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What is SOX Compliance? Requirements & Rules

Mary Shacklett — Wed, 04 Oct 2023 14:30:14 +0000

The Sarbanes-Oxley (SOX) Act is a milestone data compliance and disclosure law designed to protect investors by improving the accuracy and reliability of corporate disclosures and making corporate board members, managers, and accounting firms liable for the accuracy of their financial statements. IT plays a significant role in corporate compliance with the regulatory policies established by SOX, since related financial reports come from data housed on corporate systems and must be secured and maintained in a safe environment. This article explores the key contents of SOX, how companies can stay in compliance, and the benefits of regulatory enforcement.

Table of Contents

What is SOX Compliance?

The SOX Act protections require companies to maintain a thorough, accurate knowledge of their financial data and upkeep their network security in all areas where financial data could be breached or misrepresented. Passed by the U.S. Congress in 2002 after several major fraud cases, including the Enron fraud scandal, SOX guards investors against faulty or misrepresented disclosures of publicly traded companies’ financial data.

At a high level, SOX mandates that companies do the following:

Prepare complete financial reports to ensure the integrity of financial reporting and regulatory compliance
Put controls in place to safeguard financial data and ensure its accuracy
Provide year-end financial disclosure reports
Protect employee whistleblowers who disclose fraud

SOX also requires CEOs, CFOs, and other C-suite executives to take responsibility for honest financial data reporting, formalized data security policies and procedures, and documentation of all relevant financial details—which can all be pulled up and reviewed via audit at any time. But SOX also puts pressure on IT teams, much like other government, regulatory agency, and jurisdictional compliance policies like the European Union’s General Data Protection Regulation (GDPR), through its data and reporting requirements.

Data-Specific Rules in SOX

SOX specifically regulates the financial data of publicly traded companies, especially as it relates to corporate transactions, which can include line items like off-balance sheet transactions, pro forma figures, and stock transactions. The law enacts several rules for these kinds of financial data, obliging companies to submit for regular external audits and enabling internal reporting and controls to support financial data accuracy.

Data management and archiving are essential to SOX. IT must create and maintain a data archive of corporate records that conforms to the management of electronic records provisions of SOX Section 802, which provide direction in three critical areas:

Retention periods for records storage are defined, as are SOX best practices for the secure storage of all business records
Definitions must be made for the various types of business records that need to be stored (e.g., business records, communications, electronic communications, etc.)
Guidelines must be in place for the destruction, alteration, or falsification of records and the resulting penalties

Beyond routine audits and maintenance of financial reporting, companies are expected to report concrete evidence of changes in financial condition to the SEC. The controls that SOX requires include an Internal Control Report, which details all financial history for managerial responsibility and transparency, as well as additional documentation that proves the regular monitoring of financial data.

The SEC also requires formal data security policies with proof of communication and enforcement across a corporate network. SOX does not provide exact security protocols or expectations.

SOX Compliance

SOX compliance falls into the category of corporate governance and accountability. While it’s mainly financial, it also involves enterprise IT departments as it includes very specific guidelines for how corporate electronic records must be stored and for how long—generally, for a minimum period of five years.

SOX directs all covered companies to undergo annual audits and make the results publicly available to their stakeholders. In order to pass a compliance audit for SOX, companies need to inspect the quality of their internal controls and systems in these four key areas:

Limiting physical and electronic access to only what authorized users absolutely need
Security measures with features like endpoint security, multi-factor authentication, and anti-malware have been set up and maintained to protect against breaches
Secure backup storage for all relevant financial data that could suffer from a breach
Change management and internal auditing practices to ensure financial data remains protected when users, devices, and programs change
Appropriate reporting cycles, report formats, and data content must be put into place with a documented review process for SOX reports

SOX Enforcement

Externally, SOX is enforced by the U.S. Securities and Exchange Commission, which established the Public Company Accounting Oversight Board to oversee, regulate, and discipline auditors who work with publicly traded companies under SOX.

All publicly traded companies with American shareholders are required to comply with SOX rules, including related boards, management, and accounting firms. The consequences for non-compliance can be fines, imprisonment or both.

SOX can also be applied to international companies in certain situations—like other data laws, such as GDPR, SOX applies to any publicly traded company that does business with American citizens, even if the business itself is not located in the United States.

SOX Benefits

SOX has ushered in a level of financial accountability and liability that makes it difficult for publicly traded companies to defraud or mismanage financials. It has improved corporate data governance and ethics and made financial responsibility both a management and a board-level mandate. SOX also delivers a number of additional benefits for IT.

More Widespread Acceptance

Traditionally, management has not always recognized the return on investment of IT projects, but SOX has changed that to some extent. For example, it may be easier to approve the purchase of data integration and cleaning software, additional data storage, or expensive security and activity monitoring software if it’s necessary to help the company stay SOX compliant. Similarly, IT policies that might have been viewed as unnecessary or ignored because they might delay project deliverables now must be documented for compliance.

Reduced Complexity

SOX forces the integration of systems, work processes, and data that might not otherwise be integrated. Many companies use multiple invoicing, purchasing, enterprise resource planning (ERP), and customer relationship management (CRM) systems that IT needs to support. To maintain compliance with SOX, those systems are more likely to be integrated and business processes and systems redesigned to make everything—including data—more seamless and uniform. This integration reduces system complexity for IT in both new application development and system maintenance.

Supplier Data Sharing

SOX can improve the quality of transactions and data sharing with suppliers. While IT has traditionally struggled to integrate internal systems with those of suppliers for data exchange, SOX elevates the issue of supplier data incompatibilities into a SOX narrative for uniform data standards. This can compel supplier audits and demands for change to better integrate supplier data with corporate systems.

Improved Data Quality

The need to conform to external regulatory requirements has placed the spotlight on clean and accurate data and reporting and highlighted the importance of high quality data—even if it means investing IT staff time and budget. Standardized, high quality data is now the goal of virtually every company; without it, it’s almost impossible to run analytic and automation technologies like artificial intelligence. SOX and other compliance regulations help facilitate this work.

SOX Challenges

Despite the benefits of compliance—not least of which is avoiding punishment and fines—companies face challenges in their ongoing efforts to meet SOX regulations, which can put burdens on multiple departments and teams. Here are some of the most common.

Lack of Expertise

Inadequate resources or internal SOX expertise can be a problem for many companies, especially new and/or smaller businesses. Compliance requires implementing appropriate controls to monitor each SOX-related process—for example, purchasing might implement a control so that only someone manager-level or higher can sign off on an order in excess of $1,000. If the existing purchasing system does not have that checkpoint built into it, unsigned invoices could slip through and create a material weakness for auditors or regulators to find.

Company Culture

Some company cultures are averse to having rules and regulations foisted upon them. For example, some technology startups pride themselves on creativity, freedom, and innovation—such environments make it difficult to get management onboard with costly, time-consuming, and restrictive SOX initiatives.

Data Integration

Just because SOX requires data integration and uniform data management doesn’t make the job of data integration any easier for IT—it will take time, money, and resources. Businesses that merge or go through acquisitions and subsequently have to blend disparate systems into a composite enterprise whole for SOX reporting, especially, may find the effort daunting.

Regulatory Changes

The regulatory environment is constantly changing, and companies need to keep up. When a SOX requirement changes, the typical chain of communication starts in a regulatory agency, trickles down to the legal staff, gets reviewed by management, and then finally makes its way to IT. The challenge comes in keeping delays from happening along the way so that IT has time to implement the changes before the deadline.

The Bottom Line: SOX Compliance and Enterprise IT

SOX compliance is a fact of life for publicly traded companies. IT plays a major role in assuring that SOX guidelines and requirements are met. While the burden is high—and so are the costs for not meeting it—the advantages of compliance are widespread and benefit the companies themselves, not just their investors. SOX compliance has also elevated the role of IT in enterprise businesses, giving it a seat at the table it did not necessarily have prior. As similar new data regulations start to take hold around the world, IT teams will continue to play an important role in helping businesses stay compliant.

Read about the future of data management to learn about how other trends and policies are shaping the way enterprise organizations work with data.

Top 10 Data Center Certifications for 2023

Kashyap Vyas — Tue, 22 Aug 2023 18:40:27 +0000

Data centers are hiring in large numbers to keep pace with the growing demand for their services—but a foundational IT knowledge is insufficient if you want to work at the forefront of data center operations. Professional and advanced certifications can demonstrate your expertise and increase your value to employers. Some certifications are exam-only; others include training programs to prepare candidates for the tests. Whether offered by vendors, training providers, or professional organizations, the many available certifications offer data center professionals the chance to expand their knowledge and skills in a wide range of focus areas, from specific networking protocols to data center design to sustainability.

Here are our picks for the top 10 data center certifications for 2023.

Cisco Certified Network Associate (CCNA)

This associate-level certification demonstrates a grasp of IT fundamentals, including basic data center networking, troubleshooting, addressing schemes, switch configurations, VLANs, Nexus OS, common network services, network and server virtualization, load balancing, storage, and network access controls. The CCNA focuses on agility and versatility, certifying management and optimization skills in advanced networks, and is considered an industry standard certification.

Participants must earn a passing score on Cisco exam No. 200-301, which tests their knowledge and their ability to install, operate, and troubleshoot an enterprise branch network.

Prerequisites

No prerequisites; Cisco’s Data Center Networking and Technologies course recommended

Validity

Three years

Accreditation

Cisco

Location

Classroom and online

Cost

Course Fee: $4,500; Exam Fee: $600

Cisco Certified Network Professional (CCNP)

CNet Certified Data Centre Sustainability Professional (CDCSP)

CNet’s CDCSP certification focuses on creating a credible sustainability strategy and business implementation plan for data centers. The program covers the evaluation, analysis, planning, implementation, and monitoring of sustainability initiatives, with considerations for operational capability and business needs.

It addresses power distribution, cooling systems, IT hardware, and operational risks, and emphasizes design innovation and continuous planning cycles. It also covers compliance with national and international regulations along with the importance of demonstrating ROI and capitalizing on business, customer, social, and environmental benefits.

Candidates will learn best sustainability practices, CSR in data centers, data center performance KPIs, understanding business needs, operational risks, creating sustainable ethos, sustainability use-cases, monitoring of power sources, infrastructure, cooling capabilities, sustainability improvements, and maintenance strategies, corporate sustainability, and planning.

Graduates are encouraged to pursue further certifications and qualifications through The Global Digital Infrastructure Education Framework for career advancement in the network infrastructure and data center sectors.

Prerequisites

Two years of work experience in centers as an operations manager, designer, or sustainability engineer

Validity

Does not expire

Accreditation

CNet

Location

Online/Offline

Cost

$6,990

CNet Certified Data Center Design Professional (CDCDP)

CNet’s CDCDP certification is a 20-hour intensive training program designed to help candidates understand sustainability and energy from a professional perspective. It provides comprehensive training on data center design to meet business needs efficiently and sustainably. Participants learn best practices, compliance, and access to industry standards, with opportunities for further career advancement through The Global Digital Infrastructure Education Framework.

By finishing the five-day program, candidates gain expertise in developing projects, identifying national and international standards, availability models, structural requirements, cabinet designing, power systems, regulations, connection topologies, compliance requirements, cable management, seismic stability considerations, estimating power requirements, revising psychrometric charts, bypass and recirculation, earthing, bonding, strategizing IT requirements, virtualization, optimal testing, regulating local codes, and cable protection.

Prerequisites

Two years data center experience

Validity

Does not expire

Accreditation

CNet

Location

Online

Cost

$5,750

Bottom Line: Data Center Certifications

Experts estimate that data centers need to hire more than 300,000 new staff members by 2025 in order to keep pace with the growing demand for services. They’re also facing pressure to become more sustainable and to continually boost security to ensure the safety of client data. There’s never been more opportunity for professionals seeking to work in this expanding field, and professional certifications can expand their knowledge, demonstrate their skills to employers, and provide areas of focus and specialized expertise.

Data Migration: Strategy and Best Practices

Leon Yen — Wed, 16 Aug 2023 21:19:18 +0000

Every organization at some point will encounter the need to migrate data for any number of business and operational reasons: required system upgrades, new technology adoption, or a consolidation of data sources, to name a few. While the process of moving data from one system to another may seem deceptively straightforward, the unique dependencies, requirements, and challenges of each data migration project make a well-defined strategy instrumental to ensuring a smooth data transition—one that involves minimal data loss, data corruption, and business downtime.

In this article, we’ll explore the crucial strategies and best practices for carrying out a successful data migration, from planning and preparation to post-migration validation, as well as essential considerations for ensuring replicable results.

Data Migration Types

Since data can reside in various different places and forms, and data transfer can occur between databases, storage systems, applications, and/or a variety of other formats and systems, data migration strategies will vary depending on the migration data source and destination.

Some of the more common data migration types include the following.

Application

An application migration involves moving applications and their data from one environment to another, as well as moving datasets between different applications. These migration types often occur in parallel with cloud or data center migrations.

Cloud

A cloud migration occurs when an organization moves its data assets/infrastructure (e.g., applications, databases, data services) from a legacy, on-premises environment to the cloud, or when it transfers its data assets from one cloud provider to another. Due to the complexity of cloud migrations, organizations commonly employ third-party vendors or service providers to assist with the data migration process.

Data Center

A data center migration involves moving an entire on-premises data center to a new physical location or virtual/cloud environment. The sheer scale of most data center migration projects requires extensive data mapping and preparation to carry out successfully.

Database/Schema

A database or schema migration happens when a database schema is adjusted to a prior or new database version to make migrations more seamless. Because many organizations work with legacy database and file system formats, data transformation steps are often critical to this data migration type.

Data Storage

A data storage migration involves moving datasets from one storage system or format to another. A typical use case for data storage migration involves moving data from tape-based media storage or hard disk drive to a higher-capacity hard disk drive or cloud storage.

Learn more: Data Migration vs. ETL: What’s the Difference?

Selecting a Data Migration Strategy

Depending on the data complexity, IT systems involved, and specific business and/or industry requirements, organizations may adopt either a Big Bang or a Trickle Data migration strategy.

Big Bang Data Migration

A Big Bang data migration strategy involves transferring all data from the source to the target in a single large-scale operation. Typically, an organization would carry out a Big Bang data migration over an extended holiday or weekend. During this period, data-dependent systems are down and unavailable until the migration is complete. Depending on the amount of data involved, the duration of downtime could be significant.

Though the Big Bang migration approach is typically less complex, costly, and time-consuming than the Trickle Data migration approach, it becomes a less viable option as an organization’s data complexity and volume increases.

Benefits and Drawbacks

Big Bang data migrations typically take less time and are less complex and costly than Trickle Data migrations. However, they require data downtime and pose a higher risk of failure. For this reason, the approach is best suited for smaller organizations or data migration projects that use limited data volumes and datasets, as well as straightforward migration projects—but should be avoided for complex migrations and mission-critical data projects.

Trickle Data Migration

A Trickle Data migration strategy involves taking an Agile approach to data migrations, adopting an iterative or phased implementation over an extended period. Like an Agile project, a Trickle Data migration project is separated into smaller sub-migrations chunks, each with its own timeline, goals, scope, and quality checks. Migration teams may also use the same vernacular and tools as Agile teams in breaking the migration up into Epics, Stories, and Sprints. By taking Trickle Data’s Agile approach to data migration, organizations can test and validate each phase before proceeding to the next, reducing the risk of catastrophic failures.

A key attribute of the Trickle Data migration approach is source/target system parallelism—that is, the source and target systems are running in parallel as data is migrated incrementally. The legacy system continues to function normally during the migration process until the migration completes successfully and users are switched to the new target system. Once the data is fully validated in the new system, the legacy system can be safely decommissioned.

Benefits and Drawbacks

Because of its incremental approach and source/target system parallelism, Trickle Data migration allows for zero downtime and is less prone to unanticipated failures. However, keeping the source and target systems running at the same time incurs a cost, so organizations evaluating this migration strategy should expect a more expensive and time-consuming migration journey. Developers and data engineers must also keep both systems synchronized continuously until the migration completes, which again requires significant technical expertise and overhead to successfully carry out.

Data Migration Planning and Assessment

Regardless of which data migration strategy is in play, a successful data migration project starts with an initial comprehensive analysis and assessment of the data’s journey. This includes the following planning tasks and preparation activities:

Goals/objectives identification. Clearly define the objectives of the data migration project, illustrating specifically what data should be migrated, measures for success, completion timelines, and more.
Data inventory and analysis. Create a comprehensive inventory of all data sources, types, volumes, applications, and supporting IT assets. If one exists already, it should be analyzed for accuracy and completeness.
Risk assessment. Identify and address potential risks and roadblocks that may cause the data migration project to fail, as well as potential impacts to the organization and resolutions in the event of data loss, downtime, or other failures.
Resource allocation planning. A well-architected data migration plan will falter without the right people in place to support it. Be sure to verify that the necessary resources—staff, third-parties, and vendors/technologies—are available for the data migration, and have committed ample time to the project. This includes activities that are peripheral or may follow the actual data migration, such as user training and communications (more on this later).
Backup and contingency planning. Even the best-laid plans can go awry, and data migration projects are no different. However, with a comprehensive backup strategy in place, you can ensure that data is recoverable and systems are always operational, even if unforeseen issues occur during migration. Additionally, contingency plans should be drawn out for each potential setback/roadblock.

Migration Process Testing

After completing planning and assessment activities, the data migration project should commence with data migration process testing. The following activities should be carried out to ensure the accuracy and reliability of the data in the new system.

Create Test Environments

Perform a trial migration by creating a test environment that mirrors the production environment. This will allow you to identify and resolve issues without impacting live data.

Use Quality Data Sampling Processes

To assess the accuracy of the migration and identify any potential data quality issues, test the migration process using a representative data sample.

Implement User Acceptance Testing (UAT)

In software engineering, UAT is the crucial final phase in the software development life cycle (SDLC) before a software product is deployed to production. This phase plays a pivotal role in ensuring the successful delivery of a software application, as it verifies that the achieved success criteria matches the end-users’ expectations. For this reason, it’s also referred to as “End-User Testing” or “Beta Testing,” since the actual users or stakeholders test the software.

During this phase, real-world scenarios are simulated to ensure that the software meets the intended user/business requirements and is ready for release.

Taking cues from the software world, modern organizations will often incorporate UAT testing into their data migration processes in order to validate that they meet data end-users’ specific requirements and business needs. Adopting UAT in the migration process will bring end-users into the fold, incorporate their feedback, allow for necessary adjustments as needed, and validate that the migrated data is working as expected.

Data Migration Best Practices

Although every data migration is unique, the following principles and best practices apply universally to every data migration project. Be sure to keep these procedures top-of-mind during the course of your data migration project.

Minimize Downtime and Disruptions

Your data migration project may involve downtime or service disruptions, which will impact business operations. Schedule the data migration during off-peak hours or weekends to minimize its impact on regular business activities.

Take the Trickle Data Approach

Incremental data migrations are usually the safest route to follow—if feasible, migrate your data incrementally and allow the system to remain operational during the migration. This may require the implementation of load balancing to distribute the migration workload efficiently and avoid overloading the target system.

User Training and Communications

Ongoing stakeholder communications is crucial throughout the data migration process. This should include keeping everyone informed about the migration schedule, potential disruptions, and expected outcomes, as well as providing end-user training/instructions to smooth the transition and prevent any post-migration usability issues.

Post-Migration Validation and Auditing

Once the migration is complete, perform post-migration validation to verify that all data is accurately transferred and that the new system functions as expected. Conduct regular audits to ensure data integrity and compliance with data regulations.

Continuous Performance Monitoring

Ongoing monitoring of the new system’s performance is vital for surfacing any post-migration data loss and/or data corruption issues. Regularly assess the target system’s performance and investigate any potential data-related performance bottlenecks/issues.

Data Security and Compliance

Last but certainly not least, ensure that data security and compliance requirements are met during and after the migration process. This may include implementing data encryption at rest and in transit, access controls, and data protection measures to safeguard sensitive information.

Bottom Line: Strategies for Successful Data Migration

Data migrations may be unavoidable, but data migration failures can certainly be avoided by following a well-defined data migration strategy—one that incorporates comprehensive planning, ongoing data quality analysis, proper testing, and continuous monitoring. By planning ahead, choosing the right approach, and following best practices, organizations can minimize the risk of data loss, ensure data integrity, and achieve a successful and seamless transition to new systems or environments.

Read next: Top 5 Data Migration Tools of 2023

7 Data Management Trends: The Future of Data Management

Mary Shacklett — Wed, 02 Aug 2023 18:40:52 +0000

Data management trends are coalescing around the need to create a holistic framework of data that can be tapped into remotely or on-premises in the cloud or in the data center. Whether structured or unstructured, this data must move easily and securely between cloud, on-premises, and remote platforms, and it must be readily available to everyone with a need to know and unavailable to anyone else.

Experts predict 175 zettabytes of data worldwide within two years, much of it coming from IoT (Internet of Things) devices. Companies of all sizes should expect significant troves of data, most of it unstructured and not necessarily compatible with system of record (SOR) databases that have long driven mission-critical enterprise systems like enterprise resource planning (ERP).

Even unstructured data should be subject to many of the same rules that govern structured SOR data. For example, unstructured data must be secured with the highest levels of data integrity and reliability if the business is to depend on it. It must also meet regulatory and internal governance standards, and it must be able to move freely among systems and applications on clouds, internal data repositories, and mobile storage.

To keep pace with the enormous demands of managing voluminous high velocity and variegated data day-in and day-out, software-based tools and automation must be incorporated into data management practices. Newer automation technologies like data observability will only grow in importance, especially as user citizen development and localized data use expand.

All of these forces require careful consideration as enterprise IT builds its data management roadmap. Accordingly, here are seven emergent data management trends in 2023.

Hybrid End-to-End Data Management Frameworks

Enterprises can expect huge amounts of structured and unstructured data coming in from a wide range of sources, including outside cloud providers; IoT devices, robots, drones, RF readers, and MRI or CNC machines; internal SOR systems; and remote users working on smart phones and notepads. All of this data might be committed to long- or short- term storage in the on-premise data center, in a cloud, or on a mobile or distributed server platform. In some cases, real-time data may need to be monitored and/or accessed as it streams in real time.

In this hybrid environment, the data, its uses, and its users are diverse—data managers will need data management and security software that can span all of these hybrid activities and uses so data can be safely and securely transported and stored point to point.

IBM is a leader in the data management framework space, but SAP, Tibco, Talend, Oracle, and others also offer end-to end data fabric management solutions. A second aspect of data management is being able to secure data, no matter where it is sent from or where it resides—end-to-end security mesh software from vendors such as Fortinet, Palo Alto Networks, and Crowdstrike can meet this need.

The Consolidation of Data Observability Tools

Because many applications now use multiple cloud and on-premises platforms to access and process data, observability—the ability to track data and events across multiple platform and system barriers with software—is a key focus for enterprises looking to monitor end-to-end movements of data and applications. The issue with most organizations that are using observability tools today is that they are using too many different tools to effect end-to-end data and application visibility across platforms.

Vendors like Middleware and Datadog recognize this and are focused on delivering integrated, “single pane of glass” observability tool sets. These tools enable enterprises to reduce the number of different observability tools they use into a single toolset that’s able to monitor data and event movements across multiple cloud and on premises systems and platforms.

Master Data Management for Legacy Systems

As businesses move forward with new technologies, they face the challenge of figuring out what to do with older ones. But some of those continue to provide value as legacy systems—systems that are outdated or that continue to run mission-critical functions vital to the enterprise.

Some of these legacy systems—for example, enterprise resource planning (ERP) systems like SAP or Oracle—offer comprehensive, integrated master data management (MDM) toolsets for managing data on their cloud or on-premises solutions. Increasingly enterprises using these systems are adopting and deploying these MDM toolsets as part of their overall data governance strategies.

MDM tools offer user-friendly ways to manage system data and to import data from outside sources. MDM software provides a single view of the data, no matter where it resides, and IT sets the MDM business rules for data consistency, quality, security, and governance.

Data Management Using AI/ML

While the trend of using artificial intelligence and machine learning (AI/ML) for data management is not new, it continues to grow in popularity driven by big data concerns as the unprecedented volume of data enterprises are faced with managing collides with an ongoing staffing shortage across the tech industry as a whole—especially in data-focused roles.

AI and ML introduce highly valuable automation to manual processes that have been prone to human error. Foundational data management tasks like data identification and classification can be handled more efficiently and accurately by advanced technologies in the AI/ML space, and enterprises are using it to support more advanced data management tasks such as:

Data cataloging
Metadata management
Data mapping
Anomaly detection
Metadata auto-discovery
Data governance control monitoring

As AI/ML continues to evolve, we can expect to see software solutions that offer intelligent, learning-based approaches including search, discovery, and capacity planning.

Prioritizing Data Security

In the first quarter of 2023, over six million data records were breached worldwide. A data breach can destroy a company’s reputation, impact revenue, endanger customer loyalty, and get people fired.This is why security of all IT—especially as more IT moves to the edge and the IoT—is an important priority for CIOs and a major IT investment area.

To meet data security challenges, security solution providers are moving toward more end-to-end security fabric solutions. They are offering training for employees and IT, since increases in user citizen development and poor user security habits can be major causes of breaches.

Although many of these security functions will be performed by the IT and network groups, clean, secure, and reliable data is foremost a database administrator, data analyst, and data storage concern as well.

Automating Data Preparation

The exponential growth of big data volumes and a shrinking pool of data science talent is stressing organizations. In some cases, more than 60 percent of expensive data science time is spent cleaning and preparing data.

Software vendors want to change this corporate pain point with an increase in data preparation and cleaning automation software that can perform these tedious, manual operations. Automated data preparation solutions ingest, store, organize, and maintain data, often using AI and ML, and can handle such manually intensive tasks as data preparation and data cleansing.

Using Blockchain and Distributed Ledger Technology

Distributed ledger systems enable enterprises to maintain more secure transaction records, track assets, and keep audit trails. This technology, along with blockchain technology, stores data in a decentralized form that cannot be altered, improving the authenticity and accuracy of records related to data handling. This includes financial transaction data, sensitive data retrieval activity, and more.

Blockchain technology can be used in data management to improve the security, shareability, and consistency of data. It can also be used to provide automatic verification, offering avenues to improve data governance and security.

Bottom Line: The Future of Data Management

As businesses confront the need to collect and analyze massive volumes of data from a variety of sources, they seek new means of data management that can keep pace with the expanding need. Cutting edge technologies like AI/ML and blockchain can be used to automate and enhance some aspects of data management, and software vendors are incorporating them into their platforms to make them an integral part of the work. As new technologies continue to evolve, data management methods will evolve with them, integrating them into processes driven by increasing demand.

The Ultimate Guide to Cloud Computing

Anina Ot — Tue, 11 Jul 2023 20:00:00 +0000

Cloud computing is one of the most influential IT trends of the 21st century. Over two decades it has revolutionized enterprise IT, and now most organizations take a “cloud-first” approach to their technology needs. The boom in cloud has also prompted significant growth in related fields, from cloud analytics to cloud security.

This ultimate guide explains everything you need to know about cloud computing, including how it works, the difference between public and private clouds, and the benefits and drawbacks of different cloud services.

Jump to:
What Is Cloud Computing?
Cloud Computing Services
Public vs. Private vs. Hybrid Cloud
Cloud Computing Benefits
Cloud Computing Drawbacks
Cloud Security
Bottom Line: Cloud Computing

What Is Cloud Computing?

There are many definitions of cloud computing, but the most widely accepted one was published in 2011 by the U.S. Department of Commerce’s National Institute of Standards and Technology (NIST) and subsequently summarized by Gartner as “a style of computing in which scalable and elastic IT-enabled capabilities are delivered as a service using Internet technologies.”

NIST’s longer definition identifies five “essential characteristics” shared by all cloud computing environments:

On-demand self-service: Consumers can unilaterally provision computing capabilities (such as server time and network storage) as needed.
Broad network access: Capabilities are available over the network and accessed through standard mechanisms.
Resource pooling: Resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand to allow for location independence and high resource availability.
Rapid elasticity: Capabilities can be elastically provisioned and released to scale rapidly with demand. To the consumers, provisioning capabilities appear unlimited and highly flexible.
Measured service: Cloud systems automatically control and optimize resource use by metering appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). To codify technical aspects, cloud vendors must provide every customer with a Service Level Agreement.

Cloud also makes use of a number of key technologies that boost the efficiency of software development, including containers, a method of operating system virtualization that allows consistent app deployment across computing environments.

Cloud computing represents a major generational shift in enterprise IT.

Cloud Computing Services

Cloud computing comprises a lot of different types of cloud services, but the NIST definition identifies three cloud service models: software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). While these three models continue to dominate cloud computing, various vendors have also introduced other types of cloud services that they market with the “as-a-service” label. These include database as a service (DBaaS), disaster recovery as a service (DRaaS), function as a service (FaaS), storage as a service (SaaS), mobile backend as a service (MBaaS), security as a service (SECaaS), networking as a service (NaaS), and a host of others.

All of these cloud services can be gathered under the umbrella label “everything as a service,” or XaaS, but most of these other types of cloud computing services fall under one of the three original categories.

Software as a Service (SaaS)

In the SaaS model, users access applications via the Web. Application data resides in the software vendor’s cloud infrastructure, and users access it from any internet-connected device. Instead of paying a flat fee, as with the traditional software model, users purchase a subscription on a monthly or yearly basis.

The SaaS market alone is expected to grow from $273.55 billion in 2023 to $908.21 billion by 2030, representing a compound annual growth rate (CAGR) of 18.7 percent. The world’s largest SaaS vendors include Salesforce, Microsoft, Google, ADP, SAP, Oracle, IBM, Cisco and Adobe.

Infrastructure as a Service (IaaS)

IaaS vendors provide access to computing, storage, networks, and other infrastructure resources. Using an IaaS is very similar to using a server, storage appliance, networking device, or other hardware, except that it is managed as a cloud rather than as a traditional data center.

The IaaS cloud market, which was estimated at $118.43 billion in 2022, will be worth $450.52 billion by 2028, maintaining a CAGR of 24.3 percent over the analysis period. Amazon Web Services is considered the leading public IaaS vendor, with over 200 cloud services available across different industries. Others include Microsoft Azure, Google Cloud, IBM SoftLayer, and VMware vCloud Air. Organizations like HPE, Dell Technologies, Cisco, Lenovo, NetApp, and others also sell infrastructure that allows enterprises to set up private IaaS services.

Platform as a Service (PaaS)

PaaS occupies the middle ground between IaaS and SaaS. PaaS solutions don’t offer applications for end-users the way SaaS vendors do, but they offer more than just the infrastructure provided by IaaS solutions. Typically, PaaS solutions bundle together the tools that developers will need to write, deploy, and run applications. They are meant to be easier to use than IaaS offerings, but the line between what counts as IaaS and what counts as PaaS is sometimes blurry. Most PaaS offerings are designed for developers, and they are sometimes called “cloud development platforms.”

The global PaaS market is worth $61.42 billion, an increase of 9.8 percent over 2022. The list of leading public PaaS vendors is very similar to the list of IaaS vendors, and includes Amazon Web Services, Microsoft Azure, IBM Bluemix, Google App Engine, Salesforce App Cloud, Red Hat OpenShift, Cloud Foundry, and Heroku.

Public vs. Private vs. Hybrid Cloud

Cloud computing services can also be categorized based on their deployment models. In general, cloud deployment options include public cloud, private cloud, and hybrid cloud. Each has its own strengths and weaknesses.

Public Cloud

As the name suggests, a public cloud is available to businesses at large for a wide variety of remote computing needs. These cloud services are managed by third-party vendors and hosted in the cloud vendors’ data centers.

Public cloud saves organizations from having to buy, deploy, manage, and maintain their own hardware. Instead, vendors are responsible in exchange for a recurring fee.

On the other hand, public cloud users give up the ability to control the infrastructure, which can raise security and regulatory compliance concerns. Some public cloud providers, like AWS Outposts rack, now offer physical, on-premises server racks for jobs that need to be done in-house for security and compliance reasons. Additionally, many vendors offer cloud cost calculators to help users better predict and understand charges.

The public cloud enables companies to tap into remote computing resources.

Private Cloud

A private cloud is a cloud computing environment used only by a single organization, which can take two different forms—organizations build their own private clouds in their own data centers, or use a hosted private cloud service. They’re also the most commonly used and best option for businesses that require a multi-layered infrastructure for IT and data protection.

Like a public cloud, a hosted private cloud is operated by a third party, but each customer gets dedicated infrastructure set aside for its needs rather than sharing servers and resources. A private cloud allows organizations to enjoy the scalability and agility of cloud computing without some of the security and compliance concerns of a public cloud. However, a private cloud is generally more expensive and more difficult to maintain.

The private cloud allows a company the control and security needed for compliance and other sensitive data issues.

Hybrid Cloud

A hybrid cloud is a combination of public private clouds managed as a single environment. They can be particularly beneficial for enterprises that have some data and applications that are too sensitive to entrust to a public cloud but need it to be accessible to other applications that do run on public cloud services.

Hybrid clouds are also helpful for “cloudbursting,” which involves using the public cloud during spikes in demand that overwhelm an organization’s private cloud. Managing a hybrid cloud can be very complex and requires special tools.

It’s important to note that a hybrid cloud is managed as a single environment. Already the average enterprise is using more than one cloud, and most market researchers expect multi-cloud and hybrid cloud environments to dominate the enterprise for the foreseeable future.

The hybrid model combines public and private cloud models to enable greater flexibility and scalability.

Cloud Computing Benefits

As already mentioned, each type of cloud computing has advantages and disadvantages, but all types of cloud computing generally offer the following benefits:

Agility and Flexibility: Cloud environments enable end users to self-service and quickly provision the resources they need for new projects. Organizations can move workloads around to different servers and expand or contract the resources dedicated to a particular job as necessary.
Scalability: The same virtualization and pooling features that make it easy to move workloads around also make it easy for organizations to scale up or down as usage of particular applications increases or decreases. It is somewhat easier to scale in a public cloud than a private cloud, but both offer scalability benefits in comparison to a traditional data center.
Availability: It’s easier to recover data if a particular piece of infrastructure experiences an outage. In most cases, organizations can simply failover to another server or storage device within the cloud, and users don’t notice that a problem has occurred.
Location Independence: Users access all types of cloud environments via the internet, which means that they can get to their applications and data from any web-connected device, nearly anywhere on the planet. For enterprises seeking to enable greater workforce mobility, this can be a powerful draw.
Financial Benefits: Cloud computing services tend to be less expensive than traditional data centers. However, that isn’t true in every case, and the financial benefit varies depending on the type of cloud service used. For all types of cloud, however, organizations have a greater ability to chargeback computing usage to the particular business unit that is utilizing the resources, which can be a big aid for budgeting.

Cloud Computing Drawbacks

Of course, cloud computing also has some drawbacks. First of all, demand for knowledgeable IT workers remains high, and many organizations say it is difficult to find staff with the experience and skills they need to be successful with cloud computing. Experts say this problem will likely diminish over time as cloud computing becomes even more commonplace.

In addition, as organizations move toward multi-cloud and hybrid cloud environments, one of their biggest challenges is integrating and managing the services they use. Some organizations also experience problems related to cloud governance and control when end users begin using cloud services without the knowledge or approval of IT.

But the most commonly cited drawbacks of cloud computing center around cloud security and compliance. A hybrid infrastructure model that integrates public cloud with on-premises resources—and sometimes with a private cloud—can offer many of the advantages of both cloud and on-premises models while mitigating security and compliance risks by maintaining full control over data centers and virtual machines.

Cloud Security

Most of the security concerns around cloud computing relate primarily to public cloud services. Because public clouds are shared environments, many organizations have concerns that others using the same service can access their data. And without control over the physical infrastructure hosting their data and applications in the public cloud, enterprises need to make sure vendors take adequate measures to prevent attacks and meet compliance requirements.

However, some security experts argue that public cloud services are more secure than traditional data centers. Most cloud vendors have large security teams and employ the latest technologies to prevent and mitigate attacks. Smaller enterprises simply don’t have as many resources to devote to securing their networks.

But organizations should not just assume that cloud vendors have appropriate safeguards in place—vendors and users share responsibility for cloud security and both need to play an active role in keeping data secure.

Bottom Line: Cloud Computing

The popularity of cloud computing has grown steadily with no signs of slowing down since the phrase “cloud computing” was first used in the mid-1990s. It’s nearly ubiquitous among enterprises, with 87 percent operating a multi-cloud strategy and 72 percent a hybrid cloud strategy. Experts predict the market will continue to grow as organizations migrate more applications and data to the cloud. There are multiple models and a wide range of services available, giving organizations a lot of flexibility when it comes to cloud computing. From public to private to hybrid cloud, businesses can find or build the right configuration to meet their own particular budget, requirements, and needs.

Read next: Cloud Services Providers Comparison.

The Top 5 Data Migration Tools of 2023

Kashyap Vyas — Tue, 13 Jun 2023 16:00:11 +0000

Whether it’s about shifting to a more robust infrastructure, embracing cloud technologies, or consolidating disparate systems, organizations across the globe are increasingly relying on data migration to unlock new opportunities and drive growth. However, navigating the complex realm of data migration can be daunting, as it requires sophisticated tools to orchestrate the transfer of an intricate web of information spread across databases, applications, and platforms while ensuring accuracy, efficiency, and minimal disruption.

To help find the right tool, we’ve compared the top five data migration tools to move, transform, and optimize your organization’s data efficiently. Here are our top picks:

AWS Database Migration Service: Best for AWS Cloud Migration
IBM Informix: Best for Versatile Data Management
Matillion: Best for Data Productivity
Fivetran: Best for Automated Data Movement
Stitch: Best for Versatile Cloud Data Pipelines

Top 5 Data Migration Tools Comparison

Take a look at some of the top data migration tools and their features:

	Data Transformation	Connectors	Real-time Analytics	Security and Compliance	Free Trial?
AWS Database Migration Service	Homogenous and heterogenous migrations	20+ database and analytics engines	Yes	Yes	Yes
IBM Informix	Hassle-free data management	Wide range of connectors	Yes	Yes	Yes
Matillion	Point-and-click selection and SQL-query-based post-load transformations	80+ prebuilt connectors	Yes	Yes	Yes
Fivetran	SQL-based post-load transformations	300+ prebuilt connectors	Yes	Yes	Yes
Stitch	Part of Talend	140+ connectors	Yes	Yes	Yes

Jump to:

Key Features of Data Migration Tools
How to Choose the Best Data Migration Tools for Your Business
Frequently Asked Questions (FAQs)
Methodology
Bottom Line: Choosing the Right Data Migration Tool

AWS Database Migration Service

Best for AWS Cloud Migration

The technology giant Amazon extends data migration services to customers through AWS Database Migration Service. It removes undifferentiated database management tasks to simplify the migration process. This high-performance tool offers the additional advantage of access to other AWS solutions and services. Thus, it is best suited for businesses looking for AWS cloud migration support and features.

Pricing

The AWS Free Tier plan helps users get started with the data migration service for free. See the AWS Pricing Calculator for detailed pricing plans and information.

Features

Centralized access with AWS Management Console
Multi-AZ and ongoing data replication and monitoring
Homogeneous and heterogeneous migration support
Automated migration planning with AWS DMS Fleet Advisor

Pros

Simple and easy-to-use service
Automatic schema assessment and conversion
Supports migration among 20-plus databases and analytics engines

Cons

Large-scale data migration can be costly
Frequent changes in pricing

IBM Informix

Best for Versatile Data Management

IBM offers data management and migration solutions through an embeddable database: IBM Informix. It is a highly versatile tool that simplifies administration and optimizes database performance. It relies on a hybrid cloud infrastructure. Informix is best for multi-tiered architectures that require device-level processing.

Pricing

IBM Informix Developer Edition is ideal for development, testing, and prototyping and can be downloaded for free. The Informix Innovator-C Edition supports small production workloads and is also freely available. Other editions are available that offer a complete suite of Informix features. Contact the team for their pricing details.

Features

Real-time analytics for transactional workloads
High availability data replication (HADR) for mission-critical environments
Event-driven processing and smart triggers for automated data management
Silent installation with a memory footprint of only 100 MB

Pros

Robust processing and integration capabilities
Minimal administrative requirements
Native encryption for data protection
Real-time analytics for fast insights

Cons

Big data transfers can slow down the platform
Complex pricing policies

Matillion

Best for Data Productivity

Matillion helps businesses with next-gen ETL (extract, transform, load) solutions for efficient data orchestration. It can automate and accelerate data migration with its universal data collectors and pipelines. With its advanced capabilities, it helps extract full value from a business’s existing infrastructure.

Pricing

Matillion follows a simple, predictable, and flexible pricing model along with free trial versions. It offers Free, Basic, Advanced, and Enterprise editions and pay-as-you-go options. The minimum price for paid plans is $2 per credit. Contact the vendor to speak to an expert for details.

Features

Change data capture and batch data loading for simplified pipeline management
Low-code/no-code GUI
Reverse ETL and prebuilt connectors for easy data sync back
Drag-and-drop functionality for easier usage

Pros

Fast data ingestion and integration
Enterprise assurance
Post-load transformations
Customizable configurations

Cons

High-volume data load can cause crashes
Support issues
Needs better documentation

Fivetran

Best for Automated Data Movement

Fivetran offers an efficient platform for data migration. This cloud-based tool relies on a fully-managed ELT architecture that efficiently handles all data integration tasks. It has numerous database replication methods that can manage extremely large workloads.

Pricing

Fivetran offers a 14-day free trial option. It has Free, Starter, Standard, Enterprise, Business Critical, and Private Deployment plans with different features and pricing options. Contact the sales team for specific pricing details.

Features

More than 300 prebuilt, no-code source connectors
Quickstart data models for automated transformations
End-to-end data monitoring with lineage graphs
Fivetran API for programmatic scaling

Pros

Flexible connection options for secure deployment
Advanced role-based access control
Data catalog integrations for metadata sharing

Cons

Only cloud-based solutions
Lacks support for data lakes
Expensive option for large volumes of data

Stitch

Best for Versatile Cloud Data Pipelines

Stitch offers fully automated cloud data pipelines that can be used without any coding expertise. It helps consolidate data from a vast range of data sources. This enterprise-grade cloud ETL platform is highly trusted for extracting actionable insights.

Pricing

Stitch offers a free trial for two weeks. It follows a transparent and predictable pricing model with no hidden fees. There are three plans: Standard, Advanced, and Premium. The minimum price starts at $100 per month, if billed monthly, or $1,000 if billed annually. Contact the sales team for exact pricing details for each plan.

Features

140+ popular data sources
External processing engines like MapReduce and Apache Spark
In-app chat support

Pros

No coding is required
Centralized, fresh, and analysis-ready data
Automatically updated pipelines

Cons

Needs a more friendly user interface
Customer support issues

Key Features of Data Migration Tools

The primary purpose of using data migration tools is to simplify data transfer across different systems, ensuring integrity and accuracy. Some of the key features they include to accomplish this goal are:

Data Transformation

Data migration tools need to consolidate data from multiple sources, which requires them to have data transformation capabilities. Having a standardized data structure or format across different environments is impossible, but data transformation features can help to make these disparate data sources more manageable and uniform. These tools must optimize data for the destination system, ensuring consistency and coherence. They must also be able to identify inconsistencies or issues and transform data as per target requirements.

Connectors

Data migration tools connect various data sources and targets. Thus, they require various connector modules to help them interact with different systems during a migration. With comprehensive connector coverage, data migration tools can establish a link between the source and targets using required protocols, APIs, or drivers. As a result, data can be efficiently extracted from the source and loaded into the target.

Real-time Analysis

Efficient data migration demands real-time insights for seamless data exchange. Real-time analysis helps in the early detection of errors and accurate data mapping between the source and target. This makes it an essential feature of data migration tools, as it helps with performance monitoring, error detection and prevention, data validation, synchronization, and consistency.

Security and Compliance

Data migrations involve substantial risks like information misuse, unauthorized access, data loss, and corruption. These incidents can lead to severe financial and reputational damages, and may also involve potential legal liabilities. Due to these risks, data migration tools must adhere to strict security and compliance standards to minimize security incidents and other risky outcomes.

Customization

Different businesses have different data requirements. To meet business expectations, data migration tools must offer customization features for changing business requirements. A strong data migration tool will also provide the flexibility and adaptability to help organizations with tailored migration processes.

How to Choose the Best Data Migration Tool for Your Business

Data migrations and similar operations are risky processes, as they involve moving your organization’s sensitive information. Thus, choosing a versatile and reliable tool that ensures a smooth and successful migration is essential.

Here are some key considerations to help select the best data migration tool for specific business needs:

Configuration Type

There are two distinct types of data tool configurations: cloud-based and on-premises. On-premises data tools do not rely on the cloud for data transfer. Instead, they migrate data within the organizational infrastructure, offering full-stack control. These are effective solutions when the business desires to restrict data within its own servers.

Cloud-based data migration tools transfer and store data using cloud platforms on cloud servers. The architecture can be expanded effectively due to the quick availability of resources. These tools also facilitate data migration from on-premises to cloud systems. In addition, they are highly secure and cost-effective.

Enterprise Cloud Migration Services

Choosing enterprise-focused cloud migration services can give you an additional edge. Data migration services that are specifically designed for enterprises can more effectively take care of industry standards and maintain top-notch IT infrastructure. Besides, they offer constant updates based on the latest advancements in technologies and methodologies. They can handle complex business projects with well-designed transformation processes.

Technical Support

When choosing a data migration tool, it is also essential to pay attention to technical support capabilities offered by the vendor. Businesses especially need post-migration support to address any issues. They must also help develop robust backup and recovery strategies to deal with system failures or other potential challenges.

Additional Considerations

There are many different types of data migration, like storage, database, cloud, application, data center, and business process migration. Therefore, you should select the most suitable migration tool based on your business goals and the types of migration you want to complete.

Apart from these aspects, it is also vital that the tool you select integrates efficiently with your current business infrastructure and supports data sources and target systems. This can reduce disruptions and compatibility issues.

Frequently Asked Questions (FAQs)

How Do Data Migration Tools Benefit Businesses?

Data migration tools benefit businesses by streamlining data transfer, storage, and management processes, ensuring accuracy. Since they automate these processes, companies can focus on other essential operational aspects. Also, these tools offer the necessary flexibility and scalability to cater to specific demands.

What Types of Data Can Data Migration Tools Handle?

Data migration tools handle enormous volumes of data in different formats and structures within different systems. They deal with both structured and unstructured data and need to work with databases, enterprise applications, data warehouses, spreadsheets, JSON, XML, CSV, and other file formats.

What Are Open-source Data Migration Tools?

Open-source data migration tools are publicly accessible, typically free-to-use solutions. The source code is available on a central repository and can be customized too. Although they require technically skilled employees for proper implementation and use, community-driven support is a major plus with open-source technology, as you can get assistance from technical experts whenever it’s needed. Therefore, these are ideal options for small-scale projects involving lesser complexities.

Methodology

We implemented a structured research methodology to analyze different data migration tools available in the current marketplace. The research was based on specified evaluation criteria and essential feature requirements.

We evaluated each tool’s real-world performance based on user reviews and performance, as customer satisfaction is crucial. After in-depth analysis with several other criteria, we finally documented the top results for the best data migration tools.

Bottom Line: Choosing the Right Data Migration Tool

Choosing the right data migration tool is crucial for aligning specific business goals. Throughout this article, we explored the top five tools, each with unique strengths. When selecting a data migration solution for your business, consider factors like data complexity, scale, real-time vs. batch processing, security, and compatibility.

Remember, the key to successful data migration lies in aligning your specific business goals with the capabilities offered by your chosen tool. Take the time to evaluate and understand your requirements, consult with stakeholders, and make an informed decision that sets your organization on the path to achieving its desired outcomes.

Also See

Also See Data Migration Trends

Internet of Things Trends

Emma Crockett — Tue, 09 May 2023 18:40:42 +0000

The Internet of Things (IoT) refers to a network of interconnected physical objects embedded with software and sensors in a way that allows them to exchange data over the internet. It encompasses a wide range of objects, including everything from home appliances to monitors implanted in human hearts to transponder chips on animals, and as it grows it allows businesses to automate processes, improve efficiencies, and enhance customer service.

As businesses discover new use cases and develop the infrastructure to support more IoT applications, the entire Internet of Things continues to evolve. Let’s look at some of the current trends in that evolution.

Table Of Contents

Top 5 IoT Trends
The Future Of IoT
Bottom Line: IoT Trends

Top 5 IoT Trends

IoT devices can help companies use their data in many ways, including generating, sharing and collecting data throughout their infrastructure. While some companies are leaping into IoT technology, others are more cautious, observing from the sidelines to learn from the experiences of those pioneering IoT.

When looking through these five key trends, keep in mind how IoT devices affect and interact with company infrastructure to solve problems.

1. IoT Cybersecurity Concerns Grow

As new IoT solutions develop quickly, are users being protected from cyber threats and their connected devices? Gabriel Aguiar Noury, robotics product manager at Canonical, which publishes the Ubuntu operating system, believes that as more people gain access to IoT devices and the attack surface grows, IoT companies themselves will need to take responsibility for cybersecurity efforts upfront.

“The IoT market is in a defining stage,” Noury said. “People have adopted more and more IoT devices and connected them to the internet.” At the same time they’re downloading mobile apps to control them while providing passwords and sensitive data without a clear understanding of where they will be stored and how they will be protected—and, in many cases, without even reading the terms and conditions.

“And even more importantly, they’re using devices without checking if they are getting security updates…,” Noury said. “People are not thinking enough about security risks, so it is up to the IoT companies themselves to take control of the situation.”

Ben Goodman, SVP of global business and corporate development at ForgeRock, an access management and identity cloud provider, thinks it’s important that we start thinking of IoT devices as citizens and hold them accountable for the same security and authorization requirements as humans.

“The evolution of IoT security is an increasingly important area to watch,” Goodman said. “Security can no longer be an afterthought prioritized somewhere after connectivity and analytics in the Internet of Things. Organizations need to start treating the ‘things’ in the Internet of Things as first-class citizens.”

Goodman said such a measure would mean that non-human entities are required to register and authenticate and have access granted and revoked, just like humans, helping to ensure oversight and control.

“Doing this for a thing is a unique challenge, because it can’t enter a username or password, answer timely questions, or think for itself,” he said. “However, it represents an incredible opportunity to build a secure network of non-human entities working together securely.”

For more information on IoT and security: Internet of Things (IoT) Security Trends

2. IoT Advancements In Healthcare

The healthcare industry has benefited directly from IoT advancements. Whether it’s support for at-home patient care, medical transportation, or pharmaceutical access, IoT solutions are assisting healthcare professionals with more direct care in situations where they cannot provide affordable or safe hands-on care.

Leon Godwin, principal cloud evangelist for EMEA at Sungard AS, a digital transformation and recovery company, explained that IoT not only makes healthcare more affordable—it also makes care and treatment more accessible and patient-oriented.

“IoT in healthcare will become more prevalent as healthcare providers look to reduce costs and drive better customer experience and engagement,” Godwin said. “This might include advanced sensors that can use light to measure blood pressure, which could be incorporated in watches, smartphones, or standalone devices or apps that can measure caloric intake from smartphone cameras.”

Godwin said that AI is also being used to analyze patient data, genetic information, and blood samples to create new drugs, and after the first experiment using drones to deliver organ transplants across cities happened successfully, rollout is expected more widely.

Jahangir Mohammed, founder and CEO of Twin Health, a digital twin company, thinks that one of the most significant breakthroughs for healthcare and IoT is the ability to constantly monitor health metrics outside of appointments and traditional medical tests.

“Recent innovations in IoT technology are enabling revolutionary advancements in healthcare,” Mohammed said. “Until now, individual health data has been mostly captured at points in time, such as during occasional physician visits or blood labs. As an industry, we lacked the ability to track continuous health data at the individual level at scale.

“Advancements in IoT are shifting this paradigm. Innovations in sensors now make it possible for valuable health information to be continuously collected from individuals.

Mohammed said advancements in AI and Machine Learning, such as digital twin technology and recurrent neural networks, make it possible to conduct real-time analysis and see cause-and-effect relationships within incredibly complex systems.

Neal Shah, CEO of CareYaya, an elder care tech startup, cited a more specific use case for IoT as it relates to supporting elders living at home—a group that suffered from isolation and lack of support during the pandemic.

“I see a lot of trends emerging in IoT innovation for the elderly to live longer at home and avoid institutionalization into a nursing home or assisted living facility,” Shah said. Through research partnerships with university biomedical engineering programs, CareYaya is field testing IoT sensors and devices that help with everything from fall prevention to medication reminders, biometric monitoring of heart rate and blood pressure—even mental health and depression early warning systems through observing trends in wake-up times.

Shah said such IoT innovations will improve safety and monitoring and make it possible for more of the vulnerable elderly population to remain in their own homes instead of moving into assisted living.

For more information on health care in IoT: The Internet of Things (IoT) in Health Care

3. 5G Enables More IoT Opportunities

5G connectivity will make more widespread IoT access possible. Currently, cellular companies and other enterprises are working to make 5G technology available in more areas to support further IoT development.

Bjorn Andersson, senior director of global IoT marketing at Hitachi Vantara, a top-performing IoT and IT service management company, explained why the next wave of wider 5G access will make all the difference for new IoT use cases and efficiencies.

“With commercial 5G networks already live worldwide, the next wave of 5G expansion will allow organizations to digitize with more mobility, flexibility, reliability, and security,” Andersson said. “Manufacturing plants today must often hardwire all their machines, as Wi-Fi lacks the necessary reliability, bandwidth, or security.”

But 5G delivers the best of two worlds, he said—the flexibility of wireless with the reliability, performance, and security of wired networks. 5G provides enough bandwidth and low latency to have a more flexible impact than a wired network, enabling a whole new set of use cases.

Andersson said 5G will increase the feasibility of distributing massive numbers of small devices that in the aggregate provide enormous value with each bit of data.

“This capacity to rapidly support new apps is happening so early in the deployment cycle that new technologies and infrastructure deployment can happen almost immediately, rather than after decades of soaking it in,” he said. “With its widespread applicability, it will be feasible to deliver 5G even to rural areas and remote facilities far more quickly than with previous Gs.”

For more: Internet of Things (IoT) Software Trends

4. Demand For Specialized IoT Data Management

With its real-time collection of thousands of data points, the IoT solutions strategy focuses heavily on managing metadata about products and services. But the overwhelming amount of data involved means not all IoT developers and users have begun to fully optimize the data they can now access.

Sam Dillard, senior product manager of IoT and edge at InfluxData, a data platform provider for IoT and in-depth analytics use cases, believes that as connected IoT devices expand globally, tech companies will need to find smarter ways to store, manage and analyze the data produced by the Internet of Things.

“All IoT devices generate time-stamped (or time series) data,” Dillard said. “The explosion of this type of data, fueled by the need for more analytics, has accelerated the demand for specialized IoT platforms.”

By 2025, around 60 billion connected devices are projected to be deployed worldwide—the vast majority of which will be connected to IoT platforms, he said. Organizations will have to figure out ways to store the data and make it all sync together seamlessly as IoT deployments continue to scale at a rapid pace.

5. Bundled IoT For The Enterprise Buyer

While the average enterprise buyer might be interested in investing in IoT technology, the initial learning curve can be challenging as IoT developers work to perfect new use cases for users.

Andrew De La Torre, group VP of technology for Oracle Communications at cloud and data management company Oracle, believes that the next big wave of IoT adoption will be in bundled IoT or off-the-shelf IoT solutions that offer user-friendly operational functions and embedded analytics.

Results of a survey of 800 respondents revealed an evolution of priorities in IoT adoption across industries, De La Torre said—most notably, that enterprises are investing in off-the-shelf IoT solutions with a strong desire for connectivity and analytics capabilities built-in.

Because of specific capabilities, commercial off-the-shelf products can extend IoT into other industries thanks to its availability in public marketplaces. When off-the-shelf IoT aligns with industrial needs, it can replace certain components and systems used for general-use practices.

While off-the-shelf IoT is helpful to many companies, there are still risks as it develops—security risks include solution integration, remote accessibility and widespread deployments and usage. Companies using off-the-shelf products should improve security by ensuring that systems are properly integrated, running security assessments, and implementing policies and procedures for acquisitions.

The Future Of IoT

Customer demand changes constantly. IoT services need to develop at the same pace.

Here’s what experts expect the future of Iot development to look like:

Sustainability and IoT

Companies must embrace IoT and its insights so they can pivot to more sustainable practices, using resources responsibly and organizing processes to reduce waste.

There are multiple ways a company can contribute to sustainability in IoT:

Smart energy management: Using granular IoT sensor data to allow equipment control can eliminate office HVAC system waste and benefit companies financially and with better sustainability practices.
Extent use style: Using predictive maintenance with IoT can extend the lifespan of a company’s model of manufacturing. IoT will track what needs to be adjusted instead of creating a new model.
Reusing company assets: Improved IoT information will help a company determine whether it needs a new product by looking at the condition of the assets and use history.

IoT and AI

The combination of Artificial Intelligence (AI) and IoT can cause industries, businesses and economies to function in different ways than either IoT or AI function on their own. The combination of AI and IoT creates machines that have smart behaviors and supports strong decision-making processes.

While IoT deals with devices interacting through the internet, AI works with Machine Learning (ML) to help devices learn from their data.

AI IoT succeeds in the following implementations:

Managing, analyzing, and obtaining helpful insights from customer data
Offering quick and accurate analysis
Adding personalization with data privacy
Providing assistance to use security against cyber attacks

More Use of IoT in Industries

Healthcare is cited as one of the top IoT industries, but many others are discovering how IoT can benefit their companies.

Agriculture

IoT can be used by farmers to help make informed decisions using agriculture drones to map, image, and survey their farms along with greenhouse automation, monitoring of climate conditions, and cattle monitoring.

IoT enables agriculture companies to have more control over their internal processes while lowering production risks and costs. This will reduce food waste and improve product distribution.

Energy

IoT in the energy sector can improve business performance and customer satisfaction. There are many IoT benefits for energy industry, especially in the following areas:

Remote monitoring and managing
Process optimization
Workload forecasting
Grid balancing
Better decision-making

Finance

Banks and customers have become familiar with managing transactions through many connected devices. Because the amount of data transferred and collected is extensive, financial businesses now have the ability to measure risk accurately using IoT.

Banks will start using sensors and data analytics to collect information about customers and offer personalized services based on their activity patterns. Banks will then better understand how their customers handle their money.

Manufacturing

Manufacturing organizations gather data at most stages of the manufacturing process, from product and process assistance through planning, assembly and maintenance.

The IoT applications in the manufacturing industry include:

Production monitoring: With IoT services’ ability to monitor data patterns, IoT monitoring provides optimization, waste reduction and less mundane work in process inventory.
Remote equipment management: Remote work has grown in popularity, and IoT services allow tracking and maintaining of equipment’s performance.
Maintenance notifications: IoT services help optimize machine availability by receiving maintenance notifications when necessary.
Supply chains: IoT solutions can help manufacturing companies track vehicles and assets, improving manufacturing and supply chain efficiency.

For more industries using IoT: IoT in Smart Cities

Bottom Line: IoT Trends

IoT technology reflects current trends and reaches many areas including AI, security, healthcare, and other industries to improve their processes.

Acknowledging IoT in a business can help a company improve a company structure, and IoT will benefit a company’s infrastructure and applications.

For IoT devices: 85 Top IoT Devices

Big Data Trends and The Future of Big Data

Emma Crockett — Thu, 13 Apr 2023 17:00:00 +0000

Since big data first entered the tech scene, the concept, strategy, and use cases for it has evolved significantly across different industries.

Particularly with innovations like the cloud, edge computing, Internet of Things (IoT) devices, and streaming, big data has become more prevalent for organizations that want to better understand their customers and operational potential.

Real Time Analytics

Real time big data analytics – data that streams moment by moment – is becoming more popular within businesses to help with large and diverse big data sets. This includes structured, semi-structured, and unstructured data from different sizes of data sets.

With real time big data analytics, a company can have faster decision-making, modeling, and predicting of future outcomes and business intelligence (BI). There are many benefits when it comes to real time analytics in businesses:

Faster decision-making: Companies can access a large amount of data and analyze a variety of sources of data to receive insights and take needed action – fast.
Cost reduction: Data processing and storage tools can help companies save costs in storing and analyzing data.
Operational efficiency: Quickly finding patterns and insights that help a company identify repeated data patterns more efficiently is a competitive advantage.
Improved data-driven market: Analyzing real time data from many devices and platforms empowers a company to be data-driven. Customer needs and potential risks can be discovered so they can create new products and services.

Big data analytics can help any company grow and change the way they do business for customers and employees.

For more on structured and unstructured data: Structured vs. Unstructured Data: Key Differences Explained

Stronger Reliance On Cloud Storage

Big data comes into organizations from many different directions, and with the growth of tech, such as streaming data, observational data, or data unrelated to transactions, big data storage capacity is an issue.

In most businesses, traditional on-premises data storage no longer suffices for the terabytes and petabytes of data flowing into the organization. Cloud and hybrid cloud solutions are increasingly being chosen for their simplified storage infrastructure and scalability.

Popular big data cloud storage tools:

Amazon Web Services S3
Microsoft Azure Data Lake
Google Cloud Storage
Oracle Cloud
IBM Cloud
Alibaba Cloud

With an increased reliance on cloud storage, companies have also started to implement other cloud-based solutions, such as cloud-hosted data warehouses and data lakes.

For more on data warehousing: 15 Best Data Warehouse Software & Tools

Ethical Customer Data Collection

Much of the increase in big data over the years has come in the form of consumer data or data that is constantly connected to consumers while they use tech such as streaming devices, IoT devices, and social media.

Data regulations like GDPR require organizations to handle this personal data with care and compliance, but compliance becomes incredibly complicated when companies don’t know where their data is coming from or what sensitive data is stored in their systems.

That’s why more companies are relying on software and best practices that emphasize ethical customer data collection.

It’s also important to note that many larger organizations that have historically collected and sold personal data are changing their approach, making consumer data less accessible and more expensive to purchase.

Many smaller companies are now opting into first-party data sourcing, or collecting their own data, not only to ensure compliance with data laws and maintain data quality but also for cost savings.

AI/ML-Powered Automation

One of the most significant big data trends is using big data analytics to power AI/ML automation, both for consumer-facing needs and internal operations.

Without the depth and breadth of big data, these automated tools would not have the training data necessary to replace human actions at an enterprise.

AI and ML solutions are exciting on their own, but the automation and workflow shortcuts that they enable are business game-changers.

With the continued growth of big data input for AI/ML solutions, expect to see more predictive and real-time analytics possibilities in everything from workflow automation to customer service chatbots.

Big Data In Different Industries

Different industries are picking up on big data and seeing many changes in how big data can help their businesses grow and change. From banking to healthcare, big data can help companies grow, change their technology, and provide for their data.

Banking

Banks must use big data for business and customer accounts to identify any cybersecurity risk that may happen. Big data also can help banks have location intelligence to manage and set goals for branch locations.

As big data develops, big data may become a basis for banks to use money more efficiently.

Agriculture

Agriculture is a large industry, and big data is vital within the industry. However, using the growing big data tools such as big data analytics can predict the weather and when it is best to plant or other agricultural situations for farmers.

Because agriculture is one of the most crucial industries, it’s important that big data support it, and it’s vital to help farmers in their processes.

Real Estate And Property Management

Understanding current property markets is necessary for anyone looking, selling, or renting a place to live. With big data, real estate firms can have better property analysis, better trends, and an understanding of customers and markets.

Property management companies are also utilizing their big data collected from their buildings to increase performance, find areas of concern, and help with maintenance processes.

Healthcare

Big data is one of the most important technologies within healthcare. Data needs to be collected from all patients to ensure they are receiving the care they need. This includes data on which medicine a patient should take, their vitals are and how they could change, and what a patient should consume.

Going forward, data collection through devices will be able to help doctors understand their patients at an even deeper level, which can also help doctors save money and deliver better care.

Challenges in Big Data

With every helpful tool, there will be challenges for companies. While big data grows and changes, there are still challenges to solve.

Here are four challenges and how they can be solved:

Misunderstanding In Big Data

Companies and employees need to know how big data works. This includes storage, processing, key issues, and how a company plans to use the big data tools. Without clarity, properly using big data may not be possible.

Solutions: Big data training and workshops can help companies let their employees learn the ins and outs of how the company is using big data and how it benefits the company.

Data Growth

Storing data properly can be difficult, given how constantly data storehouses grow. This can include unstructured data that cannot be found in all databases. As data grows, it is important to know how to handle the data so the challenge can be fixed as soon as possible.

Solutions: Modern techniques, such as compression, tiering, and deduplication can help a company with large data sets. Using these techniques may help a company with growth and remove duplicate data and unwanted data.

Integrating Company Data

Data integration is necessary for analysis, reporting, and BI. These sources may contain social media pages, ERP applications, customer logs, financial reports, e-mails, presentations, and reports created by employees. This can be difficult to integrate, but it is possible.

Solutions: Integration is based on what tools are used for integration. Companies need to research and find the correct tools.

Lack Of Big Data Professionals

Data tools are growing and changing and often need a professional to handle them, including professionals with titles like data scientists, data analysts, and data engineers. However, some of these workers cannot keep up with the changes happening in the market.

Solutions: Investing money into a worker faced with difficulties in tech changes can fix this problem. Despite the expense, this can solve many problems with companies using big data.

Most challenges with big data can be solved with a company’s care and effort. The trends are growing to be more helpful for companies in need, and challenges will decrease as the technology grows.

For more big data tools: Top 23 Big Data Companies: Which Are The Best?

Bottom Line: Growing Big Data Trends

Big data is changing continuously to help companies across all industries. Even with the challenges, big data trends will help companies as it grows.

Real time analytics, cloud storage, customer data collection, AI/ML automation, and big data across industries can dramatically help companies improve their big data tools.

Top 10 Enterprise Networking Companies

Emma Crockett — Fri, 17 Mar 2023 17:00:00 +0000

Best for Enterprises: Hewlett Packard Enterprise / Aruba Networks
Best for Cloud Solutions: Arista Networks
Best for Growing Companies: Cisco
Best for Mobility: Cumulus Networks
Best for Popularity: Dell Technologies
Best for Scalability: Extreme Networks
Best for SDN: Juniper Networks
Best for Visibility: NETSCOUT
Best for Performance Management: Riverbed Technologies
Best for Versatility: VMware
How to Choose an Enterprise Networking Solution
Bottom Line: Top Enterprise Networking Companies

Best for Enterprises: Hewlett Packard Enterprise (Aruba Networks)

HPE-Aruba consistently ranks at the top of the enterprise networking solutions space and is known for its focus on unified networks. Aruba delivers SDN to scale along with an end-to-end interface. It offers zero-touch provisioning and end-to-end orchestration within a single pane of glass. It handles automated policy enforcement for the user, device, and app in both wired and wireless networking.

The platform also supports a high level of programmability through Python scripting and APIs, and a variety of cloud-based solutions designed to streamline IT operations and boost performance in SD-WANs. Users rank the company high for user experience, reconfigurability, and cybersecurity.

Aruba recently acquired Silver Peak Systems, a leader in the SD-WAN space. The platform unifies SD-WAN, firewall, segmentation, routing, WAN optimization, and more—with advanced orchestration and automated lifecycle management, self-learning capabilities through machine learning, and more.

Pros

Automated security: HPE’s networking portfolio eliminates inconsistent policies and keeps all security information safe while pushing policies to the entire organization.
Efficient network operations: The enterprise networking tools streamline analysis and identify vulnerabilities quickly for onboarding and configuration and enables segmentation for remote work, office connections, and the internet of things (IoT).
Network Visibility: HPE Aruba has a singular source to monitor data for infrastructure with any sized business. It allows the business to have alerts, performance, and client data flow.

Cons

Integration: The HPE Aruba enterprise networking tool has difficulty integrating with some systems’ technology.

Pricing

For pricing, go to the Hewlett Packard Enterprise shop page.

To learn more about HPE perspective: Q&A on Networking With Scott Calzia at Aruba

Best for Cloud Solutions: Arista Networks

Arista Networks promotes the concept of “cognitive networking” and clouds through SDN. It supports unified edge systems across networks through a portfolio of products.

The vendor offers a variety of products and solutions designed for enterprise networking. Its Cognitive Campus solution optimizes cloud solutions, specifically for performance, using an analytics-driven approach that focuses heavily on cybersecurity, visibility, and location-based services.

The software-driven approach aims to reduce networking complexity, improve reliability and performance, and boost network monitoring and security functions. The vendor’s Cognitive Management Plane incorporates artificial intelligence and a repository to automate numerous actions.

Pros

Single operating system: Arista Networks’ networking solution operates a system functioning across the entire infrastructure to reduce fear of backward capabilities.
Helpful configuration: Arista Networks customers promote better configuration than most networking software, making it easy, clear, and easy to understand.
Easy to manage: The way Arista Networks’ networking solution is laid out makes managing the platform simple compared to other platforms.

Cons

Expensive: Compared to other enterprise networking platforms, Arista Networks’ solution can be pricey for some customers.

Pricing

For pricing, reach out to Arista Networks’ Contact Sales page.

For more on Arista Networks: Arista: Networking Portfolio Review

Best for Growing Companies: Cisco Systems

Cisco Systems is an undisputed leader in networking, and key expertise and products for almost every possible organization and business need, from carrier-grade equipment to enterprise data center solutions.

Cisco Digital Network Architecture is at the heart of the company’s offerings. Cisco DNA relies on a software-delivered approach to automate systems and assure services within a campus and across branch networks and WANs.

It is designed to work across multi-cloud environments, with AI/ML tools to automate, analyze, and optimize performance and thwart security threats. Key components include automated workflows, analytics, behavioral tools, SD-WAN, and other software-defined offerings designed for both Ethernet and wireless.

In addition, the company receives high marks for its switches, routers, hardware and software, SD-WAN products, and enterprise network security tools.

Pros

Capability to scale: If a company is growing, the Cisco Systems’ networking platform offers the capability to scale for any size business.
Great management: Multiple customers address the networking platform’s ability to manage their data and infrastructure without much human help.
Visibility: Cisco has a visibility page where a customer can see every part of their infrastructure in a dashboard.

Cons

Licensing expensive: For smaller companies, the licensing cost can be very expensive.

Pricing

Pricing for the Cisco Systems’ networking package is listed here or customers can reach out by contacting sales.

For more information: Cisco Report Shows Cybersecurity Resilience as Top of Mind

Best for Mobility: NVIDIA’s Cumulus Networks

Cumulus Networks, now part of NVIDIA, delivers real-time visibility, troubleshooting, and lifecycle management functions as part of its Cumulus NetQ solution.

Cumulus promotes a “holistic” approach to networking. With roots in the Linux world, it delivers automated solutions without specialized hardware. Forrester describes the approach as an “app-dev perspective.”

Cumulus includes a robust set of tools and controls that tackle advanced telemetry, deep analytics, and lifecycle management. For example, NetQ uses specialized agents to collect telemetry information across an entire network and provide real-time insight, including state changes for data centers.

Diagnostics tools allow administrators to trace network paths, replay network states at a specific time point in the past, and review fabric-wide event change logs. The platform supports rich scripting and configuration tools.

Pros

Open networking: Cumulus uses an open network, which has open standards and separated networking hardware devices from software code.
Easy to learn: Networking tools can be difficult to learn and adjust with current systems, but customers say the platform is easy to learn.
Training time lower: Training on new tech can take hours or days to master. Cumulus saves companies time and money by making the process quicker.

Cons

Need license: Where some networking platforms do not require licensing, Cumulus requires it, raising the price for smaller companies.

Pricing

For pricing, go to NVIDIA’s Shop Networking Products page.

For more on networking: 5 Top Cloud Networking Trends

Best for Popularity: Dell Technologies

Dell Technologies offers a robust and highly-rated portfolio of enterprise solutions. The company offers a wide array of products and solutions for enterprise networks, including Ethernet switches, wireless gear, smart fabric management software, services for automated fabric management, network operating systems, and various products and tools that facilitate SDN.

Dell Technologies also focuses on maximizing connectivity at the edge with cloud integration: integrated hardware and software solutions for SD-WAN and clouds. This enables autonomous fabric deployment, expansion, and lifecycle management for software-defined infrastructures.

The company aims to “meet the demands of modern workloads and virtualization environments while greatly simplifying deployments and management” through a single pane of glass.

Pros

Automation saves time: Tasks done by automation are praised for the time and money that is saved using Dell’s networking services.
Helpful backups: When Dell Technologies backup customer data, they feel secure and protected.
Support helpful: The customer support Dell provides is helpful and knowledgeable on how to fix errors through different parts of the network.

Cons

Runs on Java: Dell’s enterprise networking services require a company to use Java, and customers say that occasionally clearing Java cache takes up time.

Pricing

To see pricing on networking tools, go to the Dell Technologies Shop.

For more: Dell Technologies: Networking Portfolio Review

Best for Scalability: Extreme Networks

Extreme Networks offers switching, routing, analytics, security, and other management solutions. The Extreme Networks product line is defined by Extreme Cloud IQ, a platform that automates end-to-end, edge-to-data-center network operations through the use of AI and machine learning. It is designed to scale to more than 10,000 managed devices per wireless appliance and includes comparative analytics and ML-driven scorecards.

Extreme Management Center provides on-premises network management in a variety of networking environments. In the realm of unified communications, Extreme Campus Controller delivers wired and wireless orchestration for campus and IoT networks.

Pros

Faster deployment: Some networking tools take time to deploy, and Extreme Networks has a positive reputation for their deployment.
Reliability: After a business installs the tools, they do the work and do not require supervision.
Easy to manage: Customers say that all of the data is in one place for the tech and business to manage their systems.

Cons

Cost: While cost is better than most networking tools, the cost is high for small to mid-sized businesses.

Pricing

For pricing, go to Extreme Networks’ How to Buy page.

For more information: Extreme Networks: Networking Portfolio Review

Best for SDN: Juniper Networks

Juniper Networks has established itself as an innovator and leader in the enterprise networking space. Juniper Networks places a heavy emphasis on smart automation within a single, consistent operating system. It receives high marks for manageability and simplicity.

Juniper offers a wide array of enterprise networking solutions designed for nearly any requirement. This includes equipment for switching, routing, wireless, packet optical, SDN, and network security. These solutions address enterprise requirements for enterprise WAN, campus networking, cloud-native, multi-cloud, 5G, and IoT/IoT devices. The vendor’s Contrail Networking solution is entirely focused on SDN.

Pros

Traffic management: Traffic is managed within the system to avoid data going into the wrong places and keeping the company secure.
Ease of use: Juniper Networks’ networking portfolio is easy to use for businesses that work with their portfolio.
Automates security: Juniper Networks keeps customers’ security tools automated at all times.

Cons

Expensive: The portfolio is expensive in comparison to other enterprise networking companies.

Pricing

For pricing, go to Juniper Networks contact sales.

For more information: Juniper Networks: Networking Portfolio Review

Best for Visibility: NETSCOUT

NETSCOUT offers a full spectrum of products and solutions designed to support digital transformation, managed services, and digital security.

NETSCOUT prides itself on delivering complete visibility within networks and clouds, as well as real-time actionable intelligence using machine learning and smart analytics. These tools help organizations gain deeper visibility into data centers, cloud frameworks, performance issues, and security risks.

One of the vendor’s strengths is its technology partners, which include AWS, VMware, Microsoft, Oracle, and Cisco Systems. NETSCOUT supports numerous vertical industries, including healthcare, retail, transportation, financial services, and government.

Pros

User-friendly dashboard: NETSCOUT’s networking portfolio offers a user-friendly dashboard that gives visibility to the customer’s company.
Troubleshooting: NETSCOUT keeps cybersecurity risks at ease by troubleshooting the tools NETSCOUT offers.
Capture: The tools help companies by capturing packet history and current movement.

Cons

Requires training: Unlike many other tools, NETSCOUT’s portfolio software needs training to be able to use the system.

Pricing

For pricing, follow the product tab and choose the product you want, then click the Try a Demo page.

For more information about networking: 10 Top Companies Hiring for Networking Jobs

Best for Performance Management: Riverbed Technology

Riverbed Technology focuses on four key factors: performance, applications, visibility, and networks. It achieves results through WAN optimization, application acceleration, software-defined WAN, and network performance management modules. The Riverbed Network and Application Performance Platform are designed to “visualize, optimize, accelerate, and remediate the performance of any network for any application.”

The open platform effectively ties together performance management, WAN optimization, application acceleration, and SD-WAN solutions. Another Riverbed product, Steelhead, delivers a technology foundation for maximizing and optimizing the efficiency and performance of networks, including SaaS products. The focus is on network performance and efficiency through information streamlining, transport streamlining, application streamlining, and elastic performance.

Pros

Easy deployment: Deploying Riverbed Technology’s networking portfolio is easy for customers.
Traffic insights: The tools give visibility to customers who want to see their traffic insights.
Long-distance success: With remote work becoming more popular, Riverbed Technology can travel to whoever needs access in the company.

Cons

No public cloud integration: Riverbed Technology cannot be integrated into the public cloud, which is a large part of data storage.

Pricing

For pricing, go to Riverbed Technology’s Free Trial Options.

Best for Versatility: VMware

VMware was a pioneer in virtualization products and solutions. Over two decades, it has distinguished itself as an industry leader with its focus on supporting multi-cloud environments, virtual cloud networking, and solutions designed to support digital business.

The company offers a network solution for several industry verticals, including retail, healthcare, financial services, manufacturing, education, and government. It has numerous partnerships that make it an attractive choice for enterprises. A core tenant for VMware is building a digital foundation.

VMware Tanzu offers products and services designed to modernize application and network infrastructure. This includes building cloud applications, advancing existing apps, and running and managing Kubernetes in Multiple Clouds. VMware’s Virtual Cloud Network provides a seamless, secure, software-defined networking layer across networking environments. The company’s VMware VRNI, which is designed to troubleshoot network issues and cyber security, is highly rated among reviewers at Gartner Peer Insights.

Pros

Versatile features: VMware’s enterprise networking portfolio offers many features within their portfolio.
Cost savings: VMware has a cheaper enterprise networking portfolio than a lot of the competition.
Easy integration: VMware easily integrates with other tools in a company’s infrastructure.

Cons

Difficult setup: Compared to other enterprise networking portfolios, VMware’s networking tools require real expertise to set up.

Pricing

For pricing, go to VMware’s store page.

For more on VMware: VMware NSX Review

How to Choose an Enterprise Networking Solution

The networking market is incredibly complicated and confusing. Dozens of vendors compete for mind share and market share. Adding to the challenge: every organization has different requirements and each solution approaches the task of networking in different ways. As SDN becomes more popular, this adds to the decision-making process. In some cases, differences among vendors, products, and approaches are subtle—yet exceptionally important. Here are five key things to consider when making a selection:

1. Does The Vendor Support The Flexibility And Agility You Require?

While all vendors promise a high level of flexibility and agility, it’s not so simple to sort everything out. Success depends on your existing infrastructure—including branch offices—and how well the current environment matches the vendor’s solution. This means taking an inventory of your current environment and understanding how the solution will change—and improve—processes. Interoperability, APIs, and support for frameworks like BiDi and SWDM might factor into the situation.

2. Do The Vendor’s Products And Solutions Rank Among The Top?

While high marks from industry analyst firms like Gartner and Forrester are no guarantee of success, they serve as an excellent benchmark for understanding where a vendor resides among its peers, what features stand out, and where a vendor lags behind the pack. Magic Quadrant and Wave report also inject objectivity into what can become a subjective and sometimes emotional process. It’s also wise to read peer reviews at various professional sites and trade information with others in your industry.

3. How Does The Cost Vs. Value Equation Play Out?

The cheapest solution isn’t necessarily the best, of course. Your goal should be to understand switching costs and find the sweet spot on the return on investment (ROI) curve. What tradeoffs are you willing to make to save money? Which features and capabilities are non-negotiable? Which solution can unlock the connectivity you require to be an innovator or disruptor?

4. Is The Vendor A Good Long-Term Partner?

Several factors that can fly below the radar are critical when selecting a vendor. Among them are financial stability, roadmap, and vision, knowledgeability of their engineers and technical staff, and customer support. The latter can be critical. You should have a clear point of contact with the company, and this person should be highly accessible. If you can’t get a strong commitment upfront, this could be a problem. Regardless, it’s wise to lock down key issues and service levels with a service level agreement (SLA).

5. Who And What Do The Vendors Support?

The days of selecting a single vendor for everything are pretty much over. In all likelihood, you will need networking products and solutions that span geographic locations, data centers, clouds, and more. In addition, you will likely have to mix and match some products.

Do the vendor’s offerings play nicely with others? Do they adhere to industry standards? Do they support open source? What kind of service provider are they for wireless network needs, like the management and deployment of mobile devices? What security standards do they adhere to? How well can they work with your existing network if you’re looking to make a shift?

Bottom Line: Top Enterprise Networking Companies

Choosing the enterprise networking solution provider is critical. As SDN becomes a centerpiece of the industry, it’s important to understand how various solutions approach networking, including whether a vendor uses a standard approach or places a hypervisor over a virtual network.

Although all enterprise networking solutions presumably address the same general tasks—centralizing complex management and administrative functions and improving manageability—the way products work varies greatly. This includes various features that vendors offer, how network management tools interact with other IT systems, troubleshooting and security capabilities built into products, and, most importantly, understanding the specific needs of an organization.

Read next: Network Security Market