Jump to:

• What do Cloud Services Cost?
• Navigating Cloud Computing Costs
• Types of Cloud Computing Services
• Cloud Providers Pricing Comparison
• Bottom Line: Understanding What Cloud Services Cost

What do Cloud Services Cost?

Determining the cost of cloud services can be a tricky proposition. While most cloud service vendors offer a pricing calculator that lets you choose services and products and enter usage requirements to generate an estimate, it’s not always obvious what your needs will be or how the charges will add up. Here’s a look at the different ways vendors approach cloud computing costs.

Pricing Factors

Several factors come into play when providers set the pricing for cloud computing, including the types and quantity of services and computing resources required, data transfer rates, and storage needs.

Networking

Cloud computing services require a robust network infrastructure for interconnectivity, and networking costs are based on bandwidth usage and data transferred in or out of the cloud infrastructure.

Storage

Cloud vendors also charge for storage used, typically on the type of storage (files, block, elastic, etc…), performance, features, and accessibility.

Hardware and Maintenance

Providers need to invest in hardware (drives, memory, processors, servers, routers, firewalls, etc…), continuous updates, and maintenance.

Hidden Charges

Providers sometimes charge hidden expenses that can drive up costs. Some of the most common to watch out for include the following:

• Data overages–cloud vendors generally offer fixed data limits and storage in their pricing plans, and exceeding limits incurs additional costs.
• Exit fees–some vendors charge them to retrieve your data if you discontinue your cloud computing services.
• Region and availability zones–most vendors charge different rates for services across different regions and availability zones; check pricing based on your region.
• Support costs–vendors may charge additional for tracking support issues.

Pricing Models

Different providers also offer different pricing models—here are the most commonly used.

On-Demand

This is a pay-as-you-go plan billed on a per-second or per-hour basis, depending on usage; this model is all about flexibility, scalable with no upfront commitments.

Instance-Based

In this model, costs correlate with the cloud instances or virtual servers being used; the bill reflects the number of dedicated servers and hosts allocated to you.

Tiered

Much like a restaurant menu, tier-based pricing presents a variety of “plans” or “bundles” from basic plans with essential features to premium offerings packed with advanced functionalities; select the level of service that aligns with your requirements and budget.

Subscription

This model turns cloud computing services into a recurring expense; you can opt for monthly, quarterly, half-yearly, or annual plans, allowing for predictable budgeting.

Navigating Cloud Computing Costs

Because cloud computing costs can be a complicated field to navigate, it’s important to know your specific needs before you commit. Here’s how to strategically navigate the cost implications of cloud computing based on your unique business needs.

Assess Infrastructure Needs

Shifting to the cloud entails investing in robust IT infrastructure. If you’re already using cloud services and are considering a change in providers, the investment might not be as substantial. Vendor terms can vary, so it’s crucial to discuss infrastructure requirements with your prospective provider first.

Estimate Your Usage

Identifying your specific needs can help you make informed decisions. Analyze your server, network, storage, bandwidth, and deployment model requirements. With a clear view of your usage, you can choose the most suitable pricing model, be it pay-as-you-go, free-usage, or subscription-based plans.

Compare Cloud Services

Evaluate different cloud computing services, their features, free usage limits, and pricing strategies. Request detailed, customized quotes from providers to understand what they offer in relation to your needs.

Types of Cloud Computing Services

There are a wide range of cloud computing services available to individuals and enterprise users. To understand pricing and make more clear comparisons, it’s important to first understand the most commonly used models.

IaaS (Infrastructure-as-a-Service)

IaaS is like a digital toolbox, offering scalable virtual resources that cater to enterprise storage, networking, and computing needs. Rather than purchasing, configuring, and maintaining servers, businesses lease those computing services from a provider. The infrastructure they are leasing is all the memory, storage, and networking they need in a virtual operating environment that is scalable and flexible.

Moving infrastructure to the cloud can help businesses curb the hefty costs of developing and maintaining physical infrastructure. What makes IaaS unique is its flexible pricing structure. Like a utility bill, costs are tied to actual usage–vendors offer a spectrum of pricing options, including long-term subscription contracts, monthly billing, or even a per-server or hourly rate.

PaaS (Platform-as-a-Service)

PaaS provides businesses with a comprehensive platform to manage their development needs without the headache of buying and maintaining each component separately. Like having an outsourced IT department, Paas is a full-suite cloud environment that includes hardware resources, databases, servers, storage, networks, operating systems, and software.

It moves more of the IT management responsibilities to the vendor than IaaS, and is often used to streamline the application development process by bundling the tools needed to create certain kinds of apps. It can be more cost-effective for many businesses than developing and supporting equal resources in-house. The pricing is typically determined by the specific service features and usage. Some providers also offer limited-time free trials or options to upgrade to subscription plans.

SaaS (Software-as-a-Service)

SaaS offers ready-to-use software applications delivered straight from the cloud. The vendor manages the entire IT stack. Enterprise users access it through a browser. The burden of updates, security patches, and feature fixes rests with the service provider, allowing businesses to focus on using the software rather than building it.

Pricing for SaaS is diverse, with vendors offering free trials, monthly or annual subscription plans, or even tiered pricing to accommodate a variety of functional needs.

Cloud Providers Pricing Comparison 

Now that you’ve learned how pricing works, here’s a look at how the cloud computing costs of the major providers compare to one another. Though many cloud services providers offer a wide range of cloud computing services, for the purposes of this guide we’ve focused on the five most widely used by enterprise clients: Amazon Web Services (AWS) Lambda, IBM Cloud Code Engine, Azure Cloud Services, Google Cloud Platform, and Oracle Cloud.

AWS Lambda

Amazon offers a wide range of products for cloud computing, but its AWS Lambda is a top serverless computing service that allows businesses to run code, automate administration and management, and package, deploy, and orchestrate multiple functions.

AWS Lambda offers one million free requests per month as a part of the AWS Free Tier plan. It has a flexible pricing model with its Compute Savings Plan, measured in dollars-per-hour. Users can save up to 17 percent with this plan in exchange for a commitment to a fixed usage amount.

In response to an event notification trigger, Lamda generates a request and charges for the functions used. The cost is calculated by duration-in-milliseconds for the time your code executes and the memory allocated to your functions and processor architecture.

View the AWS Lambda pricing page.

IBM Cloud Code Engine

IBM Cloud platform is a robust ecosystem and computing solution based on a serverless architecture. It offers a single runtime environment with automatic scaling and secure networking. IBM Cloud Code Engine is priced by resources used, and is based on HTTP requests, memory, and vCPU consumed by your workloads.

View the IBM Cloud Code Engine pricing page.

Azure Cloud Services

Microsoft’s Azure Cloud Services is a PaaS model that offers a deployment environment for cloud applications and services with high availability and flexible scalability.

It includes free trial services for a limited period—some Azure products remain free for a fixed number of requests, instances, memory used, or hours used, while others are free for a fixed 12 month period. Popular free services include Azure Virtual Machines Windows and Linux versions, Azure Functions, and Azure App Service.

The pricing plans follow a pay-as-you-go model that considers such different factors as instances, cores, RAM, and storage. There are various virtual machine series for different needs. For example, the A series is ideal for entry-level dev/testing, the B series is for moderate workloads, and the D series is for production workloads.

View the Azure pricing page.

Google Cloud Platform

Google offers enterprise-ready cloud services through the Google Cloud Platform. It includes a suite of computing products like the App Engine, Compute Engine, VMWare Engine, Spot VMs, Cloud GPUs, and more, as well as an integrated storage solution.

Google follows the pay-as-you-go pricing model with additional discounts for prepaid resources. It also has free-tier products with a specified free usage limit—new customers get $300 free credits. The Compute Engine usage is measured in gibibytes (GiB). It is calculated based on disk size, network, and memory usage.

Each Google product has different pricing, which can be estimated using the pricing calculator or by contacting the sales team for more details.

View the Google Cloud Platform pricing page.

Oracle Cloud

Oracle provides cloud computing services through its Oracle Cloud Infrastructure, a fast, flexible, and affordable solution. This multi-cloud architectural framework can be used for virtual machines, enterprise workloads, serverless functions, containers and Kubernetes, and graphics processor unit (GPU) and high performance computing HPC instances.

The Oracle Free Tier includes more than 20 cloud services which are always free with no time limits. It follows a competitive pricing policy that offers the same price regardless of region.

View the full Oracle Cloud Infrastructure pricing page.

Other Cloud Services

In addition to the top five, other cloud service vendors provide computing services at varying costs. The following chart offers a quick comparison of their pricing structures.

Bottom Line: Understanding What Cloud Services Cost

As businesses increasingly embrace digital technology, the cloud continues to evolve, offering more powerful tools and services. The immediate positive impact of cloud technology is undeniable—more than 90 percent of enterprises use cloud services, and 80 percent of them see significant improvements in their operations within months of implementation. Investing in cloud computing sooner rather than later can yield substantial benefits and keep your business competitive in the global market.

But cloud computing is not a one-size-fits all service, and not all vendors offer the same pricing structures. Understanding how the market works, the factors that affect pricing, and what your specific needs are can give your organization a leg up on finding the right service provider and the right cloud services to meet them.

Read next: Top 10 Cloud Project Management Tools

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) 

This certification bestows the professional level of Cisco Career Certification upon those who successfully complete it. It specializes in the skills needed to implement effective solutions in enterprise-class data centers. Similar to the CCNA, the CCNP requires a passing score on an exam.

The Data Center exam tests the skills needed to run a data center effectively, including knowledge of the implementation of such core data center technologies as network, compute, storage network, automation, and security. A second exam lets participants specialize in a concentration of their choosing—candidates need to pass both exams to earn the certification.

Cisco Certified Network Professionals typically hold such roles as senior network designer, network administrator, senior data center engineer, and consulting systems engineer.

Prerequisites

No prerequisites; Recommended for people with three to five years of industry experience in security solutions

Validity

Three years

Accreditation

Cisco

Location

Classroom/e-learning/private

Cost

$300 per exam

VMware Certified Professional – Data Center Virtualization (VCP-DCV 2023)

VMware offers more than 16 data center certifications, including the VCP-DCV 2023, which bridges the gap between cloud management and classic data center networking. The VCP-DCV certification tests an individual’s knowledge of VMware’s vSphere solutions, including virtual machines, networking, and storage. Professionals seeking job roles including virtualization administrators, system engineers, and consultants should apply.

VMware also offers other advanced professional courses in virtualization design and deployment: VMware Certified Advanced Professional Data Center Virtualization Design (VCAP-DCV Design),  VMware Certified Advanced Professional Data Center Virtualization Deploy (VCAP-DCV Deploy) and VMware Certified Design Expert (VCDX-DCV).

Prerequisites

Experience with vSphere 7.x or vSphere 8.x is recommended; Applicants with no prior VCP certifications must enroll in at least one training course

Validity

No expiration; recertification recommended to upgrade skills

Accreditation

VMware

Location

Online

Cost

$250

Juniper Networks Junos Associate (JNCIA-Junos)

The JNCIA-Junos certification is a beginner/intermediate course designed for networking professionals that validates their understanding of the core functionality of the Juniper Networks Junos operating system. It establishes a baseline for multiple certification tracks, including Juniper’s Enterprise Routing and Switching Certification Track and Service Provider Routing and Switching Certification Track.

Candidates can avail themselves of the resources on the Juniper Networks website and then sign up for the 90-minute, 65 multiple-choice question exam. Pass/fail status is shown directly after the exam, which certifies knowledge in data center deployment, implementation of multi-chassis link aggregation group (LAG), internet protocol (IP) fabric, virtual chassis, virtual extensible LANs (VXLANs), and data center interconnections.

Prerequisites

Juniper Networks Certified Specialist Enterprise Routing and Switching certification; Advanced Data Center Switching course recommended

Validity

Three years

Accreditation

Juniper Networks

Location

Online

Cost

$2,500-$4,750 depending on course location

Schneider Electric Data Center Certified Associate (DCCA)

This associate certification from Schneider Electric validates foundational knowledge of physical infrastructure in data centers and requires candidates to demonstrate proficiency in such aspects as cooling, power management, and physical security, among others.

Schneider offers multiple courses to prepare for the Data Center Certified Associate exam. Candidates may apply for examination after completion of the course. This certification is meant for professionals looking to work with designs or upgrades for the physical layer data centers and covers foundational knowledge of data center design, builds, and operations.

Prerequisites

None

Validity

Does not expire

Accreditation

Schneider Electric

Location

Online

Cost

$250

VCE Certified Professional

Converged infrastructure systems vendor VCE’s Certified Professional Program offers experienced IT professionals operating in converged infrastructure environments the opportunity to validate their domain-specific focus with cross-domain expertise.

Candidates begin with the Converged Infrastructure Associate credential and then choose one of two certification tracks. The Deploy track is intended for deployment and implementation professionals, while the Manage track is intended for administration and management professionals. The VCE program trains candidates in system concepts, security, administration, resource management, troubleshooting, and data center maintenance.

Prerequisites

VCE Certified Converged Infrastructure Associate (VCE-CIA) certification

Validity

Two years

Accreditation

VCE Plus

Location

Offline

Cost

$200

BICSI Registered Communications Distribution Designer (RCDD)

BICSI is a professional association supporting the advancement of information and communication technology professionals, and the RCDD is its flagship program. It trains participants in the design and implementation of telecommunications distribution systems as a part of an infrastructure development track. Being recognized as a BICSI RCDD bestows industry recognition and can accelerate career paths.

Eligible candidates must have two years of industry experience. The exam tests their knowledge of design, integration, implementation, project management, and building physical infrastructure for data centers.

Prerequisites

Two years of industry experience

Validity

Does not expire

Accreditation

BICSI

Location

Offline

Cost

$495

EPI Certified Data Centre Expert (CDCE)

EPI is a Europe-based, globally focused provider of data center infrastructure services. Its CDCE course trains and certifies IT managers and data center professionals in building and relocating critical infrastructures and data centers. The exam consists of two parts: a closed-book exam, and an open question exam in which candidates must answer 25 questions in 90 minutes.

Topics include choosing optimum centers, describing components, designing life cycle stages, business resilience, site selection, technical level design, reading electrical Single Line Diagrams (SLD), evaluating product datasheets, correlating equipment specifications, floor loading capacity, maintenance requirements, developing Individual Equipment Test (IET), and building checklists for critical data center facility.

Prerequisites

CDCS Certificate

Validity

Three years

Accreditation

EPI

Location

Online/Offline

Cost

Varies with service provider

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.

Read next: 7 Data Management Trends: The Future of Data Management

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

Data Lakehouses vs. Data Lakes vs. Data Warehouses

A data lakehouse is a new data architecture that combines the best features of data lakes and data warehouses into a single, centralized platform to store and handle data. Designed to address the weaknesses of the two, this comprehensive data platform can perform advanced analytics and generate valuable real-time insights by supporting the entire lifecycle of data processing for continuous streams of real-time and historical data.

Data lakes are vast repositories of raw data in its native format. Primarily designed for the storage of unstructured data—data generated by Internet of Things (IoT) devices, social media posts, and log files, for example—they are well-suited to storing store large volumes of data at a relatively low cost, but lack the capacity to process and analyze that data. Data stored in lakes tends to be disorganized, and because they require the use of external tools and techniques to support processing, they’re not well-suited for business intelligence (BI) applications and can lead to data stagnancy issues—sometimes referred to as “data swamps”—over time.

Data warehouses, on the other hand, are designed for the storage, processing, and analysis of large volumes of data—primarily structured data like information from customer relationship management systems (CRMs) and financial records. They excel at handling structured data, but are generally not as useful for unstructured data formats. They’re also inefficient and expensive for organizations with constantly expanding data volumes.

Data lakehouses bridge the gap by combining the storage capabilities of a data lake with the processing and analytics capabilities of a data warehouse. A data lakehouse can store, process, and analyze both structured and unstructured data in a single platform.

Learn more about data architecture vs. data modeling.

Key Features of a Data Lakehouse

Data lakehouses can facilitate high-speed data queries and other data processing efforts, consolidating data from multiple sources and in multiple formats in a single, flexible solution. Here are some of the key features that set them apart from other storage solutions:

• Unified data architecture. Data lakehouses provide a unified and centralized platform for the storage, processing, and analysis of both structured and unstructured data.
• Scalability and flexibility. Due to data lakehouses’ ability to handle vast volumes of data, they’re also capable of exceptional scalability, enabling businesses to increase their data capacity based on demand.
• Advanced analytics support. Data lakehouses can facilitate advanced analytics, including machine learning and artificial intelligence, on stored data.

Benefits of a Data Lakehouse for Business Operations

Why choose a data lakehouse over a data lake or data warehouse? They can be used across a wide range of industries to help enterprises meet their data processing and business intelligence needs. In the healthcare sector, for example, data lakehouses are used to store and keep track of patient data, enabling healthcare providers to deliver personalized care. In the finance industry, data lakehouses are used to manage and analyze transaction data, helping financial institutions detect fraudulent activities.

Here are few of the key benefits of data lakehouses for enterprise use.

Simplified Data Management

In traditional data warehouses, data needs to be transformed and loaded before analysis, while data lakes are raw and lack schema enforcement. Data lakehouses, on the other hand, enable businesses to ingest and store both types of data in the same location, simplifying the process of needing to manage multiple storage technologies. This enables businesses to focus on data-driven decisions more effectively.

Improved Data Accessibility and Collaboration

Data lakehouses facilitate data accessibility and collaboration across the various departments of an organization thanks to centralizing the repository of the enterprise data. This lets employees access a much wider range of data sets without the need for complex data request procedures or access permissions. This also enables teams to work together more efficiently by letting analysts, data scientists, and business users collaborate on data exploration, analysis, and visualization during the decision-making process.

Scalability and Cost Efficiency

When combined with cloud-based storage and cloud computing, data lakehouses allow businesses to easily scale their data infrastructure based on demand. As the volume of data grows, the architecture can expand to handle the influx of data with minimum disruptions or last-minute hardware investments. Most data lakehouse providers offer pay-as-you-go models for cost efficiency, as businesses only pay for the resources they use. This eliminates the need for expensive, upfront infrastructure costs, making it suitable for businesses of all sizes.

Real-time Analytics and Processing

Using data lakehouses, organizations can perform real-time data analytics and processing, generating immediate insights and responses to changing market conditions and customer purchasing behaviors and trends. This capability is particularly important for industries that rely on up-to-date information, such as retail, finance, and telecommunications. By harnessing real-time data, they can better optimize operations, personalize customer experiences, and gain a competitive edge in the dynamic market landscape.

Data Lakehouse Architecture

Building a data lakehouse structure from scratch can be a complicated task. For many enterprises, paying for the service from a vendor will be a better option. Databricks is one of the better known data lakehouse providers; others include Amazon Web Services (AWS), iomete, Oracle, and Google. There are also hybrid solutions that allow more control over the data lakehouse structure while working alongside a cloud provider for easier implementation.

At a high level, five levels comprise data lakehouses:

• Ingestion. This layer uses a variety of protocols to connect to disparate external sources, pull in the data, and route it to the storage layer.
• Storage. This layer keeps all the data (both structured and unstructured) in affordable object storage, where it can be accessed directly by client tools.
• Metadata. This layer deploys a unified catalog to provide information about all the data in the storage layer, making it possible to implement data management.
• Application Programming Interface (API). This layer serves as a host layer for the APIs that are used to analyze and process the data.
• Consumption. This layer is where client applications perform BI, visualization, and other tasks on the data.

While each layer is essential to the architecture, the metadata layer is the one that makes data lakehouses more useful than either data lakes or data warehouses. It allows users to apply data warehouse schemas and auditing directly to the data, facilitating governance and improving data integrity.

Bottom Line: The Future of Data Lakehouses

Data lakehouses are a relatively new architecture, but because they provide a single point of access to an organization’s entire data stores, their future looks promising. As businesses continue to generate vast amounts of data, the need for a unified data platform like a data lakehouse will only increase.

Enterprises already using data lakes will find the shift to a data lakehouse can provide better data processing capabilities while creating cost efficiencies over a data warehouse. Opting for a single platform can also cut down on costs and redundancy issues caused by using multiple data storage solutions. A data lakehouse can also support better BI and analytics and improve data integrity and security.

Advancements in technologies like machine learning and artificial intelligence will only increase the capabilities of data lakehouses, and as they become more intelligent and better able to automate data processing and analysis, they’ll become more useful enterprises hungry for more insights to give them a competitive advantage.

Read next: Data Management: Types and Challenges

The potential is huge: the generative AI market is poised to grow to $1.3 trillion over the next 10 years from a market size of just $40 billion in 2022, according to a new report by Bloomberg Intelligence.

Getting AI right relies on quality data—particularly unstructured data. AI success depends upon the appropriate curation and management of this file and object data, which makes up at least 80 percent of all data in the world. This article identifies the challenges of those efforts and offers 10 tips for addressing them.

Managing Unstructured Data and ROT

Unstructured data, given its volume and the many different types of files and formats it comprises—from documents and images to sensor and instrument data, video, and more—is vexing to manage. Often distributed across multiple storage systems in the increasingly hybrid, multi-cloud enterprise, it is hard to search, segment, and move around as needed.

Due to its growth, unstructured data is expensive to store and backup. In fact, a majority (68 percent) of enterprise organizations surveyed in 2022 are spending 30 percent or more of their IT budgets on storage. These issues are made worse in data-intensive industries as copies of redundant, obsolete, and trivial (ROT) data are rarely deleted by researchers and other teams when projects are completed.

Managing unstructured data for AI requires new solutions and tactics, including a data-centric approach to guide cost-effective storage and data mobility decisions across vendors and clouds.

There’s also a growing need to ensure that the right data sets are leveraged. New research from Stanford found that the performance of large language models (LLMs) “substantially decreases as the input context grows longer, even for explicitly long-context models.” In other words, curating the right data sets may be more important than large data sets, depending on the project.

10 Tips for Managing Unstructured Data in Generative AI

Generative AI solutions, guidelines, and practices are changing daily. But establishing a foundation for intelligent unstructured data management can help organizations flex and shift through this transformative era. Here are some tactics to consider.

Start with visibility

Data indexing is a powerful way to categorize all of the unstructured data across the enterprise and make it searchable by key metadata (data on your data) such as file size, file extension, date of file creation, and date of last access. Visibility is foundational for right-placing data to meet changing business needs for archiving, analytics, compliance and so on.

Understand key data characteristics

When laying a foundation for AI, more information is better. The more information you have on your data, the better prepared you’ll be to deliver it to AI and ML tools at the right time—and the better prepared you’ll be to ensure you have the right storage infrastructure for these new use cases. At a minimum, you’ll need to understand data volumes and growth rates, storage costs, top data types and sizes, departmental data usage statistics, and “hot” or active versus “cold” or rarely-accessed data.

Tag and segment data

Once you have a base level of understanding about your data assets, you can enrich them with metadata for additional search capabilities. For instance, you may want to search for files containing personally identifiable information (PII) or customer data, intellectual property (IP) data, experiment name, or instrument ID. Those files could be segmented for compliant storage or to feed into an analytics platform.

Collaborate with departments

With so many use cases across organizations today for AI and other research, central IT and department IT liaisons need to work together to design data management strategies. This ensures that users have fast access to their most important data but can also access older data archived to low-cost storage when they need it.

Be selective with training data

Don’t give an AI tool more data than is needed to run a query. This reduces leakage and security risks to organizational data and it may also improve the chance of highly-relevant and accurate outcomes.

Segregate sensitive and proprietary data

Security was the top concern for generative AI in a recent Salesforce survey of IT leaders. By moving sensitive corporate data– such as IP, PII, and customer data–into a private, secure domain, you can ensure that employees won’t be able to send it to AI tools. Some organizations are creating their own private LLMs to circumvent this issue altogether, even though this can be expensive and requires specialized skills and infrastructure.

Work closely with vendors

Data provenance and transparency around the training data used in an AI application are critical—data sources in generative AI applications can be obscure, inaccurate, libelous, and unethical, and can contain PII. Non-AI applications are also now incorporating LLMs into their platforms. Find out how vendors are protecting your organization from the various risks of AI with your data and any external data within its LLM. Get clear on who’s liable for what when something goes awry. Ask for transparency in data sources from the vendor’s LLM.

Create an AI governance plan

If you work in a regulated industry, you’ll need to demonstrate that your organization is complying with data usage. A healthcare organization, for instance, would need to verify that no patient PII data has been leaked to an AI solution per HIPAA rules. An AI governance framework should cover privacy, data protection, ethics and more. Create a task force spanning security, legal, HR, data science, and IT leaders. Data management solutions help by providing a means to track and monitor what data moves to AI tools and by whom.

Audit data use in AI

Related to the above, if you choose to share corporate data with a general LLM such as ChatGPT or Bard, it’s important to track the inputs and outputs and who commissioned the project in the event there are issues later. Problems can include inaccurate or erroneous results from bad data, copyright lawsuits from derivative works, or privacy and security violations. Keep in mind that LLMs not only potentially expose your company’s data to the world but the data of other organizations—and your organization could be liable for the exposure or misuse of any third-party data discovered in a derivative work.

Choose the right tools

When your results must be factually accurate and objective, some generative AI tools may not be the best fit. Consider the recent revelations that ChatGPT’s latest version is generating significantly less accurate and lower quality responses. Machine learning systems may be better when your task requires a deterministic outcome.

Bottom Line

Despite the many concerns with AI—and especially generative AI—the groundswell of adoption is on the near horizon. A survey by Upwork found that 62 percent of midsize companies and 41 percent of large companies are leveraging generative AI technology. Another study found that 72 percent of Fortune 500 leaders said their companies will incorporate generative AI within the next three years to improve employee productivity.

No matter where your organization is on the adoption curve, AI will impact your employees, customers, and product lines sooner rather than later. Be prepared by taking a proactive data management approach that encompasses visibility, analytics, segmentation, and governance to your organization can reap the benefits of AI without bringing the house down.

Krishna Subramanian is COO and President of Komprise.

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.

Read next: Structured Data: Examples, Sources, and How it Works

Once relegated to the desktop and on-premises IT environments, today’s cloud-based project management platforms have emerged as enterprise force multipliers—key enablers for organizations to execute projects at-scale. From small startups to large enterprises, firms of all types stand to benefit from the range of features in leading project management offerings: streamlined project workflows, enhanced collaboration tools, and real-time project data access from anywhere, to name a few.

In this article, we will explore the top cloud project management platforms and highlight their key features, benefits, and use cases.

Jump to:

• Top 10 Cloud Project Management Platforms
• Asana
• Basecamp
• Jira
• Monday.com
• Notion
• Planview Adaptive Work (formerly Clarizen)
• Smartsheet
• Trello
• Wrike
• Zoho Projects
• A History of Project Management Software, From Desktop to Cloud
• Evaluating Cloud Project Management Platforms
• Methodology
• Bottom Line

Top 10 Cloud Project Management Platforms at a Glance 

The next section looks at these systems in more detail, but here’s how the cloud project management platforms previously mentioned stack in terms of their key features/benefits and pricing at a glance.

Top 10 Cloud Project Management Platforms

From solutions geared for software teams to platforms targeting the enterprise, the following are the top 10 cloud project management platforms on the market today.

Asana

Launched back in 2008, Asana’s leading cloud-based project management platform has become a favorite among large and small teams alike. The platform is known for its intuitive, user-friendly interface—however, underneath its refined UI is a powerful, comprehensive feature set for effectively managing tasks, projects, and collaborations. Other standout features include tools for project planning, task delegation, and project status monitoring, as well as real-time communication/collaboration features.

Focus on Task Management

Asana places an emphasis on tasks and task management, allowing users to both access individual tasks, as well as view how those tasks dovetail into the overarching project schedule. This focus makes it easier to manage complex projects consisting of various tasks, subtasks, and processes—using its numerous views (e.g., lists, boards, timelines), teams can better visualize the status quo of a project, as well as more effectively track its progress and deadlines.

Popular Integrations

One of Asana’s strong suits is its integration with other office and business productivity tools/platforms like Google Workspace, Slack, and Salesforce, to name a few. This makes the offering a prime candidate for organizations looking to integrate it into existing workflows and processes.

Basecamp

From the onset, veteran project management vendor 37signals has specialized in project management for small teams; over two decades after its launch, Basecamp is still a leading player in the cloud project management space. The solution is known for its clean and sensible interface as well as its broad set of integrations.

Ease-of-use & Simplicity

Basecamp is mostly known for its straightforward, minimalist UI, and it’s clear that the creative team at 37signals designed it for simplicity. Front-end aside, the platform offers a range of other features like task, project, milestone, and timesheet management, to name a few.

Jira

It’s no overstatement to say that Jira dominates the software project management world; even outside of software circles, the leading project management platform has gained significant traction. That said, the tool is primarily aimed at Agile software teams looking to plan, track, and release software products more efficiently. The solution features a customizable workflow, multi-faceted issue tracking system, and other features that help to streamline the management of complex projects.

Designed for Software Teams

Because it was initially designed with software teams in mind, Jira integrates seamlessly with popular code versioning tools like GitHub, GitLab, and Bitbucket, as well as other development and CI/CD tools. And because it was designed to support Agile software development methods, modern software engineering practices such as Scrum and Kanban are fully supported in Jira.

Managing Software Projects

In terms of software project management, Jira offers a full range of features for helping to build high-quality software, faster and with less bugs/errors. Some of these include backlog grooming/management, sprint planning, and release tracking—on top of standard task and project tracking features.

Monday.com

For enterprise and corporate users, a case of the Mondays may no longer be a bad thing. Monday.com’s cloud-based project management solution features all of the compelling attributes you’d expect from a modern web application: a visually appealing UI, intelligently-designed dashboards, and easy-to-use navigation elements on top of a comprehensive project management platform. Users appreciate the platform’s unified workflows and collaborative space for planning, executing, and tracking projects via a single pane of glass. And despite being a “born-in-the-cloud” solution, Monday.com works as both a cloud-based Software-as-a-Service (SaaS) application, as well as a local/on-premises Windows/macOS application.

Customizable Interfaces, Automation, & Workflows

Monday.com’s highly adaptable interface allows for customizations per team, so unique workflows for specific needs can be created to visually represent project timelines and progress. Alongside these attributes, the platform offers automation capabilities for streamlining common and repetitive tasks, as well as workflows that incorporate efficiency and collaboration functionality.

Ready-made Templates & Assets

The solution comes with a wealth of pre-built, ready-to-use project management assets: visual boards, over 200 ready-made templates, no-code automations snippets, integration connectors, and more. Small to mid-sized organizations appreciate Monday.com’s tools for quickly improving project management processes and setting up common workflow scenarios.

Notion

Notion is a newer solution to emerge in recent years—however, despite its recent entry into the cloud project management space, the platform has amassed a large, dedicated following: at this time of this writing, the platform boasts an astounding 30 million users. Notion users appreciate the platform’s versatility, understated power, and unified interface for organizing products and enhancing productivity.

All-in-One Workspace

First-time Notion users will feel immediately comfortable with the platform’s unassuming UI. However, within that unified interface—what the company refers to as its “all-in-one” workspace—users can easily access task management, documentation, collaboration, note-taking, even knowledge base functionality. By streamlining these workflows and features under the same proverbial roof, Notion enables project teams to collaborate and centralize their efforts on the same platform, without the need to switch between different applications.

Customizations & Collaboration

Notion uses a block-based system to help teams carry out their project management processes—organizing tasks, setting deadlines, sharing/managing files, and other functions—via dynamic, visually compelling interfaces. The platform also offers powerful real-time collaboration features and support for simultaneous project contributions/contributors, and leverages common social motifs like comment threads, @mentions, and notifications.

Integrations & Automation

Notion also comes with integrations with a myriad of popular applications like Google Calendar and Slack—and even other project management tools like Trello. As far as automation is concerned, the solution offers a range of features like automatic database updates, scheduled reminders, and a host of others in its automation hub.

Planview Adaptive Work (formerly Clarizen)

Planview Adaptive Work bills itself as a collaborative work management software platform, merging cross-company project management and configurable workflow automation in a unified SaaS application. Larger organizations requiring a highly customizable solution that can enable complex work breakdown structures (WBS)—hierarchical breakdowns of all work products that must be completed by team members—can rely on AdaptiveWork as a competent option. The platform also features strong, integrated collaboration capabilities like presence awareness, team/project members chat, and automatic task/project-connected emails, to name a few.

Reporting, Resources, & Integrations

AdaptiveWork offers robust reporting features that give project managers a comprehensive view of projects, at various detail levels (e.g., resources, costs) and across teams/departments. The platform is also capable of resource management functions like demand lifecycle management for incoming projects, time/staff allocation management, and more. Organizations that use Salesforce.com as their CRM will enjoy a tight integration with AdaptiveWork, bringing together sales and operations for shared project status visibility.

Smartsheet

Smartsheet is a cloud-based project management solution geared for business users accustomed to accessing their data on a variety of devices. The application is highly functional, though limited, on mobile devices, and is (of course) fully operational on standard computers. The solution provides task management and content collaboration tools, as well as spreadsheets and dashboards for bolstering office productivity.

Integrations & Special Use Cases

A key feature of Smartsheet are some of its specialized applications—for example, firms in the retail industry use the mobile application’s barcode scanning feature to quickly track items and automatically input them into a spreadsheet. And like other competent solutions, Smartsheet integrates with popular applications, allowing firms to streamline their project management workflows.

Trello

For years, Trello’s no-nonsense yet effective Kanban solution has been the project management tooling of choice for teams looking to ramp up quickly with minimal user training. The tool’s visually minimalistic system of boards, lists, and cards for organizing tasks and projects makes it easy to get up to production speed.

Power in Simplicity

Trello’s renowned ease-of-use and low learning curve make it an attractive option for teams looking for a no-nonsense, straightforward project management solution. Of course, Trello’s simplicity may leave some users wanting for more, especially when compared to the advanced features found in other platforms. But for creative teams and independent developers/freelancers, the solution offers an ideal mix of simplicity and cost-effectiveness.

Wrike

Founded in 2006, Wrike is considered one of the veteran project management players in the lot; over the years, the stalwart platform has managed to stack up an impressive list of industry awards and citations. The solution is known for its user-friendly interface, real-time collaboration tools, automation capabilities, and features for helping manage complex projects more efficiently.

Gantt Charts & Forecasting

Wrike’s take on the trusty Gantt chart provides users with a familiar, yet advanced and uniquely interactive timeline for managing projects. This feature allows project managers to easily visualize project timelines and dependencies, identify potential conflicts and bottlenecks, forecast delivery dates, and manage overall resources more effectively and efficiently for on-time project implementation.

Automation, AI, and Integrations

Wrike offers some impressive AI features like project risk predictions and smart recommendations, branded as their Wrike Work Intelligence solution, that allow teams to realize significant time savings by automating previously time-consuming administration tasks. And to extend the solution’s functionality, Wrike offers seamless integrations with popular tools like Salesforce, Adobe Creative Cloud, and a host of other popular applications.

Zoho Projects

Founded almost three decades ago, SaaS giant Zoho offers a cloud-based project management solution called, appropriately enough, Projects. Not to be confused with Microsoft’s similarly-named offering, Projects is part of a broader suite of Zoho products, to include CRM, Mail, Calendar, and a vast array of others; naturally, the solution is an obvious choice for users of any of the products in Zoho’s vast line of enterprise solutions.

Powerful Collaboration Features

Zoho Projects provides organizations with a rich set of features for team collaboration, from dynamic discussion forums to integrated chat capabilities and other messaging tools. The solution incorporates standard project task management organizational functions and units (e.g., milestones, tasks, subtasks), as well as both list and Kanban views of tasks.

Business Intelligence Features

The Zoho brand has a longstanding footprint in the enterprise space, and its solutions leverage business intelligence and data analytics in order to address the needs of larger organizations. Projects is no exception—the solution is capable of transforming immense volumes of raw data into actionable reports and easy-to-visualize, intuitive dashboards in minutes. Project’s business intelligence and reporting capabilities include the tracking of key business metrics and long-term trends, identifying outliers, and surfacing hidden insights.

A History of Project Management Software, From Desktop to Cloud

Project management software has become an integral component of modern business, making operations and execution more systematic and efficient through the codifying and automation of project management methods. Some of these methods have been in use for almost a hundred years, albeit in analog form. For example, most of the solutions highlighted in this article are capable of generating Gantt charts for project managers to better plan tasks, allocate resources, and track project milestones—the actual charting method was developed by Henry Gantt in the 1910s.

The majority of the PC’s early days were dominated by Microsoft Project on the desktop. With its introduction in 1984, Project’s popularity was primarily driven by its familiar Office Suite interface and a steadily expanding feature set. And despite not making it to our top 10 cloud project management platform list, Microsoft Project Online still lives on to this day, enjoying a sizable market share as part of the Microsoft 365 Suite.

Agile and Web

The early 2000s saw a shift toward web-based project management solutions. These platforms provided online access to project data, enabling remote teams to collaborate efficiently. Web-based tools like Basecamp (launched in 2004) and Asana (launched in 2008) gained wide popularity for their user-friendly interfaces and collaborative features. And as Agile methodologies gained traction in software development, project management software adapted to accommodate iterative and incremental project approaches. Tools like Jira and Trello emerged as popular choices for Agile teams, offering features such as backlog management, sprint planning, and user story tracking.

With the rise of the cloud, SaaS platforms like Wrike, Monday.com, and Smartsheet emerged to offer scalable, flexible solutions with a focus on collaboration, real-time updates, and integration with other business applications. The cloud-based approach allowed seamless access to project data from anywhere, promoting global collaboration and remote work.

Integrations and Ecosystems

Today, modern project management software has become part of a broader technology ecosystem—the so-called corporate “back office.” Integrations with other business tools like CRM systems, financial applications, and communication platforms have become commonplace, allowing firms to streamline data flow, reduce redundancies, and gain a holistic view of project-related information across multiple teams and initiatives.

Modern project management applications have become a fixture of the broader technology ecosystem, and integrations with other business tools—CRM systems, finance software, and communications platforms—are now commonplace. These integrations streamline data flow, reduce redundancies, and provide a holistic view of project-related information to all project managers, team members, and project stakeholders.

Evaluating Cloud Project Management Platforms

Organizations these days have no shortage of project management platforms to choose from. Users can select from myriad options specific to their unique requirements, use cases, and environments. Chiefly, project management software—cloud-based or otherwise (i.e., desktop/on-premises)—should satisfy the baseline requirements for project creation, planning, management, and reporting.

Contemporary cloud project management platforms, including all of the solutions discussed in this article, typically offer the following key features:

• Collaboration—tools that facilitate the communication/sharing of project information amongst team members
• Task Management—features for creating tasks lists and controls for project/item tracking
• Time Tracking—tools for tracking time allocated/in-use/expended for items/tasks, and overall projects
• Scheduling—functionality for assigning due dates, creating forecasts and projections, and managing project deliverables
• Scalability and Localization—built-in support for distributed/international teams and locations

In considering these baseline features, organizations have a variety of options at their disposal—today’s leading cloud project management platforms satisfy a broad spectrum of use cases and organizational requirements. This article focuses on the following key considerations for evaluating a cloud project management solution:

• Core Features—essential project management tools, to include scheduling, task creation/management, and project tracking, to name a few
• Additional Features—features and functionality that enhance and supercharge the tool’s core features, such as AI/ML and social networking
• Integrations—the ability to hook into and leverage third party applications (existing or new) like customer relationship management (CRM) platforms, finance applications , file sharing tools, and more
• Pricing—beyond standard consumption models, the option to leverage cloud project management software via different pricing structures
• Vendor Profile—the track record, reputation, and longevity of the vendor
• Support—available documentation, vendor-provided support options, self-support, and more

Methodology

To evaluate the systems in this buyer’s guide, we researched software and rated them using a rubric based on a wide range of features, integrations, and cost, as well as additional factors including vendor profiles and support options.

The lowest scoring options were dropped from the list. We detailed the remaining systems based on how they scored in the rubric relative to one another.

Bottom Line

Cloud project management platforms have revolutionized the way teams collaborate, plan, and execute projects. The flexibility, scalability, and accessibility of these platforms make them an invaluable asset for organizations operating at all sizes, in every industry. Depending on the specific needs and preferences of your team, one of the top cloud project management platforms covered in this article should serve your needs well.

Looking to the not-so-distant future, AI will continue to transform project management through innovative, sometimes startling software features that significantly improve project efficiency, communication, and overall project success. Recent AI/ML advances have already resulted in intelligent project management tools for automating repetitive tasks, predicting project outcomes, and suggesting optimal resource allocations; indeed many of the top 10 cloud project management platforms have already incorporated these capabilities to enhance project managers’ decision-making capabilities and improve project performance.

That’s according to software vendor Vena, which published “51 SaaS Statistics, Trends, and Benchmarks for 2023“ last week. Some key findings from the guide:

• Large enterprises that employ more than 1,000 people accounted for over 60 percent of global Saas revenue in 2022.
• Private cloud companies accounted for 43 percent of global SaaS revenue in 2022, the largest market share among SaaS market segments.
• End-user SaaS spending is projected to hit $208.08 billion in 2023, adding up to 35 percent of all end-user public cloud spending.
• There are 175 SaaS companies with valuations greater than $1 billion and a collective value of almost $622 billion.

“The FED is predicting a slowdown in growth in the second half of 2023 and the first half of 2024,” said Felipe Cepero, Vena’s customer success manager. “Most would think that the SaaS market would be negatively impacted by this potential recession, but if it leads to a decrease in interest rates it may spark a much-needed boom in the industry with an increase in funding, hiring, and overall growth. The SaaS market is resilient and will continue to grow—even in the toughest markets.”

The publication explores the state of SaaS and how the market is changing by looking at key statistics and benchmarks. It also lists top challenges facing SaaS businesses and makes recommendations for overcoming them. 

Read the entire guide here.

What Is a Hypervisor and How Does it Work?

Traditional computers run one operating system, or OS, at a time. This makes them more stable, as the hardware receives only limited processing requests, but it’s also limiting. A hypervisor is a type of software that enables multiple instances of operating systems to run on the same physical resources. These multiple instances are called virtual machines.

Hypervisors work by separating the host machine’s OS, software, and applications from the underlying physical hardware and resources, allowing for multiple “guest” operating systems to share the same hardware resources without being in connection or communication with one another.

Each guest OS operates as if it has all the host’s resources to itself. The hypervisor manages available resources so that each guest OS has reliable access to enough processing power, memory, and storage to function properly. It allocates resources according to the requirements of the guest system and the applications running on it, but also according to the virtual environment’s administrator settings.

The hypervisor also ensures that activities in one virtual environment do not affect others, maintaining the privacy, independence, and security of each virtual machine.

Benefits of Hypervisors

Cloud computing has driven a rapid growth in the hypervisor market. The following are some of the benefits of hypervisors.

• Cost-Effectiveness. Companies can save resources using hypervisors by reducing the need for hardware and physical storage space. Instead of running different applications on separate machines, a hypervisor allows for multiple virtual machines to operate on a single hardware platform, leading to significant cost savings.
• Efficiency and Scalability. Hypervisors increase efficiency and scalability by facilitating the migration of virtual machines and digital assets and operations between different host machines. This feature is especially beneficial in cloud computing, where resources need to be scaled up or down based on demand.
• Host Isolation. Hypervisors allow for the complete isolation of each virtual machine. This capability is crucial because if one virtual machine fails or gets compromised by outside malicious actors, the others remain unaffected, ensuring business continuity.

Types of Hypervisors

There are two ways to deploy hypervisor technology. The choice depends on the location of the hypervisors relative to the hardware resources and OS.

Type 1 Hypervisors

Type 1 hypervisors, also known as native or bare-metal hypervisors, run directly on the host machine’s hardware. This enables them to control the hardware and effectively manage guest systems. They allow for high performance and are often used in enterprise environments where efficiency and resource optimization is paramount.

Type 2 Hypervisors

Type 2 hypervisors, or hosted hypervisors, run atop a conventional OS just like other computer software. While less efficient than Type 1, they’re easier to set up and manage, making them more suitable for smaller environments or individual use.

Hypervisor Use Cases

There are multiple scenarios for using hypervisors. Here are a few of the most popular.

Server Consolidation

Hypervisors play a critical role in server consolidation, allowing companies to reduce their physical server count by moving workloads to virtual machines. This leads to fewer costs, energy consumption, and cooling needs. They can also improve performance and reduce necessary labor.

Testing and Development

Developers can use hypervisors to create isolated virtual environments for testing and development without needing additional hardware resources. By creating a virtual environment on the primary host, developers can simulate various conditions to test their latest software or applications at a fraction of the cost.

Virtual Desktop Infrastructure

Hypervisors support Virtual Desktop Infrastructure (VDI), allowing employees to access their work desktops remotely without the need to install and maintain a separate device per employee.

What are Cloud Hypervisors?

The backbone of modern cloud computing, cloud hypervisors enable the creation of multiple virtual machines, similar to multi-tenant architecture, on which cloud services run over an internet connection. The technology provides the scalability and flexibility that cloud services require to meet varying customer demands without the need to acquire and maintain numerous physical servers.

Cloud hypervisors are essential for businesses of all sizes, from small startups to large enterprises, as they offer an easy way to build and manage cloud-based applications and services for clients and staff.

Additionally, cloud hypervisors support the automated management of resources, reducing operational costs by allowing businesses to scale up or down based on demand. By using hypervisors to build their cloud environments, businesses can focus on their core business operations while enjoying the benefits of a flexible and secure cloud computing experience.

Security Considerations with Hypervisors

As with all connected technologies, hypervisors are subject to security risks. Here are a few of the main concerns.

Vulnerability to Attacks

As the controlling element of a virtual environment, a hypervisor can become a target for cyberattacks. It’s essential to keep all software updated with the latest security patches.

Isolation Failures

If a hypervisor fails to maintain isolation between virtual machines, it could lead to data leaks or breaches.

Unauthorized Access

Without proper access control and administration, a hypervisor can be manipulated to gain unauthorized access to virtual machines connected to the same host.

Hypervisors vs. Containers

While both hypervisors and containers enable software to run reliably when moved from one computing environment to another, they function differently.

Hypervisors virtualize host hardware to run multiple operating systems, while containers virtualize the OS to run multiple instances of the same application. The main difference between hypervisors and containers is that hypervisors are more isolated from each other, while containers are more lightweight and portable.

Generally, hypervisors tend to be best suited for larger applications that require more resources, while containers are best used for smaller applications or microservices. Containers also have the advantage of providing greater flexibility, allowing applications to be moved quickly and easily between different environments.

When choosing between a hypervisor and a container, consider the size and scope of the application as well as the security requirements.

Bottom Line: Hypervisors

Hypervisors play a vital role in virtualization, providing cost savings, flexibility, and scalability. Enterprises are increasingly turning to hypervisor technology to help create, manage, and use virtual machines for a growing range of uses. As they continue to evolve, they’re becoming more efficient and secure, with greater compatibility, and are moving toward lightweight solutions designed for specific tasks and work across different hardware platforms.

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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 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.

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.

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.

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.

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