Historically, organizations were often lax about data classification, creating problems that compounded quickly and led to data sprawl, lost productivity, and security concerns. But as data becomes increasingly essential for business—and accumulates in massive volumes—organizations have begun to consider data classification a pillar of their data management efforts. Here are the six top data classification trends for 2023.

1. AI is Driving Data Classification Efforts

Artificial intelligence (AI) had a banner year in 2023, and data science—like most industries—has begun to reap the benefits. Legacy data classification systems required challenging implementations and lacked the ability to perform context-based classification, but new solutions use AI to incorporate content awareness and context analysis into classifying and sorting data.

AI powered automation in data classification can help companies analyze and label unstructured data at unprecedented scales, and with minimal human intervention. This allows organizations to classify more data more quickly. It also lets them circumvent the industry-wide qualified staffing shortage.

AI also provides data leaders with actionable visibility into how data is used, shared, and acted on by different users, making it easy to flag suspicious data.

2. More Data Regulations are Being Implemented and Enforced

As more and more data breaches come to light, especially in critical infrastructure, governments have begun to tighten their grip around tech companies that violate data management and localization principles. New data privacy laws abandon the harm-based approach—preventing and punishing violations of consumer data—in favor of a rights-based approach that gives individuals control of how their data is managed, used, and processed.

The European Union is currently undertaking its largest cross-border investigation under the General Data Protection Regulation (GDPR) and taking action against member states that allow data attacks to thrive. While the U.S. has historically had a more lenient approach toward how organizations collect and classify data, that might be changing—after passage of the watershed California Consumer Privacy Act (CCPA), other states including Colorado, Utah, and Virginia have pursued similar legislation.

Additional policies like the National Cybersecurity Strategy, Gramm-Leach-Bliley Act (GLBA), and Family Educational Rights and Privacy Act (FERPA) will create multiple federal regulators in the U.S. to oversee implementation of data governance policies, and assist with classification, usage, and archival of data in the entire data lifecycle management.

3. Better Technologies are Making Data Classification More Effective

Technology is fueling a new wave of data democratization, providing simpler access controls, more secure delivery, and greater decentralization. At the forefront is the integration of data fabric—which stitches together metadata to aid data classification—and data mesh, which can reduce information silos and aid in governance by putting the onus on teams that produce data.

The combination of technologies helps companies process data from multiple sources, producing faster insights and creating a frictionless web for all stakeholders to engage with processed data. It also helps build an autonomous, company-wide data classification and coverage interface that provides self-service access to fragmented datasets.

Enterprises can reduce operational expenses by up to 400 percent by classifying data without having to move it and creating a data abstraction layer. Enterprises can also manage their security postures with improved data access and intelligent query escalation, allowing them to build a top-down data service.

4. Zero-Trust Data Privacy Vaults are Being Used for Sensitive Data

Data classification plans must also secure confidential and restricted data by de-identifying critical datasets and exposing only the information needed to complete a task. As tech firms face greater compliance demands from regulators, privacy vaults are increasingly drawing attention as an interesting solution. A zero-trust vault eases personally identifiable information (PII) compliance concerns by providing a controlled environment to protect sensitive data.

Most privacy vaults use polymorphic encryption, two-factor authentication, and regular data audits to detect vulnerabilities and keep customer data attack-proof. They also allow governments and businesses to work together on privacy by design in big tech by redacting confidential datasets, tokenizing sensitive information, and restricting the flow of personal data in large language models (LLM) like ChatGPT.

Privacy vaults are especially popular in the pharmaceutical field, where proprietary research has to be protected across the drug lifecycle.

5. Unstructured Data is Powering Business Intelligence

Unstructured data—emails, text messages, and multimedia, for example—poses particular challenges for data classification. It is like the anti-matter of the universe in that it is difficult to detect and mostly impossible to analyze, but it accounts for a significant portion of the data enterprises collect and use.

The growing focus on unstructured data is driven by the time crunch that businesses face in a fiercely competitive market. They have to feed data pipelines faster, move only the data they need—and that has already been classified—and eliminate manual efforts to find classified datasets.

Finding ways to process and classify unstructured data can provide improved storage capacity, a data-driven way to measure consumer experience, and a better understanding of user sentiment.

Read our Comprehensive Guide to Data Pipeline Design.

6. Companies are Assessing Risks to Prevent Shadow Access

Shadow access—unintended, uninvited, and unnoticed access to datasets—is an increasingly exploited risk facing businesses with large volumes of poorly classified data. That risk is only expected to grow as more data gets stored and shared in the cloud.

About 80 percent of all data breaches occur because of existing credentials—employees intentionally or inadvertently share confidential information or access unauthorized applications and cloud services. With blurred lines between personal and professional domains and the growing complexity of cloud identity, shadow access has become an even thornier issue.

Because you can’t protect what you don’t know, new tools to assess risk for shadow access are garnering attention from data leaders. They allow them to identify data types that are vulnerable to security risks and take necessary steps to mitigate those risks.

Bottom Line: Enterprise Data Classification is Evolving

As enterprises race toward the creation of data-safe environments, their data classification policies will increasingly become one of the differentiating factors. At the moment, the field of data classification is in flux, driven by the advent of generative AI, a greater demand for customer experience, and growing pains of data sprawl. But organizations that tap into these innovations to shore up their data classification efforts and their larger data management strategies will ride the wave to a more successful, more secure, and more actionable data future.

Read The Future of Data Management to see other trends in how enterprises work with and keep tabs on mission critical information.

This article explores five ways enterprise brands underutilize existing data and looks at how they can make better use of data analytics.

5 Ways Brands Can Better Use Data Analytics

While organizations generally recognize the value of data, in many ways they’re missing out on opportunities to use it to learn more about customers and their behavior, predict their long-term relationships with the brand, and personalize messaging and promotions to them. Here are five ways brands can better use data analytics to tap into those insights.

1. To Get to Know Their Target Audience

Organizations with broad audiences can find it difficult to deliver campaigns relevant to everyone. Identifying target customers and segmenting them into small audience clusters makes it easier to meet their direct needs with more specific campaigns.

Design thinking methods and data science are effective ways to perform audience segmentation and targeting. Design thinking processes help analyze consumers in depth and identify the most relevant factors to segment them by need, while data science enables the analysis of large volumes of data, with the use of sophisticated statistical techniques that find patterns among consumers. For example, demographic characteristics, geographic information, product use, and behavioral characteristics can be used to analyze and segment consumers to better target messaging.

There are three components to a design-driven data science framework:

• Qualitative consumer interviews to understand customer profiles and needs
• Customer data analysis to generating insights about behaviors, preferences, and profiles
• Advanced analytics and machine learning (ML) to perform statistical analysis and cluster customers

This iterative process provides a means for testing hypotheses generated from the qualitative interviews. Because some insights generated from data analysis are based on correlations, which does not imply causation, for example, data insights can also suggest some points to be explored more deeply on the qualitative interviews.

2. To Predict Lifetime Customer Value and Optimize Acquisition Cost

Data analysis and machine learning can be great tools to reduce the cost of customer acquisition. Data can support the estimation of the customer acquisition cost (CAC) as well as the customer lifetime value (CLV), which predicts estimated profits over each customer lifetime.

By calculating the CLV, companies can evaluate how much to invest in a customer based on the potential return. Segmenting customers according to lifetime values lets you optimize acquisition costs by investing more in campaigns targeting leads that will likely generate more revenue throughout the whole lifecycle. The desired result is an inverse relationship between CAC and CLV, with a higher CLV.

There are several different ways to calculate CLV depending on the type of business. A complete CLV methodology uses probability models and requires advanced statistical knowledge in order to perform a more accurate estimation of the CLV of each customer, providing a more thorough, more dynamic metric. But even a simple approach to segmenting customers with this metric lets you understand demographic and behavioral traits of your valuable customers.

This could be used to train a machine learning model to predict the CLV segment of new leads, facilitating an optimal customer acquisition budget, for example. It could also be used to perform customer look-alike targeting to find similar new leads.

Learn more about data modeling.

3. To Forecast Behavior Using Propensity Models 

Marketing teams often use one-size-fits-all approaches to engage leads. Data analytics can drive greater personalization and better results by modeling consumer behavior through the use of propensity models. Proper use of these models helps to predict the likelihood that leads and consumers will perform certain actions, such as make a purchase or convert to the next step of the funnel.

Many companies struggle with getting good outcomes with the use of propensity models. One reason is that they use propensity scores generated by customer relationship management (CRM) tools or marketing automation platforms, which are not specifically designed for their business. A propensity model should be dynamic and adaptable. Automating data pipelines and processes can help retrain the model on a regular basis. The model should also be scalable so it can be used in future campaigns.

A propensity model should also be aligned with variables specific to the business—for example, demographics, product use, and buying history—that make good predictors. Actionable propensity scores can help increase conversion rates by defining the incremental impact of being targeted, letting you target customers with better incremental responses. You can also offer higher discounts for customers with a lower propensity score who need more incentives.

Learn more about data pipeline design.

4. To Listen and React to Consumer Sentiments

Customers share a lot of information about their needs and their relationship with brands and products. Acquiring and analyzing this information provides a way to measure user satisfaction and loyalty. Approaches like netnography and social listening let companies understand customers’ emotions and their reactions to campaigns, making it possible to boost use and consumption and build loyalty.

Data analytics using natural language processing (NLP) enables the analysis of large volumes of text data based on opinions and complaints left by consumers on social networks. Machine learning techniques can perform sentiment classification (negative, neutral, or positive for example) using text as input data. The results can be used to understand customer opinion.

Sentiment analysis has become an essential tool for marketing campaigns because it enables scalable analysis in real-time, making it possible to act on consumer feedback and personalize messaging to attract the target audience.

5. To Increase Lifetime Value by Personalizing Actions

Customer lifetime value should guide acquisition and retention activities. Analytics can also help companies increase CLV through personalized offers and recommendations. Research indicates that if a customer buys once, there’s a 30 percent chance on average they will return—but if the customer buys a second time, the chance return increases significantly. Which means it’s important to act quickly to transform one-time buyers into two-time buyers.

One way to re-engage customers is through a welcome campaign that makes personalized offers based on customer profile or segment. Those offers can also be triggered by a next-best action model, which uses predictive machine learning to estimate the likelihood of the customer buying specific products and considers the one that has the highest chance of converting in that moment to create the offer.

A next-best action model can prescribe content and messages relevant to the customer’s segment, stage of sales, and propensity and suggest the right sales opportunities, specific offerings, sales actions, and even actions to minimize churn. A good recommendation or next-best action model can increase conversion rate, CLV as well as consumer satisfaction, if the actions recommended meet the user needs.

Bottom Line: Make Better Use of Existing Data

Enterprises understand the value of data. They gather it in increasingly massive volumes, invest in ways to store it, and report on it to measure performance, fuel dashboards, and track results. But rethinking how they use the data they’ve already gathered to add data analysis for forward looking predictions can create a higher return from those investments and provide a far deeper understanding of their audience, making it easier to engage with them through marketing and sales campaigns.

Read Top 7 Data Analytics Tools to see the best software enterprises can use to implement the strategies covered in this article.

Jump to:

• Data Simulation Features
• Benefits of Data Simulation
• Data Simulation Use Cases
• Types of Data Simulation Models
• Data Simulation Software Providers
• Bottom Line: What Is Data Simulation?

Data Simulation Features

Simulated data can be used to help validate and test complex systems before applying them to authentic data. Simulated data is also complete, and rarely has any gaps or inconsistencies, making it suitable for checking the validity and quality of an analytics system under ideal conditions. While this all can be done using real-life data, with data simulation it comes at a fraction of the cost, and without all the legal and ethical concerns that may arise in handling and storing user data.

Data simulations are attractive to individuals, teams, and enterprises that work with data for myriad reasons beyond just affordability. Its features can be considered in three main areas—flexibility, scalability, and replicability:

• Flexibility. Since the data is manufactured, it can be adjusted to simulate a wide range of scenarios and conditions without ethical constraint, allowing a system to be studied in more depth. This is particularly useful when testing out large-scale simulation models and predictive models. It’s also of benefit when visualizing complex data, making it possible to test for accuracy in extreme situations.
• Scalability. In addition to data quality, data volume plays a critical role in training machine learning and artificial intelligence models. The scalability of simulated data elevates its value for such use cases—since the data is artificial, it can be generated as needed to reflect the randomness and complexity of real-world systems.
• Replicability. Similar circumstances and conditions can be reproduced in a different simulated dataset to ensure consistency in testing. This consistency is crucial for validating models and hypotheses, as it allows you to test them repeatedly and refine them based on the results.

Benefits of Data Simulation

Data simulation is just one tool in an enterprise’s larger data management toolbox. Depending on the use cases, there are numerous benefits to using it in the place of actual data—here are the most common.

Enhanced Decision Making

Data simulation can inform decision-making by simulating various conditions or events and predicting outcomes based on actions. This provides insight into hypothetical scenarios, allowing for the creation of suitable protocols for all possibilities.

Cost Efficiency

Using data simulation instead of harvested data is more cost-effective, as it reduces the need for physical testing and active data collection. Simulating different scenarios and observing their outcomes provides valuable insights without the need for costly and labor-intensive data collection efforts.

Improved Model Validity

Data simulation can aid in model testing and refinement. Creating a virtual representation of a real-world system makes it possible to test different models and refine them based on the results, leading to more accurate models that are better at predicting scenarios in great detail.

Risk Reduction

Data simulation can provide data on crises and potential issues, allowing organizations to identify pitfalls or challenges before they occur in the real world. This foresight can help mitigate risks and avoid costly mistakes.

Learn the best practices for effective data management.

Data Simulation Use Cases

Data simulation can be used in numerous applications across a wide variety of industries. But some industries rely more on data than others, making data simulation particularly beneficial for them.

Finance

In the finance industry, data simulation is primarily used for risk assessment and investment portfolio simulations. Analysts can test different scenarios to gauge potential risks and returns associated with a particular transaction or investment strategy. This helps them make more informed investment decisions and manage client portfolios more effectively.

Healthcare

Data simulation can be used in healthcare to train models for drug testing and epidemiological predictions. Data mimicking patterns of diseases spreading, for example, enables epidemiologists and healthcare professionals to estimate their impact and plan response plans accordingly. Drug simulations provide the opportunity to assess a drug’s efficacy and safety before beginning human trials.

Retail and Marketing

Data simulation can be used to predict customer behavior and optimize stock for purchasing trends in retail and e-commerce. By simulating customer behavior, retailers and marketers can predict purchasing trends and optimize stock levels accordingly, leading to improved customer satisfaction and increased profits.

Types of Data Simulation Models

There are multiple types of data simulation models, each with its own unique features and capabilities. Here are the most common:

• Monte Carlo simulations. This type of simulation uses random sampling to obtain results for uncertain situations and is widely used in finance, physics, and engineering to model complex systems and predict behavior.
• Agent-based modeling. This type of simulation focuses on the actions and interactions of individual, autonomous agents within the data systems and is particularly useful for studying complex systems where the behavior of the system as a whole is influenced by the behavior of individual components.
• System dynamics. System dynamics helps to understand non-linear feedback loops in more complex systems and is often used in economics, environmental science, and public policy to simulate complex systems and predict their behavior.
• Discrete-event simulations. These models focus on individual events in the system and how they affect the outcome, and are widely used in operations research, computer science, and logistics to simulate processes and systems.

Learn more: Data Modeling vs. Data Architecture

Data Simulation Software Providers

Various providers offer data simulation solutions, including commercial software such as MATLAB, Simul8, and AnyLogic Cloud. These tools provide a wide range of features, including graphical user interfaces, scripting languages, and extensive libraries of mathematical and statistical functions.

Open-source data simulation solutions often come in the form of libraries in languages such as Python and R. They’re freely available, widely used in the scientific community, and offer extensive libraries of mathematical and statistical functions. Because they’re highly customizable, they can be tailored to specific needs. Other open source simulation tools include OpenModelica, OpenSimulator, and Logisim.

Bottom Line: What Is Data Simulation?

Data simulation is a powerful tool for studying complex systems and predicting their behavior. It lets you simulate a wide range of scenarios, predict their outcomes, and test different models and hypotheses. Whether you’re a data scientist, a business leader, or a policy maker, data simulation can provide you with the insights you need to make informed decisions.

By using data simulation, you can enhance your decision-making, improve your models, and reduce your risks. With its flexibility, scalability, and replicability, data simulation is a valuable tool for anyone interested in understanding complex systems and making accurate predictions.

Read What is a Digital Twin? to learn how enterprises use virtual environments as another means of simulating real world conditions to test and monitor systems under controlled conditions.

This comprehensive guide explores these two models in detail, examining their structures, purposes, operational mechanisms, and when to use them effectively in different scenarios.

Hierarchical Data Model vs. Relational Data Model at a Glance

Hierarchical and relational data modeling paradigms have distinct characteristics, differing significantly in structure, how they represent data, and their operational mechanisms. Here’s a high-level look at how they compare.

Read Data Modeling vs. Data Architecture: Key Differences to learn more about how data models work and what goes into them.

What is a Hierarchical Data Model?

A hierarchical data model organizes data in a tree-like structure. Data elements are represented as nodes with parent-child relationships. Due to this approach, hierarchical databases are especially adept at representing structured data with well-defined relationships. Each parent can have multiple children, but each child has only one parent.

Hierarchical data models were the first to be adopted in enterprise environments, making their way into various early business computing applications and solutions including file systems, corporate datastores, and certain specialized databases.

How do Hierarchical Data Models Work?

In a hierarchical data model, data is organized in a top-down manner, with a single root node at the top. Each subsequent node in the hierarchy has a parent node, and a child node can have multiple parent nodes. Retrieving data in a hierarchical model involves traversing the tree-like structure, starting from the root node and moving down to the desired node—in this capacity, the model is particularly suited for representing data with clear hierarchical relationships.

This hierarchical arrangement and parent node/child node linkage system makes it ideal for representing large volumes of data in a structured manner with well-defined and nested relationships. Hierarchical data models are ideally suited for applications with data that naturally exhibits a hierarchical structure.

Uses and Examples of Hierarchical Data Models

A computer file explorer is one of the most well-known examples of hierarchical data models. It uses a tree structure for file systems, where directories act as parent nodes, and files serve as child nodes. A primary use case is organizing and managing data in file systems, where directories and subdirectories are arranged hierarchically.

Hierarchical data models are well-suited for representing organizational structures, such as company hierarchies and organizational charts. They’re also widely used in representing product categories in e-commerce platforms, with parent categories branching into subcategories, allowing for efficient navigation and catalog management.

In recent years, hierarchical data models have been instrumental to the design of information retrieval systems, formats, and standards like XML and HTML. In XML, elements are structured hierarchically to allow for the representation of complex data, such as web content, configuration files, and data exchange between systems. Hierarchical data models are still a viable option for representing and managing data that exhibits clear parent-child relationships, enabling efficient storage and retrieval in various domains.

What is a Relational Data Model?

A relational data model represents data as tables consisting of rows and columns. Each row in a table represents a specific record, while each column represents an attribute or field.

The relational database management system (DBMS) continues to serve as a reliable workhorse for the modern enterprise. Most database administrators and developers are accustomed to the relational data model and its representation of data in tabular form, where data elements are organized into tables with rows and columns.

Read our Guide to Database Management to see how data models fit into the larger picture of enterprise data.

How do Relational Data Models Work?

In the relational data model, data is organized into tables. Each table represents an entity and each row represents a specific instance of that entity. Relationships between entities are established through foreign keys, which link one table to another.

This model allows for complex querying and data retrieval using structured query language (SQL). Relational data models emphasize data normalization to reduce redundancy and maintain data integrity, and provide powerful mechanisms for filtering, joining, and aggregating data.

The relational data model is renowned for its simplicity, flexibility, and ability to handle complex data structures efficiently. Subsequently, they are widely used across a diverse range of applications and industries, though perhaps most prominently in business and enterprise use cases and environments.

Uses and Examples of Relational Data Models

Most business datasets—for example, customer information, sales transactions, and inventory records—are best represented and managed as relational data types. Organizations that employ relational databases to store, organize, and retrieve this data efficiently have a wealth of tools, knowledge, and resources at their disposal.

Relational data models are prevalent in enterprise resource planning (ERP) and customer relationship management (CRM) systems, as well as web development and e-commerce platforms. Content management systems (CMS), online shopping platforms, and social media websites use relational databases to store user profiles, posts, comments, and other dynamic content. The WordPress CMS, which powers nearly half of all websites on the internet, uses the MySQL relational database as one of its underlying technologies.

Main Differences between Hierarchical and Relational Data Models

Hierarchical and relational data modeling paradigms have distinct characteristics, strengths, and weaknesses. While both models serve the purpose of organizing data, they differ significantly in their structures, data representation, and operational mechanisms.

Data Structure

In terms of data structure, hierarchical data models organize data in a tree-like structure with parent-child relationships. In contrast, relational data models organize data in tables with rows and columns.

Relationships

Data model relationships represent the connections or associations between different entities or data tables within a database. These relationships define how data from one entity relates to data in another, providing a crucial means to establish connections, dependencies, and constraints in the data structure.

Common types of relationships include one-to-one, one-to-many, and many-to-many, each reflecting the cardinality of how data elements in one entity correspond to those in another. In general, hierarchical data models represent one-to-many relationships effectively, but struggle with many-to-many relationships. Relational data models can handle both one-to-many and many-to-many relationships efficiently through foreign keys and join operations.

Flexibility

Hierarchical data models are better-suited for data applications with clear hierarchical structures; complex data relationships can be difficult to model using a hierarchical approach. Relational data models tend to be more flexible in this regard and adaptable to a wide range of data structures and relationships.

Complexity

If you look at a typical org chart, it’s not hard to quickly identify the key leaders and decision-makers in the organization. Hierarchical data models are simpler to design and understand for hierarchically structured data, whereas relational data models can handle complex data scenarios but may require more complex queries and joins.

Main Similarities between Hierarchical and Relational Data Models

Despite their differences, hierarchical and relational data models share some characteristics. Here are the most common:

• Storage and retrieval. When implemented correctly, both allow data professionals to better organize and structure data for efficient storage and retrieval and provide mechanisms to ensure data integrity and consistency through various constraints and relationships.
• Search and query. Both models support querying and data retrieval, although the methods and languages used may differ (for example, traversal in hierarchical models, SQL in relational models).
• Data normalization. Lastly, both models emphasize data normalization to reduce redundancy and improve data consistency, though normalization is generally more of a concern with relational data models.

Normalization involves organizing data in such a way that it minimizes redundancy and ensures data integrity while maintaining relational connections between tables. One of the primary reasons for data normalization is to reduce data duplication.

Breaking down data into separate tables and eliminating repetitive information saves storage space and enhances data consistency. This not only reduces the risk of data inconsistencies but also makes it easier to update and maintain the database, as changes only need to be made in one place rather than multiple locations.

When to Use Hierarchical Data Models

The choice between hierarchical and relational data models depends on the nature of the data and the specific requirements of your application. The following guidelines and example use cases can help you decide when to use a hierarchical data model.

Organizational Structures

When representing organizational hierarchies, such as company departments or reporting structures, the hierarchical data model can be effective for clearly conveying levels of rank and priority.

File Systems

File systems on computers and servers often follow a hierarchical structure, making the hierarchical data model a natural fit for data modeling in this context.

Taxonomies and Classification

When dealing with data that can be categorized into hierarchical taxonomies, such as product categories or species classifications, hierarchical models work better than relational models for visually representing descent and cascading effects.

Simple Hierarchies

Lastly, hierarchical data models are ideal for representing data with clear, relatively simple hierarchical relationships.

When to Use Relational Data Models

The following guidelines and example use cases are better-suited to the relational data model than the hierarchical.

Complex/Many-to-Many Relationships

The relational data model is better equipped to handle scenarios with many-to-many relationships, where one entity can be related to multiple others (and vice versa). For example, when dealing with data that involves complex relationships between entities, such as customer orders, products, and suppliers, the relational data model is usually a more flexible option.

Ad Hoc Queries and Data Integration

Relational data modeling and their RDMBs allow for ad hoc querying and reporting capabilities using SQL-based querying—a significant advantage for data professionals looking to customize their organizations’ business intelligence instruments. And in scenarios where data needs to be integrated from various sources and consolidated into a single database, the relational data model is a more streamlined option for facilitating this process.

Bottom Line: Hierarchical vs. Relational Data Models

Both hierarchical and relational data models have strengths and weaknesses, making them uniquely suitable for specific use cases. While the hierarchical data model excels in representing simple hierarchical structures, the relational data model provides more flexibility and can handle complex relationships effectively.

Ultimately, your decision should be guided by the nature of your data and the requirements of your application/use case—and in many cases, a hybrid approach may also be possible through integrating hierarchical data into a relational database for a best-of-breeds approach.

Read Best Data Modeling Tools and Software to learn about the top products to help enterprises with data modeling needs.

In this guide, we will explore the intricacies, benefits, challenges, and best practices of data pipeline design, and look closely at the design principles that allow for the safe transport of data.

What is Data Pipeline Design?

Data pipeline design involves the systematic planning, creation, and optimization of a structured sequence of data processing and transformation stages that together enable seamless data transport from multiple sources to one or more destinations. These end locations could be data lakes, warehouses, or analytics platforms, for example.

Data pipelines are the conduits for ingesting, cleaning, enriching, transforming, and delivering data efficiently for analysis, reporting, and decision-making purposes. As such, their design considerations must take data quality and scalability into account.

Data Integrity and Quality

Strong data fidelity is the bedrock on which robust enterprises are built, as it empowers business leaders to make informed decisions based on trustworthy information. And with artificial intelligence and machine learning (AI/ML) becoming commonplace across virtually all industries, data accuracy, completeness, consistency, and timeliness are necessary to make accurate projections and predictions. Proper data pipeline design therefore plays a pivotal role in ensuring this data integrity and quality are preserved and continuously validated.

Data Processing Scalability and Agility

As the volume and velocity of data generated by organizations continue to grow, enterprises with a well-designed data pipeline have a unique competitive edge in adapting to increased workloads and evolving data sources. Optimal data pipeline design enables this scalability and agility in data processing, and provides the flexibility to integrate new data sources or technologies seamlessly, enabling organizations to embrace innovation while maintaining robust practices.

The Importance of Data Pipeline Design

Data pipeline design plays a pivotal role in the success of data-driven organizations, especially when it comes to enterprise operations. Enterprises in particular are in the midst of a relentless and exponential data explosion.

With global data creation anticipated to surpass 180 zettabytes by 2025, data industry analysts claim that big data will be “dead” in a handful of years—not because data itself is going anywhere, but because all data will be big data, without need for the distinction. Firms should pay special attention to their data pipeline design to ensure that it can support the growth.

How Does Data Pipeline Design Work?

Data pipeline design starts with considering the critical components that make up a typical data pipeline. Each component plays a part in creating, transforming, moving, or storing data:

• Data Ingestion/Sources. These data origins can include databases, applications, external APIs, sensors, or streaming sources like social media feeds. Data is collected from various sources and ingested into the pipeline. This step involves handling different data formats, protocols, and error handling.
• Data Processing. Raw data undergoes transformations like cleaning, validation, and aggregation to make it usable. This phase includes data cleansing, enrichment, transformation, and aggregation to ensure that the data is accurate, consistent, and in the right format for analysis.
• Data Storage. Once processed, data needs a secure and scalable storage solution such as data lakes, databases, or data warehouses. Processed data is stored in a suitable repository. This can be a data lake for unstructured data, a relational database for structured data, or a combination of both.
• Data Movement/Destinations. These are the endpoints where data is delivered, such as analytics platforms, reporting tools, or machine learning models. The data is moved from storage to its destination. This can involve batch processing or real-time streaming, depending on the use case.
• Data Movement/Delivery and Schedulers. The data reaches its intended destination, whether it’s a dashboard, reporting system, or analytics platform. More commonly, these systems sit on top of a data lake or warehouse.

The next challenge is how to accurately capture, structure, and analyze data for decision-making. Two common data integration strategies or approaches to data pipeline design can help in determining the best course of action: extract, transform, and load (ETL) and extract, load, and transform (ELT). Both methods consolidate data, but each does so in a different order.

Extract, Transform, and Load

In ETL, data is first extracted from source systems. Then it is transformed into the desired format and, finally, loaded into a destination system. ETL primarily consists of three steps:

1. Raw data is extracted from various sources.
2. A secondary processing server transforms the data.
3. The transformed data is loaded into its target location—for example, a relational database management system (RDBMS).

During transformation, the processed data is first validated to conform with the target location’s schema and structural requirements before it arrives at its target destination. For this reason, ETL is suitable for situations where data needs to be cleaned, enriched, or aggregated before storage.

Extract, Load, and Transform

In ELT, the order of the transform and load steps is essentially reversed, as follows:

1. Raw data is extracted from various sources.
2. The data is loaded as-is into a data lake or warehouse.
3. The data is transformed on-demand where it resides—in the data lake, warehouse, or other type of target system, for example.

With ELT, data is first extracted, then loaded into storage and transformed on an ad-hoc basis. Data cleansing, transformation, and enrichment occur within the data warehouse, typically accessible in raw form directly by data engineers.

ETL vs. ELT: How to Choose

ELT generally is preferred for data lake and warehousing scenarios, as it allows analysts to work with raw data and perform transformations when necessary. However, the choice between ETL and ELT depends on specific organizational needs and priorities.

ELT may be more appropriate for a data pipeline for organizations that need to do the following:

• Ingest and analyze large volumes of raw data quickly
• Preserve/optimize data agility and speed over immediate data quality
• Support modern, cloud-based data warehouses
• Scale horizontally to handle massive amounts of data.
• Enable data scientists and analysts to work with raw data
• Support advanced analytics and machine learning projects

On the other hand, traditional ETL may be more ideal for data pipelines if the following are true:

• Data quality and consistency are paramount
• You have the resources for up-front data cleansing and modeling
• You need a structured, well-organized data warehouse
• You need fast access to historical data

Benefits of Proper Data Pipeline Design

Efficient data pipeline design offers several benefits that directly impact the effectiveness of an enterprise’s data-driven initiatives. Here’s a look at some of the most common.

Enhanced Data Quality

Data pipelines play a fundamental role in maintaining data quality. By enforcing data cleansing, validation, and transformation processes, pipelines ensure that data is accurate, consistent, and reliable. High data quality is a prerequisite for meaningful analysis and reporting. Well-designed data pipelines ensure that data is processed efficiently, reducing latency and enabling faster data-driven decisions.

Real-Time Data Access

Efficient data pipelines ensure that the right data is accessible to the right people or systems when needed. With well-constructed pipelines, data professionals can access and analyze real-time data, enabling them to respond swiftly to changing conditions and emerging opportunities. This accessibility is critical for data professionals, as it enables them to conduct analyses, generate insights, and drive data-driven decision-making.

Real-Time Data Monitoring

Data pipeline design can incorporate real-time monitoring and alerting capabilities. This is particularly valuable for data professionals, as it allows them to respond promptly to data quality issues, anomalies, and potential security threats.

Scalability

As organizations experience data growth, well-designed data pipelines can scale to handle increased workloads without compromising performance. This scalability is vital in today’s data-rich environment, where data volumes continue to expand exponentially. Scalable data pipelines can handle increasing data volumes and accommodate future growth in the organization’s data needs.

Data Governance

Robust data pipelines facilitate tracking and auditing of data access and usage, aiding organizations in maintaining compliance with data protection regulations. Data professionals need to ensure that data is handled in a compliant and secure manner. Data pipeline design can enforce data governance policies, ensuring that data is handled, stored, and processed in compliance with organizational standards and regulatory requirements.

Data Pipeline Design Challenges

While data pipeline design offers numerous benefits, it also presents several challenges of which data professionals should be aware. Here are the most common:

• Data Security. Securing data as it moves through the pipeline is a complicated affair involving encryption, access controls, and data masking to protect sensitive information, to name a few.
• Data Privacy. Ensuring compliance with data privacy regulations (e.g., GDPR and CCPA) is also a significant hurdle in data pipeline design and maintenance, especially for organizations operating in highly-regulated industries. Additionally, data must be anonymized or pseudonymized as necessary.
• Data Integration. Integrating data from various, disparate data sources is a constant challenge in data pipeline design; however, when done right, effective integrations provide data professionals with a unified view of data for comprehensive analysis and reporting.
• Data Latency. Data pipelines supporting real-time applications must be designed for minimal data latency, as delays in data processing can impact the effectiveness of monitoring and impact service levels.
• Fault Tolerance. Data pipelines should be resilient to failures and support normal data processing and applications,even in the face of hardware or software issues.

8 Data Pipeline Design Best Practices

Whether you’re a machine learning infrastructure engineer, a DevOps engineer, or a data scientist, the following best practices can help address many of the challenges involved in creating effective data pipelines.

1. Define Clear Objectives

Your data pipeline design project should start with a clear definition of objectives and requirements—this should entail a thorough assessment of your organization’s needs, and determinations regarding data that needs to be processed, who needs access to it, and what actions should be taken in response to events. Documenting these objectives will guide the design and implementation process, ensuring that your data pipeline aligns precisely with your goals.

2. Implement Data Encryption

Your data pipeline design should include end-to-end encryption to protect data while in transit and at rest. Other considerations include using strong encryption algorithms and key management practices to safeguard sensitive information, as well as data encryption at the field or column level. This level of encryption granularity allows you to control access to specific data elements within your pipelines.

3. Enforce Strict Access Control

Enforce strict access controls and permissions to restrict data access to authorized personnel only, and implement role-based access control (RBAC) to ensure that individuals or systems can only interact with the data they are explicitly permitted to access. Regular reviews and updates to access permissions will ensure that as personnel roles and responsibilities change, access is adjusted promptly (or revoked when no longer required).

4. Use Data Masking

Data masking techniques should be applied at various points in the pipeline to obfuscate sensitive information and prevent unauthorized exposure. Anonymizing or pseudonymizing data can provide an additional layer of protection. However, ensure that your data masking methods maintain data usability for legitimate purposes, such as analytics, while rendering the data unreadable for unauthorized users.

5. Establish Monitoring and Alerting

Continuous monitoring allows you to respond swiftly to incidents and minimize the impact radius of potential data breaches and security incidents. Your data pipeline should include robust monitoring and alerting mechanisms to detect anomalies and cyber threats in real-time. Use information and event management (SIEM) systems that collect/analyze logs and generate alerts when suspicious activities or threats are identified.

6. Maintain Compliance

Regularly audit and assess your data pipelines to ensure compliance with data protection regulations. Establish a systematic compliance management framework that includes regular audits, documentation of  controls, and the ability to demonstrate compliance to regulatory authorities. Keep abreast of evolving regulations and adapt your data pipeline accordingly to maintain compliance.

7. Plan for Disaster Recovery

Data pipeline designs should include comprehensive disaster recovery plans to ensure data availability in the event of pipeline failures or data breaches. Design your data pipelines with redundancy, failover mechanisms, and backups to minimize downtime and data loss in the face of unexpected disruptions. Regularly test your disaster recovery procedures to validate their effectiveness.

8. Draft Data Retention Policies

Stale and/or unused data is both costly to store and a security/compliance risk; your data pipeline designs should therefore include data retention policies for managing the lifecycle of data within the pipeline. Define clear guidelines for how long different types of data should be retained, considering both operational needs and regulatory requirements. By implementing automated data archiving and deletion processes, you can ensure compliance while freeing up storage resources for mission-critical data.

Bottom Line

Proper data pipeline design is a strategic imperative for today’s enterprises and a foundational element of effective data management, empowering data professionals and decision-makers to harness the full potential of data while mitigating risks. By understanding these principles of data pipeline design and adhering to best practices, experienced data professionals can create robust pipelines that support their organization’s data-driven objectives.

Read about data migration strategies and best practices to learn more about enterprise data handling.

What Is Big Data?

Big data is more than just information in large quantities—more specifically, it’s data too large and complex to manage or process with conventional methods. Processing even a fraction of the millions of terabytes of data generated daily takes considerable computing power and storage capacity. It also takes data quality, data management, and data analytics expertise to maintain all that data and unlock its potential.

Even a minor amount of data can be helpful to businesses that know how to use it to learn more about customer behavior, product performance, and market trends, for example—but small volumes of data also provide limited reliability. Just as a larger sample size ensures scientific experiments are more representative of the real world, big data provides a better look into actual events and trends.

The Big Data “3 V’s”

The “big” in big data covers three primary categories, known as the Three V’s—volume, velocity, and variety:

• Volume. This is the most straightforward of the three, as big data naturally involves huge amounts of data. The sheer scale of information in these datasets renders conventional storage and management systems effectively useless.
• Velocity. Big data is also big in its velocity, or how fast new information is gathered and processed. Processing must be rapid to keep up with the pace of information.
• Variety. information in these data sets comes in multiple formats from numerous sources—industrial devices, social media channels, emails, for example—and can include text, sales data, videos, pictures, or sensor information, to name just a few. This rich variety provides a more complete picture of what the business wants to understand.

These three dimensions provide a useful way to think about big data and the challenges of working with it. It involves unthinkably huge amounts of data coming in like a firehose at blistering speeds in too many shapes and sizes to easily manage.

Challenges of Big Data

This volume, velocity, and variety of data can push businesses further than ever before, but the majority of big data projects fail. Here are seven of the most common reasons why, and solutions to help overcome these obstacles.

1. Cybersecurity and Privacy

Security is one of the most significant risks of big data. Cybercriminals are more likely to target businesses that store sensitive information, and each data breach can cost time, money, and reputation. Similarly, privacy laws like the European Union’s General Data Protection Regulation (GDPR) make collecting vast amounts of data while upholding user privacy standards difficult.

Visibility is the first step to both security and privacy. You must know what you collect, where you store it, and how you use it in order to know how to protect it and comply with privacy laws. Businesses must create a data map and perform regular audits to inform security and privacy changes and ensure that records are up to date.

Automation can help. Artificial intelligence (AI) tools can continuously monitor datasets and their connections to detect and contain suspicious activity before alerting security professionals. Similarly, AI and robotic process automation can automate compliance by comparing data practices to applicable regulations and highlighting areas for improvement.

2. Data Quality

Data quality—the accuracy, relevance, and completeness of the data—is another common pain point. Human decision-making and machine learning require ample and reliable data, but larger datasets are more likely to contain inaccuracies, incomplete records, errors, and duplicates. Not correcting quality issues leads to ill-informed decisions and lost revenue.

Before analyzing big data, it must be run through automated cleansing tools that check for and correct duplicates, anomalies, missing information, and other errors. Setting specific data quality standards and measuring these benchmarks regularly will also help by highlighting where data collection and cleansing techniques must change.

3. Integration and Data Silos

Big data’s variety helps fill some quality gaps, but it also introduces integration issues. Compiling multiple file types from various sources into a single point of access can be difficult with conventional tools. Data often ends up in silos, which are easier to manage but limit visibility, limiting security and accuracy.

Cloud storage and management tools let you shift information between databases to consolidate them without lengthy, expensive transfer processes. Virtualization can also make integration easier—data virtualization tools let you access and view information from across sources without moving it, which increases visibility despite big data’s volume and velocity.

4. Data Storage

Storing big data can be a challenge—and a costly one. Businesses spent $21.5 billion on computing and storage infrastructure in the first quarter of 2023 alone, and finding room to store big data’s rapidly increasing volumes at its rising velocity with conventional means is challenging, slow, and expensive.

Moving away from on-premise storage in favor of the cloud can help—pay for what you use and scale up or down in an instant, removing historical barriers to big data management while minimizing costs. But the cloud alone won’t be sufficient to keep pace. Compression, deduplication, and automated data lifecycle management can help minimize storage needs, and better organization—also enabled by automation—allows faster access and can reveal duplicates or outdated information more readily.

Read our 2023 Cloud Computing Cost: Comparison and Pricing Guide.

5. Lack of Experience

Technical issues may be the easiest challenges to recognize, but user-side challenges deserve attention too—and one of the biggest is a lack of big data experience. Making sense of big data and managing its supporting infrastructure requires a skillset lacking in many organizations. There’s a nationwide shortage of jobseekers with the skills being sought by enterprises, and it’s not getting any better.

One solution? Rather than focusing on outside hires, foster data talent from within existing workforces. Offer professional development opportunities that pay employees to go through data science education programs. Another is to look for low-code or no-code analytics solutions that don’t require skilled programmers—similarly, off-the-shelf software and open source big data solutions are more common than ever, making it easier to embrace big data without extensive experience.

6. Data Interpretation and Analysis

It’s easy to forget that big data is a resource, not a solution—you must know how to interpret and apply the information for it to be worth the cost and complexity. Given the sheer size of these datasets, analysis can be time consuming and tricky to get right with conventional approaches.

AI is the key here. Big data is too large and varied to analyze quickly and accurately manually. Humans are also likely to miss subtle trends and connections in the sea of information. AI excels at detail-oriented, data-heavy tasks, making it the perfect tool for pulling insights from big data. Of course, AI itself is just a tool and is also prone to error. Use AI analytics as a starting point, then review and refine with human expert analysts to ensure you’re acting on accurate, relevant information.

7. Ethical Issues

Big data also comes with some ethical concerns. Gathering that much information means increased likelihood of personally identifiable information being part of it. In addition to questions about user privacy, biases in data can lead to biased AI that carries human prejudices even further.

To avoid ethical concerns, businesses should form a data ethics committee or at least have a regular ethical review process to review data collection and usage policies and ensure the company doesn’t infringe on people’s privacy. Scrubbing data of identifying factors like race, gender, and sexuality will also help remove bias-prone information from the equation.

While size is one of big data’s strongest assets, consider whether you need all the information you collect—not storing details that don’t serve a specific, value-adding purpose will minimize areas where you may cross ethical lines.

The Bottom Line: Eliminate Challenges to Succeed with Big Data

Big data is a complicated issue. The sheer volume and variety of the data and the speeds at which it collects poses technical challenges to enterprises looking to establish the infrastructure to process, store, and analyze it. The nature of the work also demands expertise that’s not always easy to come by. As a result, most big data projects fail. But the payoffs are also big, and enterprises that approach big data strategically and prevent or overcome common obstacles can capitalize on the promise of big data.

Read The Future of Big Data to learn about the trends shaping this field and how they will affect the way enterprises work moving forward.

What is SOX Compliance?

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

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

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

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

Data-Specific Rules in SOX

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

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

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

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

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

SOX Compliance

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

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

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

SOX Enforcement

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

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

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

SOX Benefits

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

More Widespread Acceptance

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

Reduced Complexity

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

Supplier Data Sharing

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

Improved Data Quality

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

SOX Challenges

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

Lack of Expertise

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

Company Culture

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

Data Integration 

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

Regulatory Changes

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

The Bottom Line: SOX Compliance and Enterprise IT

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

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

We compared the most popular data quality tools on how well they met enterprise requirements for pricing, core features, customer support, data management features, and security. The following are our picks for the seven best data quality tools and platforms available today:

• Ataccama ONE: Best for working with big data
• Data Ladder: Best for managing and enhancing address data
• DQLabs Data Quality Platform: Best for automation
• Informatica Intelligent Data Management Cloud: Best for data integrations
• Precisely Data integrity Suite: Best for responsive customer support
• SAS Data Quality: Best for collaboration
• Talend Data Fabric: Best for understanding your data reliability

Top Data Quality Tool Comparison

All of the data quality tools we explored provided a full range of data cleansing and transformation functions. Differentiators included the level of automation the tools support, the depth of the data management functionality, and pricing.

Jump to:

• Key Features of Data Quality Tools
• How to Choose the Best Data Quality Tools for your Business
• How we Evaluated Data Quality Tools
• Frequently Asked Questions (FAQ)
• Bottom Line

Ataccama ONE

Best for working with Big Data

Overall Rating: 4/5

• Cost: 2.4/5
• Core Features: 4.75/5
• Support: 3.5/5
• Data Management: 4.65/5
• Security: 5/5

Ataccama ONE is an enterprise-grade modular platform that combines a full set of data quality features with data governance and data management capabilities. The tool can be deployed as a cloud-based solution, on-premises, or in a hybrid environment.

Pricing

• Vendor does not provide pricing information
• Starts at $90,000 per year on the Azure Marketplace
• Ataccama’s data quality tool is available as a free download

Features

• AI-enhanced data quality tools
• Anomaly detection and alerts
• Data catalog and governance tools

Data Ladder

Best for managing and enhancing address data

Overall Rating: 2.3/5

• Cost: 1.25/5
• Core Features: 3.9/5
• Support: 3.5/5
• Data Management: n/a
• Security: 3.5/5

Data Ladder is a specialized data quality and matching tool used by a wide range of businesses to profile, clean, and transform customer data. Since it does not include data management and governance functionality, it is less costly than most of the other systems reviewed here. The tool includes a built-in USPS database to ensure consistency in address formats.

Pricing

• Vendor does not provide pricing information
• 30-day free trial available

Features

• Data profile reports
• High-quality data matching
• Address verification
• Match and classify product data

DQLabs Data Quality Platform

Best for automation

Overall Rating: 3.65/5

• Cost: 1.75/5
• Core Features: 4./5
• Support: 3.25/5
• Data Management: 4.65/5
• Security: 4.4/5

DQLabs Data Quality Platform takes an automation-first approach to data quality, harnessing machine learning to perform quality checks on data without the need for extensive coding. Its user-friendly interface is accessible to non-technical users as well as data engineers, and its data observability tools facilitate data governance activities.

Pricing

• Three tiers of service; pricing provided by custom quote
• Free trial available

Features

• Automated incident detection
• Pre-built connectors to data sources
• Auto-discovery of business rules

Informatica Intelligent Data Management Cloud

Best for data integrations

Overall Rating: 4.5/5

• Cost: 2.9/5
• Core Features: 5/5
• Support: 4.75/5
• Data Management: 5/5
• Security: 5/5

A cloud-native solution, Informatica Intelligent Data Management Cloud offers thousands of connectors and integrations to data sources and apps, making it easy and fast to ingest data from nearly any source on the web. The platform’s CLAIRE AI engine facilitates data matching and rule creation, reducing the time it takes to classify data and making it faster to find actionable insights. Its data catalog and Master Data Management tools allow for ongoing monitoring and governance of your business data.

Pricing

• Vendor does not provide pricing
• Subscriptions are based on a package of processing units, which provides access to platform
• Starts at $129,600 per year on the AWS Marketplace
• 30-day free trial available

Features

• Data profiling, cleansing, and standardization tools
• Automated data discovery
• Data sharing across teams via a data marketplace

Precisely Data Integrity Suite

Best for responsive customer support

Overall Rating: 3.3/5

• Cost: 0/5
• Core Features: 5/5
• Support: 4/5
• Data Management: 3.5/5
• Security: 3.9/5

A modular suite of seven interoperable data quality and management tools introduced in 2020, Precisely Data Integrity Suite includes: Data Integration, Data Observability, Data Governance, Data Quality, Geo Addressing, Spatial Analytics, and Data Enrichment. The platform supports all major cloud data warehouses and receives high marks for their responsive customer support.

Pricing

• Vendor does not provide pricing
• Software costs $500,000 per year on AWS Marketplace

Features

• Automated data anomaly and outlier alerts
• Curated datasets for data enrichment
• Plain language search

SAS Data Quality

Best for collaboration

Overall Rating: 4.2/5

• Cost: 1.25/5
• Core Features: 5/5
• Support: 4.5/5
• Data Management: 5/5
• Security: 5/5

SAS Data Quality offers a user-friendly interface for data cleansing, transformation, and monitoring that allows teams across the business to collaborate on the development of a business glossary and data lineage. Visualization and reporting tools make it easy to monitor and share information about data health.

Pricing

• Vendor does not provide pricing
• Two-week free trial is available

Features

• Data profiling, standardization, cleansing, and monitoring
• Business glossary, lineage, and metadata management
• Data visualization and reporting

Talend Data Fabric

Best for understanding your data reliability

Overall Rating: 3.5/5

• Cost: 0.75/5
• Core Features: 4.65/5
• Support: 4/5
• Data Management: 3.5/5
• Security: 4.9/5

Talend Data Fabric is a platform that brings together the company’s Data Integration, Data Quality, and Data Integrity and Governance tools, along with its Application and API Integration. The tool automatically cleans and profiles data as it enters your systems and assigns trust scores to your data to help you understand its reliability at a glance.

Pricing

• Vendor does not provide pricing
• Two-week free trial available

Features

• Data profiling with built-in trust score
• Build reusable data pipelines
• Automated recommendations for addressing data quality issues

Key Features of Data Quality Tools

All of the data quality tools included here provide a full range of data cleansing and transformation functions. But data quality tools have begun to evolve with the rise of artificial intelligence and machine learning, which help automate workflows and alerts. In addition, several tools on the market have moved past cleansing and transformation into data management and governance, allowing businesses to continually monitor their data for any issues, trace problems back to the root cause, and take steps to mitigate any errors. Here’s a look at the key features of data quality tools.

Data Profiling

At the heart of data quality tools, data profiling is the function of analyzing the data to determine structure, content, and relationships. This data parsing helps determine what needs to be done to integrate data into your systems.

Data Validation

Data validation applies a set of business rules and criteria to the data to flag any issues that need to be addressed before adding it to your system.

Data Standardization

Data often comes into your systems in a variety of formats and with different column headers and vocabularies. Data standardization is the process that applies a set of uniform attributes to the data so that it can be combined.

Deduplication

In large data sets, there is usually some overlap and duplicate content. The process of deduplication finds matching data, compares the data using rules set up by the business, and either combines the data or removes any duplicate records.

Data Matching and Enrichment

Data matching is the process of comparing different sets of data to determine if there is any commonality or overlap. Data matches can be exact or software can use “fuzzy logic” to determine if there’s a match. Data enrichment is the process of connecting data in the system to additional data held elsewhere to develop a more comprehensive profile or record.

Automation

As data sets get larger and data collection is more frequent, automation is a cost- and labor-saving feature that can free up staff time to work on other projects. One of the most common areas of automation in data quality tools is automated workflows. Another common area is automated alerts, which are triggered when the system detects certain conditions or thresholds.

Error and Anomaly Detection

In order to keep data as high quality as possible, it is important to be notified of any errors or potential anomalies as it is ingested so that steps can be taken to mitigate any problems before they arise.

Data Pipeline Integration

Standalone data quality tools need to be able to bring in and work with data from a variety of sources and in a variety of formats. Tools that have pre-built connectors to common data sources and formats help simplify the process of data ingestion.

Data Management

As the functionality of data quality tools has expanded, they have taken on a variety of data management functions. This includes the ability to catalog the data in your systems, facilitate data governance, monitor data over time and alert users to any issues, trace the lineage of data in the system, analyze the root cause of any problems, and analyze the impact of any problems.

How to Choose the Best Data Quality Tool for Your Business

There is a great deal of diversity in the data quality tool market. All of the platforms included in this roundup excel at core functions, but some are better suited for different applications. Here are a few tips to help you narrow the choices.

• If you need a standalone data quality tool for your business—Consider your budget, the amount of data you need to analyze and transform, the type of data you collect, how much you can and would like to automate your data quality processes, and whether you also need data management and governance functionality.
• If you are limited in budget—Look at tools that specialize in data quality rather than a larger platform or fabric, as specialized tools tend to be lower cost.
• If you work with vast amounts of data—Look for tools that integrate well with large data warehouses and data lakes so that it is easy to set up a pipeline for big data. Certain systems have better ability to work with and transform specific types of data and others have sophisticated automation features.
• If you need a tool that can catalog your data and help with governance and risk management—Look for platforms that combine several different types of tools rather than trying to find separate tools that need to integrate.

Learn about the 10 best enterprise tools for master data management.

How We Evaluated Data Quality Tools

To rate these data quality tools, we considered five key composite criteria: cost, core features, data management features, support, and security. We then analyzed the products using a weighted scoring rubric—our scoring system ranks products from 0 to 5. The best solutions are chosen from that short list. Percentages represent the weight of the total score for each product.

Cost | 20 percent

In evaluating the top seven data quality tools, pricing considerations include the advertised cost, the price of add-ons and options, available pricing tiers, any upgrades or discounts, and whether a free tier and/or trial version is available. If the vendor did not provide pricing for a tool, they did not receive any score in that category.

Core Features | 25 percent

The core features evaluated for each data quality tool included data profiling, data validation, data standardization, deduplication, data matching, data enrichment, automated workflow, automated alerts, rule creation, error detection, and data pipeline integration.

Data Management Features | 25 percent

Since many top data quality tools have expanded to include data management features, we also looked at whether the platform offers a data catalog, data governance, data monitoring, data lineage, root cause analysis, and impact analysis.

Support | 15 percent

The support evaluation included the availability of training and onboarding, phone and online support, a knowledge base, a user community and videos and webinars.

Security | 15 percent 

We looked at data access controls, logging and auditing, encryption, how the systems handle data masking, the results of security audits (SOC 2 Type 2, ISO 27001), and whether they facilitate GDPR and CCPA compliance.

Frequently Asked Questions (FAQs)

FAQ #1 What features should businesses look for in data quality tools?

• Data cleansing: ability to profile, validate, standardize, match, deduplicate, and enrich data.
• Ability to create and automate workflows and alerts.
• Connections with a variety of data sources and formats.
• Ability to manage and monitor data over time.
• Responsive customer service.
• Robust security and privacy tools.

FAQ #2 Can data quality tools address data privacy and compliance concerns?

Data privacy and compliance is a critical feature of data quality tools. Look for tools that offer end-to-end encryption of data in transit, that encrypt data at rest, and that make it easy to mask personal identifying information (PII) data. Make sure that the tool creates and allows you to download logs of all user activities so that you can use them for audit purposes. Ask vendors how their platform can help you comply with regional privacy regulations, such as GDPR and CCPA.

FAQ #3 Are there specific industries that can benefit the most from using data quality tools?

Any business can benefit from using data quality tools. If your business collects and wants to use customer data, sales data, operations data, or industry data, it is important to ensure that you have data that is clean, accurate, and complete to help you make business decisions.

Bottom Line: Enterprise Data Quality Tools

Technological advances have allowed businesses to continually collect more data about their customers, prospects, and operations in a diverse array of systems and formats. The challenge for companies is in transforming this data into useful and actionable insights. Evidence-based decision making is only as good as the data that informs the process and small problems with data sources can compound to create faulty insights down the road.

Data quality tools help organizations with a wide range of functions that together help ensure that it is accurate and reliable. If you want to use evidence-based decision-making to grow your business, you need high quality data to inform those decisions—and the right data quality tool to help you clean, validate, and standardize that data is a critical component of your analytics software stack.

Learn about the 7 best data analytics tools for enterprises and how they fit with data quality as part of a larger data management strategy.

Data governance is a holistic approach to your data—at its core, it refers to the set of rules and permissions by which the data is orchestrated and accessed. Master data management (MDM) is the overlap of technology and business operations to enforce the policies of data governance, ensuring the uniformity, accuracy, and accessibility of data.

This article explores the differences between data governance and master data management, where they overlap, and how they can be used together to help businesses ensure their data is accessible, reliable, and secure.

Jump to:

• The Challenge of Managing and Governing Data
• What is Data Governance?
• What Is Master Data Management (MDM)?
• Data Governance vs. Master Data Management: Key Differences
• Role of Technology in Data Governance and MDM
• Can Data Governance and Master Data Management Work Together?
• Bottom Line: Data Governance vs. Master Data Management

The Challenge of Managing and Governing Data

The challenges of managing and governing data go beyond the cost of storage to retain the massive volumes that businesses have come to rely upon for decision-making, operations, and competitive advantage. They also include security—through policies like the European Union’s General Data Privacy Regulation (GDPR), the California Consumer Privacy Act (CCPA), and the Health Insurance Portability and Accountability Act (HIPAA), the regulatory landscape has redefined the baseline for adequate data protection and management practices.

Maintaining data quality is another concern. The accuracy and reliability of data are critical. If either suffers, the decisions they inform aren’t grounded in truth.

The intertwined concepts of data governance and master data management serve distinctive roles. Differentiating between them is essential to mastering your data landscape.

Learn the 10 Best Practices for Effective Data Management.

What is Data Governance?

Data governance is a holistic approach to the how, why, and by whom of the data in the care of your organization. At its core, data governance refers to the set of rules and permissions that determine how the data is handled and who gets to access it. These rules have an effect on the data’s availability, usability, integrity, and security.

Not just a technical endeavor, it’s also directly related to business processes and organizational culture. Think of data governance as a rulebook, dictating processes and policies to ensure the data is strictly accessed by authorized individuals and isn’t used in any way that could harm the organization, its stakeholders, or clients.

The Core Principles of Data Governance

While every organization’s data governance policies will depend upon its needs and goals, here are the core principles they should follow:

• Data Quality—Ensures the data is maintained to be as accurate, reliable, and actionable as possible.
• Data Access—Makes the data readily available to all who need access to it, creating a streamlined process for access requests and approval procedures.
• Data Privacy—Protects the data and personal information of clients, employees, and stakeholders.
• Data Protection—Maintains the security of the data from potential threats and malicious actors using security measures to protect data assets.
• Regulatory Compliance—Ensures that the data handling methods meet legal or jurisdictional requirements.

Benefits of Data Governance

In addition to helping organizations improve the processes around the core principles listed above by improving quality, enhancing security, and ensuring regulatory compliance, data governance also helps in other areas of the business.

For example, more accurate and reliable data better informs decision-making at all levels, leading to better outcomes. It also increases the agility of the business—in fluctuating market conditions, organizations are able to pivot more readily when they have a solid grip on their data.

Data governance isn’t purely an IT strategy. It’s an organizational imperative that ensures data, in all its forms, is treated with the importance and security it entails.

What Is Master Data Management (MDM)?

Master data management (MDM) is the overlap of technology and business operations to enforce the policies of data governance, ensuring the uniformity, accuracy, and accessibility of the enterprise’s data. By consolidating the data, MDM ensures it’s consistently synchronized across the various systems, bridging data islands to create a seamless flow of up-to-date data and information.

MDM combines software and other tools, processes, and the mechanisms outlined by an organization’s data governance policies to ensure that master data is governed and coordinated across the enterprise with high accuracy and integrity.

Components of MDM

MDM isn’t a single entity, but a combination of multiple technical and operational components. Each of the following plays a role in the data management process:

• Data Integration—Integrates incoming data from legacy systems, CRMs, ERPs, and external data feeds into a central repository to offer a comprehensive view.
• Data Quality—Ensures that the consolidated data maintains its quality by removing duplicates, rectifying errors, and filling in gaps to ensure a complete set.
• Data Governance—Controls who can access the data, how it’s categorized, and how often it gets updated.
• Business Process Management—Updates data seamlessly as processes change (for example, during a product launch or merger).

Benefits of Master Data Management

Master data management helps organizations by creating a single, reliable repository of information. This means higher quality data that’s devoid of redundancies and inaccuracies. It also streamlines business processes by consolidating the data and providing a unified view to remove roadblocks or discrepancies and allow for smoother operations in the supply chain, finance, and consumer relations management.

With reliable access to data-backed decisions, business leaders can craft strategies that are grounded in reality and aligned with goals and aspirations. MDM can also increase operational agility by ensuring businesses are highly adaptable, using real-time data access to shift strategies and responses as needed.

Master data management is not just an IT tool, it’s a strategic imperative. As organizations grapple with growing data complexities, MDM stands out as the beacon, ensuring data coherence, quality, and agility.

Data Governance vs. Master Data Management: Key Differences

While largely interconnected, master data management and data governance serve different facets of the data ecosystem. Here’s a look at what sets them apart.

Purpose and Objectives

Data governance focuses primarily on policies ensuring data integrity, security, and overall quality, treating it as one of the organization’s valuable assets. MDM, on the other hand, zeroes in on the various fragments of the data and how each of them should be precisely managed for comprehensive and reliable access to the data around the clock.

Processes Involved

The policies created by data governance are enforced organization-wide, often taking into account regulatory compliance. MDM is more process-centric, delving into data integration, quality assurance, and aligning data with business operations.

Stakeholders and Responsibilities

Data governance includes data owners, stewards, users, and stakeholders. It takes their input, both directly and indirectly, as to how the data is going to be kept accurate and safe from privacy violations. MDM, on the other hand, is solely concerned with input from the IT and business operations, allowing them to integrate data sets into necessary processes, upholding data standards, and refining business processes.

Role of Technology in Data Governance and MDM

Technology stands at the forefront of efficient data governance and master data management, and is often the catalyst for their effectiveness.

Organizations have access to specialized tools and platforms, allowing them to amass large amounts of data from varied sources while maintaining its integrity and long-term usefulness. But beyond the collection and storage of data, businesses need technological solutions to help them process and dissect data and extract actionable insights in real-time.

Combined with the advancements of processing algorithms and machine learning, the automation of repetitive tasks within data governance and MDM has become more affordable, making it more accessible to smaller organizations with limited resources.

The use of technology in data governance and management doesn’t only streamline operations—it also significantly reduces the rates of human error. On the security front, technological solutions allow for robust encryption of the data, alongside a myriad of protective and mitigative measures. This ensures the data remains shielded from potential breaches and leaks. It’s further aided by monitoring and reporting software that keeps an eye on the data both in-rest and in-transit at all times.

Can Data Governance and Master Data Management Work Together?

The fusion of data governance and MDM can lead to maximized outcomes. This is most evident when considering the overlap between the need to set and enforce solid policies while managing the data for processing and accessibility. Data governance and MDM are not only complementary—they thrive when implemented in unison.

Combining both concepts allows organizations to come up with frameworks that emphasize best practices while championing clear roles that uphold data quality and integrity. Through such integration, organizations can foster a seamless and efficient data management strategy that leverages the strengths of both disciplines.

Bottom Line: Data Governance vs. Master Data Management

Data governance is about managing data as an asset, while MDM is about creating a single, unified view of data. Despite the unique approaches of data governance and master data management and their myriad differences, they’re closely related and perform better when combined.

By understanding the key differences and similarities between these two disciplines, you can make more informed decisions about your data strategy and achieve better outcomes that allow you to effectively manage and govern your data, refining raw material into valuable insights that drive business growth and innovation.

Read about the 10 Best Master Data Management Tools for 2023 to learn how enterprises are implementing their own MDM strategies.

Why does the U.S. Need a National Cybersecurity Strategy?

The risk posed by cybersecurity threats is enormous, and the ramifications of targeted attacks are larger still. At the individual level, data breaches can cause identity theft and loss of income; at the corporate level they can disrupt business continuity, damage reputations, and steal intellectual property; and at the government level, they can cripple agencies, shut down power grids, and cut off communications networks. 

The National Cybersecurity Strategy is a government effort to expand public/private partnerships, shore up cybersecurity defenses and alliances, and protect networks, systems, functions, and data while continuing to promote tech innovation. Some of the goals of the plan include the following:

• Simplifying threat reporting 
• Creating a first-touch response to cyberattacks
• Developing timelines and execution methods
• Allocating resources and mapping responsible government agencies 
• Incentivizing cyber hygiene
• Improving public-private partnership 

Recent years have shown an increase in state-sponsored cyberattacks—a 300 percent growth from 2000 to 2022, according to government data. For enterprises, the average financial cost of a ransomware attack is already over $4.5 million, and those attacks are only getting more sophisticated.

Learn more about top data security software and solutions.

What is the National Cybersecurity Strategy?

The NCS is a five-pillar action plan to ramp up cybersecurity efforts and bring all stakeholders together to ensure success. A solid national cybersecurity policy is essential to building on the promise of emerging technologies while minimizing the risks they pose.

Pillar One: Defend Critical Infrastructure

Defending critical infrastructure, including systems and assets, is vital for national security, public safety, and economic prosperity. The NCS will standardize cybersecurity standards for critical infrastructure—for example, mandatory penetration tests and formal vulnerability scans—and make it easier to report cybersecurity incidents and breaches. 

It seeks to label Infrastructure as a Service (IaaS) providers as a “critical infrastructure,” putting more of the onus on them to ensure data security and protection and using legal accountability to eliminate insecure software products and unpatched vulnerabilities. It will also implement the zero trust cybersecurity model for federal networks.

Pillar Two: Disrupt and Dismantle Threat Actors

Once the national infrastructure is protected and secured, the NCS will go bullish in efforts to neutralize threat actors that can compromise the cyber economy. This effort will rely upon global cooperation and intelligence-sharing to deal with rampant cybersecurity campaigns and lend support to businesses by using national resources to tactically disrupt adversaries.

Components of this pillar include building awareness about threat actors, ransomware, IP theft, and other malicious attacks and creating a Cyber Safety Review Board (CSRB) to review catastrophic incidents and strategize based on repeated attack patterns. It will also implement new guidelines for already-impacted industries—manufacturing, energy, healthcare, and public sectors, for example—and new software bill-of-materials standards to lower supply chain risks.

Pillar Three: Shape Market Forces to Drive Security and Resilience

As the world’s largest economy, the U.S. has sufficient resources to lead the charge in future-proofing cybersecurity and driving confidence and resilience in the software sector. The goal is to make it possible for private firms to trust the ecosystem, build innovative systems, ensure minimal damage, and provide stability to the market during catastrophic events. 

The priority plan under this stage includes efforts to protect privacy and personal data security by creating federal grants to encourage investments in secure infrastructure and investing in cyber insurance initiatives to help private firms recover from high-scale attacks. It will also implement an Internet of Things (IoT) security labeling program to improve consumer awareness of IoT device risks.

Pillar Four: Invest in a Resilient Future

To aggressively combine innovation with security and forge an impregnable shield against the growing number of cybercrimes, the government has earmarked funds to secure next generation technology while ensuring necessary tech transfer and information dissemination between private and public sectors. The NCS will put a special impetus on data discovery, protection architecture, and encryption in all government to business communications.

This pillar also includes cybersecurity apprenticeships and a National Cyber Forensics and Training Alliance to train the workforce and improve cyber literacy, and the deployment of a unique digital identity authentication to thwart phishing attacks and create a trusted digital identity framework (TDIF).

Pillar Five: Forge International Partnerships to Pursue Shared Goals

Global leaders are learning that cyber diplomacy is the most forthcoming strategy to turn adversaries to allies. With pillar five, the government will commit to continue global initiatives against digital warfare and build a trust surplus among allies.

Among the ways it hopes to accomplish this is by creating a centralized tracker for coordinating cost-sharing initiatives, creating secure and dependable global supply chains, and strengthening partner nations’ capacities to shield themselves against cyberthreats. It will also establish a threat intelligence infrastructure to collaborate with allies and global agencies. 

Learn how to develop an IT security policy.

What Do Businesses Need to Know about the NCS?

Businesses will have to change some of their thinking around cybersecurity under the NCS. It makes the point that voluntary progress toward better cybersecurity and data privacy practices are no longer sufficient, and maybe weren’t working at all. More than that, the government will implement new standards and regulatory frameworks and shift liability to hold enterprises accountable for not doing their part. It will also incentivize cybersecurity best practices.

Here are the three main actions businesses will be pushed to take by the NCS:

1. Identify and minimize vulnerabilities by taking proactive measures to test and secure their threat landscape and holding partners and third-party vendors to similar cybersecurity standards.
2. Address supply chain vulnerabilities by sharing information through new public/private partnerships, patching known vulnerabilities, providing employee cybersecurity training, and designing critical incident response plans.
3. Put cybersecurity front-of-mind when developing software, processes, products, and networks to protect privacy and data—the NCS makes it clear that it expects businesses to take on more responsibility and will seek to enforce it. 

Bottom Line: Enterprise Changes in the NCS

The National Cybersecurity Strategy is the U.S. government’s first cybersecurity initiative in 15 years. As such, it’s a living document, an ever-evolving blueprint to build cyber-resilience and protect the U.S. and its allies from threats. More than just filling gaps, it ambitiously seeks to pave the way to a strong, equitable, and inclusive cyber future. Businesses of all sizes will have to play a role in its rollout and will be essential to its success, but it targets enterprises especially—the stakes are higher, the resources are more plentiful, and their responses have the potential to serve as frameworks and best practices for smaller businesses to follow. 

Keeping data secure is just one component of an effective data management strategy. Learn the 10 best practices for data management to make sure your business has its own data efforts under control.