Businesses rely on trustworthy data to make good decisions. But increasingly, organizations get their data from a variety of disparate systems — and as the volume of business data increases exponentially, it’s often challenging to manage the information and use it for analysis and decision-making. Data management involves gathering, organizing, securing and storing data to provide a single source of trusted information that organizations can use to make more-informed business decisions. A well-designed data management system helps companies gain insights into key aspects of the business, such as customer preferences, inventory management and sales efficiency.

What Is Data Management?

Data management refers to a set of activities for efficiently gathering, organizing, securing and storing the business’s data. Effective data management enables companies to better analyze information, increase operational efficiency, enhance security and comply with regulations.

Data management is more important than ever as companies undergo digital transformation and automate their business processes. Many businesses now get data from disparate data sources, including software as a service (SaaS) applications, application programming interfaces (APIs), mobile apps and Internet of Things (IoT) devices. Data management enables companies to combine data from all those sources to create a single, consistent set of information. Data management should involve people from across the business groups as well as the IT group because it requires a deep understanding of the company’s data, how it is used, and what the business needs from its data.

Data management functions include:

  • Understanding and documenting data flows between systems.
  • ETL (extract, transform and load) processing, which takes data from operational systems like point-of-sale (POS) applications and transforms it into a model optimized for analytical processing.
  • Data cleansing, part of ETL processing, to eliminate data duplication and check data quality and integrity.
  • Creating data catalogs, which hold a complete picture of the data assets in your organization, stored in one easy-to-find location. It details the location, security levels and content of each data set, as well as information such as the date of last change and the quality of the data. This enhances security and allows your organization to derive more value from its data analysis.
  • Creating data warehouses, data marts and/or data lakes that join and store data from a variety of systems. Traditional on-premises data warehouses store structured data for analysis. Modern data management systems also support cloud data warehouses as well as data lakes, which can handle unstructured information, such as streaming data, as well as structured data.
  • Establishing data governance, which applies standard practices and company policies to maintain the security and integrity of the data over time.
  • Securing data through encryption, access control and network architecture so that the company can use best practices to prevent attacks as well as limit loss in the event of breaches and understand which data has been affected.

Key Takeaways

  • Data management involves gathering, organizing, securing and storing data to provide a single source of trusted information.
  • Data management helps businesses manage the growing volume of data from disparate applications, IoT devices and other internal and external sources.
  • Data management helps businesses analyze information for better decisions, improve data quality, share information among users, improve sensitive data security and comply with regulations.
  • Key data management functions include data governance, cataloging data, cleansing and moving data between systems and managing data warehouses and data lakes.

Data Management Benefits Explained

Data management gives businesses more confidence in their data and helps make data more easily accessible across the organization. When business users can gain easy access to the data they need to make decisions, and they trust that the data is up to date and accurate, they can efficiently respond to changing market conditions. Data management can also help limit data silos — where different groups within the organization have different versions of the truth.

Good data management helps your organization scale as the volume and velocity of data grows. It facilitates audit and compliance functions and can make it easier for your organization to take advantage of new technologies, such as artificial intelligence (AI) tools, that analyze large volumes of data for unique and predictive insights.

Key data management benefits include:

Eradicate data redundancy.

Many businesses use multiple systems that each store business data. Often, data is duplicated across these systems. For example, product information could be stored in point of sales, manufacturing and inventory systems. Data stored in multiple locations can become inconsistent and is often stored in different formats. Data management systems aim to provide a single, accurate source of data for analysis. ETL processes pull data from different systems, resolve inconsistencies and cleanse the data to remove errors. The data is then stored in a data warehouse or data lake.

Improve data sharing.

In the past, it was typically very difficult for organizations to share data with business partners or even among their own business units. In the early days of data warehouses, for example, it was hard to share the entire volume of data contained within a single database, so any data sharing was typically limited to a subset of reports.

Now, in many cases it is important to share the entire data set in order to support advanced data analysis techniques based on data science and AI. The trend toward scalable, object-based cloud storage makes it much easier to securely share large sets of data with other organizations, especially when it’s combined with effective governance and cataloging processes that enable organizations to understand exactly which information is contained within each data set and whether it should be shared. While security is always a concern with sharing data across organizations, data loss prevention tools can be used to add a layer of protection by providing controlled access to data over a limited period.

Tighten data privacy and security.

Data security and privacy are important functions of data management. With a growing number of regulations such as Europe’s General Data Protection Regulation (GDPR) and HIPAA, the risks of failing to secure your data can be extremely significant. Effective security depends heavily on data governance and data catalogs, which allow you to know where sensitive data is stored, whether it should be encrypted and which users and groups should have access. In the event of a data breach, the metadata in data catalogs can allow you to understand the scope of the data that has been compromised, which can dramatically limit potential fines.

Aid backups and recovery.

In the age of prevalent and potent ransomware attacks, backups are top of mind for any organization. Data management plays an important role in creating a backup and recovery strategy that matches your company’s priorities and data model. By cataloging your data assets, you can understand which of those data sets are the most critical to your business. Why is this information important for backup and recovery? One reason is that it may affect your backup frequency because, in most cases, you will want to minimize the potential data loss for critical systems. Even more important, it helps you plan your recovery. In the case of an attack or a natural disaster, you almost certainly won’t have enough human or network bandwidth to restore all your systems at once. Understanding which systems are most critical helps you build a well-crafted recovery plan to help get your business back online.

Whether you manage your own infrastructure or use cloud-based services, it is also important to know whether your backups are immutable and how they are stored. Immutable means that once written, the files can never be updated — or, in the event of a ransomware attack, encrypted by attackers and made inaccessible. Air-gapped means that the backups are stored on inaccessible media. In the old days, this meant storing backup tapes in a metal box. Now it typically means offline storage in a separate data center.

Create real-time data consistency.

When companies start using data analysis and business intelligence tools, they often want to include additional data sources to enrich their analysis. Many businesses also need to make decisions based on rapidly changing data, so they want to obtain data for analysis in real time or as quickly as possible.

Traditionally most data warehouses and analysis projects have used a batch process, loading new data every night or perhaps every four hours. That’s because many data management systems have mature ETL processes that are straightforward to implement.

To deliver real-time data, however, you may need to capture data streams from various data sources. Data management helps companies achieve this goal. It allows you to better understand which data sets need to be captured in real time to help inform your business decisions. Data catalogs can also provide information about when each data set is updated so that you can understand how current the data is.

Key Benefits of Data Management

Eradicate data redundancy Data stored in multiple locations can become inconsistent and is often saved in different formats. Data management systems aim to provide a single, accurate source of data for analysis.
Improve data sharing Data management systems help ensure consistent data is shared securely among all users. Cloud-based data management systems facilitate sharing large amounts of data.
Tighten data privacy and security Data management systems help the organization focus its security efforts on the most critical data assets. They help the company understand where sensitive data is stored, whether it should be encrypted and which users and groups should have access.
Improve backups and recovery Data management helps companies create and manage backup approaches tailored to the data. It helps set appropriate backup frequencies based on the importance of the data and minimize recovery time in the event of a problem.
Real-time data consistency Modern data management systems help businesses capture data streams and share information in real time.
A well-designed data management system helps provide insights into key aspects of the business.

Types of Data Management

Data management includes a wide array of functions and activities involving a broad range of people, from chief compliance officers to IT architects and data scientists. Some key focus areas are:

  • Data governance: Data governance is a set of functions and approaches to manage organizational data through its life cycle. Data governance aims to ensure that data is accurate, secure, available and usable throughout the organization. It includes the development of data management principles, policies and standards. Data governance should be an ongoing activity. It’s best driven by cross-functional teams representing the organization’s business and technology groups.
  • Master data management: Master data encompasses the core data that’s essential for running the business, including the company’s charts of accounts, suppliers, locations, employees and sales prospects. Master data management involves business experts and IT working together to ensure the uniformity, semantic consistency and accountability of this data. It enables organizations to create a consistent set of data attributes and identifiers, which helps to ensure accurate reporting.
  • Data stewardship: Data stewardship is a subset of data governance that focuses on the enforcement of policies that maintain the company’s data governance principles regarding data quality, security and integrity. A company’s data steward typically plays a leadership role within the data governance team and is responsible for making final decisions in line with the firm’s data governance policies.
  • Data quality: Data quality is the area of data management responsible for unearthing and eliminating inaccurate, duplicative, and out-of-date data. Data quality practices also aim to ensure that data is being used appropriately to obtain correct answers to business questions.
  • Data security: With the continuous onslaught of cyber threats, data security has never been more important. Data management techniques enable you to apply more effective security because they help you gain a better understanding of what data you have, where it is stored and who has access to it. These key pieces of information allow you to focus your security efforts on that data, applying appropriate access controls, encryption and other security controls as necessary.

Why Data Management Is Important

Data analysis is a key function in every organization. Companies use data analysis to make decisions and discover new ways to streamline operations, create marketing strategies, adapt to unforeseen market changes, open new lines of business, enhance customer relationships and improve security. That’s a key reason that data management is so important. Well-run modern businesses don’t make decisions unless they have data to support them. Without solid data management practices that create a reliable source of data for analysis, an organization’s decision-making suffers.

An effective data management approach plays a critical role in creating a single source of information that everyone in the organization can rely on for making decisions. Many larger organizations face the common challenge of siloed data — the organizational intelligence that managers need to make decisions is so spread out that it can take weeks to pull it together for analysis. Siloed data also means there is no shared version of the truth. As a result, decision makers have a hard time reaching consensus on important issues because discussions are based on each person’s view of the data, not the problem or the solution.

Data silos are an organizational issue, not simply a technical problem. A cross-functional data management team that centralizes the administration and management of data can build consensus and gain understanding of the company’s data challenges. The team can take advantage of modern data management tools to help standardize the data so that it can be used by multiple systems and users without worrying about its provenance or accuracy.

Data management also helps organizations extract valuable information from huge amounts of raw data. The sheer number of applications and devices today means organizations are bombarded with a firehose of data. Data management strategy and software can help you understand which data has the most value so you can filter data early in the process. This helps reduce the data to a more manageable size for processing and analysis. Consider the case of a sensor on the manufacturing floor that records temperature every 5 seconds. If the value of the temperature is within the normal range, you could discard those records and replace them with an average over 15 minutes. If you start to receive out-of-range values, you could collect all the records for a shorter period.

Manage — Then Model

Business that grow fast and profitably have a secret: They’re great at financial modeling. Sounds complicated, but it’s simply combining finance and business metrics to create a forecast of future results. When resources are scarce, knowing where to expend effort for max effect is a superpower.
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Data Management Best Practices

Establishing best practices is key to making data management a success in your organization. It is important to understand that this is a business project with IT support — not a project that’s driven by the IT group. IT-driven data management projects usually offer limited value because technology professionals lack business users’ deep understanding of the organization’s data.

Define your goals.

Since you can’t reach your destination if you don’t know where it is, the first step on your data management journey is to set goals for what you want to achieve. Defining goals helps determine the processes you will use for collecting, storing, organizing, categorizing, risk-profiling, maintaining, cleansing, securing and analyzing your data. Clearly defining your goals also ensures you’re only managing the data you really need.

Make a plan.

Once you have firmly established your goals, develop a plan of action. The first step is to identify and gather input from people throughout the business who really understand the data that their department uses — and what it needs. This will help you build a data management plan that includes calculating data usage estimates, setting accessibility guidelines, determining approaches to archiving, assigning data ownership and stewardship and much more. As time goes on, the plan will serve both as a reference guide and a living document that evolves as your data needs change.

Maintain data quality.

Data is constantly changing. Customer and vendor addresses are a classic example. If a customer or vendor changes their location, you need a mechanism to allow them to update their address — or better yet, a mechanism that notifies you of the change and updates your systems automatically. As you can probably imagine, many other changes frequently occur in a living system, and it can require a significant amount of effort to ensure that data remains accurate.

Secure your data.

Data security is of paramount importance to all organizations today. When threat actors infiltrate an organization, they typically try to steal, corrupt, delete and/or encrypt data in order to force the company into paying a ransom. Data management plays a key role in protecting the organization’s vital information. It gives you a better understanding of your data, so you can selectively apply stronger levels of control and isolation to your more sensitive information. But remember that security is an ever-evolving target, and static approaches to security policies will be quickly out of date. It’s important to continually monitor the latest threats and determine whether you need to adjust your security strategy accordingly.

Create a data management function.

Data in even a midsize organization is complex, and no single person can understand all the company’s data to the level needed to manage it. That’s why it is important to build a data management team that includes people who have a good understanding of the data as well as the organizational knowledge to know who is responsible for the data within each department. For a successful data management initiative, you need people who understand the business and the data that business groups produce and consume. A cross-functional data management team allows you to gather best-in-class practices and build synergies within your organization.

NetSuite: The Data Management Platform of the Future

NetSuite provides businesses with a comprehensive suite of cloud-based applications built on a single database. NetSuite policies and infrastructure ensure that your data is always replicated, backed up and available from any location, whenever you need it. NetSuite data management also ensures that your users have real-time visibility into the company’s financials, customer data and inventory, allowing them to make informed decisions quickly and easily. Replication and synchronization across data centers provide built-in disaster recovery capabilities and business continuity assurance. The cloud-based system is designed from the ground up to support steady increases in transaction volumes and spikes in usage.

NetSuite also offers a set of capabilities specifically designed for product data management, so that master design and development data is centrally stored and accessible to all stakeholders.

With more than 33,000 customers making more than 1.5 billion requests daily and 6 petabytes of data already under management, NetSuite offers proven scalability to support business growth.

Modern businesses are data-driven. Success hinges on the ability to use data to gain insights and make better decisions that increase revenue and reduce costs. But analyzing data can be a time-consuming and fruitless exercise when you’re relying on inaccurate data spread across the organization. Data management systems help solve the problem, ensuring that the company has accurate, reliable information by addressing data capture, maintenance, security, management, categorization and access. Data management helps make information the engine that drives value for the organization, its customers and stakeholders.

Data Management FAQs

What is the purpose of data management?

The purpose of data management is to ensure that an organization has timely, well-secured access to clearly defined, accurate, up to date information – and that the available information is easily understood by everyone who needs to access it.

What are the benefits of data management in every enterprise?

Data management systems improve data accuracy and security, enhance data sharing, ease maintenance and save time through automation. They improve any organization’s ability to use data to create value for itself and its customers.

What is the importance of big data management?

Data management systems are essential for managing large volumes of data. Without a data management system and a well-designed data management strategy, it is impossible for most organizations to effectively manage all the data they create and consume daily. Data management systems are designed specifically to tackle the challenges presented by high data volumes and velocity.

What is the importance of data management in research?

Data management is important for any kind of research, whether it’s for a product launch, internal communications about an issue relevant to the organization or scientific discovery. Data management helps ensure that the information being used for research is well-organized, accurate and reliable, so researchers can analyze it and have confidence in their results. A good data management system facilitates collaboration among researchers and helps manage compliance with regulatory and other standards.