What Is Data Management? Types, Benefits and Examples

“What is data management?” we hear you ask.  

Data management is the act of collecting, organizing, processing and storing data. The aim is to make smart,  data-backed decisions that enable enterprises to become more productive and efficient.

Data management encompasses everything from data gathering and preparation to data catalogs and governance. Even in its simplest incarnation, it enables people in an organization to quickly find the information they need for analysis. 

Innovations within the hybrid cloud, artificial intelligence and edge computing have paved the way for the growth of big data. However, this is hugely complex for businesses to manage and is largely responsible for the need for data management solutions. 

Data management solutions allow business managers to gather valuable business insights through data visualization tools. These solutions provide the backing for data science teams to explore more complex areas and advance their analytical capabilities. 

In this article, we explore the different types of data management, together with what it brings to your business, before finishing off with some use cases to determine if data management is something you should implement. 

Think of this as Data Management 101. Read on to learn how data management can handle structured and unstructured data to simplify your business architecture into an integrated and centralized database.

Data management explained

Data management refers to building and maintaining a framework to ingest, organize, store, analyze and archive data. Closely linked to process management, it ensures your business works with the freshest and most complete data available.  

Organizations implementing a strong data management plan understand the importance of correctly using this to inform smart business strategies. Data management helps people, businesses, and connected entities maximize data usage and take actions that optimize business benefits and opportunities. 

Data management encompasses a broad scope of policies and procedures, some aspects of which include: 

  • Creating, accessing and updating across varying tiers of data
  • Storing data across multiple clouds
  • Delivering robust disaster recovery strategies
  • Implementing data in different analytics and apps
  • Securely sharing and governing data across organizations 
  • Protecting and securing data by ensuring data privacy protocols 
  • Transforming data into structured forms

Elements of data management

Types of data management strategies can be categorized according to the following areas:

MDM (master data management)

MDM focuses on delivering a single source of truth. Acting as a consistent set of identifiers, it defines your business’s core entities and creates a master record related to customers, sites, hierarchies, prospects, suppliers and so on.

For example, master records are used as the most up-to-date information to inform tasks such as new prospect marketing emails. In this instance, MDM ensures only relevant prospects receive promotional outbound emails and eliminates records of those who are irrelevant or have previously shown disinterest.

MDM applies data integration, enrichment, reconciliation, quality and governance to create these master records. As data sources are ingested, it initiates processes to identify, gather, transform and repair data.

Artificial intelligence (AI) and automation play a part in identifying, matching and merging data in storage systems before cleaning it. During the merging phase, MDM can record inconsistencies in records, capture information about where data originated, and create an audit trail of changes in case compliance regulations require it.

Upon its creation, master data can be managed and shared throughout your organization to reduce data errors, enhance reporting and help with smarter decision-making.

MDM is used by global businesses with highly distributed data across multiple systems and also organizations with frequent acquisition or merger activity. MDM mitigates the associated data integration challenges and often advances the time-to-value from a company acquisition or merger.

Master data is categorized into certain domains. Some common master data domains include:

  • Customer master data management (B2B and B2C)
  • Asset master data management
  • Product master data management
  • Reference data master data management
  • Location master data management
  • Employee data master data management
  • Supplier master data management

With a single source of consistent and reliable data, businesses gain clear oversight of their products, suppliers, prospects and customers to deliver a consistent experience to consumers and team members.

Data stewardship

Data stewardship is the practice of ensuring accessible, reliable, safe and usable data across businesses. It oversees the entire data lifecycle and encompasses creating, preparing, using, storing, archiving and deleting duplicate data.

The overarching goal of stewardship is to enforce established data governance principles during collection and movement. A data steward acts as a liaison between the business side and the IT department, with common responsibilities including risk management, data security and information governance.

Data stewards are responsible for dictating how corporate data can be accessed and where it’s required and have the ultimate say in its accuracy and accessibility. Data stewardship offers better documentation and tracking, transparent data policies and processes, and can reduce security risks.

Data quality management

Data quality management ensures integrity is maintained and quality data is delivered by identifying underlying management errors.  

A data quality manager often acts as an assistant to a data steward to accelerate processes. Typically, they focus on issues such as inconsistent data and duplicate records, together with any discrepancies.

Data governance

Data governance is more than a data management process. It’s the oversight that ensures data brings meaningful value and aligns with a business’s overarching strategies. 

It provides the framework across data, technology, people and processes to support business objectives. This framework strives to ensure consistent and high-quality data is gathered and that it adheres to industry regulations and compliance requirements.

To establish governance, businesses often build data architectures for analytics on a lakehouse. A data lakehouse is an architecture that provides secure engineering, data warehousing, machine learning and business intelligence.

Data governors oversee data stewards, quality management professionals and security teams. While data management also focuses on compliance with policies, data governance takes a longer-term approach to implementing these.

Data security

A key element of data management is security. Security processes must be in place for every data exchange — typically including encryption management, provisions against accidental data deletion, unauthorized access, and other managed data incidents.

Data security management can be defined as:

  • Ensuring data in any form is protected from corruption or unauthorized access
  • Monitoring data storage, acquisition, retrieval and deletion for potential security threats
  • Encouraging service developers to test against data security standards
  • Harnessing encrypted cloud storage and encrypted cloud networks to secure data sharing and transfers 

Staff working from home and using devices that fall outside corporate oversight can negatively impact data security management. After all, personal devices can affect an entire company due to malware or hacker interventions. 

Protecting sensitive data is paramount to organizations, and businesses must be able to prove they take appropriate measures to secure organization and customer data. 

The European General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) specify standards for data control and give governments the power to fine organizations to improve security management. In order to avoid fines, any technology you use should have transparent security and trust policies and certifications available.

Big data management

Big data management describes the business process of gathering, analyzing and implementing information to enhance operations, often through a unified management system. This solution handles data sets that are too complex and large for processing in the traditional way. 

Big data analytics is the process of analyzing and processing massive data sets to discover patterns, market trends, new business insights, and customer preferences. 

Data lakes usually store big data from different sources in a raw format as either structured, semi-structured or unstructured data. Sources can include audio recordings, social media channels and Internet of Things (IoT) devices. 

Big data management typically encompasses:

  • Developing a strategy for the volume of data and deciding how and where it will be stored 
  • Identifying different sources of data to apply the best ingestion tools 
  • Researching and deploying the right technologies to process data in real time 
  • Ensuring data is accurate and clean 
  • Building a big data ecosystem to harness better decisions 

Before big data was on the scene, managing enterprise data was achieved on-premises. However, cloud-based data management software now enables remote data analysis with decreased time to implement.

Effective data management benefits

Effective data management processes point the way for businesses to resolve internal problems, gain a clearer perspective and provide an improved customer experience.

For example, good data management leads to a better customer experience because consumer preferences and buying habits are logged, thus helping companies to create more personalized offers and opportunities. This results in a tailored shopping experience and more revenue for businesses. 

Additionally, data management can identify interactions that could cause potential problems in network infrastructure, software applications and security protocols.

Effective data management can also uncover valuable business intelligence and inform decisions such as:

  • How to target marketing according to consumer interests, behaviors and interactions
  • How to implement security protocols to protect sensitive information
  • How to enable machine learning that powers continuous data-driven business processes
  • How to reduce operating expenses by restricting power for peak performance

Let’s look at some other important data management benefits.

Data visibility

As good management increases the visibility of your business data, your team can quickly access the information they need to do their jobs. The data scientists at your company can perform their analyses more efficiently, and your operations will run more smoothly as a result.

Data accuracy

Following data management best practices ensures data is as accurate as possible. Implementing stringent processes means your business minimizes potential data management mistakes and provides a single source of truth.

When your data is current, you can rely on it to inform market decisions and quickly adapt to customer needs.

Data security

Management protects your business and team from data breaches, loss and theft. Data security encryption tools, such as secure authentication, are in force to ensure essential business information is accessed only by authorized personnel.

Encryption is used to protect sensitive customer or employee information from misuse and disclosure, not to mention internal threats and hackers trying to access a business’s network.

Other security management practices include data backup and recovery, which help reduce the risk of data loss due to hardware failure or natural disasters. These are also an integral part of successfully storing data in the long term to meet compliance requirements. Automated data management processes can back up data at regular intervals without human intervention.

Security is especially crucial when data contains personally identifiable information because this must be carefully managed to comply with consumer protection laws.

Data scalability

Last but by no means least, data management enables companies to scale data to create repeatable processes according to business needs.

Processes that are repeatable eliminate duplication costs and streamline operations.

The significance of data management systems

Data is one of the most valuable assets an organization has, so it’s essential it’s optimized for maximum impact.

We already know data leads to smarter business decisions, influences product launches and marketing campaigns, reduces business risk, and fosters fruitful customer relationships. For data to achieve these overarching goals, an effective data management system must be in place.

Data management systems work with businesses to get the right information to the right people at the right time. Delivering analytical insights through data management workflows, they’re essential to ensure business-critical data is accessible, secure and scalable.

Your data management system should:

  • Generate, manage and clean data across your organization
  • Enable all of the use cases you have for your data 
  • Help plan for disaster recovery
  • Secure and control access to data from any location 
  • Audit and delete data to meet compliance requirements 

Information is power, so effectively managing and optimizing data puts your business in the driving seat. 

Examples of data management

Examples of effective data management use include the automotive industry, which often uses metadata-driven cloud management platforms to increase data visibility across different locations. In this example, entire fleets of rental vehicles can be managed using large data sets while ensuring the correct governance is followed.

Another example of data management in practice is a grocery delivery service. This business use case consists of an internal database and tools to respond to requests. Shoppers are increasingly demanding a more personalized experience, and intelligent data aids this goal.

Unsurprisingly, digital-native companies need strong data management strategies in place. One example of a company doing this well is Grammarly. It provides real-time writing and editing assistance with text documents, and users provide constant feedback by accepting or rejecting suggestions. With about 5 billion events per day, that’s a lot of data. By using the Databricks Lakehouse Platform, all this data is stored in one place and can be accessed for insight in as little as 15 minutes. That means Grammarly can focus on creating a great customer experience, instead of sorting through different data sets and silos.

Another tech-focused example is Abnormal. As an email security company, they know how important it is to act fast. That means data needs to be dealt with quickly, and a lot of information needs to be easily accessible to their machine learning models. By having good data strategies in place, Abnormal can process thousands of emails per second — and continue scaling up in future if needed.

From tailored product recommendations to real-time security responses, data management is deployed to consistently meet customer expectations. 

Want to learn more about digital-native companies and data management? Take a look at our case studies for Rivian, Scribd, Grab and YipitData.

Elevate your experience with cloud data management

Hopefully, this article has answered the question we started with — i.e., “What is data management?”

Data management platforms such as Databricks data lakehouse elevate cloud data management by combining the best of data warehouses with the best of data lakes to eliminate data silos. Robust governance and performance enable a flexible approach to cloud data management and simplify your business’s data stack. 

Built on open source, Databricks has the data management tools your organization needs to innovate faster and operate more efficiently. Give your machine learning teams the power to access and harness any data at scale by putting it to work. 

Why not unlock the power of your data assets today by using the best data management solution on the market?

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