What Is Data Management? A Comprehensive Guide

What Is Data Management?

Data Management Defined

Data management is the process of ingesting, collecting, storing, organizing, governing, and protecting data to achieve organizational goals. It is the proactive approach organizations must take to comprehend and safeguard their data throughout its lifecycle. It also involves creating and enforcing policies and guidelines to keep data practices consistent and coherent across the organization.

Managing data is more than simply handling and sorting it; it’s about implementing robust systems, or frameworks, that validate, secure, and provide a clear understanding of the information at hand. However, this does not mean that it’s just an enterprise-level concern—for that, we have enterprise data management. Even small businesses stand to enhance their revenue, productivity, and customer experience with effective data management and efficient business processes; and this is why organizations need an effective data management strategy.

Why is Data Management Important?

Businesses need healthy data for business intelligence (BI) and analytics, training AI and machine learning models, and other data-driven initiatives. Relying on unrefined data to power business decisions is like setting sail without a map (or a well-defined plan in this case). Think of it this way: When you have a lot of raw information—whether it’s numbers, text, or pictures—you must structure it and improve its quality before you can use it. This is precisely what data management helps organizations with; it keeps business data in order so that it’s easier to find, use, and protect.

Today, businesses have several reasons to manage data effectively, particularly as they are compelled to deal with an unprecedented growth in the amount of data they create and collect. This is also why organizations resort to using data management platforms. Whether it’s to improve the performance of marketing campaigns, forecast demand, or unlock hidden opportunities for innovation, businesses need an effective framework to manage data and make the right decisions.

However, if the data is managed poorly, that will mean that the requisite information is not only disorganized but also inaccurate and inaccessible to decision-makers. Relying on this data leads to wasted resources, frustrated employees, and ultimately, flawed decisions that hinder business growth.

Here are some more reasons why data management is important for businesses:

• Effective data management streamlines processes and reduces redundancies to improve the overall efficiency of business operations.
• It keeps critical business data backed up and accessible for disaster recovery.
• It enables businesses to comply with relevant regulatory requirements, for instance, data privacy and security.
• Provides a standard procedure to properly archive and dispose of data and optimize data storage costs.
• When data is organized and accessible, different departments can work cohesively, sharing insights and working towards common goals.

Data Governance vs. Data Management

One of the key points to remember is that data governance and data management are not the same concepts—they are more different than similar. Yet, both go hand in hand when it comes to using data to achieve organizational goals.

Whereas data governance involves a broader set of activities, such as establishing policies and procedures, that guide and oversee how an organization uses its data, data management is all about its implementation. It essentially supports the overall data governance framework.

The table below summarizes data governance vs. data management:

Learn more about data governance and management.

Who Is Responsible for Managing Data in an Organization?

Managing data in an organization is a collaborative effort involving several roles, each with specific areas of focus. Here’s a general breakdown of key personnel responsible for the management of data in an organization:

Leadership and Strategy

In larger organizations, a chief data officer (CDO) is generally responsible for overseeing the entire data strategy. They define the vision, establish data governance policies, and ensure alignment with business goals.

Governance and Oversight

The data governance council, a cross-functional group with representatives from IT, business units, and data governance specialists, collaborates on defining data policies, standards, and procedures, ensuring compliance.

Technical Implementation

Data architects design the architecture for storing, accessing, and integrating data across different systems. They ensure scalability, security, and performance of the data management infrastructure. Database administrators (DBAs) manage and maintain the organization’s databases, ensuring smooth operation while maintaining data integrity.

Data Ownership and Use

Data stewards are business-unit representatives who own and are accountable for the accuracy and quality of specific data sets. They understand the data needs of their department and ensure data is used appropriately. Data analysts analyze data to extract insights and inform business decisions. They may also be involved in the cleansing, transformation, and management of data. With advanced skills in statistical analysis and machine learning, data scientists develop models and algorithms to uncover hidden patterns and trends within the data, enabling more sophisticated data-driven decision making.

Data Security

The IT security team implements security measures, access controls, and data protection protocols to safeguard sensitive information.

The Data Management Process

Unlike a one-off data migration project, data management is not a one-time process—it’s an ongoing endeavor. As businesses evolve, so does their data. Regular updates and maintenance are essential to provide data that is relevant, secure, and, most importantly, aligned with the ever-changing needs and objectives of the organization. Finally, as data ages and becomes obsolete, there are procedures for archiving or appropriately disposing of it.

Here’s what the data management process generally looks like:

Collecting Data: The process begins with the collection of raw data from various sources. This could be customer interactions, transactions, or any other relevant information. Once collected, the data needs a home, so it’s stored in databases, data warehouses, or other storage systems, making it easily accessible.

Storing Data: This step involves selecting and maintaining the appropriate infrastructure to house your organization’s information. It also includes decisions about storage types, such as relational databases, NoSQL databases, or cloud data warehouses. Considering the volume and type of data your organization handles, you must also determine the optimal storage capacity.

Structuring Data: Raw data often lacks a defined structure, so it must be structured in a way that makes sense, usually through databases with defined tables and relationships. This step also involves cleaning and transforming the data so that it’s standardized and consistent.

Improving Data Quality: Data quality is fundamental to ensuring accurate and reliable information. Start by implementing robust data validation checks during the data entry phase to prevent errors. Collaborate with data owners and users to establish and enforce data quality standards. Utilize data cleansing tools and techniques to correct inaccuracies and inconsistencies.

Securing Data: Protecting data from unauthorized access or loss is a critical aspect of data management, which involves implementing security measures such as encryption, access controls, and regular audits. Organizations must also establish policies and procedures to guarantee data quality and compliance.

Accessing Data: This is where you establish access controls to ensure that individuals have appropriate permissions based on their roles within the organization. Implementing encryption and authentication mechanisms further helps in protecting sensitive data during transmission and storage.

Consuming Data: With the data now processed and secured, organizations can perform analysis, whether it’s through statistical methods, machine learning algorithms, or other analytical tools, and make reliable business decisions.

Data Management Types, Components, and Functions

Being a broad concept, data management can have several types of aspects, or functions, that reflect the diverse ways organizations manage data. Here’s a look at the different types of data management functions:

Big Data Management

Growing data volumes compel organizations to invest in scalable data management solutions. Today, this means they should have the necessary resources and infrastructure to be able to deal with big data—large volumes of structured and unstructured data—efficiently. This also includes maintaining data quality while facilitating easy access to the needed data.

Master Data Management

Another widely used strategy for managing data is master data management (MDM). It focuses on maintaining a consistent and accurate version of master data, such as customer information or product details, across an organization. MDM leads to data that remains a reliable and trustworthy asset for the organization.

Database Management

Involves the organization, storage, and retrieval of data using database management systems (DBMS). Database management is the process of creating and maintaining databases. It allows users to manage data, maintain its integrity, and optimize performance. There are several types of DBMS, with two being the most common ones: relational databases (also called SQL databases) and non-relational databases (also called NoSQL databases). Other DBMS include object-oriented database management systems, columnar databases, graph databases, etc.

Learn more about the types of databases.

Data Integration

The goal with data integration is to consolidate data into a centralized repository. Organizations rely on scalable data pipelines to integrate their data to make reliable decisions and drive growth. Two of the most widely used data integration techniques are extract, transform, and load (ETL) and extract, load, and transform (ELT).

Data Warehousing and Data Lakes

Data warehouses and data lakes serve as essential components in the modern data management architecture.

Data warehousing is all about consolidating data into a central data warehouse—or an enterprise data warehouse in case of a large organization—for various BI and analytics initiatives. Although organizations widely use ETL pipelines for this purpose, ELT has gained considerable traction lately. Data warehousing is especially important in managing data with a defined structure for streamlined data retrieval.

A data lake, on the other hand, takes an unstructured approach to data storage. Unlike relational databases with predefined schemas, data lakes can store vast amounts of data in its original format, regardless of structure. This includes text documents, images, sensor data, social media feeds, and more. Data lakes are increasingly being used for big data management.

Data Quality Management

Data management lays a strong emphasis on data quality management. The goal is to provide business leaders with easy access to accurate data for reliable decisions. Breaking down data silos and building a single source of truth (SSOT) are some prerequisites that organizations must do right to ensure data accuracy.

Data Governance

Data governance provides strategic oversight and a framework to uphold data as a valuable asset, managing it in a way that aligns with organizational goals and industry best practices. It maintains data quality, consistency, and compliance with regulations.

Data Modeling

Data modeling is all about creating abstract representations of how data should be organized, structured, and related within a database or information system. It serves as a blueprint that guides the design and implementation of databases and facilitates the storage and retrieval of data in a logical and efficient manner. 

Business Intelligence and Analytics

Modern data management systems are equipped with features and components that enable you to analyze data, generate insights, and make forecasts. Specifically, BI offers a comprehensive overview of historical and real-time data. It transforms raw data into actionable insights, equipping you to make informed decisions with a forward-looking perspective.

On the other hand, analytics enables you to sift through information, identify patterns, and extract valuable details that contribute to a granular understanding of your business landscape. Together, BI and analytics form a pivotal component of your data management strategy to enhance decision-making.

Data Management Benefits

Staying competitive in a dynamic business landscape means organizations must base their decision-making on healthy data. Effective data management sets up organizations for success, here’s how:

Improved Decision-Making

Effective management of data allows organizations to track and monitor various aspects of their operations. With access to a well-managed and integrated data repository, executives make more informed and timely decisions. Consequently, the increased visibility into relevant data allows for a better understanding of market trends, customer behavior, and internal performance, facilitating strategic and data-driven decision-making.

Enhanced Operational Efficiency

An effective data management strategy becomes a foundation for reliable and cohesive information by streamlining processes and workflows. This way, everyone works with the most up-to-date and accurate data. As a result, not only does operational efficiency improve, but the entire team can also respond more promptly to challenges and opportunities, contributing to an agile and responsive work environment.

Improved Data Security

The repercussions of data loss and cyber threats continues to keep organizations on their toes. This is why managing data also extends to protecting sensitive information. With data security being a key data management component, it involves setting up access controls, encryption, and authentication mechanisms permitting only authorized individuals to access and modify data. Regular monitoring and auditing of data activities further enhance data security by detecting and addressing potential threats proactively.

Better Compliance

Managing data isn’t just about keeping data organized; it’s as much about following the rules. Industry standards and regulations set clear guidelines for handling data, and following them helps organizations avoid hefty fines. But that’s not all. By handling data responsibly, organizations show they take protecting sensitive information seriously. This builds trust with stakeholders and strengthens the organization’s reputation for ethical and responsible practices.

Lower Costs

Organizations with effective data management processes see a significant reduction in costs due to optimized utilization of resources. For example, businesses can substantially reduce storage costs by eliminating redundant and obsolete data, so that only relevant and valuable information is stored. Additionally, efficient data processing and retrieval mechanisms streamline operational workflows, which in turn lead to increased productivity and decreased labor costs.

Data Management Challenges

While there are many benefits of data management, its implementation can be a complex endeavor. Here are some challenges that become obstacles when managing data in an organization:

Accessing Organizational Data

A significant aspect of data management involves knowing your organization’s data sources and where the business data resides. However, many companies struggle in the identifying the most critical data elements. More than 42% of tech professionals surveyed face difficulty locating organizational data.

The solution is first to determine the analytics and reporting objectives. Based on these objectives, craft a comprehensive data management strategy to collect, prepare, store, and consume data efficiently. The goal should be to make organizational data accessible across the organization.

Rising Data Volumes

Data collection has increased vastly due to the growing digitalization of information. It is pushed further by the increasing use of social media and smartphones, with IoT systems being another significant driver of big data. However, the limited storage capacity of on-premises systems is a big obstacle in data management, especially when it comes to big data. Continuously rising data volumes require additional storage space and advanced storage systems, which lead to higher costs.

Many businesses move their data to the cloud to overcome for cheaper storage. Consequently, cloud-based data warehouses have become a necessity for storing large amounts of data.

Consolidating Data Across Systems

Tech companies use multiple information systems, including accounting software, CRM, and ERP systems. Each department usually has its own data repository, resulting in siloed systems. The contrasting systems can lead to data duplication and lack of synergy, which impact data analyses.

Tip: a no-code data integration platform is the best option for preparing a centralized data warehouse.

Managing Unstructured Data

More than 80% of business data is available in an unstructured format. This data contains valuable insights that can drive effective decision-making. For instance, emails from potential leads contain information, including their location, budget, and use case, which helps salespeople design more personalized and relevant demos.

However, unstructured data is not machine-readable and must be structured, i.e., in columns and rows for reporting and analysis. To leverage unstructured data to their advantage, companies must use tools and technology that enable them to gather and manage data, whether it’s structured or unstructured.

Maintaining Data Quality

According to a survey by Forrester, 80% of IT professionals cite poor-quality data as a major challenge to data management. With businesses facing an onslaught of data from various directions, many data sets inevitably contain missing or incorrect information. Leaving these issues unattended leads to misleading insights and poor decision-making. Using relevant data quality metrics helps measure data health by identifying duplicate values, missing fields, and null values.

Ensuring Data Security

Rising data stores increase the risk of cyber-attacks and data leaks. Large volumes of data need protection both during rest and transmission. A data governance framework is usually the answer, however developing and implementing comprehensive data governance policies and procedures requires substantial resources. Maintaining consistency and adherence to these policies across diverse data sources and systems requires organizations to determine the sensitivity and regulatory requirements of data, manage access controls and implement encryption methods, all the while ensuring consistency in data governance practices across different departments.

Handling Multiple Data Solutions

Many organizations use separate tools for different data management processes, such as extraction, integration, warehousing, etc. Using various data solutions leads to data silos and inefficient data management. Additionally, using multiple software has the following disadvantages:

• Lack of connectivity between systems
• Higher total costs associated with multiple tools
• Each tool has varying compliance levels
• Lack of consistent UI

Unified data management solutions help address and overcome these challenges.

Data Management Best Practices

Organizations can effectively tackle the challenges of managing data discussed above if they incorporate some best practices into their overall data strategy. Here are some data management best practices:

• Implement a centralized data access strategy with clear protocols for data retrieval, access control, and discovery to streamline information flow.
• Implement a layered security approach with encryption, access controls, and regular audits to safeguard sensitive data.
• Invest in scalable data storage solutions to efficiently manage growing data volumes.
• Incorporate effective data integration tools and processes streamline data flow and enhance interoperability.
• Utilize data management tools that can handle unstructured data in bulk.
• Establish a robust data quality management framework and processes to keep data health in check.
• Create a comprehensive data governance framework to ensure consistent and secure management of data across the organization.

Leveraging Data Management Tools for Success

Given the proliferation of data and organizations’ growing need for data democratization, leveraging data management tools is no longer optional; it’s essential for success. A modern data management tool empowers organizations to:

• Tame the data deluge by organizing, cleansing, and integrating data from various sources while ensuring its accuracy, consistency, and accessibility.
• Break down data silos to dismantle information barriers and enable self-service analytics.
• Enhance data-driven decision-making at all levels by offering user-friendly interfaces that make it easier for everyone, regardless of technical expertise, to explore and analyze data.
• Ensure data security and compliance with built-in data governance features that protect sensitive information and guarantee adherence to data privacy regulations.

How Astera Helps Businesses with its Data Management Platform

Astera provides a unified data management platform with a consistent user interface and experience.

Whether its unstructured data management, integrating data from multiple sources via ETL, ELT, or CDC, building a custom data warehouse from scratch, building and publishing APIs, or EDI management, Astera empowers its users to accomplish everything without writing a single line of code.

With the ability to connect to a wide range of source and destination systems, whether they are on on-premises or in the cloud, users get a truly seamless experience when moving and integrating data across systems.

Streamline your data management initiatives with a reliable partner. Get in touch with one of our data solutions experts at +1 888-77-ASTERA to discuss your use case. Alternatively, you can download a 14-day free trial to test our data management suite and see if it fits your requirements.