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Data Management: What Is It? Why Is It Important?

March 20th, 2024

This article serves as a comprehensive guide to data management, covering its definition, importance, different processes, benefits, challenges, and best practices.

What Is Data Management?

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

Businesses, both large and small, find themselves navigating a sea of information, often using unhealthy data for business intelligence (BI) and analytics. Relying on this data to power business decisions is like setting sail without a map. This is why organizations need an effective data management strategy in place.

Managing data is more than simply handling and sorting through data; 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 teams stand to enhance their revenue, productivity, and customer experience through an effective data management strategy.

Why is Data Management Important?

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 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 software. Whether it’s to improve the performance of marketing campaigns, forecast demand, or unlock hidden opportunities for innovation, businesses need data to make the right decisions, especially since the end goal is to maximize revenue and profit.

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. This leads to wasted resources, frustrated employees, and ultimately, flawed decisions that hinder business growth.

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

  • Effective data management streamlines processes and reduces redundancies to improve the overall efficiency of business operations.
  • It enables businesses to comply with relevant regulatory requirements, for instance, data protection and privacy.
  • When data is organized and accessible, different departments can work cohesively, sharing insights and working towards common goals.

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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 managing organizational data.

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

Think of data governance as the captain steering the ship, setting the course and overall strategy, while data management is the crew carrying out the day-to-day tasks to keep everything running smoothly.

The table below summarizes data governance vs data management:

Data Governance Data Management
Focus Strategic and organizational aspects of data. Tactical and operational aspects of data.
Scope Establishing policies, standards, and oversight. Implementation of processes and day-to-day tasks.
Responsibility Decision-making and accountability. Execution and handling of data operations.
Objective Ensure data is healthy and secure. Efficient and effective handling of data.
Activities Policy creation, enforcement, and monitoring. Data collection, storage, processing, and usage.
Long-term view Aligns data-driven initiatives with organizational goals. Addresses immediate data handling requirements.
Examples Defining data ownership, access controls. Database administration, data cleansing.


Learn more about data governance and management.

Who Is Responsible for Managing Data?

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 management 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 is often messy, 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:

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 ETL (extract, transform, load) and ELT (extract, load, transform)

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 stored in a database, 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.

Data Warehousing and Data Lakes

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 is gaining quite a lot of traction these days. Data warehousing is especially important in managing data with a defined structure for streamlined data retrieval.

A data lake, on the other hand, takes a more 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.

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.

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.

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.

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Data Management Benefits

Staying competitive in today’s digital business landscape calls for organizations to base their decision-making on healthy data. An effective data management strategy 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.

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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 a well-planned data management strategy involves knowing your organization’s data sources and where the business data resides. However, many companies experience trouble in the identification of 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, design a comprehensive strategy for data collection, preparation, storage, and distribution. The goal should be to make organizational data accessible across the organization. Once a plan is formulated, the management should identify human and IT resources to execute the processes successfully.

Rising Data Volumes

The limited storage capacity of on-premises systems is a big obstacle in managing big data. Continuously rising data volumes require additional storage space or more advanced storage systems, leading to higher costs.

The main culprit is the rise of big data, and the tech industry is one of the biggest consumers of it. 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. IoT systems are another significant driver of Big Data.

Many businesses move their data to the cloud to overcome this problem. Cloud-based data warehouses are becoming increasingly popular for storing large amounts of data. They offer powerful querying abilities for analysis and are highly scalable. As data growth isn’t likely to slow anytime soon, an expandable storage solution provides long-term benefits.

Consolidating Data Across Systems

Tech companies use multiple information systems, including accounting software, CRM, and ERP systems. Each system has its own data repository, resulting in siloed systems. The contrasting systems can lead to data duplication, lack of synergy, and, consequently, can hinder data analysis.

Businesses should create a single source of truth by consolidating the scattered data into a centralized location to overcome this challenge. Pooling all the data into a unified system help eliminate data silos and unlock business insights.

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. This information can help salespeople design more personalized and relevant demos.

Similarly, a tech company can extract unstructured data from PDF documents, including purchase orders and feedback forms, to derive meaningful insights about procurement and sales departments.

As unstructured data is not machine-readable, it should be converted into structured data, 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 from 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 datasets inevitably contain missing or incorrect data.
Leaving these datasets unattended can lead to misleading insights and poor decision-making. For instance, missing the contact details of a customer group can result in missing opportunities with potential leads.

To combat this challenge, it’s imperative to introduce data validation checks with defined quality metrics. These checks will help identify duplicate values, missing fields, null values, and the overall integrity of data. Ideally, a solution should have real-time data prep functionality to ensure data quality.

Ensuring Data Security

Cybersecurity is poised to become one of the major disruptors of the tech industry. Rising data stores increase the risk of cyber-attacks and data leaks. Large volumes of data need protection, and IT executives have complained about the rising severity of cyberattacks.

Consequently, executives must devote resources to building a secure data management infrastructure. Businesses can use established frameworks as a starting point. The ISO 27000 series is one example of globally recognized best practices for IT security. However, the framework you choose will depend on multiple factors, such as the nature of your industry and its compliance requirements.

Building a centralized data repository with proper security protocols also minimizes the risk of a data breach. Companies use end-to-end ETL tools to integrate data sources and achieve a secure, single source of truth.

Handling Multiple Data Solutions

Many organizations use separate software for different data management processes, such as extraction, integration, warehousing, etc. Using various data solutions can lead to data silos and inefficient data management, resulting in poor data oversight and vulnerabilities.

Using multiple software has the following disadvantages:

  • A steep learning curve
  • Lack of connectivity between systems
  • Higher costs
  • Compliance risks
  • Lack of consistent quality

Unified data management solutions can help address and overcome these challenges. Companies must search for a single data management vendor. A unified data platform helps eliminate data silos and data vulnerabilities so that companies can gain more control and visibility of their data.

Take the First Step to Unified Data Management With Astera

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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:

Prioritize Data Security 

There are several measures you can take to protect sensitive data. For example, implementing robust data encryption to render information unreadable to unauthorized personnel. Another line of action could be enforcing access controls, granting access to authorized users only after they meet rigorous verification procedures. Additionally, conducting regular security checks and audits helps identify and address potential weaknesses in your data security. These weaknesses could be vulnerabilities in your access controls or compromised credentials.

Implement a Centralized Data Access Strategy

A centralized data access strategy functions as the command center of your organizational data, establishing clear protocols that define how data is retrieved, who can access it, and when. It acts as a single point of control that streamlines access and makes it easy for everyone to find the information they need.

Invest in scalable Data Storage Solutions

When dealing with increasing data volumes, scalability is the name of the game. Implementing scalable data storage solutions enables systems to handle growing amounts of data without breaking a sweat. Cloud-based or distributed storage systems are the heavy lifters here—they provide the flexibility and capacity to manage large data volumes efficiently. Additionally, optimizing storage capacity based on evolving data requirements ensures that businesses don’t waste resources or run out of space when it’s needed the most.

Incorporate effective data integration solutions and processes

Data integration tools streamline and accelerate the overall integration process. Consequently, these tools have become an essential part of implementing a data management strategy. Different data integration techniques and technologies, such as ETL tools, not only maintain data consistency but also enhance accessibility and analysis.

Utilize data management tools that can handle unstructured data

Managing unstructured data requires specialized approaches and tools. While NoSQL databases are well-suited for handling unstructured data, organizations still need a dedicated tool that enables them to manage unstructured data in bulk. For example, extracting and cleaning data from disparate data sources in bulk is a tedious task. In this case, specialized unstructured data extraction software becomes an indispensable tool.

Establish robust data quality management processes

A data management strategy should have a robust data quality management framework. Data profiling is a critical component of this framework that allows for in-depth analysis of data patterns and anomalies, guiding the identification of potential issues. Similarly, data cleansing plays an equally important role—it rectifies inaccuracies, errors, and redundancies. Additionally, implementing validation rules and automated checks within the data management workflow enforces data quality standards in real-time to prevent the entry of erroneous information.

Create a comprehensive data governance framework

It’s common to have multiple data solutions in a single organization, and with that, it’s also common to hear data teams talk about how managing them is a hassle. The key is to standardize data management practices and create uniform processes for data collection, storage, retrieval, and usage, reducing the likelihood of errors and inconsistencies. In short, establish a data governance framework with clear policies, procedures, and responsibilities to ensure consistent and secure data management across diverse solutions.

How Astera Helps Businesses with Data Management

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

Astera - Unified Data Management

Astera – Unified Data Management

Whether its unstructured data management, integrating data from multiple sources via ETL or ELT, 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.

Take the First Step to Unified Data Management With Astera

Whether it's unstructured data management and integration, API management, building a custom data warehouse, or EDI management, streamline data management from end-to-end with Astera's 100% no-code platform. Try for Free.

Download Trial
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