What Is Data Management?

What is data management?

Data management is the process businesses use to gather, store, access and secure data from various platforms. Managing this information properly helps organizations utilize data analytics to gain insights that improve business operations and achieve better outcomes. By establishing a framework for accessing the vast amounts of data they generate, companies can make more informed decisions and enhance their ability to deliver valuable products and services to their customers.

“Data management involves multiple disparate functions and systems working together to move, organize and secure data such that it is accurate, precise, accessible and protected,” explained Christopher Risher, managing director and fractional chief information officer at 1Path.

Developing an effective data management strategy can give businesses a competitive advantage and strengthen customer-facing and internal operations. While data management is critical, organizing a business’s data into a centralized system can be challenging. 

How does data management work?

Data management requires a thorough understanding of a business’s software tools and platforms. Systems such as accounting software, customer relationship management (CRM) software, point-of-sale systems and credit card processing platforms all collect large volumes of customer and financial data, often referred to as big data. This data must be effectively filtered, analyzed and interpreted to inform business operations and strategic decisions.

“Managing data typically begins with a project that’ll get started in one of the knowledge areas and iterate through the other knowledge areas,” Risher explained. “Utilizing cloud-enabled tools can assist in the rapid development of a data management platform. These cloud tools can empower an organization regardless of the location of their data.” 

To build an effective data management strategy, two critical practices include standardizing data and ensuring high data quality:

• Standardizing data: An effective data management strategy must serve standardized data. Not every software platform collects data in the same way or collects the same types of data. Data management unifies these data silos so they become useful when combined. “Organizations are dealing with more data from more sources than ever before,” explained Todd Wright, former head of data management solutions at SAS. “They have come to realize that all this data can provide a wealth of new insights into customer buying behavior and the dynamics of their industry ― but only if this data is managed and trusted.” 
• Ensuring quality data: How data is managed directly relates to data quality, which must be unassailable if data analytics efforts are to bear fruit. Decisions made based on faulty data will, in turn, be faulty decisions, so data quality should be of the utmost importance to any business relying on this information. 

“Nowadays, every company has data, from the multinational giants of IT to the small local breweries,” noted Rosaria Silipo, head of data science evangelism at KNIME. “Some data are sensitive, some are history, some can be used for future predictions, some for auditing and so on.” 

Silipo emphasized the complexity of proper data management. “With so much data and so many different properties and usages, a different set of rules and competencies is required to handle each subset of data,” Silipo explained. “You can see then that data management can quickly become a quite complex and tricky task, which can bring further prosperity or further problems to the company.”

What are some types of data management systems?

Data management systems make data complexity more manageable, automating many arduous aspects of unifying and reviewing key data. These systems incorporate databases and analytics tools that help businesses store and organize crucial data and query the system as needed. The best systems consolidate data into helpful reports with contextualized data visualizations. Some even incorporate automated decision-making recommendations empowered by machine learning, helping key stakeholders make more informed choices about business operations.

Several types of data management systems exist, including the following:

• Data governance: Tools like Informatica, Azure Data Catalog and Talend improve a business’s ability to track data and associate it with metadata for better retrieval. Metadata helps improve data structure, making it more accessible and useful. Data tracking tools help businesses understand each data asset at their disposal and ensure it is accounted for and understood. Together, these elements are critical for making vast databases truly useful. Data governance is all about how data is organized, stored and secured. Through data governance, businesses can guarantee data quality.
• Business intelligence (BI): BI tools like Microsoft Power BI, Azure Synapse Analytics, Tableau and Snowflake improve data storage, processing and security while presenting data to decision-makers in an organized and contextualized manner. These BI tools are crucial for managing and analyzing the extensive datasets that would be impossible for humans to sift through to extract actionable insights manually. With features, such as data visualization, automated reporting and integration with various data sources, BI tools enable businesses to make data-driven decisions efficiently.
• Data integration: Tools like Azure Data Factory, Logic Apps and Azure Functions offer user-friendly interfaces for connecting and centralizing disparate data sources, leading to comprehensive insights. For example, data from accounting software and one of the best CRM software platforms might initially appear unrelated; however, when combined, these datasets can help paint a more complete picture of cash flow and revenue, offering deeper business insights.
• Data observability: Tools like Amazon CloudWatch and IBM Instana Observability monitor data management pipelines and processes, offering visibility into a business’s data health to ensure its security and reliability. These tools automate the data monitoring process by detecting patterns and anomalies within data sets, offering businesses a clearer understanding of potential issues or shortcomings. 
• Data catalogs: Data catalog tools, including IBM Knowledge Catalog, Collibra and Alation, organize data assets and make them easily retrievable across multiple systems by using metadata to classify and label them. Many of these tools leverage artificial intelligence to provide intelligent search capabilities, speeding up the data discovery process. They also deliver smart insights and recommendations by analyzing metadata and usage patterns. Additionally, they enable businesses to trace data lineage more accurately, ensuring they always know where data is coming from and how it’s being used. 
• Data modeling: Tools like erwin Data Modeler, Toad Data Modeler and Lucidchart represent data elements visually, helping businesses understand how their data entities are connected and structured. These tools support data management by mapping the relationships between data entities and systems, ensuring the structure aligns with the business’s needs. Data can be modeled using various techniques, including data maps or entity-relationship diagrams. 

“The goal of data management is to give an organization reliable and quickly accessible data through which decisive action can be taken in a secure manner,” Risher said.

Why is data management important?

A data management system is crucial for the following reasons: 

• Organization and structure: A data management system organizes and structures data, boosting visibility into a business’s operations. 
• Patterns and insights: A data management system can help you identify patterns and deliver insights into everything from operation improvements to customer behavior analytics. 
• Better decision-making: Easily accessible, accurate and organized data can help business owners make timely, informed decisions to improve and scale their operations. 
• Enhanced security: Data management systems provided an added level of security. They are often guarded by encryption and authentication to protect data from breaches. 
• Reliability: These systems are highly reliable, with many offering disaster recovery services to back up your valuable data and protect your bottom line from data breaches. 
• Automation: Workflow automation features within data management systems minimize repeatable processes requiring human input. This reduces errors and ensures consistent, reliable insights from collected data, allowing teams to work more efficiently and make data-driven decisions. 

What are the challenges with data management?

Data management presents several challenges: 

• Vast amounts of data: One of the biggest challenges with data management today is the increasing volume of data collected across various platforms. While this data may be valuable, it can be challenging to determine its true meaning and effective use. Without an efficient way to process real-time or near-real-time data, its value diminishes and risks becoming obsolete by the time it’s analyzed. 
• Financial costs: Implementing and maintaining data management systems comes with significant financial costs for businesses. However, these investments are crucial for maintaining competitiveness, as other businesses also leverage data-driven strategies to enhance their operations. 
• Laws and regulations: Businesses must continuously adjust how and what data is being collected to comply with ever-changing data privacy laws and regulations. Staying up-to-date on current requirements and consulting with a legal professional can help businesses stay in compliance.

How do you develop a data management strategy?

Risher stressed that a data management strategy provides the necessary structure to ensure analytical models produce reliable insights. “Without proper implementation of data management controls, some level of the pipeline that feeds an analytical data model can be rendered unreliable,” Risher cautioned. “If we are basing strategic, forward-thinking decisions off poorly gathered data, then we are likely impacting the business by making incorrect decisions.”

Here’s how to develop an effective data management strategy:

1. Define your objectives: Start by defining your business’s key objectives, such as increasing operational efficiency, gaining customer insights or improving compliance. Ensuring your business’s goals are at the heart of your data management strategy is crucial to successful implementation. These goals shape your strategy and determine the tools and technology you’ll need. “Depending on the goal, you need a different tool to manage a different subset of data — from classic databases to highly secure data repositories, from data visualization tools to more advanced data analysis tools,” Silipo explained.
2. Identify flaws in your current data management practices: Next, assess how your current data management strategies, including your data sources and types, support these goals, paying attention to any gaps or misalignments that may hinder your objectives. 
3. Design a data management process: When establishing a data management process, you must consider how your data will be:
   1. Gathered: What data will you collect and from what sources? How will it be structured?
   2. Organized: After collection, how will you prepare raw data for analysis? What will your organization process look like, including adding metadata and assigning standardized field names? 
   3. Saved: Where will your data be kept (for example, on a cloud service or on-premise servers) and how will you store it securely?
   4. Analyzed: Who on your team will need access to the data and what permissions will be required? 
   5. Shared: How will your data be communicated with end users and how will you share its insights?

Silipo says businesses must take responsibility for managing their data and understand how that data fits into the overall business strategy. “Based on [each type of data’s] role and features, we need to define a sub-strategy for protection, storage and usage,” Silipo advised. “A successful data management strategy allocates a place, a task and a policy to each subset of the data, in terms of privacy, storage and usage.”

What are some data management best practices?

The following data management best practices can improve your organization’s relationship with the data it collects and stores, securing it and making it easily accessible for improving business processes. 

Maintain a team with data management skills.

Data management is a vast and complex objective requiring a knowledgeable and dedicated team of professionals.

Matthew Franzyshen, business development manager at Ascendant Technologies Inc., warns that file permissions and data access are complex tasks best left to an information technology (IT) pro. “Data management is a skill that requires an IT professional to properly manage access,” Franzyshen explained. “It is too easy to expose data to employees (or the internet) who should not have access to sensitive data [like] HR data, payroll data, etc.,” Franzyshen added that employees should only have access to the data they need for their specific roles.

An experienced and skilled team with advanced data management skills is crucial to developing and refining a global approach to data management. Rely on your in-house or outsourced team to act as managers and consultants when it comes to connecting your business’s data to its wider operations. Equip this team with user-friendly tools to monitor, access and organize data, both while it’s stored and as it’s collected. 

Establish a data compliance plan.

As data privacy laws, such as the European Union’s General Data Protection Regulation or California’s California Consumer Privacy Act, become increasingly common, data privacy compliance is critical. Your data management plan should enhance your business operations while being auditable so you can demonstrate compliance to regulators and business partners easily.

“You need to know how each subset of data must be protected, stored and analyzed depending on its nature and on its strategic importance,” Silipo advised. “Here, a number of skills are joined together: legal skills to design the rules, IT skills to see the implementation of the rules, programming skills to retrieve the data and some statistics and data analysis to understand how these data can become useful.”

Regularly review data security policies.

The cyber threat landscape is constantly changing and malicious actors are becoming more sophisticated in infiltrating systems. Small businesses are prime targets because hackers know they’re typically less protected than large enterprises. As a result, small business owners must regularly revisit their cybersecurity plans and revise them to meet current threats. 

Franzyshen recommends testing backups regularly to ensure you can restore data before a loss occurs. “We have seen many companies find out they don’t have the data when they need to restore it,” Franzyshen warned. “If you have an IT department or IT vendor give you a ‘story’ vs. your data back, you need to find someone else to manage your backups.”

Franzyshen also suggested implementing multifactor authentication (MFA) to protect company data further. “A simple username and password is not enough today,” Franzyshen cautioned. “MFA is critical to mitigating compromised mailboxes and access to sensitive data/services.”

Additionally, equip your IT team with the capability to monitor and respond to emerging threats proactively to protect centralized data.

“Security is always top of mind,” Risher said. “So, having access to a security professional, such as a CISO [chief information security officer], to validate the security parameters is extremely valuable.”

Miranda Fraraccio contributed to this article. Source interviews were conducted for a previous version of this article.