Proper data management can unleash a world of opportunities while ensuring a business remains compliant with data privacy and integration standards. Industry leaders are using data management best practices to streamline the data discovery, quality, and processes that make sure the data management plan aligns well with business goals.
The enterprise data management market is projected to reach a value of $221.58 billion by 2030, showing a compound annual growth rate of 12.4% between 2025 and 2030. Database management systems statistics also revealed that relational database management techniques owned 72% of the market share in 2022. Data management best practices are more important than ever.
New research also shows that 50% of data stewards battle with organizing unstructured data for retrieval augmented generation while another 52% struggle with structuring data for machine learning applications. Other data management challenges have led financial firms to increase their budgets. Follow data management best practices to avoid many of the common challenges.
What Is Data Management?
Data management is the practice of collecting data, analyzing it, and using the collected data efficiently, accurately, securely, and cost-effectively. However, good data management requires finesses and a few best practices that consolidate success in data integration solutions, analytics, and quality. Using data management best practices can unlock a world of opportunities.
The Benefits of Following Effective Data Management Best Practices
Ensuring data transparency, data integrity, or data governance are some ways companies can turn customer data into a competitive advantage. However, here are more benefits businesses can obtain when using proper data management best practices:
- Data-Driven Decisions: Effective data collection and management can help businesses access accurate and consistent data sets lead to better decision-making processes for future users.
- Enhancing Operational Efficiency: Data scientists who implement the best practices often lead to an improvement in the overall efficiency of data collection and use throughout the data lifecycle.
- Access Valuable Insights: Collecting customer data accurately in ways that ensure data consistency can help to achieve business goals and deliver next-level customer services.
- Ensure Compliance: Manage data following the best practices to ensure accurate data ownership, seamless reporting, data privacy, and data governance in every process.
- Data Quality Management: Eliminate data quality issues by following the best practices for data management to ensure data quality that provides priceless insights.
The Complete Guide to Data Management Best Practices
The key best practices that play a pivotal role in an effective data management plan can unlock all the data management advantages that align with your business objectives. Implement our expert best practices that help you achieve seamless data management processes with visible results.
Understand Data Management Fundamentals
There are many expert guides for the best practices (data management) available from trusted institutions. Even data scientists continue to research data management best practices as technology and analytics changes quickly. The research data management best practices are outlined by Michigan State University, or read the data management best practices checklist from the Santa Barbara Library.
Define Clear Data Management Processes
First, you must identify business objectives to decide on the strategic alignment, allocation of resources, and accountability and measurement of how you manage data. Ultimately, what do you wish to gain through improved data management? Once answering the question, you can allocate resources, make sure the company’s strategic initiatives align, and establish key performance indicators (KPIs).
Implement a Data Governance Framework
The success of any research data management plan depends on accountability and shared responsibilities. Implement effective data governance by assigning clear roles and responsibilities for all team members involved. Also, establish clear data management processes and policies for everyone to follow, highlighting specific data governance workflows and activities for each team member.
Add Data Quality Management in the Data Lifecycle
A recent data quality survey found that 50% of data stewards believe that the quality originates from data engineering. Part of the ultimate data management best practices is to ensure data quality throughout the data lifecycle, whether accessing customer or research data. Assess current data quality, implement data profiling, automate quality checks, and ensure data lineage and traceability.
Integrate Robust Data Security Measures
Data privacy and ethical concerns often challenge seamless data management. It’s essential to focus on robust data security measures that ensure data security and privacy. Follow data security and privacy guidelines specific to your industry. For instance, you’d implement HIPAA laws and regulations in healthcare, PCI DSS security standards in banking, or GDPR regulations for information technology.
Ensure Data Transparency and Centralized Storage
Transparency is a cornerstone to guaranteeing compliance and makes up one of the most important data management best practices. Understand research data management laws outlined by the Digital Curation Center (DCC) before collecting, storing, analyzing, or sharing the data assets.
You also need to know where to keep research data, according to the DCC. Use a centralized repository for data storage. Users must securely store data files and any other resources related to the metadata in a secure location with strict access controls to respect data privacy laws and maintain transparency.
Streamline the Data Integration Process
Research data or customer data often lies in organizational silos, which simply wastes resources and challenges various aspects of governance and compliance. Another data management best practice is to streamline the data integration process with automation tools that improve data flows. Remove the need for manual data entry using advanced technologies and different types of AI.
Gather and Describe Data Comprehensively
Data management best practices suggest that users must gather data extensively, keeping raw data raw to preserve it, using the appropriate field delimiters, and applying consistent codes. Then, users must describe data properly by assigning descriptive names, using standard terminology to make it discoverable, creating a data dictionary, defining the data model, and describing the format.
Leverage Proper Data Documentation and Metadata Management
Follow data documentation and metadata management best practices for improved data management. Data documentation should include naming conventions within all data files, data lineage descriptions, codes of reasoning, and the reasons for missing values. You should document all data assets and data files as metadata, meaning it should mention the origin, time, location, process, creator, and access.
Use Master Data Management
Implement master data management best practices within your internal process to manage data across the entire enterprise using tools, technology, and unified master data processes that consolidate key data assets for the entire company. Key data assets include research, customer, and location data, and product details. Master data management eliminates duplicate data in all departments.
Prepare Metadata Using Flexible Manipulation Techniques
Use flexible manipulation techniques to prepare data for analysis. For example, explore non-parametic methods for data analysis that don’t force you to structure data into traditional parameters. Use different file formats and data structures to prepare metadata using old-school statistical methods. One example is aggregating source data from multiple sources into an analytic base table (ABT).
Include Data Analytics and Reporting
Learn how to use AI in software development to automate reporting and analytics for improved data management across the board. If you already have data analytics, speak to an expert about integrating automation tools and other technologies that play a pivotal role in streamlined reporting and analytics.
You can also extract actionable insights from data analytics to improve decision-making. Ensuring that data is well-analyzed is about describing the methods used to derive data products, making sure the data is reusable, documenting every step of data processing, and identifying outliers.
Rely on Multiple Datasets From Multiple Sources
Defining your metadata requires you to gather information from different sources to outline your various data sets. Consistent data is achieved by using various sources to improve the overall efficiency and accuracy of gathered data. Gathering, storing, analyzing, and sharing data from different sources will also help your business understand the geospatial data parameters to better describe and share it.
Publish and Share Data Securely
Know how to share files securely, whether data files or data itself. Always use end-to-end encryption with access controls and audit logs with secure file transfer protocols. Also, remember to securely manage your endpoints when sharing data. However, there are also data sharing guidelines to follow.
Then, follow the University of Cambridge guidelines for sharing your data, which starts by knowing which data to share and meeting the FAIR principles that is particularly important to sharing research data. The FAIR acronym means data must be findable, accessible, interoperable, and reusable.
Other data management best practices relate to how you publish the data assets. Start by adding descriptive names and using datacasting tools that streamline the process. Also, check the data outputs to ensure web and print accessibility and review whether the data is reusable for publishing it.
Perform Frequent Audits to Ensure Compliance
Finally, it helps to conduct frequent audits that make sure you still remain compliant. It’s easy to steer away from the guidelines set out by different regulatory boards for specific industries. However, be sure to manage data while conducting frequent audits that make sure the data integration, storage, and distribution remain compliant. For instance, GDPR fines and penalties alone can exceed $23 million.
Summing Up Data Management Best Practices
Following best practices (data management) ensures that all data users are accurately and consistently using data profiling and collection techniques to your fullest advantage. The key best practices guide how data assets should be stored, collected, analyzed from various sources and its future use.
Follow our data management best practices to ensure you meet all aspects of effective management, whether naming conventions, sharing metadata, allocating resources, identifying missing values, or simply assigning access controls. Eliminate duplicate data and ensure effective data flows today.
Best Practices in Data Management FAQs
Why is data management important?
Effective data management is essential in business today because it empowers the organization to use the data to its fullest potential. Good data management can ensure compliance and describes accurate, accessible, and consistent data management processes. That way, organizations can access a competitive advantage over others by using data for informed decision-making.
What are the 5 steps of data management?
The five steps of data management also incorporate some of the best practices. A 5-step data management plan or simple data lifecycle should include a process to collect, prepare, store, analyze, and distribute the data. You can also use the Digital Curation Center’s 5 steps to decide what data to keep for future use, which further guides how you can use the data assets continuously.
What are the four C’s of data management?
The four C’s of data management can also guide data management best practices. Always ensure data is clean enough for accuracy, curate enough to be well-organized, connected enough to be unified across different systems, and controlled enough to be secure and remain compliant with industry standards. If data meets these criteria, then you’ve established good data management best practices.
