Considering the volumes of data enterprises gather and store—and how much of their decision-making hinges upon its accuracy—data management best practices are essential to their success. Unmanaged or badly managed data can be a wasted resource, or worse, it can lead to process failures, operational bottlenecks, and excessive exposure to various dimensions of risk. Proper data management practices should be a foundational component of every enterprise strategy. This article outlines the top 8 data management best practices that can help businesses harness the power of their data for better decision-making, compliance, and overall efficiency.
1. Understand Data Management as an Organizational Function
The days when Excel covered most of the organizational needs around data are long gone. Half-hearted efforts like hiring a tech-savvy graduate or contracting a service provider for some reports provide short-term promise and long-term headaches. Pushing the responsibility to IT – often a last-ditch effort – is like asking a mechanic to drive a race car. Ultimately, sustainable data management requires (1) goal setting, (2) strategic alignment, (3) resource allocation and (4) accountability paired with measurement. Those are the characteristics of core organizational functions like Sales, Finance or HR. Missing out on any of those elements inevitably results in short term, reactive or decentralized efforts usually falling short of potential and expectations.
2. Create a Data Governance Framework
A well-designed data governance framework serves as the foundation of effective data management. A robust data governance framework outlines roles, responsibilities, policies, processes, and standards for data management within your organization.
Clarifying Roles and Responsibilities
Data governance framework development starts with defining clear roles and responsibilities for teams and members within the organization. This includes appointing key users accountable for specific data domains, tool owners who have overall responsibility for data assets, and power users who manage data on a day-to-day basis.
Establishing Data Policies and Standards
A data governance framework should address the development and enforcement of data policies and standards. Data policies define how data should be collected, stored, accessed, and used within the organization, and cover aspects such as data quality, data security, data privacy, and data retention. Data standards, on the other hand, provide guidelines for data formats, naming conventions, and metadata. For example, a data policy may specify that customer data must be encrypted during transmission, while a data standard may outline the format for storing customer addresses consistently across all systems. By setting clear policies and standards, organizations can continuously maintain data consistency and integrity.
Data Governance Processes
The framework should outline the processes and workflows for data governance activities. This includes data documentation, data quality assessment, data access requests, or data issue resolution. Collectively, these processes enable organizations to manage data effectively by ensuring that data is credible, relevant, and timely available.
3. Ensure Data Security and Privacy
Data security and privacy should be top-of-mind concerns for data-driven organizations operating in today’s high-risk digital landscape. Implement robust data security measures to safeguard your organization’s data against breaches and unauthorized access, including strong encryption, access controls, and regular security audits. Additionally, continuously validate compliance with data privacy regulations such as the General Data Protection Regulation in Europe (GDPR) and the California Consumer Privacy Act in the US (CCPA) to mitigate the risk of non-compliance across your organization’s data estate. Employee training and awareness programs also play a pivotal role in mitigating security risks, as many data breaches start with human errors or social engineering attacks.
Data Ethics and Transparency
Beyond legal compliance, data security and privacy practices should align with the organization’s ethical considerations. Firms should be transparent with users about how their data is collected and used, seeking explicit consent where required. Ethical data handling also involves minimizing data collection—to the extent necessary for the intended purpose—for bolstering data accuracy and providing individuals with control over their data. Implementing ethical data practices builds trust with customers and partners, enhancing an organization’s reputation and long-term business sustainability.
4. Streamline Data Integrations
Data often resides in various silos within an organization, making it challenging to access and analyze. Automation and integration tools can help streamline the process by enabling data to flow seamlessly between systems, eliminating the need for manual data entry or data transformation. Implement data integration solutions that provide a unified view for analysis, reporting, and decision-making, consolidating data from disparate sources into a widely accessible format or platform.
Data integration not only accelerates data access—it also enhances data accuracy and consistency. When data is integrated from various sources, it undergoes transformation and cleansing processes to ensure that it aligns with predefined data standards. This results in a more reliable and consistent dataset, reducing the likelihood of errors or discrepancies in analysis and reporting. Streamlining data integrations also allows organizations to implement data validation checks, further enhancing data quality.
5. Enforce Proper Data Documentation and Metadata Management
Effective data management goes beyond just storing and organizing data; it also entails understanding the context and meaning of the data. Maintaining comprehensive data documentation and metadata is therefore essential to understanding the context and meaning of your data—you should therefore document all data sources, definitions, and transformations to ensure that users can always make sense of the information. Effective metadata management solutions can help immensely in this regard, especially during the process of data discovery and data lineage tracing.
6. Enforce Data Lifecycle Management
Data has a shelf-life and corresponding lifecycle that spans from creation to archival or deletion. You should develop a data lifecycle management strategy that defines when data should be retained, archived, or disposed of. This practice helps to reduce storage costs, ensure compliance with data retention regulations, and mitigate the risk of retaining outdated or irrelevant data. By implementing a well-structured data lifecycle management strategy, organizations can strike a balance between retaining valuable historical data and reducing the burden of storing obsolete information, ultimately resulting in enhanced data management capabilities.
7. Implement Master Data Management
Master Data Management (MDM) is crucial for maintaining consistent and accurate core data across the organization. Establish a centralized repository for master data like customer information, product details, or employee records. Clear accountabilities and rigid procedures around the usage and maintenance of Master data are crucial to avoid the emergence of clones of master data across the organization.
8. Manage the ‘Last Mile’ of Data Value Extraction
Advanced analytics tools and reporting mechanisms can extract actionable insights from your organization’s data. However, converting those insights into tangible results is a surprisingly challenging task. The “last mile” of data management is a management discipline that is much more of an art than a science. It boils down to making people change and adapt, often outside comfort zones, and starts leveraging data where guts and perceptions were predominant.
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