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7 Data Management Best Practices for Enterprises

For CEOs and CXOs, harnessing their organization’s data from offline and online sources can be a game changer in promptly analyzing trends, understanding user behavior, and making fast yet impactful decisions to stay ahead of competitors. Imagine having real-time insights into customer preferences, streamlined decision-making processes, and a resilient data infrastructure that empowers innovation.

Sounds promising for your data management journey, right?

During the height of the COVID-19 pandemic, Harvard Business Review Analytics Survey proved that data-driven companies outperformed their peers by 5–10% in profitability. And here’s the fascinating part – this wasn’t just a one-off event. Whether in manufacturing, healthcare, finance, or any other industry, a well-framed data infrastructure can foolproof organizations in economic ups and downs.

Oftentimes, organizations are overwhelmed with the amount of data they extract from various sources and wrestle with it to derive actionable insights. To overcome this universal challenge, we have written about the best practices for data management that can help elevate your organization’s data landscape.

7  Best Practices For Enterprise Data Management

1. Create a Foolproof Data Strategy

Before diving into implementing best practices in data management, organizations need to establish a clear and comprehensive data strategy. A well-crafted data strategy serves as the futuristic roadmap that guides the entire data management journey. It outlines the organization’s data-related goals and priorities and steps to achieve them seamlessly. That is fundamentally developing a deep collaboration between data professionals and key stakeholders/decision-makers (CEOs and CXOs) to advance these strategic goals.

Steps to deploy a data strategy:

  • Understand your organization’s current data landscape.
  • Identify pain points, data silos, and data quality issues.
  • Engage with key stakeholders to define clear business objectives.
  • Prioritize data initiatives based on business objectives.
  • Create a data governance framework with roles and responsibilities.
  • Implement data quality, security, and compliance standards.
  • Choose the right tools and technologies to support your data strategy.
  • Consider solutions for streaming/real-time data processing, metadata management, data virtualization, master data management, and data modeling.

data management best practices

Source: Harvard Business Review

2. Enable Seamless Real-time Integration

Real-time data integration is one of the best practices for data management for enterprises requiring instant access to data for real-time decision-making and operational efficiency. Unlike traditional batch processing, real-time data integration involves the continuous and near-instantaneous movement of data from source systems to target systems, enabling organizations to extract the power of up-to-the-minute data for analytics, reporting, and instant decision-making.

Real-time integration also supports data analytics and business intelligence initiatives. By delivering data in real-time, organizations can perform advanced analytics, detect trends, and uncover valuable insights faster. This agility in data analysis can lead to better strategic planning and innovation.

Steps to establish real-time integration:

  • Choose a data streaming platform that aligns with your organization’s requirements.
  • Implement connectors to various data sources and targets to facilitate data ingestion and distribution.
  • Configure data streams to handle different data formats, schemas, and structures encountered within the organization.
  • Design an event-driven architecture that can react to data changes in real-time. This architecture should enable seamless event publishing and subscriptions.
  • Implement event-driven triggers and workflows for processing, transforming, and delivering data.

3. Implement an Enterprise-wide Data Catalog

Simplifying data discovery and access has been a trend in modern enterprise data management. Most organizations prefer to implement an enterprise-wide data catalog that serves as a centralized metadata repository and provides a comprehensive view of all data assets within the organization. It plays a pivotal role in democratizing data access, enabling users to find and utilize data efficiently, and helping establish a culture of data-driven decision-making.

With a data catalog, organizations can develop more effective data strategies. It helps identify gaps in data coverage, data quality issues, and opportunities for data enrichment. This strategic approach enables organizations to derive greater value from their data assets.

Steps to establish an enterprise data catalog:

  • Choose an enterprise data catalog tool that offers advanced search capabilities, including natural language processing.
  • Ingest metadata and data asset information for indexing, utilizing automated processes wherever possible.
  • Implement a user-friendly search interface with filters, keywords, and a relevance-ranking algorithm to deliver accurate search results.
  • Establish data lineage tracking processes that automatically capture and update information on data sources, transformations, and storage.
  • Visualize data lineage with clear diagrams or flowcharts, making it simpler for technical and non-technical users to understand how data flows through the organization’s systems.

4. Create a Metadata Management Framework

Effective implementation of a metadata management framework stands out as a best practice for data management in industries including healthcare and finance. As organizations today handle a vast amount of data from multiple sources, creating a robust metadata management framework is essential for understanding, organizing, and effectively utilizing data across the enterprise. This practice involves the creation of a framework to capture, store, and leverage metadata, empowering data professionals to gain insights into data lineage and usage patterns and making data assets more valuable and accessible.

Metadata management is also closely tied to data quality management. Metadata can include information about data sources, data validation rules, and data transformation processes. With this information readily available, organizations can identify and address data quality issues more efficiently. Metadata also allows data stewards to track data quality over time, facilitating continuous improvement efforts.

Steps to create a metadata management framework:

  • Document metadata attribute definitions and naming conventions.
  • Continuously update and refine metadata standards based on evolving data needs and industry trends.
  • Select a metadata repository solution that aligns with organizational goals.
  • Implement access controls to ensure only authorized personnel can modify metadata.
  • Establish versioning and auditing mechanisms to track changes and maintain metadata integrity.
  • Review and update data definitions regularly to reflect changes in business processes and data usage.

5. Adapt to Data Virtualization Techniques

Data virtualization is a sophisticated form of data integration that started making waves in the 2000s, empowering enterprises to simplify the data integration process by providing a unified, virtual view of data scattered across various sources. It eliminates the need for complex, time-consuming ETL (Extract, Transform, Load) process, allowing organizations to access and utilize data in real-time without physically moving it. This simplification streamlines data management processes, reducing operational overhead.

Steps to establish data virtualization in your data management system:

  • Identify the specific objectives and benefits you aim to achieve through data virtualization, such as improved data agility, reduced redundancy, or simplified data access.
  • Conduct a thorough inventory of your organization’s data sources, including databases, applications, cloud platforms, and data warehouses.
  • Document the types, formats, and locations of data.
  • Ensure the solution supports a wide range of data sources and provides robust security features.
  • Implement connectors or adapters to integrate diverse data sources into the data virtualization layer.
  • Develop mappings and metadata to establish relationships between data sources.
  • Create a virtual data layer that represents the integrated data sources.
  • Define data models and transformations that facilitate data abstraction for different use cases.

6. Build a Data Lifecycle Management Strategy

Data Lifecycle Management (DLM) is a foundational practice in enterprise data management to effectively manage data from creation to disposal. This practice ensures that data remains accurate, accessible, and secure throughout its lifecycle, optimizing storage, compliance, and operational efficiency. DLM involves defining clear stages, processes, and policies for data, helping key stakeholders and CEOs decide when to retain, archive, or delete data.

DLM also optimizes data storage and resource utilization. By archiving less frequently accessed data and deleting no longer needed data, organizations reduce the burden on their primary storage systems. This efficiency translates into cost savings as organizations can avoid unnecessary storage expenses and improve the performance of their data infrastructure.

Steps to create DLM for your organization:

  • Implement data capture mechanisms at the source.
  • Ensure data is accurately recorded and timestamped.
  • Maintain data lineage to track its origin.
  • Define data access policies and usage guidelines.
  • Implement data quality checks during usage.
  • Monitor data usage patterns for optimization.
  • Establish automated archiving processes.
  • Define criteria for data archiving (e.g., based on age).
  • Store archived data securely and catalog it for retrieval.
  • Develop data retention and deletion policies.
  • Implement secure data disposal methods (e.g., shredding).
  • Document the deletion process for compliance purposes.

7. Establish a Data Governance Framework

Data Governance framework empowers organizations to make data-driven decisions seamlessly. When data is managed, governed, and trusted, CEOs/CXOs can rely on it to inform critical choices, improve operational efficiency, and identify opportunities for growth and innovation.

While data breaches, data loss, or misuse can have severe financial and reputational consequences, data governance helps mitigate these risks by enforcing disaster recovery plans, data security measures, and data access controls. It also facilitates risk assessments and audits to identify vulnerabilities and weaknesses proactively.

Steps to set up data governance in an organization:

  • Form a council comprising senior executives, data stewards, and representatives from various business units. This council will oversee and drive data governance initiatives.
  • Develop comprehensive data management policies and standards covering data classification, access control, retention, and sharing. These policies should align with the organization’s strategic goals.
  • Implement a data quality framework that defines data quality metrics and Key Performance Indicators (KPIs). Use data profiling tools to assess data quality continuously.
  • Stay informed about data protection regulations relevant to your industry and geography (e.g., GDPR, HIPAA, CCPA). Align data governance policies with these regulations.

Are you looking to take your first step into data management or struggling to extract your organization’s data from multiple sources? Fret not! We have been supporting organizations in managing their data efficiently for years now. Right from setting up the data infrastructure to deriving ground-breaking insights, our team’s forte extends across data engineering, analytics, and science.

We can be your go-to technology partner for all data-related requirements. Talk to our experts and schedule a 30-minute consultation call to understand your organization’s data management needs.

Naresh Kumar

A web-analytics nerd, speaker - here delving into (Big)-data.

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