Data management encompasses all disciplines involved in creating, ingesting, validating, storing, protecting, processing and using data. Managing data is essential because of data’s potential to drive innovation, business insights and a competitive edge. As the speed and scale of data grows, so does the ability to access new information sources and advance analytics to drive new products and services and evolve business models. However, as data mounts, organizations must prioritize implementing innovative data management best practices.
Increasingly, organizations are applying common DevOps practices to data management to manage data more effectively as an enterprise-wide asset. Known as DataOps, the approach utilizes DevOps best practices to democratize data in an agile manner. This enables collaboration across the enterprise while building trust in data.
If data is to be used to optimize business processes, reduce costs, increase efficiencies, improve customer insights and achieve critical business objectives, organizations must manage data like a valuable resource. While data management encompasses a broad range of disciplines, to truly increase data trust, any successful data management strategy requires cataloging data in a way it can easily be governed and validated. However, despite our best efforts, ensuring data is easily accessible, understandable and trustworthy is more of a challenge than ever.
There are several risks when failing to protect and manage enterprise data. Mismanaged data assets lead to inaccurate, untrustworthy information that will quickly turn into major data liabilities. Widespread data mistrust in an organization leads to negative organizational and reputational impact, resulting in the refusal from business users to utilize data.
For instance, a bank stores enormous amounts of customer information. That data might contain insights that reveal opportunities to sell new financial services, personalize customer experiences or reduce customer churn. But, if you are simply storing the data and not using it to uncover insights, what is the point?
Just like fresh groceries, your data has a shelf life with an expiration date. The longer information sits in storage, the more likely it will go to waste. Meaning, insights that can support business growth and innovation will end up at best in the trash. Or worse, leading the organization down the wrong path.
To ensure enterprise data remains an asset, organizations must integrate different data management capabilities.
Data management has been around for decades. But over recent years, DevOps practices are increasingly being leveraged to build cross functional DataOps teams and improve data management. DataOps focuses on improving enterprise-wide collaboration, integration and automation. As a result, organizations increase data delivery speed while minimizing doubts for end users, regardless their role.
Managing data starts with data governance.
Data governance is an enterprise framework that coordinates people, processes and technology to understand and transform data into an asset.
Data governance lays the foundation for data visibility and understanding. And a data catalog collects and organizes enterprise information for broad usage. With a data catalog, organizations deliver transparency into enterprise data to not only strengthen accountability for data assets but also enable data users to uncover analytical insights that improve business outcomes and operational efficiency. It also provides business users and data engineers alike with self-service tools to access and prepare high-quality data.
The most value an enterprise should receive from a data catalog is providing 3D data lineage. 3D lineage is like a GPS that helps navigate the business process highways. It provides traceability and relationships between data, systems and business processes so data leaders in any role can make more informed decisions quicker and with confidence. 3D lineage provides context into the full process, data supporting each process step, stage gates, ownership hand-off points, essential business rules, data quality impacts and more. This enables data leaders to view and share information across many dimensions contextualized for that particular usage of the data.
While data governance and the data catalog provide data understanding and usage, once data users find what they are looking for, it is equally critical to trust their information, making data quality essential. By tracking, monitoring and documenting where data came from, where it is going and how it changed, users can verify and trust the quality of their information today and how that trended over time.
By integrating profiling and other data quality routines, such as completeness and conformance, directly into a data governance framework, organizations can ensure all of their data at any given point in the process is accurate and consistent throughout the entire data landscape.
Equally important, as data moves from one location to the next, is timeliness and validating information at each point. Data validation is critical for data privacy, financial reporting, and customer communications. By enhancing basic data quality checks to include balancing and reconciliation to assure data arrives accurately, at its expected location and on time, missing or inaccurate information doesn’t negatively impact the company.
With high-quality, trustworthy information across the data supply chain integrated into the data catalog, with data governance best practices built in, data users can confidently rely on enterprise data for analytical insights to improve revenue, reduce costs, validate regulatory compliance and more.
Integrated data governance and data quality lay the foundation for successful analytical initiatives.
When business users can quickly locate and access data, companies can significantly reduce time to valuable insights. Consequently, teams can rapidly build analytical models to develop meaningful business intelligence, better serve customers and uncover new business opportunities.
Data management comprises every aspect of transforming enterprise data into valuable analytical insights. By merging a multitude of DataOps best practices to enhance data management, organizations can continue to benefit from enterprise data, even as more information comes in every day.
Are you looking for additional information on data management? Download our white paper, Agile Data Management: Four Critical Use Cases.
For additional resources on data management, read this article from Dataversity, What is Data Management?