With business leadership focused on meeting their key objectives and IT teams focused on keeping the lights on and dragging old apps into the cloud era, there’s often less attention than there should be on the elephant in the office that is legacy business data.
Departments have spent years building their apps, adopting cloud services independent of IT—so-called shadow IT—and relying on legacy applications and data that other departments have long since forgotten about. The business might also rely on third-party data from partners or external services for marketing, billing or other purposes across the business that has value or requires careful management.
All of this creates risk for the business, requiring greater visibility across all departments and a joined-up approach to data management to ensure that teams can access data, use it to gain insights into operations and build a data-based strategy for a coherent future.
As part of that effort, businesses need to empower staff with the self-service immediacy to the details and context needed to understand data, guide its use with governance and put it to work smartly. As Gartner puts it, "Data literacy is an essential part of a data-driven culture".
Identifying and managing dark data
Recent reports suggest that between 50% to 90% of data is “dark data” – information that the business leadership is unaware of and that IT hasn’t captured, creating operational risk and meaning the business doesn’t have all the tools at its disposal to gain insights, differentiate and strategize.
One solution to this growing problem is a data audit, or data cataloging, which is essential as part of compliance efforts, but can also help uncover new ways in which the business can use data to drive value and gain fresh operational insights.
While this used to be a heavily manual process, data intelligence software is taking over much of the heavy lifting, identifying hidden data and extracting useful metadata to help the data team gain critical insights, helping define the “truth” about the business.
With improved visibility, and a formal method of data democratization, the business will be able to let teams and employees self-serve data, maintain security and compliance and understand and improve data fitness - all with strong governance intact. The following best practices will help boost data visibility and literacy across the business:
5 best practices for effective data management
1. Have a single source of truth
As with many data-led initiatives, the goal is to have a single version of the truth on which the enterprise can rely to deliver insights and make progress in the market. Whatever data there is, and the tools used to capture them, the end-result needs to be that single version providing timely, accurate data that makes both daily operations and strategic planning a less stressful experience.
2. Classify data as sensitive, important or stale
There can be many other categorizations, but these immediately identify data as requiring specific attention. Sensitive data needs governance rules applied, ensuring they’re stored in compliance with GDPR or other mandates. Important data needs to be regularly checked for accuracy, validity and currency, while stale data should be escorted off your digital footprint as soon as possible to avoid any team or department relying on it.
3. Get the most from data visualization
Data visualization is becoming its own science and your enterprise needs the right roles and talent in place to ensure that you get the best information processed using the most suitable tools. Solutions like erwin can identify data across the business, categorize it and provide the metadata, graphical literacy aids like data lineage and mind map knowledge graphs, and the integration capability to advanced visualization tools to deliver smart insights.
4. Incorporate people into the data cycle
People are part of the data cycle, too. From senior leaders, clients, data architects and DevOps down to the users, the business should identify all stakeholders responsible for data management and usage. They should ensure that each role is appropriately trained and follows the enterprise’s monitoring, auditing and tracking processes, along with following compliance rules. On top of this, the business should inform the appropriate leaders when data assets or the processes that impact users or data change. Data governance platforms offer the governance, data stewardship, policy management and socialization capabilities to ensure all users are on the same page.
5. Identifying gaps in your data
Identifying gaps in your data is another critical best practice. Could there be data in an unidentified black hole? Or is some data simply not being recorded? If it is vital or useful to the business effort, then it should be captured and leveraged.
As these processes and the scope of work becomes clear during an investigation phase, it’s easy for discoveries to spiral out of control. A center of excellence or other team structure should be created to ensure that the data visibility effort has suitable sponsorship, funding for the tools and a clear set of goals and metrics to deliver success to the business.
Conclusions for data leaders
When it comes to your data visibility and literacy needs, consider erwin Data Intelligence to establish enterprise-wide data visibility with data governance and best data practices.
As all businesses become more reliant on their data, governance, context and accessible asset information are key features that will enable IT and business leaders to understand their data use and extract maximum value from it.
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