Their dependence on manual input from subject matter experts has quickly seen them become obstacles to the efficient management of data and the proper harnessing of its value to the business. As more organizations than ever aim to move to a data-driven culture, new technologies and strategies are being adopted to facilitate this change. Still, many enterprises are building on sand – too often forging ahead without proper technological and people-based foundations for data management. You may be hearing about ‘Active Data Governance’ as the solution to these common pitfalls. Here’s what you need to know about active data governance to judge whether it is right for your organization.
What is Active Data Governance?
The data growth explosion requires a new approach to governing data and maximising its usefulness to the enterprise. At the high level, data governance refers to the creation and maintenance of strong internal practices around data, including but not limited to data discovery, risk management and usage. Each of these aspects of data governance have traditionally required specialist data users to both design and implement processes on an ongoing basis. This has the effect of effectively siloing intimate knowledge regarding the data and relegates data governance and use to a specialist few. Active data governance is a new data governance philosophy which aims to create simple, defined and accessible data processes that broaden the scope of who can approach and be accountable for data and its management.
The process of adopting active data governance is multifaceted, but non-negotiables include:
Begin by building a data literate culture. Assess and formalize data governance practices and educate and incentivize people across the organization treating it as their responsibility.
Document and formalize what already exists. De-silo knowledge stored in the heads and every day activities of the minority of people within the organization specialised to handle data. Democratise this knowledge to guide new data users in the use and governance of data.
Harness technologies which enhance efficiency, collaboration, accountability and processes through automation. Without automation, active data governance is impossible. Processes will remain anarchic and ungoverned, largely manual and only administered by experts. Intelligent data management platforms can provide a foundational basis for active data governance adoption across the enterprise.
What does Active Data Governance give me that past models don’t?
The key improvements made by active data governance to previous models of data governance are closely related to its core elements. For example:
Getting your workforce data literate, de-siloing knowledge and making processes defined and easy to follow requires unification and standardization of both data and governance processes. The enterprise is almost always split between several complex systems and applications both in the cloud and on-premises. Having a comprehensive active data governance scheme requires technology that can facilitate implementation through unification of entire systems with clear, end-to-end process management. This can mean an automated data catalog and business glossary.
In order to open up data for use by a wide user base across the enterprise, automated data quality assurance and exception tracking are required. The consequences of having unclear or false data quality information, or missing exceptions can be major for the business. Lost opportunities and compliance concerns are just the beginning. Active data governance requires technology that can automatically measure the quality of data and assess its fitness for purpose. Manually combing through huge amounts of data to establish quality is far too slow and resource intensive. Automation is required to assess data quality issues in real time and make them visible to all users, with simple, guided remediation processes for any exceptions provided.
With active data governance, root cause analysis and impact analysis of issues and prospective changes become more efficient. By creating end-to-end visibility for data’s transformations and governance processes, the wasted or narrow efforts of traditional data governance can be avoided. One way to realise this benefit of active data governance is through data lineage, which can provide rapid and easily understandable visual tracing and diagnosis of any exceptions.