The evolution of data governance spans a continuum as illustrated by the Gartner data governance maturity model, and each step builds upon the previous step in order to progress towards an automated data governance program focused on business process automation. What starts out as a documentation exercise on spreadsheets or SharePoint quickly becomes an unsustainable model of data governance. While it often has value to IT, the business user sees technical lineage as information overload since their use case is to quickly understand abstract lineage as easy-to-comprehend business lineage. Although the technical lineage is extremely valuable and necessary, the organization is hamstrung unless it can evolve data governance to a business audience who is yearning to understand data governance from a business context.
Metadata management has been around for decades with mixed fortunes due to its technical focus, complexity and lack of business context. Data governance 1.0 saw the introduction of provided business definitions and ownership/stewardship responsibilities for an organization’s most critical data elements (CDE), in many cases using home grown tools such as Microsoft Excel, Wikis or SharePoint sites. Yet the need to get a full 360 degree of one’s data still remained a major hurdle. The evolution to data governance 2.0 saw a convergence of both business and technical data worlds with singular views of the data, basic lineage, and workflows to manage and provide transparency into the data assets, and the rollout of more vendor based solutions.
As the volume and breath of data grows due to the introduction of various big data stacks, streaming data, and data lakes, more is being asked of an organization’s governance platform than ever before. Regulatory concerns such as BCBS 239, GDPR, CCAR, Solvency II and MiFID all place an emphasis on data. Users are also finding new and creative ways to use data and desire the ability emulate the ‘Amazon Marketplace’ experience when searching, requesting and accessing the organization’s data assets.
Data governance 3.0 has started to address many of these challenges by leveraging machine learning, automation and recommendation engines in the collection, validation and reconciliation of the data, with the goal of easing the burden on considerably large manual efforts historically involved in populating and maintaining a robust data governance platform. In addition, data governance 3.0 seeks to abstract the complexity of business users shopping for data by presenting an intuitive and easy to use interface tailored to how business users consume data.
As data governance continues to evolve and mature to remain relevant within organizations, the core principals will remain the same: to provide accountability and transparency into an organization’s data assets; however the scope and types of data will change as will the metrics, visualizations, and automation techniques needed to ensure that governance is correctly applied, less burdensome, and valued.
To make the move, the key is expanding both access along with data quality and machine learning analytics to business users that wish to self-service their data needs, rather than be continuously dependent on IT. Often business users need to find, sort, and analyze data immediately for a project that is time sensitive. Data governance 3.0 is about connecting previously different and siloed disciplines into one platform for business users to self-service their own needs. The connected disciplines in a data governance 3.0 business-friendly platform are visual data prep, data quality, machine learning, governance workflows and dashboards which empower business users to perform functions which previously required the technical intervention and expertise of IT resources. Utilizing a Visio-like drag-and-drop interface to quickly combine data sets, apply pre-packaged data quality routines without arduous coding, and subsequently analyze data by applying machine learning analytics to enrich the analysis enables users to quickly consume the output in visual dashboards with meaningful data metrics upon which decisions can be made. Unless this can be done, the organization’s data governance maturity will be impeded in the evolution to a more automated and self-sufficient governance framework that has a design center around the business user.
Evolution to a business-centric tool requires one more necessary step. Zero-code workflows, with a Visio-like drag and drop interface and pre-defined workflow routines, are a requirement for business users to expedite the creation of new workflows or to edit existing workflows without dependence on coding expertise. This facilitates the maturity model one step closer to data governance business process automation.
Turning enterprise data into insights at a rapid pace requires an integrated data strategy to ingest, prepare, analyze, and act on data to then communicate insights derived. A single, business-oriented solution suite with data quality, governance and analytics capabilities that work in conjunction can enhance business user control over data and help solve business impacting questions. Here is how it should work.
Data Quality: Ensuring data quality is about establishing trust in data. Business users need to assess enterprise data quality and decide if that data can be applied and analyzed for business planning, strategic decision-making, and day-to-day business operations. The solution suite should ensure data remains complete, accurate, relevant and consistent across complex data supply chains to make sure that business users have continued confidence in data integrity, and will utilize all the data assets available to them.
Data Governance: Delivering a business-friendly perspective requires data governance to help translate the IT department’s technical nomenclature into easily understandable business terms with enriched data quality metrics to eliminate confusion among business users struggling to perform critical business functions. The solution suite should foster collaboration by delivering business users an all-inclusive view of their data landscape to easily define, track the lineage of, and manage all aspects of their data.
Analytics: Turning data into insights at an accelerated pace requires removal of manual processes and implementation of automation wherever possible. A solution suite with analytics capabilities should enable machine learning to monitor data quality and bolster governance efforts, ultimately enabling data governance to improve data quality automatically. With data quality automated, business users can minimize data preparation time and focus on data analysis to make smarter business decisions at breakneck speed.
A solution suite with a set of comprehensive capabilities can completely revolutionize the way organizations handle data across their enterprise.
For far too long, anything data related has fallen on the IT department’s shoulders across many organizations. With a comprehensive, business data governance collaborative solution suite, that is no longer the case. Business users and IT can collaborate across diverging lines of business to answer any questions about data and facilitate resolution. Business users will no longer require help from IT every time they have a question about their data, freeing up IT resources to handle higher-value functions and empowering business users to quickly analyze data for business purposes. The end goal is to continually evolve data governance maturity and expand the audience to business users.
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