Enterprises today are hard-pressed to define what a good data governance strategy is. While setting up a data governance framework they acquire a considerable amount of technical debt and spiraling complexity that restrains innovation. They should be slightly acrobatic to avoid these issues. There are some principles to keep in mind for honing or developing a data governance strategy that thrives.
Data is the oil that lubricates the digital transformation engine and keeps it humming. However, it needs to be collected, managed, and stored in the right way for digital use cases. In many scenarios, the enterprise data governance model consists of different data sets held in silos for different transactions and purposes. This model creates bottlenecks as IT teams struggle in transforming large files or manually preparing data for business intelligence. It takes too long for teams to onboard data, or push the data into business processes for insights or operational decision making. A considerable amount of time is wasted in aggregating data, extracting insights, and measuring business performance.
In a siloed environment, the unstructured data is a significant impediment of speed. It restrains teams from spotting patterns in business-critical data, i.e., emails, images, documents, etc. Therefore, enterprises should have one universal data framework that scales naturally for diverse transactions.
Many enterprises are still looking to define their data governance strategy with a distributed dataset approach. It doesn’t return the right answers to a multi-cloud environment where risks of performance impacts and downtimes are relativity higher. Enterprises should get their data governance framework sorted out for enabling broader-scale transformation and swiftly navigating through data challenges. A single enterprise-wide data governance strategy is the right solution to do this out-of-the-box, without heavy lifting & additional expenses. Here are some rules to do this efficiently:
Replace Batch Processing: Enterprises should replace batch loading or processing models with an information fabric that ensures data virtualization and caching to databases and data platforms. Such a data fabric should make sure that data quality, semantic data models, and data security across different layers are service oriented. It delivers the advantage of a coherent business focussed data model for ingesting and provisioning data between source and target systems.
Set Up An On-Demand Data Management Platform: Enterprises should set up an on-demand data management platform that helps them in scaling linearly across all operational environments. After this, they should interwire analytics with data management services to get quick insights for optimizing affected areas. Start-to-End integration helps in mapping business processes to data processes, accessing data and fixing gaps. Moreover, they get a unified & in-depth view of business processes and customers.
Enable Self-Service: Enterprises should ensure that their data governance and data management policies invoke a greater degree of trust. They need to have access to self-service tools for information and data sciences. Self-service tools help in pushing production-ready data to different environments in consumable formats without development support. This advantage helps in making their data governance framework more dynamic, agile, and intelligent.
Create Differentiation Between Applications and Data Management Tier: Enterprises should ensure that applications only focus on application logic & interface and not on technology integration functions. This differentiation simplifies data management and accelerates data moment without much dependencies. Simplified data management for all data formats, i.e., structured, semi-structured, unstructured, etc. brings down the time-to-value that goes in lengthy code marathons.