Using AI and Machine Learning with Data Governance
Anomalous data can be disastrous to enterprise Data Management. Data corruption frequently occurs due to data silos or inconsistent data formats, divergent views of data through different systems, human errors, multiple data entries of the same data by multiple users, poor Data Governance and many more reasons.
With an ever-growing stream of high-volume, high-velocity data choking the data pipelines in an average organization, business leaders are increasingly concerned about the reliability of such incoming data. If you cannot trust the data, you cannot trust any data activity from there on.
Data Governance (DG) “is a collection of components – data, roles, processes, communications, metrics, and tools – that help organizations formally manage and gain better control over data assets.” In layman’s words, the absence of a Data Governance framework can lead to data inconsistencies and anomalies.
For example, the same customer data may be documented differently in sales, logistics, and customer service systems. This will lead to mismatched data during the data integration phase, and create data integrity problems, further impacting data analytics, BI, or reporting systems. These issues will reflect poor Data Governance, opening up regulatory compliance issues.
The primary objective of an enterprise Data Governance program is to standardize data definitions and develop common data formats for use across the enterprise, so that it boosts data consistency for both business and regulatory purposes.
According to the World Economic Forum, “463 exabytes of data will be created every day by 2025.” This phenomenal rise of data volumes will very soon necessitate the use of automated Data Quality measurement tools to support DG exercises in enterprises. The good news is that an AI-or ML-assisted DG program is a step in the right direction.
Although advanced technologies are now available to enhance the role of Data Governance, many organizations still use outdated practices. The new AI- and ML-assisted DG framework can help “reduce the risks and maximize the value of the data and algorithms that increasingly drive competitive advantage.”
Some recent technology trends have necessitated the modernizing of DG programs, which are increased cloud adoption, omnichannel data, agile methods, self-service platforms, and the popularity of AI and ML solutions for maximum value.
Here are some common challenges to traditional Data Governance:
DG coach and author Nicola Askham, while describing the six principles of a successful DG program, mentions that business executives are eager to know the benefits of a governance program at the very beginning. According to Askham, “If you can’t answer that in a way that they really are interested in and benefits them, they’re just not going to be interested.”
Like any strong business case, a burgeoning Data Governance program requires a kick-off meeting demonstrating the business benefits of a proposed program. Then as the program takes off and proceeds, periodic meetings or presentations with actual metrics, must become routine to convince the business users about the importance of an effective DG program.
