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.
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What Does Data Governance Have to Do with All This?
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.
High-quality data is viewed as a strategic asset a competitive differentiator.
Although advanced technologies are now available to enhance the role of Data Governance, many organizations still use outdated practices.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.
Data Governance Without AI or ML: Challenges
Here are some common challenges to traditional Data Governance:
- Lack of consistency in data across business functions
- Divergent views of data available across business functions
- Lack of common data definitions
- Lack of documented Data Governance strategy
- Misuse of data in self-service analytics or BI platforms
- Big 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.
So, what kind of metrics can Data Governance program coordinators use in live presentations to discuss the merits or demerits of a running DG program?
Some probable Data Quality (DQ) improvement metrics can come from demonstrating data rectifications on a quarterly basis, cost savings from rectifiction, data accuracy and error rates in data sets, or number of data errors fixed per quarter and so on.
Many Data Management consultants discussed different types of metrics during the DATAVERSITY® Enterprise Data Governance Online conference (EDGO).
AI and ML for Data Governance: The New Vision
In today’s business environment, the competitive edge is viewed as an organization’s ability to use the most effective data analytics or BI platform for DM. To this end, global businesses are heavily investing in AI- and ML-powered Data Management solutions, which includes AI and ML Assisted DG platforms. These advanced governance platforms offer maximum value while saving unnecessary costs.
The article Challenges for Data Governance and Data Quality in a Machine Learning Ecosystem article states that organizations will invest in advanced data technologies such as AI and ML to “achieve quality, compliance, and security at scale.”
The primary purpose of a modern AI- and ML-assisted DG solution is to ensure improved Data Quality, reliability and accuracy while preserving data security and privacy of its customers. Thus, well-governed Data Management practices imply accurate and responsible data usage within the boundaries of DG policies and procedures. Here are some implicit goals of an assisted DG platform:
- Reliability of data source
- Improved Data Quality
- Seamless data integration
- Meeting regulatory requirements
- Improved data security and privacy
And lastly, and most importantly, protecting customer data.
The Modern DG Platform: Embedded AI and ML
The present day DM challenges do not end with data deluge, growth of cloud service providers, or stringent data privacy laws; the problems escalate with a serious lack of understanding of how to make people, process, policies, technologies, and tools fit together within a defined Data Governance strategy framework.
Thus, the current need is assisted DG or modernizing the DG platforms with embedded AI and ML features. It all boils down to acknowledging that many critical DG processes like user access controls, metadata management (MDM), and data security can be easily automated through AI or ML features.
Role of AI and ML in Enterprise Data Governance
Author Ann Marie Smith describes how assisted DG ensures that ML models are aligned with organizational “policies and standards for Data Management and usage.”
Here are some definite examples of AI- and ML-assisted DG in organizations.
- Data Stewards Use ML to Complete Tasks: ML algorithms assist data stewards in monitoring the data, and specifically, the metadata of millions of data elements in large organizations. Smart algorithms accomplish tasks in a flash, leaving the data stewards to engage in more challenging but less labor-intensive tasks.
- Data Cleansing Time is Reduced: AI and ML tools substantially reduce data cleansing time while improving the Data Quality. This step also enhances organizational reliance on accurate and well-governed data for further use.
- Faster Implementation of DG Policies and Standards: The trained ML models apply approved policies and standards faster and more accurately.
Here is an interesting post by a Forbes author, who thinks that “DG, AI, and ML are interdependent.” According to this author, what a business does with the “good data” is as important as the quality of the data. AI and ML together enhance the data-analytics process by automating data cleansing and preparation tasks; extracting insights from piles of raw data in seconds; and helping the business staff with faster and better decisions.
The Final Anecdote: Automation of DG
When employees in an average-sized business have to manually perform data-related tasks, it can become overwhelming due to the volume and complexity of the data. Any IT department is usually overburdened with information requests, and top executives have short tolerance for outdated data processes. So, who is to be blamed?Of course, the lack of infrastructure.
Businesses generally use archaic, labor-intensive, manual processes that add to the cost and overhead of the business. Automated Data Governance can easily fix these problems. The blog post explains how AI- and ML-assisted DG can be a savior.
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