Data Governance (DG) ensures that enterprise data, the most valuable business asset, is preserved and used in the most efficient and safest manner. That said, Data Governance puts immense demands on organizational policies, processes, technologies, and lastly on accountable staff to develop an executable framework, from its core architecture to implementation stages.

Enterprise Data Governance is a holistic framework involving qualified personnel and planned policies and processes to make the best use of advanced data technologies to ensure the best preservation and use of data. The primary governance aims of any business are to enhance the quality of data, reduce Data Management costs, and provide access to data to all in a highly controlled environment.

The way a business usually achieves its DG goals is by setting up stringent policies, standards, and metrics to arrive at intended data-driven outcomes. At the most fundamental level, DG policies mandate rules for accessing and managing data sets while adhering to all applicable privacy and security regulations.

The Data Governance Institute defines Data Governance as:

“A system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.”

The Pandemic Amplified the Value of Enterprise Data

According to a Teradata study conducted in October 2020, 91% of the survey respondents agreed that the pandemic led to “skyrocketing” value of data in their organizations. Moreover, 94% of surveyed executives agreed that “data is an essential asset.”

While the importance of data-driven insights and decisions continue torise, theview on the flip side is “garbage in, garbage out,” indicating insights or decisions based on bad-quality data can only yield bad outcomes. Bad data has a long-lasting consequence for businesses — it can permanently destroy the trust that customers and other stakeholders have!

According to Gartner, “87% of enterprises have low BI and analytics maturity.” As a result of this dismal state of affairs, most organizations struggle to make use of their data assets. Gartner has categorized organizations with low BI as “basic” or “opportunistic.” Gartner reiterates that organizations at the basic level are limited to spreadsheet-type analytics, while the opportunistic level organizations contain business units pursuing siloed data and analytics activities without any centralized control.

One of the four recommendations of the study is that a Data Governance program needs to be implemented in every organization to raise awareness about the importance and benefits of DG in enterprise Data Management and analytics.  

DG in Business Analytics or in BI

The speaker at the webinar on BI governance explains that “BI Governance” and “Data Governance,” two oft-used terms in the BI environment, are quite different from each other. This webinar dives deep into the governance activities associated with enterprise BI and is worth a review.

As data-driven organizations have gradually moved away from task-specific applications, data silos have disappeared into oblivion. Modern businesses welcome seamless “data flows” across diverse business units and functions. 

With the increasing popularity of next-generation technologies like AI and machine learning (ML), the importance of Data Quality and Data Governance is also on the rise. According to the same Teradata study previously cited, 77% of surveyed executives believe that their “organizations are more focused on data accuracy” now.

Usually when business data is further analyzed for comparative reviews or competitive intelligence, this practice is called business intelligence (BI) or business analytics. In case of BI, a Data Governance program indicates “the process of executing and enforcing authority over the management of data and its related resources.”

With Data Governance-enabled BI, enterprises hope to make better decisions, and in a shorter span of time. According to industry watchdogs, over time, advanced BI capabilities will become easier and free to use. A Forbes study confirms that organizations with effective DG-powered BI practices have all reported “breakthrough ROIs” from their BI investments.

In The Ultimate Guide to Data Governance, author Elizabeth Mixson commented, “At the heart of data analytics lies data governance, the unsung hero of data quality, usability and security.”

These are some of the ways DG components collectively improve organizational analytics and BI practices:

• Data Architecture offers the blueprint for aligning organizational Data Management strategy with organizational strategy.
• Data Quality (DQ) acts as the gatekeeper of “accurate, complete, timely, and consistent data” used within an organization. 
• Data stewards develop controls and checkpoints for every data interaction point throughout the Data Governance framework.
• Data Modeling helps organizations decipher database design at a physical level or a business function at a logical level.
  Data storage policies provide controls to Database Management, data lifecycle management, licensing, and more.
• Metadata Management breaks down the granular details of stored data.
• Data security policies help combat security breaches, the average cost of which was $3.92 million in 2019. 
• DG also helps establish seamless data integrations and interoperability between diverse systems, which is essential for the success of predictive analytics.

Finally, see how Medium author Kavika Roy describes the 4 Ways Data Governance Can Improve Business Intelligence.

Data Governance for Self-Service BI

In most organizations, an important goal of the DG team is to “mitigate the risk of improper use of data.” According to an author who attempts to share the real story behind DG for self-service business intelligence, the adventurous business users often use their creative solutions to avoid facing punishment from “DG polices” within their organizations. These risk-takers use “Excel files and SharePoint lists with manually-entered data” to achieve their goals in their own way.

A halfway compromise is probably the managed self-service BI, where business users access data they need, but by strictly adhering to systems and processes set up by DG teams. In this scenario, the DG group and business users become allies, and data access is provided under stringent conditions.

The greatest takeaway from the insightful article hyperlinked above is that DG and self-service BI are allies, and should be viewed as allies. DG initiatives typically begin with plans, but the plan often changes when the organizational strategy changes. This unique balancing act is achieved in global enterprises with the help of Power BI.

On the flip side, BI enhances DG practices by having smart analytics built in (embedded in) organizational systems and processes. The ultimate goal of BI-enabled DG practices is to move beyond quality metrics and start exploring the root causes of systemic errors like detecting sources of bad data, monitoring the frequency of errors in particular systems, or analyzing whether Data Quality has improved over time, and how. This kind of sophisticated analytics for DG practices can only happen with embedded BI.

Data Governance in BI: Examples and Benefits

Here are some DG use cases for enterprise BI:

• In aSaaS-based business model with high-volume user base, it is imperative that client data is stored and used for analytics in a secured manner. Thus a strong DG strategy is required to ensure client data security.
• Customer-behavior data tracking and deep analysis of that data have become critical practices for improving products and increasing revenue in a business. DG plays a key role in handling customer-behavior data for deep analytics and BI.
• Data breaches have played havoc in global businesses in the recent years. DG can help establish and preserve the necessary regulatory policies to avoid catastrophes associated with data breaches.

DG benefits for BI:

• DG ensures timely and accurate analysis of business data, beginning with data acquisition and ending with reports. DD-enabled BI has higher chances of discovering risks and opportunities.
• DG improves operational processes by streamlining data flow across organizational systems and processes, which in turn enables faster and better decision-making. 
• DG enhances security of email systems and reduces the possibility of loss of classified information.
• DG improves quality of data (DQ), which is the life blood of enterprise BI.
• DG ensures that there is only one version of the truth for enterprise data, which is highly controlled and trustworthy.
• Because of DG, Data Management for BI is compliant with all regulatory policies and standards.

While BI indicates a highly technology-driven Data Management practice, DG refers to a holistic strategy or framework for aligning overall business goals with enterprise analytics goals. DG converts a data into a strategic asset.

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