Loading...
You are here:  Home  >  Data Education  >  BI / Data Science News, Articles, & Education  >  BI / Data Science Articles  >  Current Article

Why Data Governance is Important for Business Intelligence Success

By   /  August 24, 2017  /  No Comments

data governanceBusinesses have started to realize that a sound Data Governance strategy can significantly improve the returns from enterprise Business Intelligence (BI) investments. According to a study published by Forbes Insights, this market-watcher claims that Strong Data Governance Enables Business Intelligence.  The Forbes study claims that this trend has been reinforced by direct feedback from global organizations.

However, the study warns that Data Governance requires a healthy balance between consistency and flexibility, which is a tough proposition to meet. As indicated in the Insights report, data inconsistency, many versions of data view, and slow adoption rates still continue to create barriers to enterprise BI success.

Forbes makes one critical observation, which over 75 percent of corporate executives agree to and that is that as Data Governance is enforced in enterprise Business Intelligence operations, advanced BI capabilities will be easily available to mainstream business users.

So, What is Data Governance?

Without oversimplifying, one can state that the diverse data sources, complex data types, sensor-driven intelligence, and interdependent data-technology platforms have necessitated the use of a highly managed and monitored Data Management strategy throughout the data-driven enterprise. Data Governance sets the blueprint for managing data assets of an organization, which includes many layers such as the architecture, the operational framework, and the processes.

The way to begin this highly structured and monitored Data Management strategy is to standardize the use of terminology across business units and enforce consistency of use. The final goal of Data Governance is to facilitate a “unified and consistent view of information” across the enterprise for advanced Business Intelligence activities.

Also, review this interesting article from CIO magazine:  Understanding Data Governance. Further, take a look at DATAVERSITY®’s Improving Data Governance Business intelligence with a Common Data Vocabulary, and you will know why organizations are facing problems managing their small data, let alone Big Data.


The Relative Anonymity of Data Governance in Media Circles

Data Governance has not created ripples in the media as Big Data or sensor-driven data has.

Although Data Governance is relatively unnoticed in business circles and in the broad media world, its low visibility does not take away from the reality. In a data-driven environment, businesses can never realize the full potentials of their data technologies and tools unless the data is well governed. In short, as Data Governance involves defining and categorizing different data types, it affects every data activity in the complete BI ecosystem – beginning with Data Quality (DQ) and Master Data Management (MDM).

A sound Data Governance program is usually aligned with and enhances the corporate Data Strategy, thus increasing the possibilities of deriving higher value from BI initiatives.

The article titled 5 Reasons Why Data Governance Matters to Data Driven Marketers very aptly describes why Data Governance is important to an overall enterprise Data Strategy for operational success.

Just look at the multiple benefits of Data Governance from any angle – it reduces risks associated with Data Quality, lowers operational costs by eliminating duplication of data and Data Management tasks, helps enterprises to know their customers better, and helps extract higher ROI from marketing analytics. Thus effective Data Governance can help reduce costs and generate new channels of growth and revenue.

A Gartner news flash states that the rise of data preparation and data discovery tools, along with smart analytics platforms, will enhance “grassroots BI,” which precludes the need for governance. The favorable outcome of such a trend is that the current BI vendors are on a race to deliver a variety of “data-mashup” capabilities unheard of before. The goal of the self-service BI community is to expand the market reach of advanced BI.

Enterprise Data Management via Data Governance Program

The biggest danger of providing Advanced Analytics tools to business users is that their limited data and data technology knowledge may lead to inconsistent data definitions, metrics, and data interpretations across working teams. The proper Data Management and Data Governance program will help ensure that the in-house BI platforms are closely monitored and audited in a timely manner to eliminate the possibilities of bad Data Quality, data inconsistencies, or data inaccuracies by defining and enforcing strict standards and protocols across the entire BI architecture.

Data in enterprises are stored in widely disparate repositories such as databases, Data Warehouses, Data Marts, or in operational systems, which should be managed via strict, standardized policies and procedures for effective BI outcomes.

The author of the article titled How Data Management and Governance Enable Successful Self-Service BI asserts that a strong Data Management and Data Governance program will help standardize the operating policies, procedures, and metrics for Self-service BI in the enterprise. Please review this article to understand how the DG program can address issues related to data policies, data ownership, Data Quality, and data technology standards across the enterprise. DATAVERSITY’s article titled Complete Data Management: Data Governance, Analytics, and Data Integration All in One reveals the approach to breaking down data silos.

Data Governance for Data Consistency, Repeatability, and Reliability

The fragmented insights hidden in diverse business data repositories cannot be unified and retrieved unless the data is consistent, repeatable, and reliable for analytics purposes. Data Governance ensures the consistency, repeatability, and reliability of data through its sustainable models that control both Data Quality and data usage.  The best DG blueprint will provide data accessibility, data reliability, and data activation. Although Data Governance is not a highly visible business activity, its contribution to the enterprise data architecture is immense.

Data Governance for Self-service BI

When enterprises need Self-service BI and explorative data discovery, Data Governance comes to the rescue of ordinary business users who shy away from complex data models or ETL processes necessary for BI. Data Governance ensures a quick, seamless, and friendly Data Analytics environment for these mainstream users.  In Self-Service BI vs Data Governance , Rita Sallam, vice president of research at Gartner, notes that current self-service BI platforms need to be able to handle data preparation tasks and the basic DG requirements in order to be market ready.

If data preparation and Data Governance are built into Self-service BI tools, then they can be true game changers in the enterprise BI world. This, according to Sallam, can only happen if BI platform vendors pay attention to in-built Data Governance technology for BI solutions. Also read how Infosys provides Data Governance solutions to enterprises in Effective Data Governance.

Future Success of BI Depends on Quality and Governance

When the business users are empowered with direct access to clean and consistent data sources and to data discovery tools, they can rapidly transform the future of analytics through self explorations. By enabling the power business users to discover and act upon market intelligence exactly when it is needed, the Self-service BI solution providers will bring complex BI activities to the user’s desktop. As non-technical users frequently miss seizing a golden opportunity  in absence of appropriate insights at the right time, Self-service BI will cut across the technology confusion and deliver instant, actionable intelligence as and when the users need it. By eliminating highly tech-savvy Data Scientists, Data Engineers, or Data Analysts from the self-service BI systems, data solution vendors are turning business users into Data Evangelists of sorts.

Data Governance Facilitates a True Self-service BI Environment

As the true impact of a Self-service BI environment depends Data Quality, an effective enterprise DG strategy is required to mitigate the negative effects of quality issues.  The global organizations, which focus more on visualization and data discovery aspects of Self-service Analytics rather than on DG will risk circulating inaccurate or incomplete information leading to business disaster. Thus, as organizations reach out to compare available Self-service BI solutions in the market, they should try to evaluate whether a particular solution fits in with the enterprise Data Quality initiative. The final word in data activities in “trust,” thus any analytics solution implemented must be able to deliver trustworthy information through the existing Data Governance funnel.

Governed Data Promises “Just-in-time” Business Analytics

In business, it is all about timing. Whether it is better customer service, improved marketing ROI, discovering new business opportunities, or enhancing business processes, the enterprise BI can deliver results if and only if the right business user gets the right data (intelligence or insight) at the right time. The report titled Forrester Best Practice Tips for Business Intelligence Success reveals this best kept secret of business success. The article further notes that providing the best analytics setup, along with the processes and tools to decision makers at the right time is critical for business success.

Data, which is inaccessible, inaccurate, or untrustworthy, is of no use to an average business user. In the best case scenario, probably both the data and the BI environment will require continuous governance and monitoring. To get the needed competitive advantage, enterprises will need to know exactly what data is available where in both internal and external data pipelines. This will become more a reality as data volumes and complexity continue to rise.

 

Photo Credit: everything possible/Shutterstock.com

About the author

Paramita Ghosh has over two and a half decades of business writing experience, much of which has been writing for technology and business domains. She has written extensively for a broad range of industries, including but not limited to data management and data technologies. Paramita has also contributed to blended learning projects. She received her M.A. degree in English Literature in 1984 from Jadavpur University in India, and embarked on her career in the United States in 1989 after completing professional coursework. Having ghostwritten and authored hundreds of articles, blog posts, white papers, case studies, marketing content, and learning modules, Paramita has included authorship of one or two books on the business of business writing as part of her post-retirement projects. She thinks her professional strength is “lifelong learning.”

You might also like...

Property Graphs: The Swiss Army Knife of Data Modeling

Read More →