The Impact of Data Governance on Self-Service Analytics

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Self-Service ComputingAn important reason for global enterprises of all sizes to embrace Self-Service Analytics & Business Intelligence (BI) is to allow business users and business units to conduct their daily Analytics work without relying on IT. According to Gartner, by 2019, business users indulging in self-service tools will deliver more Analytics-related output than qualified data professionals.

At the same time, Gartner also acknowledges that the Data Governance model at work must also be capable enough to support these self-service initiatives. This concern indicates that for a flexible Analytics & BI platform to accept Explorative Analytics by non-technical persons, current Data Governance issues pose a serious challenge.

Current Challenges in Self-Service Analytics

Self-Service Business Intelligence (BI) has remained on the wish lists of many enterprises for many years, and the essential features of a typical Self-Service BI platform benefit both novice and expert users. Surprisingly, many users may have been using self-service features for a while now without being explicitly aware of the term “self-service.”

Right now, Analytics requirements in many organizations vary between free-form data analysis and tweaking reports to modifying data models. While the novices may succeed in conducting routine Analytics tasks on this platform with little help from IT personnel, the savvy BI users will enjoy the tremendous flexibility of self-service functionality.

According to Rita Sallam, VP Research at Gartner, “Data preparation is one of most difficult and time-consuming challenges facing business users of BI and data discovery tools, as well as advanced analytics platforms.” Thus, right now Data Preparation and Data Governance tools must be designed within the Self-Service BI platforms  if organizations want the agility of Self-Service BI and Data Discovery without compromising Data Quality.

Challenge 1:
The danger in this approach is that Data Quality and Data Governance will continue to remain troubling issues with no straight solution. In Self-Service Analytics, there may also be a genuine attempt to make “one size fit all,” by accommodating feature sets for a wide range of users from novices to power users. According to the author of the article titled Governing Self-Service Analytics, the “one size fits all” approach may work for self-service but it will certainly not work for Data Governance as the variety of Analytics activities conducted by different users is too wide.

Challenge 2: In the CIO article 5 Pitfalls of Self-Service BI, the author claims though Self-Service Computing ensures that business users get the insights exactly when they need it without any IT-staff intervention, different business units within a large organization may end up creating their own data models and metrics, which will lead to a big problem later if the reports are exchanged or cross-tabulated between the BUs. The problems usually found in data silos will surface again, only a hundred times magnified. In the article Self-Service BI Success Depends Upon Data Quality & Governance, the author clearly explains that when multiple business users own and manage their own data marts, the enterprise has the potential to completely lose control of Data Security and Data Governance.

Challenge 3: Data Quality has for long remained a critical, but unreachable goal for many organizations. Now organizations, especially large ones with many business units, realize that Data Quality can only be guaranteed through the implementation of an extensive Data Governance strategy, which includes roles, responsibilities, ownerships, policies, and procedures.

Further, all the policies and procedures can actually be put into practice through the use of advanced technological frameworks. Without proper Data Governance in place, organizations run the risk of have poor quality data resulting in erroneous results. The article Why Data Governance is Important for Business Intelligence Success indicates that solid Data Governance can improve the ROI from BI investments.

Challenge 4: The exponential growth of data volumes, data variety, and data sources during the Big Data and IoT eras have changed everything.  Enterprise Data Management (EDM) remains a huge challenge for large organizations with complex data networks. Of all the Data Management requirements outlined in this CIO article, the author stresses on the need for Data Governance for Self-Service Analytics the most, as data complexity continue to increase with time.

Challenge 5: As Data Preparation is the most critical phase of Analytics’ processes, and self-service platforms either partially or wholly automate this phase, this technology may actually serve as a “driver for greater Data Quality and Data Governance not an inhibitor.” The DIY approach to Data Preparation in Self-Service Analytics will force users to explore why and how Data Quality affects the quality of Analytics and the outcomes. Read this blog post from SAS Institute to Data prep and self-service analytics – Turning point for governance and quality efforts?

Challenge 6: Although the biggest benefit of Self-Service Computing is that is offers complete democratization of complex Data Management tasks in the daily life of a business user, are there any disadvantages of having so much freedom over critical business data? The DATAVERSITY® article How to Encounter the Negative Side Effects of Data Democratization? discusses the other side of the apparently greener pasture. As all types of business users will have access to critical data, aren’t Data Security and Data Privacy at high risks of loss or corruption? Unless Data Governance policies take these risks into full consideration through the rules, procedures, and access controls, the whole purpose of self-service may be compromised.

Risks Involved in Ungoverned Self-Service Analytics Systems

Here are some probable consequences of not governing a Self-Service Analytics & BI platform:

  • Unusable data models with flawed business logic and metrics
  • Business decisions emerging from bad or incorrect data
  • Lack of single version of truth
  • Audit failures in case of data verifications
  • Reporting errors and diminished credibility
  • Compliance failures and regulatory penalties
  • Analytics & BI system maintenance nightmares
  • Huge data security issues

How Sound Data Governance Transforms Self-Service Computing

  • Data Governance can drive fast adoption

When business users experience the superior results achieved in a sound Data Governance framework, the results will serve as a “tell all” for the consumers of the results. That itself can drive fast adoption of self-service platforms.

  • Data Governance Can Empower the Citizen Data Scientists

In Data and Analytics: Five Ways To Foster A Culture Of Empowerment And Governance, the Forbes author states that while Data Governance indicates “control” and rule-based usage restrictions on one hand; on the other hand, it brings tremendous power to the right business user. The focus shifts to “common users” from “IT experts.” Through effective DG policies, enterprises can enable carefully monitored freedom in Data Analytics and BI without compromising Data Quality or Data Governance.

Desirable Governance Features for Self-Service Computing

Here are some highly desirable features that a survey established about what most businesses want in their Self-Service Computing platforms:

  1. Customizable administrative features
  2. Tools for easily migrating or exporting data to a proprietary repository without the chance of data locks
  3. Accurate reporting of time-lagged data time
  4. Single loop authentication for access
  5. Permission-based and role-based access to data
  6. Watermarking for “sanctioned data sources”
  7. Version control
  8. Clear display of data lineage
  9. Collaboration tools to discuss reported content

Future Success of BI Depends on Data Quality and Data Governance

Self-Service Computing, undoubtedly, has come a long way in adding value to daily business decision making. Nonetheless, much is still lacking in terms of Data Governance, Data Security, and Data Privacy. According to this Jen Underwood post, at a recent industry conference, a Director of Analytics from a global 2000 retailer discussed how their Data Security team uncovered loopholes in a top Self-Service BI solution available in the market today.

In Data Governance Trends in 2018, the primary objective of a Data Governance framework is the ability to add real value to an organization through the Data Management infrastructure, which is at a different level than issues of Data Security or access control. With clean and consistent data at their disposal, ordinary business users can singularly concentrate on their Analytics and BI tasks with embedded tools, without having to worry about wrong computations or bad results. If self-service technology really succeeds in overcoming the “technology confusion,” then it does have the promise of delivering actionable insights and market intelligence just when the users need it.


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