Setting Up for Success: Governing Self-Service BI
To successfully adopt a self-service BI and analytics program, enterprises need a mature governance strategy that is effective, all-encompassing, and realistic. Here are five key components to that strategy.
- By Manish Kedia
- October 2, 2020
The immense growth of data, a pressing need for data-driven decision making, and the increased availability of business intelligence (BI) tools led to organizations looking for a solution that gives more power to their business units. Self-service BI had the answers for data discovery, quick access, and uncomplicated analytics enlightenment. There’s no arguing that self-service BI has come a long way from being a trend that only a handful of organizations leveraged to a norm now thanks to its sustained enterprisewide benefits. Self-service BI rose in part to provide greater agility to lines of business. IT can take longer to give you the data or develop the insights you need to capitalize on business opportunities.
However, without a balanced governance strategy, the incredible benefits of self-service BI can also lead to data chaos and risking the security of critical enterprise information. Let’s look at some of the hidden yet crippling challenges of self-service BI:
Data redundancy and inaccurate reporting. With an increased number of users given access to the data layer, more reports and dashboards are generated to support business decisions, especially in the early stages of self-service BI adoption. When multiple individuals utilize the same data source at different times, it can lead to discrepancies in the data reported and redundant reports and dashboards generated from the same data set. Different business users also create their own versions of data sets derived from huge and more complex data sets. These activities, when compounded, will eventually lead to inconsistent reporting, which can set back executives making time-sensitive, data-driven business decisions.
Performance and capacity issues. It’s not surprising how often and soon organizations run into performance issues with their self-service BI tools. Redundant data sets and reports can increase the load on systems, leading to capacity issues. Though some of the most powerful BI tools available provide best practices to improve report development, load testing, and capacity management, it still boils down to how end users are handling the technology in the absence of effective governance.
Handling critical information. An overloaded system, with redundant data sets and reports, may still have recourse, but when a security breach happens, it is one of the hardest setbacks that CIOs and organizations endure. With the increased use of self-service BI tools, many individuals within the organization may access proprietary business information. How and where users store it, how data is encrypted, and who can access it -- these are only a few security concerns.
User experience. Another unintended outcome of increased access to the data layer is a less optimal experience for the end business user, which negates the whole benefit of self-service BI -- to empower business users with increased efficiency. When data is manipulated outside of a well-established environment by many business users with different levels of skill and expertise, maintaining data quality becomes challenging.
Business users have different goals than IT; users are more concerned about a quick evaluation of a developing trend than about long-term data set maintenance and security. Ensuring the maintenance of data integrity, accuracy, and consistency may fall through the cracks. End users question the reliability of their insights as they become aware of data validity and integrity issues. Eventually, such problems will hamper the adoption of self-service BI tools. It’s like one step forward and two steps back.
The Solution: Intelligent Governance of Self-Service BI
To successfully adopt a self-service BI and analytics program, enterprises need a mature governance strategy that is effective, all-encompassing, and realistic. Such a strategy is instrumental in maintaining the integrity, validity, and security of data. It primarily focuses on sharing the responsibility among the users through a tiered, hands-on training approach and user education.
Eventually, governance percolates through this “train-the-trainer” approach, which includes:
- Identifying super users within an enterprise. A super user is an avid user of BI and data analytics -- typically (but not always) a data analyst or a data scientist. A general grasp of the cloud and BI technology is a must. Super users have an intrinsic desire to support other users in the organization.
- Estimating how many super users are needed. Typically, the proportion is one for every 30 BI end users, but the number varies depending on the intensity of technology adoption and consumption as well as the size of the organization and complexity of its data.
- Defining the training period. Train super users from the early stages of technology implementation to give them ample opportunity to learn. Super users play a critical role during the testing, reviewing, and user adoption stages.
- Structuring the training process. Effective training should include a hands-on approach and set up the best practices. Oversee and provide support to the super users initially as they start training the end users in the organization.
- Prioritizing governance of high-risk BI. Begin by focusing on the governance of high-value projects with critical business information and outcomes before gradually bringing the rest of the projects into the fold.
A Final Word
Although many enterprise-ready self-service BI tools will offer built-in governance capabilities, we still need a nuanced approach to maximize their value. A carefully designed strategy will enable organizations to empower their professionals with the immense functionalities that self-service tools offer along with a shared sense of responsibility to maintain data integrity and security.
Manish Kedia is a senior entrepreneurial leader and executive manager. He has over 20 years of experience working with leading companies, growing products and technology organizations. Starting his career with Siemens AG, Manish has led fast-growing high-tech start-ups and innovation at industry leaders such as Microsoft. He is passionate about applied technology and reimagining business to leverage disruptive innovation of cloud, AI, data, and analytics. As the Co-Founder & CEO of CloudMoyo, Manish brings his experience and passion for applied technology as CloudMoyo builds solutions that harness the power of the cloud, AI, and data analytics to empower railroads in their digital transformation.