Top 4 Challenges of Self-Service BI and Analytics: Understanding the Mounting Problems
The ability to quickly access and analyze information is required in today’s competitive business environment. Self-service business intelligence (BI) and analytics tools have completely transformed the way that businesses utilize data – often to great benefit. However, organizations now also have to deal with some growing challenges and issues introduced by it.
The ZenTalk with featured speaker Claudia Imhoff entitled, “What Have We Done? The Mounting Problems with Self-Service BI and Analytics Part 1 of 3,” explores the difficulties of self-service BI. We have summarized the significant takeaways in the form of four primary challenges organizations face.
Inconsistency in Data Introduces Trust Issues
One of the glaring issues organizations face is the inconsistency in data and the subsequent trust issues that arise. In many organizations, different stakeholders come to meetings armed with diverse sets of data and reports that often include conflicting data points. This inconsistency in data definitions and calculations within reports can breed skepticism and mistrust in analytics. ZenTalk speakers highlighted a real-world example where a financial services company incurred a staggering $40 million rounding error due to the use of incorrect analytics assets. Although this is a dramatic example, such mishaps underscore the need for standardization and data definition uniformity in the analytics process.
Another prevalent challenge in the realm of self-service BI is report sprawl. The ease with which modern BI tools allow users to create reports and dashboards has led to a proliferation of reports and dashboards. The issue arises when anyone, regardless of expertise or understanding of the inherent business rules in data source structure, can generate reports. The consequence? A lack of control over the quality and accuracy of these reports. Plus, other people in the organization do not know which reports or dashboards should be used for analyses and decision making.
Lack of Analytics Governance
While data governance is a priority in many organizations, the usage of data in the form of reports and dashboards requires analytics governance as a complement to data governance programs. This layer of governance ensures that the analytics assets are accurate, relevant, and in alignment with organizational goals. It’s not just about managing data; it’s about managing the entire analytics process. The absence of governance not only results in report proliferation but also contributes to unverified accuracy of reports.
Adverse Impact on Decision Making
Perhaps the most critical issue of self-service BI and analytics challenges are the direct impact on decision making. When analytics assets lack standardization and are riddled with inconsistencies, individuals within an organization risk making decisions based on inaccurate data. Further, with the proliferation of reports that may be similar in nature, a decision-maker may not know which report or dashboard contains the appropriate information for use. This lack of validation can lead to a fractured decision-making process. The mounting problems in self-service BI are not merely operational issues; they can significantly impact the strategic direction of organizations and, in some cases, the company’s bottom line.
The ZenTalk concludes by highlighting how critical it is to identify and resolve the issues surrounding self-service BI. Critical issues that require attention include data inconsistency, report sprawl, the necessity for analytics governance, and the possible detrimental effect on decision-making. To realize the value of data as an asset, organizations must resolve the challenges with self-service BI and analytics.
To listen to the full ZenTalk discussing these challenges, and to hear the follow-up segments on how organizations can tackle some of the issues, please click here.