This is part of Solutions Review’s Premium Content Series, a collection of contributed columns written by industry experts in maturing software categories. In this submission, Anexinet Director of Applied Intelligence Brian Atkiss outlines key considerations for securing self-service BI without a side order of data spaghetti.
If your enterprise has been weighing the pros and cons of adopting self-service business intelligence (BI) tools, know that the benefits are sure to outweigh the risks—but only when ample attention and data governance are in place. For this reason, devising a Self-Service BI strategy beforehand is critical to success. Because while self-service BI tools simplify the process of insight generation through data analysis (compared to traditional BI tools which tend to be too complex for the untrained user), they do not completely eliminate the need for user education to return value and ensure accurate results.
Most compellingly, the adoption of Self-Service BI tools democratizes analytics by simplifying those tools, enabling more folks at your organization to analyze data as they see fit. Employees no longer need to spend weeks to months designing, building, and testing a visualization solution, only to find that it fails to meet the needs and requirements of the business. A self-service BI solution empowers your organization to leverage the power of data to generate more actionable insights in real-time than ever before, improving resiliency by enabling fast strategy-pivoting, based on query results, while costing next to nothing to support.
Better yet, many of today’s self-service BI features may be embedded into platforms your employees already use, minimizing the training necessary to ensure accuracy and achieve results.
However, developing a full understanding of your data model can still prove a daunting task. The typical BI-tool user isn’t a power user but rather, a casual or standard user with limited skills and training regarding data interpretation. But without this understanding, erroneous conclusions and interpretations are likely to result.
Traditional BI platforms were maintained by data professionals and IT Teams. And while this presented a bottleneck that often stymied adoption and slowed development, it was not without its advantages. Because the most detrimental, even dangerous, outcomes result when self-service users take faith in inaccurate results achieved by manipulating erroneous data—in the end making it extremely difficult to determine where the real truth lies. In other words, basing business decisions on an ad-hoc data model can be more disastrous than making no decision at all.
Since the adoption of self-service BI tools is currently being driven predominantly by business users, having adequate data governance policies and procedures in place is essential to ensure your decisions are based on accurate and reliable data. On the bright side, new self-service BI tools also provide performance data to let IT know if their training and governance programs are proving effective.
To maximize the value of their BI strategy, organizations that adopt self-service BI tools must also promote and encourage data exploration and knowledge sharing to propagate a deeper appreciation for how the organization’s analytic initiatives can best solve day-to-day business challenges, based on the data and technology available.
Thankfully, the next generation of business intelligence (augmented analytics) leverages AI, machine learning, and natural language processing to improve usability and accelerate data preparation and analysis (even predictive analysis) to enable even more effective data-driven decision-making.
Self-service BI tools can easily help a larger portion of employees discover untapped revenue sources and new business opportunities while reducing training costs and improving efficiency and productivity—because now it’s easier than ever to leverage data to generate new insights. However, to maximize results, your self-service BI strategy must include formalized training around data governance and a well-designed communications strategy that employs periodic checkpoints and coaching.
Because as intuitive as the new BI tools are becoming, there is no shortcut or substitute for these essential processes when it comes to aligning your team and ensuring the entire organization is in sync.
The key to leveraging self-service BI tools is to find just the right balance between governance, data availability, and ease of use. Companies need to keep in mind that the most dangerous and detrimental outcome is when self-service BI gets too comfortable and begins storing a secondary source of the truth. Combining and manipulating this data can lead to “data spaghetti”, and eventually gets out of control to the point where you cannot even determine what is the real truth.