This is part of Solutions Review’s Premium Content Series, a collection of reviews written by industry experts in maturing software categories. In this presentation, Brian Atkiss, Director of Applied Intelligence at Anexinet, outlines key considerations for securing self-service BI without accompanying data spaghetti.
If your business has weighed the pros and cons of adoption self-service business intelligence (BI) toolsknow that the benefits are sure to outweigh the risks, but only if sufficient attention and data governance are in place. For this reason, developing a self-service BI strategy in advance is critical to success. Because during self-service BI tools simplify the process of generating insights 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 training to achieve value and ensure accurate results.
Most compellingly, adopting self-service BI tools democratizes analysis by simplifying those tools, allowing more people in your organization to analyze data as they see fit. Employees no longer need to spend weeks or months designing, building and testing a visualization solution, only to find that it does not meet the needs and requirements of the company. A self-service BI solution enables your organization to leverage the power of data to generate more actionable insights in real time than ever before, improving resilience by enabling rapid pivoting of strategy, based on results queries, while costing next to nothing to support.
Better yet, many of today’s self-service BI features can be integrated in the platforms your employees already use, minimizing the training needed to ensure accuracy and get results.
However, developing a full understanding of your data model can still be a daunting task. The typical BI tool user is not a power user, but rather an occasional or standard user with limited data interpretation skills and training. But without this understanding, erroneous conclusions and interpretations are likely to result.
Traditional BI platforms were managed by data professionals and IT teams. And while this presented a bottleneck that often hindered adoption and slowed development, it was not without its benefits. Because the most detrimental and even dangerous results occur when self-service users rely on inaccurate results obtained by manipulating faulty data, which ultimately makes 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 driven primarily by business users, having the right data governance policies and procedures in place is essential to ensure your decisions are data-driven. precise and reliable. On the positive 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 should also promote and encourage data exploration and knowledge sharing to spread a better appreciation of how analytical initiatives organizations can best solve day-to-day business challenges, based on available data and technology.
Fortunately, 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 analytics) to enable even more efficient data-driven decision making.
Self-service BI tools can easily help more employees discover untapped revenue streams and new business opportunities while reducing training costs and improving efficiency and productivity because it is now easier than ever to use data to generate new insights. However, to maximize results, your self-service BI strategy should include formalized training around data governance and a well-designed communication strategy that uses periodic checkpoints and coaching.
Because as intuitive as new BI tools become, there is no shortcut or substitute for these essential processes when it comes to aligning your team and ensuring the entire organization is synchronized.
The key to leveraging self-service BI tools is finding the right balance between governance, data availability, and ease of use. Companies should keep in mind that the most dangerous and detrimental outcome is when self-service BI becomes too comfortable and begins to store a secondary source of truth. Combining and manipulating this data can lead to “data spaghetti” and eventually spiral out of control to the point where you can’t even determine what the real truth is.
Brian Atkiss is Director of Applied Intelligence at Anexinet. Brian has over a decade of experience building analytics solutions that drive actionable insights for the Fortune 500, and has extensive experience in social listening and advanced analytics solutions around integration data, machine learning and artificial intelligence.
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