As the quantity of business data keeps growing at a rapid pace, businesses around the world are compelled to divert their focus to managing the data and analyzing it the right way. Business intelligence has helped businesses to better manage and analyze huge amounts of data and glean the right insights that can be leveraged to foresee future outcomes. It also takes appropriate action to avoid obstacles.
The best aspect of self-service BI is, it helps to unearth the full potential of analytics to a wider audience across an organization and connects them to the database, and creates reports in an intuitive manner. But even as it keeps getting better, it is also showing up its flaws in the sense it can be misused, especially by people who lack the skills to handle it. And were used properly, the data dictionaries need to be examined, loopholes plugged, and finally, organized by data analysts.
In this article, we will discuss some major pitfalls of self-service business intelligence.
Induces A False Sense Of Security
Self-service BI was started by IT departments who wanted to rid themselves of ad hoc tasks for different business units. However, at times, this can backfire and end up producing mediocre results, lulling companies into a false sense of security.
It must be noted that a good BI is driven by realistic and concrete data engineering with the ability to properly interpret results which is possible only with the competence and skills of an expert. Such an expert can achieve a good BI even with sub-par tools. On the other hand, a self-service business user cannot be expected to achieve a good BI even with the assistance of advanced tools.
Fuels Data Anarchy
They say ‘time is the essence of the hour.’ This holds true in the case of self-service BI, which instead of saving time, ends up consuming more time. Even a simple model that if built, handled, and tested by a professional will consume a mere 15 minutes while consuming thrice the same amount of time if done by an end-user. What happens eventually is that the task ends up being passed to the IT department to make it secure and available globally.
Experts also point out the fact that although self-service BI tools do a good job at ad hoc analysis, they fall short when it comes to conventional reporting. As a result of this, companies spend more time bifurcating their reporting into ad hoc and regular categories and spreading it across various platforms. Eventually, this will lead to more splintering of reporting tasks and aggravate the issue of data silos.
Increases Licensing Costs
In many cases, departments in a company often independently select separate BI vendors rather than a common BI vendor for all. What happens as a result of this is that each department will want to have their own system with a separate license to conduct their own BI, which eventually will only increase the licensing costs and inflate the budget. In such a scenario, large organizations will not be able to take and enjoy the benefits of large bulk discounts offered by the BI vendors.
With self-service BI, there always lies the tendency to skip or miss some important processes and also bypass the best practices and architectural layer roles well-developed and well-consolidated by IT over the last few decades. When that happens, it will not be possible to conduct analysis at a high level, as a result of which inaccuracies creep in, leading to more errors. In addition, users will make more mistakes in terms of defining the calculations or handling data quality issues.
Promotes Improper Skillsets
There will be some data concepts that are complex which means they can be handled by only competent professionals possessing the right skill-sets to handle them. Self-service BI requires devolution of such tasks to users down the line. In most cases, the users lack the requisite skill-sets as well as the capabilities to handle complex tasks and end up producing wrong output, which can be costly in terms of money and time.
To avoid such occurrences, it is important to ensure the devolution to self-service aligns with the right users equipped and the right skill-sets.
Also, the purchase of self-service BI tools does not automatically guarantee the fact that they will be used appropriately. This is a wrong notion since such tools can, at times, be improperly handled by professional consultants.
Breeds Wrong Accessibility
A major say in favor of self-service BI, as it makes ad hoc reporting easier and more accessible for employees. But this can backfire and instead fuel the risk of sub-standard reports, which can be a danger. For instance, non-data scientists or people too deeply involved in business issues often glance at or analyze the reports in a few seconds. Such reports may also end up getting shared across a broad section of the audience. This can breed the spread of misinformation and create chaos.
In some cases, users may have tools that can spot out general patterns but cannot produce proper details. Such users may also lack the ability to handle BI software that can deliver the right insights for increasing revenue or even prise out the right details beneficial for solving real business problems.
The visualizations and dashboards from self-service BI tools will be precise as long as the user can properly tap and query the data. To make self-service BI viable, there lies a dire need to train the users and equip them with the right skill-sets to handle the BI tools regularly and properly. Else there will always be the recurring danger of inaccurate reports, which means more time and resources get used and overshoot the budget as well. On the other hand, BI, if handled by skilled data analysts, can deliver the proper quality and accuracy of business analytics and reports. With their high-level business intuition, they can also spot out key performance indicators (KPIs) and test new metrics.
Overall, the future of self-service business intelligence depends on a company’s need for business intelligence. If the company wants swift reports with insights, then self-service may work. Else, if the company looks for reports that delve deeper into the data and produces more insights, then full-scale business intelligence will make for a better choice.
As advances in AI in natural language processing accelerate further, voice-friendly platforms will gain more prominence. When that happens, users can be expected to interact with their software and ask for recommendations.
ConclusionFor businesses, the need of the hour is a proper data strategy and BI strategy with a focus on data engineering to facilitate accurate data pipelines. This is possible only if they align their needs with a company that has skilled data experts with the ability to extract insights for critical reporting.