How many of our beliefs are so deep-seated that we would never think to question them? In reality, probably more than we think. Humans are not as rational as they may believe, and at any rate there’s no time to investigate every claim that’s made.
We have a habit of repeating what others tell us and accepting what others say as fact. Some of these assumptions are true, but of course in some cases they can leave us believing a lie. For example, it’s a common misconception that the Great Wall of China is the only man-made structure visible from space; however from the edge of space you can see cities and some buildings in clear view. Similarly, Vikings are commonly represented as having worn helmets with horns; however this sartorial detail was in fact created by composer Wagner in his opera The Ring Cycle.
These misconceptions, unfortunately, are not restricted to interesting trivia. Myths and misconceptions can also be found in the world of business, even though harbouring these beliefs can ultimately prove to be more costly. In particular, as the business intelligence (BI) industry has changed so rapidly over the last ten years, businesses often believe and evangelise conventional wisdoms around data analysis. In the interest of trying to dispel these myths and outdated concepts once and for all, I have identified the following six outdated business intelligence platitudes:
1. Only Decision-Making Managers Need BI
Despite years of effort and huge spending, BI isn’t in the hands of enough decision makers. Use of BI often rests with decision-making managers within an organisation, but this needs to further widen out to all kinds of employees as decision making is widespread in organizations, and all decisions need data. Interestingly, this concept dates back to antiquated hierarchical structures in the 19th century – from long before software even existed. When first deployed BI used this structure which meant that its aim was for auditing and control, rather than for enabling people to make better decisions through analysis. Fast-forward to today and even though BI has become more sophisticated, this same thinking is often still in place, meaning that most people aren’t benefiting from the extra layers of data we have access to.
2. Good Reporting = Good BI
Almost all BI projects start with the laudable aim of producing management reports. Often this is for financial reporting and an IT department is often prescribed with what the BI tool needs to do. However, the problem is that reporting is static information and tells the user relatively little. This means that the data is near impossible to ‘interrogate’, and yet, the ability to be analysed is the key characteristic of a good BI system. Users need to be able to question the data and build BI systems that can help explore root causes, interrelations, trends and shifts in the data iteratively.
3. In-Memory BI Will Fix Adoption Problem
In the modern world, anything that takes longer than a Google search to respond is in danger of being abandoned by users. However, there’s more to adoption than speed alone. Even if it’s fast, BI systems can become rigid due to lacking a powerful back-end and users spending too long building reports or visualisations. The end result tends to be that the user is put off using it altogether. To create a culture of analytics software therefore has to be fast, easy to use and flexible enough to always be relevant.
4. We Don’t Have The Analytical Skills
Why pay analysts or data scientists to interpret data? Human have evolved natural analytical capabilities, including pattern recognition (distinguishing between clumps and single dots), outlier detection (noticing something different about a room) and categorisation (relevance detection). Instead, businesses need software that democratises data analysis and uses the innate analytic skills we all have. They need to take power away from data scientists and put it right in the hands of employees at all levels for overall better data analysis.
5. More Visuals To Help People ‘Get’ Data
It’s true that roughly 60% of our neural sense processing is dedicated to things we see and therefore data visualisations are very important. However, a picture alone is not sufficient. Some tools have gorgeous visualisations but don’t allow unfettered navigation of data, and obviously that is anathema with today’s gadget-empowered, Internet-informed, application-savvy employee-consumers. It’s crucial for users to be able to interact with these visualisations so they can understand its meaning and also make further discoveries. Staring at a static or interactively limited report kills this.
6. Better Access To Data = Better Decision-Making
Having all the information in the world at their disposal did not help bankers to avert the sub-prime mortgage catastrophe and 2009 financial crash. Just because the data is there it doesn’t mean that it’s being used well. Better decision making requires practice and competency. It comes from developing those skills that help protect users from tunnel vision, getting blindsided and doing things more often. Famously Malcolm Gladwell wrote that it takes ten thousand hours for true expertise in any activity, so likewise business intelligence users need to keep practicing data analysis as well. Doing so will help them make smarter decisions and discover more useful insights.
As we enter 2015 and companies look for growth opportunities, it’s critical that they are not weighed down by believing in myths. Companies need to revisit their attitudes to data analysis and take steps to move in the right direction. Ultimately, taking these steps can improve business operations and ultimately its bottom line, which is a fact I’m sure we can always believe in.