How many of our beliefs are so deep-seated that we would never think to question them? In reality, more than we think. Humans are not as rational as we believe, and, in fact, there is no time to research every claim we make.
We have a habit of repeating what others say and accepting it as truth. Some of these assumptions are true, but of course, in some cases, we may be led to believe a lie.
For example, it is a common misconception that the Great Wall of China is the only man-made structure visible from space; it is also possible to observe, from space, cities and some clearly defined buildings. Similarly, Vikings are commonly depicted as wearing horned helmets; however, this detail of their attire was created by the 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 business world, even though protecting ourselves from them can be very costly.
Faced with rapid changes in the Business Intelligence (BI) industry over the last ten years, we often tend to believe myths regarding data analysis. My goal is to try to dispel some of them, as well as outdated concepts, once and for all.
Let's look at six myths about BI:
1 - Only decision-making managers need BI
Despite years of effort and massive investment, BI does not reach the hands of everyone responsible for decision-making processes. The use of BI is often limited to a certain number of decision-making managers within an organization, and this type of tool should be accessible to all types of employees, since the decision-making process is increasingly distributed within organizations and companies, and because all decisions require data.
Interestingly, this concept dates back to old-fashioned hierarchical structures in the 19th century – long before the existence of software. When first deployed, BI utilized these structures, which meant its purpose was about auditing and control, rather than enabling people to make better decisions through analysis.
Nowadays, and despite BI having become more sophisticated, this mindset is often still in place, meaning that most people are not reaping the benefits of the extra layers of data they have access to.
2 - A good BI tool generates good reports
Almost all BI projects start with the laudable goal of developing management reports. Often, financial reports. And, almost always, the IT department is told what the BI tool should do. However, the problem is that the information obtained is static and "says" little to the user.
This means it is almost impossible to "interrogate" the data, and yet, analytical capability should be the key feature of a good BI system. Users need to be able to interact, question the data, and build BI systems that can help explore root causes, interrelationships, trends, and changes in the data.
3 - In-Memory BI solves the adoption problem
In the modern world, anything that takes longer than a Google search to generate an answer risks being abandoned by its users. However, there is more to adoption than speed itself. Even if they are fast, BI systems can become rigid due to the lack of a powerful back-end and the time spent by users producing reports or visualizations. The result? The user may stop wanting to use the systems...
To create an analytics culture, the software must be fast, simple to use, and flexible enough to remain relevant to the user.
4 - Not having the necessary analytical skills in-house
Why pay analysts and data scientists to interpret information? Humans have natural analytical capabilities, including pattern recognition (distinguishing between clusters and individual points), change detection (noticing something different in a room), and categorization (detecting relevance).
Instead, companies need software that democratizes data analysis for everyone and leverages the innate analytical skills we all have. Organizations need to democratize data analysis and put it directly into the hands of their employees to drive better results.
5 - We need more visual resources to help people "get" data
It is true that about 60% of our neural processing is dedicated to things we see, and therefore, data visualizations are very important. However, an image alone is not enough. Some tools have beautiful visualizations but do not allow data navigation, and obviously, this fact is a curse, given the capabilities of today's gadgets used by employees/users who increasingly search for information on the Internet through various applications.
It is critical that users can interact with visualizations so they can understand their meaning and make new discoveries. Looking at a statically or interactively limited report puts an end to this process.
6 - Better access to data means better decisions
Having all the information in the world did not help bankers avoid the 2009 financial crisis. Just because the data is there does not mean it is being used correctly.
Improving decision-making capability requires practice and competence. These skills stem from developing competencies that help users protect themselves from unexpected situations and from a higher frequency of certain tasks.
Malcolm Gladwell wrote that it takes us 10,000 hours to become experts in any activity. Likewise, users working with any BI system need to continue practicing data analysis. This practice will help them make smarter decisions and discover more useful concepts.
In 2016, companies are looking for growth opportunities, and it is crucial that they are not restricted by the weight of believing in myths. They need to review their attitudes toward data analysis and take steps to move in the right direction. Ultimately, taking these steps can improve business operations – which is the goal of any company. And in that fact, I am sure we can always believe.
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