Data & Analytics have become main-stream. Executives and their boards are increasingly starting to question whether their organizations are truly realizing the full value of the insights. A study suggests that 58% of organizations have difficulties evaluating the quality of the data and its reliability, raising a big question to the stakeholders as to “can you trust your data?” On one hand these is this set of people who are worried about the authenticity of their organizational data, or the data they intend to use.
On the other hand you may encounter a set of people coming up with lame excuses, and claiming that they are happy with their data-sets and find their data to be trustworthy. They are not in need of any kind of data cleansing or data processing or assistance of data management experts. They are not wrong completely at what they feel and so what they say. The recent reports by Gizmodo, The Independent, New York Post and various others, about “Balls have zero to me to me” where Facebook’s AI chatbots Bob & Alice created their own language. Such incidents are enough to send chills down your spines.
Investigations to aforementioned incidents are on, and most likely it would be the bad data or absence of data cleansing process; the root cause. Don’t get us wrong. We advocate data driven decisions. However, on a thoughtful note, all this and much more can happen only if your data is in place. When we are talking about the trustworthiness of your data, it’s the appropriateness and accuracy that we are referring to.
We as a society have moved away from decisions made based on limited information or gut feel, to a data and information driven society; where applicability of common sense is minimal or nil. However; the challenge is that though the society has evolved, people have not. Business and enterprises are still being led by baby boomers that are better suited to hunt mammoths and not take financial decisions based on accurate data and insights derived from them.
Now that, everyone has realized that human judgement in a business context is poor, organizations are increasingly basing decisions on data driven facts. But is their data trustworthy? Let’s see why they should not trust numbers blindly?
Believe it or not, but a lot of things can go wrong. Even Google Analytics is prone to mistakes, which is backed up with this discussion on GA data. Anything and everything starting from data collection to data integration, data interpretation to data reporting; should be questioned rigorously. For example, events not named in an explanatory fashion, inclusion of start date and many more; can lead decision analysts to commit errors while calculating results.
