As a Computer Illiterate growing up in the new millennium, I had tremendous difficulty making sense of how to do most of my stuff. Most of my decisions were snap, on-the-moment and intuitive ones. By the time I was in my teens, I had learnt my way around the big computer problem. My experiences and the Great Indian “Jugaad†mind-set had provided me with a sufficient enough method wherein I only worked on the computer when absolutely necessary. If there was any other way to do the task, any escape route, I gladly followed it.
By now you must have realised that I was and am sceptic and data-averse. Data to me was always those unreadable files that eat a hell lot of my storage space. I was gaining proficiency in getting around this data problem and along came Big Data. Everyone from Google to Barrack Obama was using it.[1] As a student at one of the most prestigious B-Schools in the knowledge market, not only was I supposed to know what it was, but was expected to be able to tune in and utilise it to make a difference. Life has strange ways of getting back at us, mere mortals, and here was my customised gut wrenching sucker punch. The fighter in me knew I had to do this, but I did not find a way I could.
A 3 month long research in the field led me to Data Driven Decision Making or D3M. Simply put, it is the pleasure of sipping a coffee in your office chair while you watch your computer work up some algorithms and provide you invaluable decision making tips to face some of your most frequently encountered problems.
Immediately after, I experimented with the different sorts of decisions that can be made with D3M. The results were a true eye-opener. If we think of decision making as a broad spectrum from operational decisions at one end to strategic decisions at the other. Operational decisions can be characterized as highly structured, routine, short-term oriented and increasingly embodied in sophisticated software applications. On the other hand strategic decisions are taken by the top management and serve to set the long-term directions, policies and procedures of an organization. They tend to be complex and unstructured because of the uncertainty and risks that generally accompany longer term decisions. In between these two extremes, we can have varied decisions including non-routine ones in response to new or unforeseen circumstances beyond the scope of operational processes, and tactical decisions dealing with the necessary adjustments required to implement longer term strategies. [2]
Half a decade ago, D3M could have only helped you with the more structured forms of decision making but no more. With the advent of Big Data, machines know much more about humans and human behaviour than humans themselves. Sample this, personal analytics can actually allowed me to analyse my Whatsapp chat history and find why most of the girls I chatted with refused me a date. The word cloud usage showed me that my texting skills were uninspiring to say the least with the most common words being “ok†and “yaâ€. I realised I needed to be more creative and engage better. What did not help was that most girls chatted in the window of 8-12pm whereas I had the habit of taking a short nap at the time. Also it helped me identify my most productive work hours and helped me plan out my work better.
Thus far, all seemed well with D3M but on further research most early moving corporates into the field were still undecided on its benefits. A closer look exposed a distinctly similar pattern in them. Most early moving managers thought that D3M will help them save money or time or both. Research shows that they are misguided to say the least. D3M does neither, atleast not in the short term. What D3M does allow though is discovering solutions you never knew existed. What it will do though is finding needles in the haystack consistently. Another interesting insight was that D3M depends a lot on the data collection. Great collection leads to great results. All we are required to do is ensure data cleanliness, variety and velocity.
In his book, Data Driven: Profiting from Your Most Important Business Asset, Prof. Thomas Redman summarizes the whole decision making via data as “Good decision makers follow at least three Bayesian principles. First, they bring as much of their prior experience as possible to bear in formulating their initial decision spaces and determining the sorts of data they will consider in making the decision. Second, for big, important decisions, they adopt decision criteria that minimize the maximum risk. Third, they constantly evaluate new data to determine how well a decision is working out, and they do not hesitate to modify the decision as needed.â€
After months of thorough investigation and experimentation, I have arrived at this conclusion, “To stay a step ahead in this ever competitive world, using Data Driven Decision making is a must. So let D3M take care of all the external data that you need to work with and let your mind focus on understanding “the data from insideâ€. That will surely lead you to not just success but contentment.
References
2. http://blogs.wsj.com/cio/2013/09/27/data-driven-decision-making-promises-and-limits/
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