A bigdata initiative must start with a bigdata strategy. We comment on the recommended approach.
Today, a large number of companies from all sectors and sizes (although especially the largest ones) are launching bigdata initiatives, partly due to the pressure of competition and new business challenges and, partly, why to hide it, for a certain ‘fashion’ or pressure of the environment (if my competition is getting into this, I too, I will not be less …).
The reality, surprising as it may seem, is that as recent IDC studies show, most companies are starting or planning the start of bigdata initiatives in the short term but do not know where to start.
In this context, in many cases the results are tragic because they make big mistakes that costs a lot of money. One of the most common is to start with the technological component: “We set up a DataLake”. The result, as I say, is tragic, partly due to the fact that the commercial wizards of the sector have created a series of myths and legends that have been internalized.
Myth 1: New technologies based on Hadoop are cheap and are deployed in a jiffy.
This is substantially false (or only partially correct and an oversimplification) that leads to throwing us in an enthusiastic technical race without an armed strategy and, of course, without a business case and moderately robust economic model. Basically we use the false mental scheme, curiously usual in many top executives of bulky payrolls that if it is much cheaper than my usual technologies (typically a DWH in this case) we should save money, no matter how we do it. And in this context, Murphy’s Law always comes out triumphant and the result in most cases is a lot of budget consumed with nothing to take to the mouth of ‘real’ result.
Initial Recommendation: Strategy and economic model first. Processes and organization later. Technology, at the end. Experiment, prioritize and monitor (adapt and adjust your model).
Actually they are concepts that I have been using for many years in transformation consultancy of any technological area, but it is more applicable than ever to this field.
A great weakness, particularly of the Latin countries, is our proverbial animosity for the strategy. We are action. Planning is about cowards. The adaptability and improvisation of the Latin character is a great asset in my opinion but always adequately integrated into a robust strategic planning.
In this sense, a first mistake is to confuse ‘data-driven’ business (driven by data) with bigdata technology. One thing does not necessarily imply the other. We must identify our data-driven business scenarios and how we are going to execute them. We will only adopt new bigdata technologies if we have a clear justification for it; and we will model it (I do not mean technically, but with a business case, I will talk about it in more detail in later posts of this blog).
Our approach to the bigdata strategy is a bidirectional model (top-down and bottom-up). The business top-down model will be in charge of identifying those ‘data-driven’ business scenarios, modeling it economically (business value) and data requirements (what data and potential analytical models I need to implement it). The bottom-up model is to model & cataloging what data sources (potential or real) we have in our business processes or we need to solve our business use cases. The intersection of both will give us the feasibility analysis and a first cost modeling. At this point we can make decisions and translate it into a strategic plan; this is, substantially, which scenarios we tackle first (the cheapest ones among those with the most impact); how are we going to monitor progress (what are my KPIs?) and how we are going to feed our model with reality as we execute it, allow us to adjust our expectations. And have fun!