23 Feb 17

    Data: The oxygen of digital transformation

    unlokq

    oxygen-therapy-300x198Data is the oxygen of digital transformation that allows it to breath, as it allows you access into your customers world, and allows you to take action to change your own organizations behavior, AND potentially that of your customers, the CEO is the lifeblood pumping a consistent vision through the organization, and technology is the skeleton that holds it all together. But enough of the analogy we know that ‘digital transformation is about doing better business in a digital world’ but how do you know you are getting better if you can’t measure the improvement, the answer lies in your data.

    However, I still hear people asking; ‘what is the difference between data and big data?’ and ‘why is analytics different from reporting or business intelligence?’ Personally I don’t see a great deal of difference in the data. ALL data is important. I DO see difference in the questions, and this has an influence on the data required to answer them.

    It’s kinda like a chicken an egg problem. Do you believe the needle, in the form of a question, or the haystack in the form of the data came first?

    data_and_artim_in_haystacks

    Traditionally, you start by knowing the question that you want to answer and gather data that you believe that is going to answer that question. This is usually the case for older, or larger organizations, they tend to believe what they have always believed, or assuming what was right before is still right now, and that the questions that are being asked are still the right questions. This commonly flows from the fact that the questions are derived from models of the world in which the organization operate (and in some cases created), but the issue with that view is that ALL models without exception are flawed, in the fact that they are based on assumptions, these assumptions are in place to make the model simpler, not to make the answers better. Look at the economic models that were in place prior to the 2008 crash as an example.

    Younger organizations recognize that they only know a fraction of the total number of questions that need to be asked but collect ALL available data in the hope that it will become useful as knowledge increases through continuous learning through available data. i.e. they START with the data, and then work out what the questions are. In this scenario the existence of more data can reveal answers to questions that should have been asked but were not. This is like saying that the needle is easier to find when the haystack is larger, or more extreme, the needle did not exist until the haystack was there.question-mark

    The evolution of the above reverts back to question first, but the question is framed differently, for example, what might we need to do to change the data going forward since a good scientist will experiment in an attempt to disprove assertions rather than to prove them, once all avenues have been explored then the assertion must be fact.

    In order to do this, you must have the data available to you that is able to support this questioning of model against reality. Large and historic organizations are actually best placed to do achieve this with a wealth of data to discover new questions and alter behaviors but have organizational and political battles to fight since the data does not exist in a single location. They have to work out firstly where the data is, and oftentimes deduplicate the same data collected by different methods by different departments.

    It remains to be seen whether younger organizations fall into the same trap of assuming that their models of the world are persistent. But some notable failures including MySpace and FriendsReunited may have fallen into this trap. Being too firm in your belief in your models is what leaves you open to disruption, adapt and evolve and change your questions accordingly

    Start your evolution by;

    1. Questioning your answers
    2. Questioning your questions
    3. Questioning your models and their built-in assumptions.