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In the age of information, Data is expected to be the magical potion which can solve all your issues. In fact, Data is not a potion but a tool. A tool can only be useful if it is used correctly. Despite how enthusiastic your organization may be to bring in a culture of making “Data based Decisions”, if you make one or more of the misses I list below, it will be an awful effort.
Not capturing the Sense of Business
Data analysts or Data scientists are not often Domain experts. They know best to analyse data, make patterns out of it or predict possible outcomes based on those patterns. However, enlightening them on “what to analyse and what are the outcomes expected” rely on Domain experts. Best decisions are made with the combination of years of tacit knowledge accumulated in doing a business and expert analysis of underlying Data. Thus, prior building your framework of “Data based Decision making”, it is necessary to consult the actual users of those outcomes, and understanding the approaches they used to solve the same problem until now. Generally that is the very first paving stone for your Data model.
Garbage in – Garbage out
This refers to the effect that it makes if you feed wrong data into your data model. You will inevitably get wrong information. It is necessary to verify how clean your primary data sources are and how accurately do they capture the data. Even if you have the best data models, and frameworks to analyse your data, if the inputs that you feed into those are wrong, the outputs it will generate will also be wrong and will not bring any benefit to you. Hence the second step of building your data framework is validating your data for accuracy.
Think about Data like a Gem. When you discover it, it looks worthless covered with mud and dust, unpolished. The task of a clever data scientist is to clean and polish (analyse) it to the extent where it displays its true form of beauty. The incompetent Data scientist will over-craft (over analyse) the Gem (data) until it loses its true shape and it loses some of its precious parts.
Likewise, over analysis of Data will result in giving you rather biased (to the opinion of that of the one who analyses Data) and altered information. They say “if you torture Data enough, it will confess to anything that you want”.
Change Management and User Readiness
Making decisions based on insights can be a new experience to your business users. Most of the medium scale organizations, even today thrive on making decisions based on the tacit knowledge, business sense and cognitive capture of patterns. Even after making a framework for insight driven decision making, still there can be a tendency to practice older way of making decisions among the users. This can be due to several reasons, such as being non familiar with using technology associated, being skeptical about the information, or simply trusting own gut/ opinion better.
Even if you have the world’s best insight generator, if your people (and you) are not using them, it is a waste.
At Zkewed we always consult our users first and go with a problem solving approach. We help them to find solutions to the problems they had, and even what they didn’t know they had, using their Data. We always look at the data objectively to make sure there is no over analysis. And our graphical dashboard like interface is user
which helps faster user adaption.