Data driven leaders have long realised the importance and significance of leveraging on the power of data analytics in both economic and non-economic decision-making. Leadership in today’s world means not only having key data available but also at the right time, right place and right format ready to be used to take swift action. Turing insights into fruitful engagement is the bottom line of engaging in some sorts of data transformation.

The combined power of Artificial Intelligence and Data Science is a force that propels successful businesses to greater heights. By mining data and passing them through the furnace of business analytic tools, uncommon insights are obtained about questions that hitherto have kept C-Suites executives awake at night.

What you don’t know about your business today will hurt your business tomorrow

Being competitive requires that a company understand their business inside and out.  Your company will be massively hurt in no distant future if you have no idea of the correlations between your processes, operations and customer expectations for example.

This kind of understanding can only be yours when you have a robust data management strategy in place and the right business technology to support it. It goes beyond having Silo sub systems that have no way of linking to each other in a reasonable way.

We don’t live in a society that lacks information. Rather however, we live in a time where our success largely depend on our ability to combine our different siloed data in such a way that meaningful economic sense can be made out of it.

What don’t you know about your business today?

The above line is one pertinent question that all CEOs should continually ask their subordinates at the beginning of every board meetings. You will be surprised at what you will find out simply by asking your team members to list out what they think they do not currently know about your organization.

The response I get each time I ask the question ‘what don’t you know about your business today?’ is that I see Executives scratching their head thinking of what answer to give. Those that manage to come up with answers simply say ‘there is a lot I don’t know about our business’.

The above question can also be asked in strategic brain storming sessions. Companies that adopt this kind of questioning technique when done correctly hardly fall victim of existing unknown problems. It’s like a medical X-Ray constantly being performed on your business in a bid to maintain healthy finance for your entity.

Benefits of analysing data

• Provides insights: one of the most pronounced usefulness of analysing data is that it provides an unbelievable insight that one can only imagine without it. Many managerial blunders that have been made in the past would not have been made if enough insight was gathered through analyzing data that already exists.
• Gives competitive advantage: competitive advantage is a major rock that guarantees a company survival. I cannot think of a better way of obtaining useful strategic information that can be used to achieve this other than being scientific with the use of data.
• Can be used as evidence and proof should anything go wrong: I have heard of cases where businesses have failed even when all measures were taken to ensure success. Yes, management is not a game that seeks to exonerate managers from mistake but, having scientific proof of due diligence will go a long way in clearing the air.
• Reveals trends before they get out of hand: data mining and data analytics are powerful tools that reveals any kind of trend before they become obvious. There is a saying that trend is your friend. No, this is not only true if you are playing the FOREX market, it cuts across all facets of life.

In this piece on the implications of neglecting data mining and data analytics, I have attempted to touch some salient issues surrounding the current data-centric super highway world that we now live in. a business of any sort will be doing herself great disservice if they don’t harness the potentials that are submerged in raw data.