USING BIG DATA AGAINST DISRUPTION

Lack of Data used to be a Problem

More than twenty years ago, when I was an MBA student writing a strategy project report, I recall going to the library and going through thousands of physical magazines to do basic research. Back then, data was hard to find and access. But over time, data availability and access increased exponentially; by the time I was in graduate school writing my Ph.D. thesis, data was abundantly available.

The problem today is the opposite

Today, businesses are sitting on immense amount of data. Now the problem is the opposite of what I faced as an MBA student; today there is too much data and we need to find ways to mine that data to make better decisions. This is where big data analytics comes handy – it is a powerful force for business managers. It is being increasingly used across many areas for high impact decision-making.

Empirical and Conceptual Insights

This article shows that by marrying the empirical insights from big data analytics with conceptual insights, managers can not only magnify the power of big data analytics but also deal with strategic issues in their businesses. It also uses the example of disruption as a strategic issue where big data analytics can be a powerful tool.

Big Data Analytics

Big data analytics provides trends across different types of data or correlations among data points hidden deep within a data repository; I term such insights as empirical insights. Empirical insights are often indicators of a micro behavior that may have major strategic impact but to understand that larger impact one needs another type of insight which I term conceptual insight.

Conceptual insights allow you to model the world in abstract concepts and see the relation between different parts of the world. For example, ‘lower the competition in an industry, easier it is to enter the industry’ or its corollary ‘intense competition makes new entry harder’ is a conceptual insight developed with observation across data points. In order to make a leap from empirical insight to a conceptual insight, one needs conceptual frameworks to perceive the world.

Use Case

To understand this better, let us use the example of blades and razor industry where Dollar Shave Club has made a sizeable dent. Big data repository with tweet data may show that some people are unhappy with the price of shaving blades and blog comment data may provide information on potential avenues for cheaper blades. However, these empirical insights don’t automatically enable you to make a leap that a Dollar Shave Club may be emerging and may be successful.

To make that leap you need to leverage a conceptual insight by looking at the strategic position of various players in the industry. Once you notice that low price – low performance segment in the market has been vacant, you can marry the two to see how Dollar Shave Club could be a disruptive force in the industry. In this way, conceptual insights have a multiplier effect on the empirical insights of big data analytics.

Big Data and Disruption

The example above points to a larger issue of disruptive forces managers face today where big data can make a powerful impact. It would be fair to say that we are living in the age of disruption; today no industry is immune from disruptive forces.

Not only are disruptive forces emerging across industries, managers have little time to react to them. Although it took 30 years for digital cameras to disrupt analog cameras, it took the iPhone less than 5 years to displace blackberry. Uber and AirBnB came out of nowhere to play havoc with taxi and hotel companies respectively.

And finally, as mentioned above, Dollar shave club emerged fairly rapidly to make a dent in the blades and razor industry. In short, managers face powerful disruptive shocks today but have virtually no time to react. This observation made me write “The dark side of innovation” which I hope will help managers not only think about disruption but also use it to deal with disruptive forces before they emerge in an industry.

The Dark Side of Innovation Framework

In my book “The Dark Side of Innovation” I introduced a conceptual framework to deal with potentially disruptive events in any industry. The conceptual framework is a three step process to manage disruptive forces in any industry. These steps are:
1. Predict potential disruptive events in an industry
2. Imagine the future states of the industry based on these possible events
3. Design solutions to prevent disruption, to transform disruptive change, and to create new value

This framework, based on extensive research and data from across many industries, provides a pathway to conceptual insights. Using the conceptual tools in the book, you can see your business in a new light and generate conceptual insights. When results from big data analytics is combined with this framework, it can provide powerful insights to help you deal with disruptive events in your industry.

Using Big Data Against Disruption

In fact you can appropriately marry big data insights with each step of the framework above. In the first step, you can leverage the empirical insights from your big data to feed into the models used for predicting disruption in an industry. Just as in the example of Dollar Shave club above, you can use multiple empirical insights along with the conceptual insights from the tools in the book to predict disruptive events in your industry.

Similarly, in the second step, where imaging the future states of your industry is concerned, you can use big data analytics to predict future scenarios in your industry to see if your organization is ready for those future scenarios. Finally, in the third step where you need to device solutions to be prepared for the future scenarios, big data analytics can be a powerful tool for you.

This is where you will see high impact of changing consumer behaviors, data driven insights for product development, superior customer segmentation and high impact marketing actions. In short, this framework example shows how you can leverage big data insights by using conceptual insights as a multiplier force to deal with major strategic decisions.

Takeaway

In conclusion, big data analytics is a powerful force that can aid you in many ways when dealing with strategic as well as tactical issues. However, to reap the real power of big data analytics you need to marry the empirical insights from big data analytics with the conceptual insights.
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(This article appeared in a compiled book titled The Big Analytics)