Why Big Data is Relevant

I am continuously surprised by people who tell me that Big Data has no relevance to their organization. To me it’s like the 90s where people said that the internet was not relevant to business. If you are one of the people battling with the concept, let me ask you to consider three key data sources: Social Media, Operational Technology and Third-party Data.

Social Media: If your company sells, services or markets to consumers, you have to leverage this goldmine, (or prepare to fall down a mineshaft!). Gone are the days when you control your brand. The growing influence of social media and the voice of the crowd will become the major driving force for your brand perception. If you doubt this claim, consider the profitability of hotels dramatically affected by their Expedia score!

Operational Technology: Sometimes referred to as the internet of things, this refers to the instrumentation of just about everything. If your organization makes, fixes or works with physical stuff, then consider how fine grain information about that stuff could change your operation, your cost base and your efficiency. As an example, the car insurance industry has always been based on average risk derived from basic demographic information. Today, sensors in cars are being used to assess an individual’s risk based on their driving habits. This fundamental change in the way risk is calculated results in lowers premiums and increased profits.

Third-party Data: This is data that is available to you if you go looking for it. Many governments, not-for-profit organizations and charities are opening up access to their data for external consumption. Combined with your data, this could provide a new perspective to your decision making. For example, demographic projections from census data could be invaluable when deciding upon infrastructure developments, new store location, telecommunications tower positions, etc…

I think the key ‘trick’ to getting started with Big Data is to change the interface between IT and the business. Take a large mobile phone carrier investigating the impact of a service outage as an example. A data scientist is asked to look into the relationship between customers leaving their service after this reported incident. Upon further investigation, the data starts to reveal patterns about customers that leads the data scientist to ask, “What is the real issue here?” Eventually, the data reveals that the company has a problem retaining customers and it has little to do with the service outage.  A social map is derived from the data and it shows that once a person cancels their service, a number of the people they talk to the most also leave. With this insight the company can now target at-risk customers with a special offer, which can dramatically reduce their churn and allows the business to focus on attracting new customers and increasing profits.

The challenge really is to understand the fundamentals of what your organization does and figure out how it creates value. Then, the magic is to find a way to enhance that value by adding a side-order of data.

About the Author: Clive Gold