by Francois Swanepoel, chief technology officer, Stone Three Venture Technology

Big Data is fast becoming the biggest buzz phrase in business. It is such a strategic imperative that recent research from Accenture showed that 87% of enterprises believe big data analytics will redefine the competitive landscape of their industries within three years. Does this mean the data scientist - the person responsible for analysing and extracting value from the data - should get a seat at the boardroom table?

Big Data is a broad term referring to any set of data - or information - that is too large or complex to analyse using traditional methods. Service providers around the world are building new products and services that focus on analysing, capturing, curating, storing, and transferring these massive sets of data. Think of the intelligence that goes into Amazon.com’s recommendation engine, or Google’s search results, both of which deal with vast amounts of data that is captured, sorted, analysed and represented in a way that delivers value to these organisations’ customers.

It’s such a radical shift in the way companies deliver products and services that a joint study by Accenture and General Electric found that 73% of companies are already investing more than 20% of their overall technology budget in Big Data analytics, and just over two in ten are investing more than 30%. The same study found that 76% of executives surveyed expect spending levels to reach even higher levels.

Francois Swanepoel, chief technology officer,
Stone Three Venture Technology

However, to unlock the true value of Big Data, you need to apply the specialist skills of a data scientist. The Data Scientist analyses the Big Data and extracts value from it that enables organisations to create and deliver better products or services. Accenture and General Electric make reference to the Industrial Internet, a collective term for the use of sensor, software, machine-to-machine learning and other technologies to gather and analyze data from physical objects or other large data streams.

A Data Scientist will then use those analyses to manage operations and in some cases to offer new, valued-added services. When we look at decision-making at the executive level, the applications of data science are virtually endless. Chief Marketing Officers (CMOs) can get rich customer data to improve campaigns quickly and accurately, leading to better customer relationship management and, ultimately, better customer service. Data Scientists can provide sentiment analysis using vast volumes of information, helping the CMO make fast, informed decisions in real time.

Chief Financial Officers (CFOs) can benefit from the data scientist’s ability to automate some of the financial intelligence activities and be freed up to keep a better eye on the organisation’s long-term financial well-being. And by building in predictive models that take into account a wide array of current and potential influences on the organisation’s finances, Data Scientists can empower the CFO to navigate the complex economic landscape much more effectively and with lower risk to the organisation.

Chief Procurement Officers stand to benefit hugely from data science - imagine being able to make quicker and more accurate decisions about supply chain and logistics issues, potentially saving the company huge amounts of money.

Most organisations that are looking into Big Data right now are focusing on the first step, namely collecting the data. But it is immensely difficult and complicated to understand what to do with that data in order to gain any value from it. The Chief Data Scientist (CDS) can take the lead in interrogating the data and extracting meaning and substance from it, empowering key decision-makers with invaluable insights that can drive the future success of the organisation.

The Chief Data Scientist may not have a seat at the boardroom table yet, but those that do sit at the top of the organisational chart should certainly listen to what he has to say.