Competing in a world with big data

I was excited to be at Harvard Business School in June 2016 participating in a course on Competing on Business Analytics and Big Data. The premise of the program is:

As digital technologies continue to transform the marketplace, companies across the globe are collecting an enormous amount of data. Unfortunately, many of these same businesses lack the talent and structure to turn big data into a competitive advantage.

The first question I have is how do businesses currently think of this? Is big data an opportunity or a curse? I’m on the side of the opportunity and a twitter poll agreed. However, it’s worth considering as there are countless examples of where reading data wrongly has driven the wrong decisions. As the volumes of data increase and as leaders we get further from the raw data, how do we trust the insight? As a blog post from Dataloft says:

Yet, when there are sometimes conflicting views from different sources reporting on the same subject, how do we know which one to believe? … It’s why big data is both a blessing and a curse. The capacity to manage and crunch enormous data sets creates incredible opportunities for new insights … but the daily bombardment of new and often contradictory statistics, can be utterly bewildering.

Getting answers to good questions is a way to think of getting value from business analytics and big data. As Julian Birkinshaw, Strategy Professor at London Business School, says in his article on Drowning in numbers:

Big data is everywhere, promising better and faster decision-making. But more information doesn’t necessarily yield better solutions. Computers are good at providing answers, but they are hopeless at knowing what questions to ask.

We’re also challenged by Ronald van Loon in an article to think about if some skepticism on big data and machine learning is …. glass half empty?

The reality is that technology is shifting faster than leadership can understand what threats it poses. Large bureaucratic companies that simply adopt the big data paradigm will struggle to keep up with explosion of unstructured data and data streams that has caused the role and value of information to drastically change.

We, of course, need to think positively about the opportunity and there are a building list of great examples of where real value is being generated. For example, Evan Sinar writes that big data is now an integral part of talent recruiting. Perhaps the way to think of these opportunities is as McKinsey’s Nimal Manuel and Shilpa Aggarwal wrote for the World Economic Forum:

Regardless of how it’s practised in many companies, big data analytics is not trawl fishing: it’s spear fishing.

So here’s the next question I have. How ready is your organisation to take this opportunity? I’d like to bet most organisations are just getting started.for example, only 20% of participants in a study by the American Society for Quality (ASQ) believe their organisation is using BigData to gain competitive advantage.

I’m hoping this Harvard course and discussions with participants will help me turn business analytics and big data to opportunity by considering these topics:

  • Competing on business analytics to build customer trust and generate more data
  • Recognising and exploiting the difference between correlational and causal patterns
  • Exploring the data explosion in the “four Vs” — variety, velocity, voracity, and volume
  • Applying experimentation and data analysis to build breakthrough business models
  • Improving company’s analytics capabilities to create and capture more value
  • Managing company with real-time analytics to improve overall efficiency
  • Comparing big-data analytics with a traditional small-data approach
  • Understanding the strategic implications of supply chain and marketing analytics
  • Attracting and developing data analytics teams to build and hold a competitive edge
  • Using data analytics to create smart, connected products that reshape your company and industry

Good luck with your journey to leveraging business analytics and big data.

Originally published at

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