Strength in numbers: Data-driven decision making
Published on 09/05/2012 by Peter Dorrington, Director of Marketing Strategy (EMEA), SAS
What value does your organization place on business analytics to help understand and solve problems? MIT Professor Erik Brynjolfsson has spent years researching the value of business analytics to business and has quantitative data demonstrating why businesses that use data-driven decision making perform measurably better than those that don’t.
He shared those findings in a Premier Business Leadership Series Amsterdam keynote titled: Strength in Numbers: Data-Driven Decision Making. According to Erik, most revolutions in science begin with better methods of measurement. When we can see (and measure) new things, we are driven to seek answers and thus new ways of thinking and operating.
Now we have a flood of ‘big data’ that we can capture, store and measure, all of which enables new capabilities.
For example, online retailers can measure far more about their customers than their traditional bricks-and-mortar counterparts — giving them new options to innovate and transform their relationships with customers.
But this revolution of big data and big measurement is still in the early stages, notes Eric. Innovative organisations are not only adopting new technologies, they are creating new corporate cultures where there’s more reliance on data than experience or opinion. But it also gives organisations opportunities to predict the future with far greater accuracy than before. Citing an example of house purchases, Erik described how a model they generated to predict house purchases outperformed the experts in the field.
In Eric’s studies, organisations that relied on data-driven decision-making performed at rates 4-6% higher than their peers in a number of different categories. This was demonstrated to statistically- significant levels. But, those that are most likely to perform at the highest levels have data, expertise and leadership in abundance.
Erik thinks that ‘nano-data’ will be the successor to big data – i.e. the ability to make measurements and analysis on the basis of groups of one, but doing that within the deluge of big data.