Big Data in the running of a company

Achieving competency in Big Data is a three-part process that requires setting the ambition, building up the analytics capability and organizing your company to make the most of the opportunity. Leaders build up their analytics capabilities by investing in four things: data-savvy people, quality data, state-of- the-art tools, and processes and incentives that support analytical decision-making. About a third of companies don’t do any of these well, and many of the rest excel in only one or two areas. But to build a high- performing analytics machine, you need to do all four well. Success in each capability depends on strength in the others.

Companies need a strategic plan for collecting and organizing data, one that aligns with the business strategy of how they will use that data to create value. In our analytics survey, 56% of the companies didn’t have the right systems to capture the data they needed or weren’t collecting useful data, and 66% lacked the right technology to store and access data. A good data policy identifies relevant data sources and builds a data view on the business in order to—and this is the critical part—differentiate your company’s analytics capabilities and perspective from competitors. A critical aspect of good data policy is to focus on identifying relevant sources of data. For example, capturing all queries made on the company website or from customer support calls, emails or chat lines, regardless of their outcome, may have significant value in identifying emerging trends; however, keeping detailed logs of requests that were easily handled might be less valuable. 

Aim high in your aspirations of what’s possible. Advanced analytics and Big Data tools are developing so rapidly that they’re likely to help you get to potential insights and statistical novelties in ways that were not possible even as recently as a year ago. Tools and platforms like Hadoop, HPCC and NoSQL are rapidly emerging and evolving to address analytics opportunities, as is the rich eco- system of mature analytics, visualization and data management. Today, these tools are available from a wide range of vendors and an even larger community of open-source developers.

In our survey, 56% of executives said their companies lacked the capabilities to develop deep, data-driven insights. Most agreed they were not up to the challenges of identifying and prioritizing what types of insights would be most relevant to the business. Successful analytics teams build those capabilities by blending data, technical and business talent. Think of a band as the model: a team with different but overlapping skills that knows how to effectively and efficiently communicate and collaborate.