Big Data has quickly become an established fact for Fortune 1000 firms — such is the conclusion of a Big Data executive survey that my firm has conducted for the past four years.
The survey gathers perspectives from a small but influential group of executives — chief information officers, chief data officers, and senior business and technology leaders of Fortune 1000 firms. Key industry segments are heavily represented — financial services, where data is plentiful and data investments are substantial, and life sciences, where data usage is rapidly emerging. Among the findings:
- 63% of firms now report having Big Data in production in 2015, up from just 5% in 2012
- 63% of firms reported that they expect to invest greater than $10 million in Big Data by 2017, up from 24% in 2012
- 54% of firms say they have appointed a Chief Data Officer, up from 12% in 2012
- 70% of firms report that Big Data is of critical importance to their firms, up from 21% in 2012
- At the top end of the investment scale, 27% of firms say they will invest greater than $50 million in Big Data by 2017, up from 5% of firms that invested this amount in 2015
Four years ago, organizations and executives were struggling to understand the opportunity and business impact of Big Data. While many executives loathed the term, others were apostles of the belief that data-driven analysis could transform business decision-making. Now, we have arrived at a new juncture: Big Data is emerging as a corporate standard, and the focus is rapidly shifting to the results it produces and the business capabilities it enables. When the internet was a new phenomenon, we’d say “I am going to surf the World Wide Web” – now, we just do it. We are entering that same phase of maturity with Big Data.
So, how can executives prepare to realize value from their Big Data investments?
Develop the right metrics. While a majority of Fortune 1000 firms report implementing Big Data capabilities, few firms have shown how they will derive business value over time from these often substantial investments. When I discuss this with executives, they often point out that the lack of highly developed metrics is both a function of the relative immaturity of Big Data implementations, as well as a function of where in the organization sponsorship for Big Data originated and where it currently reports. Organizations that have the executive responsible for data report to the Chief Financial Officer are more likely to have developed precise financial measurements early on.
Another issue with measuring the effectiveness of Big Data initiatives has been the difficulty of defining and isolating their costs. Big Data has been praised for the agility it brings to organizations, because of the iterative process by which they can load data, identify correlations and patterns, and then load more data that appears to be highly indicative. By following this approach, organizations can learn through trial and error. This poses a challenge to early measurement because most organizations have engaged in at least a few false starts while honing Big Data environments to suit their needs. Due to immature processes and inefficiencies, initial investments of time and effort have sometimes been larger than anticipated. These costs can be expected to level off as experience and efficiencies are brought to bear.
Identify opportunities for innovation. Innovation continues to be a source of promise for Big Data. The speed and agility it permits lend themselves to discovery environments such as life sciences R&D and target marketing activities within financial services. Success stories of Big-Data-enabled innovation remain relatively few at this stage. To date, most Big Data accomplishments have involved operational cost savings or allowing the analysis of larger and more diverse sets of data.
For example, financial firms have been able to enhance credit risk capabilities through the ability to process seven years of customer credit transactions in the same amount of time that it previously took to process a single year, resulting in much greater credit precision and lower risk of credit fraud. Yet, these remain largely back-office operations; they don’t change the customer experience or disrupt traditional ways of doing business. A few forward-thinking financial services firms have made a commitment to funding Big Data Labs and Centers of Excellence. Companies across industry segments would benefit from making similar investments. But funding won’t be enough; innovating with Big Data will require boldness and imagination as well.
Prepare for cultural and business change. Though some large firms have invested in optimizing existing infrastructure to match the speed and cost benefits offered by Big Data, new tools and approaches are displacing whole data ecosystems. A new generation of data professionals is now emerging. They have grown up using statistical techniques and languages like Hadoop and R, and as they enter the workplace in greater numbers, traditional approaches to data management and analytics will give way to these new techniques.
When I began advising Fortune 1000 firms on data and analytics strategies nearly two decades ago, I assumed that 95% of what was needed would be technical advice. The reality has been the opposite. The vast majority of the challenges companies struggle as they operationalize Big Data are related to people, not technology: issues like organizational alignment, business process and adoption, and change management. Companies must take the long view and recognize that businesses cannot successfully adopt Big Data without cultural change.
Source: https://hbr.org/2016/02/just-using-big-data-isnt-enough-anymore
