An initial focus on specialized, short-term outcomes informs a collaborative, long-term approach.
A recent CapGemini survey indicates that two-thirds of all executives describe their organization as data-driven. But when the survey probed a bit deeper, executives acknowledged challenges with “Big Data” pertaining to the management, aggregation, and real-time application of multiple forms of data.
While industries such as financial services, energy, healthcare, and pharma indicated priority in managing data, other industries heavily dependent on marketing and promotions such as retail and entertainment fared far worse.
More than half of the retail and entertainment executives surveyed indicated data “isn’t much of a priority.”
Big Data Adoption Barriers
Why is this? Some executives acknowledged narrowly-timed promotion windows and overall emphasis on shopper conversion as barriers to adoption of Big Data. In most cases, marketers in retail and entertainment rely on getting the greatest amount of reach in the shortest amount of time.
But the challenges in companies applying big data are shared across a wide variety of industries. The study showed:
- 70 percent of all executives are concerned with making sense of all the data;
- 73 percent are concerned with integrating the data in a cross-channel fashion;
- 91 percent are concerned with ROI.
Fortunately a road map exists for addressing the challenges of Big Data and managing concerns around ROI and cross-channel insights.
Road Map to Big Data Success
In 2011, an IBM research report “Analytics: The Widening Divide” outlined three types of companies gaining a competitive advantage through analytics and Big Data.
- Transformed companies that have strong and sophisticated analytics capabilities and use data to guide both day-to-day operational decisions and inform strategic priorities;
- Experienced companies comfortable with analytics and use data to inform a number of important decisions, but lacking a sophisticated platform driving insight;
- Aspirational companies with basic analytical capabilities, perhaps in finance or another data-heavy area such as operations, but rarely use data to answer but the most basic questions.
When analyzing how companies made the change from Aspirational to Transformed, the research report uncovered two paths: the specialized path and the collaborative path.
Choosing the Right Path
Most organizations seem to prefer the collaborative path (though the MIT study does not quantify the difference between the two options) due to its ability to deliver cross-functional insights to be used in strategic planning. But many times these projects fail because the needs across departments vary a great deal, the data being integrated is unique to a specific discipline, no one can agree on a standard platform and finding a place to start can be difficult.
As a result, more organizations need to consider the specialized path and abandon the theoretical higher order benefits of a collaborative approach and simply get in the habit of working with data in very specialized ways.
A Matter of Timing
Companies will benefit more from building a quality approach to data analysis, even if it occurs on a small-to-medium scale within the organization. This process can then be repeated across the company and these new stakeholders can sit down and build a collaborative platform to identify strategic insights.
Companies hinder their ability to build collaborative platforms because the collaborators have little experience managing data projects and have nothing to bring to the table.
Being honest about the organization’s data maturity and recognizing the need to be more performance oriented within specific departments can jump start data initiatives. Focusing on short-term outcomes will help overcome short-term performance windows and provide better understanding to apply to a future-state collaborative platform.
:: By Matt Booher, director, digital analytics
