Our research indicates that while companies have been furiously collecting and storing data, both internal and external, in a laudable attempt to become increasingly data-driven, they have allowed their data models to age. While the collection, storage, organization, and preparation of vast volumes of new data has been a labor-intensive job, that should not distract from the fruitful work of refining and revisiting the models that companies use to gain insight from all that data. And new tools, automation and artificial intelligence can increasingly do much of the heavy lifting of such tasks.
Models must be continually reexamined and refreshed to optimize the data’s value. This requires a holistic view of the organization’s data and analytics landscape, beginning with an understanding of the data available to the enterprise and how it is being used at every level. And as is often the case with technological challenges, achieving this enterprise-wide view begins with an organization’s people, not with its machines or even its investments. “Many companies have invested heavily in technology as a first step toward becoming data-oriented, but this alone clearly isn’t enough,” writes Thomas H. Davenport, co-founder of the International Institute for Analytics. Companies “must become much more serious and creative about addressing the human side of data if they truly expect to derive meaningful business benefits.”8
Indeed, ADPMN has found that companies that are becoming leaders in their sectors by virtue of their digital proficiency have broad board-level and C-suite support for their data-driven strategies. That’s because unless digital transformation and innovation is a priority for the senior management team, the necessity of funding the data gathering and insight analytics to drive innovation will fall outside the core budget of most organizations, and they will tend to be limited to scattered pilot projects and one-off proofs-of-concept that will rarely roll-up to enterprise-level returns on investment. However, with senior-level support, companies can create specific budget lines for investments in data analytics, including not just the machines but in both manager- and employee-level training in data literacy. (In some companies, especially larger ones in which business units may have their own vested data interests, it is advisable to appoint a Chief Data or Chief Analytics Officer both to discourage data hoarding and to establish data accountability at the C-level.)
A recent survey ADPMN conducted of 62 Dutch organizations indicates that their success in leveraging data and analytics is proportional to the extent to which they have instilled a culture around data and analytics. The high-performance digital organizations (HPDOs) use data and analytics to focus on such behaviors as driving mass customization, creating exponential value, taking full advantage of digital ecosystems, and embracing risk. Consider the case of one typical HPDO: the company spends 10% of revenue on digital initiatives, outsources very little of its analytics (less than 10%), automates between 50% and 70% of its analytics, operates analytics on a cloud platform, has an enterprise-wide data literacy program, and follows agile methods for all organizational processes.9
In fact, a sure way to measure an organization’s commitment to becoming a digital business is to assess its strategic investments in data, analytics, AI and automation — and track their growth over time.
HPDOs do not do all this work for its own sake; they focus on data monetization. This monetization primarily comes from improved processes and the democratization of data across the organization for informed decision making. Monetization may also come directly by adding value to products and services by wrapping them in information or selling information offerings in existing or new markets.