If using data is not part of your job today, make no mistake – it soon will be. Data is rapidly becoming the common currency within leading organizations. Data is the new digital super hero busting down silos in large companies, dissolving the “not invented here” syndrome and dissembling fiefdoms that hark back to the Industrial Age. In a very real way, data proliferation is forcing us to acknowledge a kind of digital Darwinism that has already begun transforming organizations – ready or not.
Now, data sets can be so large and detailed that they offer value to multiple departments and stakeholders, not just a few. Data is moving to become the central driver of Customer Value Creation, making it the biggest propellant for innovation companies large and small have ever seen. Tapping predictive analytics and other tools, data can peak at the future in ways that are impossible for human beings.
In a recent survey by a leading publication, here are the top-5 areas targeted by organizations seeking to upgrade their use of data in driving innovation:
- Identifying new sources of revenue
- Retaining and acquiring new customers
- Developing new products and services
- Winning and keeping customers
- Enhancing the customer experience.
While the prospect of using data to scale innovation in any one of these areas is intriguing, converting the “data dream” into innovation reality is a tougher endeavor. In order to adapt and avoid the fatal pull of digital Darwinism, organizations must become more nimble in their data strategies and data usage.
Four Challenges Of Digital Darwinism
The challenge for many organizations is not the realization that the data revolution is here. Rather, it’s the inability to keep pace with the rapid emergence of new data frontiers. Gil Sadeh, CEO of Signals Group – a predictive analytics and data intelligence provider – notes that “Technology is being produced whether or not we have applications for it, and whether or not we know how to adapt.”
Organizations are experiencing major upheaval around what radically expanded data availability means for their business models. Leaders are uncertain how to synthesize and grasp data analytics tools. They’re fumbling for relevant ways to drive Customer Value Creation while wrapped in a mindset of Industrial Age go-to-market strategies. Even as customers vault ahead with new shopping and decision-making patterns, organizations are struggling to adapt to these new behaviors and rethink the playing field of their own industries.
Here are four key challenges companies face in this new era of digital Darwinism, along with recommendations on how to newly sculpt an innovation-forward approach to addressing these challenges.
1. Inability to agree on who’s in charge of the data. Last year, IBM created a new moniker for the individual who – theoretically – is charged with leading this brave new data-driven world inside an organization: the Chief Data Officer (CDO). Setting aside for the moment whether IBM’s flagging fortunes allow it to anoint any new titles at all, the envisioned role of the CDO is “to realize the value of data across the enterprise.” The CDO is charged with applying organizational data resources in support of the enterprise business strategy. In other words, the CDO is the individual who champions the importance of data as a competitive tool, then links it to a distinctive vision for growth. But CDO’s often face walled kingdoms built by the Chief Innovation Officer (CIO) or Chief Analytics Officer (CAO), locking out the very data strategies the CDO is charged to implement. To overcome this barrier, the CDO must champion data as a common currency for innovation. He or she must evangelize the value of data to every division and brand, linking arms with every other C-level executive in the firm. Driving Customer Value Creation is the end goal. Each C-level officer must serve as a co-evangelist for data and its myriad applications even if this position is the central mission of the CDO.
2. Low perceived ROI on data investments. Many management teams claim they’re not seeing enough immediate financial return from their accelerated data crunching efforts to justify additional investments. As noted in a recent McKinsey Global Institute study, most managers lack an understanding of or confidence in data analytics, and hesitate to employ it. Existing organizational processes are often unable to accommodate rapid advancements in analytics due to resource burdens or long internal sell cycles. Perhaps even more fundamental is the fact that, per Sagence – a Chicago-based data analytics firm – a small team of data scientists can spend up to 80% of its time simply preparing (aka cleaning) data for proper analysis. That doesn’t even include interpreting the data. So an organization must often engage five data scientists to provide the equivalent of one full-time executive who can process and also understand the final output. This is a worrisome figure given the scarcity of data scientists, and the inability to sustain a ratio like this at scale. To overcome this barrier, organizations must aggressively train broader swaths of executives in how data serves as an innovation driver. Data analysis should become a baseline skill for all new hires so the organization can begin ‘baking in’ new analytics capability across multiple functions. Leading companies like American Express, Procter & Gamble, and Walmart have made major investments in democratizing the use of analytics through dashboards and data visualization tools. They’ve started analytics meet-ups, and champion data-related activities in their leadership communications, and newsletters. Data is becoming the new language – the common currency – that spans their brands and departments.
3. Unwillingness to use data as a tool in decision-making, or innovation. Often, even though extensive data resources do exist within an enterprise, none get used by leadership teams because senior executives prefer to “go with their gut.” Many senior leaders are not using any data now as a driver of their decisions – even big ticket decisions. According to a recent study by the Economic Intelligence Unit and PriceWaterhouseCoopers (PWC), 79% of executives make a “big” decision every quarter, with the value of these decisions exceeding $1 billion. However, when it comes to connecting these decisions to data or analytics, just 32% of the executives in the PWC study characterize their decision-making process as highly data-driven. There is thus a huge mismatch between existing gut-based business practices and the use of data-driven decision making. To overcome this barrier, companies must realize that there is not just a downside to profits when data is not incorporated into key decisions. There is a productivity downside as well. Companies that fall behind in their data capability will be less and less able to embrace the linkage between data analytics and emerging technology/data platforms like the Internet of Things. Key innovation opportunities will be lost in that gap. As one leader who supports the use of data-driven decision making noted, “Data is one of the cornerstones of digital transformation.” Companies cannot realize the productivity gains data brings without taking more focused steps to nurture data applications for decision-making and innovation.
4. Fragmented focus on which areas within the firm can most benefit from data-driven applications. In a major study among IT managers conducted by Accenture, more than 60% of respondents said their companies have successfully completed at least one big data implementation effort, but 36% haven’t even begun a single project yet. According to the study, “frontline managers and business users fall back on their historic rules of thumb” when they don’t trust data analytics tools, or the recommendations yielded by them. This is particularly true if the analytics-based tools are not easy to use or are not embedded into established workflows and processes. To overcome this barrier, select a single area within the organization to focus on for increased innovation results and data-driven applications. Good starting points include customer retention, or understanding customer purchase behaviors. Once a single area has been identified and launched, research conducted by Mu Sigma – a leading data analytics firm – shows that rapid use of the data dramatically increases the likelihood that its findings will “stick.”
Radical Shifts Ahead In The Corporate Landscape
As the digital Darwinism moniker suggests, data giveth, and data also taketh away. Use of predictive analytics as a core data tool threatens to dissolve entire departments – Research & Development (R&D) prime among them. As Gil Sadeh of Signals Group noted at a recent innovation conference, “The reason traditional R&D models are becoming obsolete is largely because companies are unable to keep pace with predictive analytics solutions.” Data and machines will be able to predict trends with greater speed than humans can, dramatically accelerating product development, and bringing together complex factors that can impact future business activity.
Thomas Edison – whose 168th birthday is marked today – stands not only as the father of Research & Development, but as America’s first data scientist. Long before computers took center stage, he realized the power of working with data to yield new markets and new products. Through constant experimentation and observation, Edison tapped his own laboratory data to yield quantum leaps in technology. In the digital age, we must each become “data capable” innovators. Now, the power of an entire R&D laboratory lies in the hands of an individual employee. Rather than shying away from how you can use data to innovate, embrace it, and see where your newfound data capability can lead.
This article was written by Sarah Miller Caldicott from Forbes and was legally licensed through the NewsCred publisher network.