Gaining value from big data is all the rage right now – and for more than one reason. Investments in the right tools and applications can spell big payoffs. According to McKinsey, data-driven organizations are 23 times more likely to win new customers, and six times more likely to retain the ones they already have.
But there’s a disconnect. It’s clear that companies are buying into data solutions, but often times they aren’t doing anything with that investment. Ovum states, for example, that more than 70 percent of telecommunications companies have invested in big data analytics, yet less than 20 percent have actually deployed them. Similarly, Gartner predicts a massive failure (85 percent) across Fortune 500 organizations to effectively make use of big data to gain competitive advantage from this.
At every phase, and within every department, data projects face roadblocks: from strategy and architecture, to implementation, to adoption, and all the way through to analytics. Without the proper buy-in and support, these roadblocks will halt a project. The solution to navigating obstacles boils down to changing the way an organization sees itself. What we need is a cultural shift. In order to make any initiative successful, it takes a team consensus and a strong leadership. And big data is no exception.
Creating a Data-Driven Culture
Step 1: Getting C-suite buy-in. Culture is largely determined by an organization’s leadership, and to make the most of big data investments, it is vital to build a data-driven culture that is supported by the C-suite. Organizations are hiring data scientists, but with these hires they are addressing just one part of the picture. We have seen time and again that neither people nor departments can reach their full potential working in a silo. Rather, they benefit from a multidisciplinary team.
Creating a data-driven culture is about seeing the full picture and building an organization that has teams with these skills that work together. The C-suite is well-positioned to lead this charge, provide a united goal for the business that every function of the organization can rally behind, and to instill trust. Sharing data and analytics across business units with a shared understanding of their proper use (as opposed to hoarding) can lead to holistic improvements across the whole business.
Step 2: Brainstorm the art of the possible. It’s OK to start big at this stage, questioning what different initiatives would mean for the business and creating a big picture or vision for the project. During this process, a strong leader is critical to help bridge divides across departments and demonstrate the business potential across functions for the organization as a whole.
The C-level leader in this phase has the power to spark imagination. “Imagine what we could do with this consumer behavior data in marketing,” she says. You can replace “marketing” with “service,” “product development” (and device data), and the list goes on. It’s important to open the floor to all discussions and encourage creative thinking about the reach of the data. Often that means forgetting the “rules” of all the limitations on analysis that have been learned in past and thinking outside the box.
Step 3: Determine the capabilities needed to support the vision. Cross-functional teams with an enterprise-wide perspective are critical. Each case is unique but, generally speaking, a project likely will need support from a business sponsor, architect, data scientist, operations leader, and business analyst – all brought together with a shared goal to provide value.
C-suite leaders need to evaluate how best to utilize each team member’s strengths to meet the determined goals, and hand over that ownership to the team with the mission to break silos, solve change-management problems, and improve skills in an orderly manner.
Assembling the team can be tricky in and of itself. According to the IDG Enterprise 2015 Big Data & Analytics Survey, 48 percent of respondents cite a skills gap as their top challenge, edging out the previous top challenge, which was budgetary constraints. To attract a well-rounded team, organizations need to create a welcoming environment for new talent. Just as collaborating across the enterprise is critical, so too is collaborating across generations. Often the skills gaps are deep – they are about to collaborate using new big data techniques, and the tradecraft of best practices that isn’t captured in any formal theory.
Step 4: Test and learn with agile iterations. The testing phase is important for any number of reasons, but arguably the most important reason is for the learning process. Contrary to what we might expect, when it comes to understanding how well data initiatives are performing, KPIs, not ROI, will be a better gauge of success in the early stages. C-level leaders should focus on the learning process as the project takes form, and take advantage of rapid feedback loops to engage the business.
You can’t argue the facts and, in the case of data, that’s a very convenient thing. The business has to take ownership of metrics and sign up for the longer-term view. At the C-level, leaders can reach a broad audience to demonstrate, from the data, adoption and results, learning across the organization, and whether the project is on track to be deployed across the business and, ultimately, to affect the ROI in a big way.
Step 5: Create a 2-3 year roadmap. Having a longer timeline can seem frustrating for some, especially when wanting to understand ROI and see results more quickly, but this longer-term approach leads to big payoffs of the investment. It’s the job of the C-suite to help keep this view in focus and motivate cross-enterprise teams to forge ahead when the benefits of their work haven’t yet been realized.
ROI might not be apparent immediately, but over several years, the payoff will be realized. In the meantime, leaders should establish milestones and measurable goals that keep that end result in mind, and guide the team to keep them on track.
Ron Bodkin is the founder and CEO of Think Big Analytics, a Teradata company. Ron founded Think Big to help companies realize measurable value from big data. Think Big is the leading provider of independent consulting and integration services specifically focused on big data solutions. Our expertise spans all facets of data science and data engineering and helps our customers to drive maximum value from their big data initiatives.
Ron will present a session titled “How to Build a High Impact Data-Driven Culture” Oct. 21 at the Teradata 2015 PARTNERS Conference and Expo in Anaheim, CA. For more information about the conference and to register, click here.
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