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Manage organizational change

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Introduction

Your organization’s clients pursue big data and analytics with the expectation of a positive return on their investment. Only 63% of companies achieve that goal within the first year. 1 That’s because when companies embark on pursuing big data and analytics strategies, they embark on a journey that is inherently fraught with organizational and cultural challenges, and those obstacles can result in sub-par adoption rates and ultimately impede the realization of expected benefits.

By contrast, organizations that flex, iterate, experiment and evolve continually not only make the most breakthrough gains using advanced analytics – think Amazon, Starbucks, Google, and Nordstrom, each leaders in their respective industries – but they also rack up the strongest growth and customer fan- following. An MIT/CapGemini study finds that companies with stronger digital intensity drive nine percent more revenue than their peers and those with stronger transformation management intensity are 26 percent more profitable.

But ongoing data-driven innovation is extremely difficult to manage, much less inculcate as part of corporate DNA. Implementing and refreshing big data initiatives in a way that is repeatable, sustainable and scalable takes more than just technology and vision, it takes a shift in culture, organization, processes, data, and talent structures.

Chief Data Officers face this challenge a lot. Often the first to hold the role in their organizations, current CDOs have a mandate to bring about change in the way businesses manage, integrate and monetize data. Here are clear steps to start managing that change.

Step 1: Communicate the value of culture shifts

The organizations that have the greatest success imbuing a change-is-good mindset are those that rally their base around bold, breakthrough strategies, reward calibrated risk-taking, and embrace rapid test-and-release cycles. They focus less on “first time perfect” and more on managing continuous change, supported by robust data analytics. “They do this by constantly reinforcing, both externally and internally, the message that they are brave enough to rewrite the rules of traditional corporate culture, challenging and rewarding employees to think and act like a start-up but within the scope and resources of a large organization,” says Eric Denkhoff, Associate Partner in IBM’s Global Organizational Change Management Center.

Companies like Amazon, for instance, eschew the idea of modest improvements in favor of large-scale, market-making innovation. Amazon’s Auction, the Kindle, and the Fire are all examples of bold bets and a comfort with experimentation. As the company’s CEO, Jeff Bezos explains in this interview with Business Insider. “…A few big successes compensate for dozens and dozens of things that didn’t work. Bold bets — Amazon Web Services, Kindle, Amazon Prime, our third-party seller business — all of those things are examples of bold bets that did work, and they pay for a lot of experiments.”

Those strategies raise the bar and create a level of challenge and stimulus that is rewarding to employees – and because the stakes are high, teams must be inspired and motivated to give the effort all they’ve got. But Google doesn’t go bold for the sake of it. Their initiatives are intensely data-driven, fueled by rigorous analytics, and iterated through a near-constant, real-
time track-and-react process. As Harvard Business Review noted, “Google simultaneously tests and markets [products] to the user community. In fact, testing and marketing are virtually indistinguishable from one another. This creates a unique relationship with consumers, who become an essential part of the development team as new products take shape and grow.” This also means that some media-hyped innovation initiatives (think Google Glass) must be let go if the numbers don’t justify the investment. Data-enabled innovation gives management the ability to de-risk customer-based strategies and gives development teams the ability to make more informed decisions about where to place their bets.

Step 2: Enable a more malleable organizational structure

Companies in various industries are realizing that hierarchical and tiered reporting structures are often too rigid, slow, and impermeable to allow for fluid collaboration, point-and-click responsiveness, and accessible information exchange. Adaptive organizations are breaking down those artificial barriers and creating flatter structures, modular buildouts, and smaller project-based working teams and incubators (think Walmart Labs, which creates mobile apps and is reinventing both e-commerce and brick- and-mortar shopping, based on consumer data). Those elements create a more nimble operating model. They also recognize that the currency of change is communication. Rather than static reporting cycles, adaptive organizations put the emphasis on transparent information flows.

Executive dashboards offer management an at-a-glance look at core metrics and project teams share milestones, bottlenecks and status in daily standup meetings and through more formal whiteboard reporting.

Step 3: Digitize customer and corporate journeys end-to-end

Fractured, outmoded and silo-based processes are the ball-and-chain of change. But simplifying and streamlining the tens of thousands of processes large established organizations manage presents a mammoth challenge. According to McKinsey, the most flexible players get around that issue by zealously prioritizing the handful of customer and corporate journeys that matter most. For a retailer, a customer’s ability to browse in the store, order online and ship to their home might rank among the highest value journeys. Recognizing that, the brand would unpack and redesign that process end to end, automating routine steps to maximize speed, convenience, efficiency and engagement. Digitizing processes also results in trackable accountability as well as databases of information to analyze for patterns and insights and measure success. Todd Cullen, the Global CDO of Ogilvy, notes, “The ability to discern key patterns of information is enormously helpful in selecting audiences, segmenting customers and planning campaigns.” That makes it easier – and faster – for businesses to refine and iterate in response to market moves and customer insights.

Step 4: Drive decisions with data analytics

Operating at blink-speed leaves little time for second-guessing. Often, it seems as if businesses shouldn’t wait until strategic decisions have been totally de-risked – by which time the market has inevitably moved on – but they do need to be directionally correct to avoid squandering resources and wallowing through a dispiriting cycle of false starts. Organizations with robust data analytics capabilities will be in the strongest competitive position not just to interpolate data from a variety of internal and external sources (such as online activity, service consumption, and billing and payments) but to tease out critical patterns that can influence customer behavior in important ways. Ogilvy’s Cullen says, “We had a couple of cases, for example, where shipping container manifests – not really a ‘marketing’ data source – provided Ogilvy with a snippet of information that, combined with other data, allowed us to glean important product and market launch information.”

Operating at blink-speed leaves little time for second-guessing. Often, it seems as if businesses shouldn’t wait until strategic decisions have been totally de-risked – by which time the market has inevitably moved on – but they do need to be directionally correct to avoid squandering resources and wallowing through a dispiriting cycle of false starts. Organizations with robust data analytics capabilities will be in the strongest competitive position not just to interpolate data from a variety of internal and external sources (such as online activity, service consumption, and billing and payments) but to tease out critical patterns that can influence customer behavior in important ways. Ogilvy’s Cullen says, “We had a couple of cases, for example, where shipping container manifests – not really a ‘marketing’ data source – provided Ogilvy with a snippet of information that, combined with other data, allowed us to glean important product and market launch information.”

Establishing a steady cadence for reporting those insights gives management the catalytic, hard evidence needed to direct their teams and execute swiftly. “Periodically show results—it might be old news for you, but not for stakeholders from the main line of business,” advised Sankar Bala, Chief Data Officer at Flagstar Bank, at IBM’s Spring 2015 CDO Summit in San Francisco.

Step 5: Develop the right talent and incentive model

While executive vision and support is key when managing organizational change, the unsung heroes in making change stick are middle managers. They play a critical role in both defining the mind-set shifts needed and in modeling the new behaviors expected. In top performing companies, such as Procter and Gamble, the archetypal command-and-control manager is evolving into a coach, empowering team-members to take on greater decision-making (which aids retention and boosts morale) while giving them greater guidance, support and training (this case story on how the conglomerate transformed one of its manufacturing plants offers a good example of the more fluid management style they were adopting).

In subtle but powerful ways, they are tasked with helping people change their mind about things. Data-driven decision making is essential in making the model work – both to support managers as well as enable greater employee autonomy. By applying analytics to HR, for instance, Google has demonstrated that exceptional technologists can have a performance differential of up to three hundred times an average employee. The HR team’s mission statement is for “all people decisions at Google to be based on data and analytics.” For Lazlo Bock, Google’s SVP of People Relations and his head of People Analytics, Prasad Setty, talent decisions deserve the same data-based insights as do any other value-generating function. That creates a more consistent, measurable and objective means of gauging performance, rewarding progress, and charting personal, team, and corporate goals.


Sources

1 Interview with Eric Denkhoff, Associate Partner in IBM’s Global Organizational Change Management Center, on primary research by IBM.


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