Everything comes at a cost, especially during a time of continuous transformation. From technology adoption to business-model innovation, companies are challenged to make the “right” tradeoffs in their digital strategies. At the heart of such decision-making, they need to unleash their data’s real purpose.

From time to time, an executive will discuss how the success of their digital strategy can mean the difference between success and failure. They fear becoming an industry dinosaur left behind in a constantly changing marketplace, and dream of emerging as a unicorn that almost instantly reaches a billion-dollar market cap.

Yet companies rarely know how to run fast, adaptive, and agile operations that can take on a world of economic volatility, uncertainty, complexity, and ambiguity. Instead, they are finding that the questionable quality and complex accessibility of their data is restricting innovation, profitability, organizational optimization, and even opportunities for strategic growth and expansion.

In such cases, data is not the problem. Instead, a proactive data management strategy is needed to reveal the purpose of that data while optimizing investments in machine learning, analytics, compliance, and cybersecurity. Once this is done, data becomes a significant profit driver, not a profit barrier.

Separating Unicorns from Dinosaurs with the Right Data Management Strategy

The ultimate goal of a data management strategy is to guide the business ecosystem, as a whole, toward the productive use of data. It considers how people experience this intelligence across five dimensions:

1. Transform the organizational structure, develop the agile muscle
Reconsider traditional organizational charts that establish clear boundaries in leadership hierarchies, team structures, and data silos.

While it may be efficient in mass-producing products and messaging, this approach limits a company’s ability to deliver results during shifting expectations as one united, responsive front. This is often the case, whether the business is interacting with customers, partners, suppliers, or employees.

2. Foster a data culture
Promote a workplace culture that goes beyond viewing data as just another “cool toy” for innovation or experimentation. Establish a culture to measure at every touchpoint of value creation and throughout the journeys of employees, customers, and partners.

By establishing a data culture, companies can get the buy-in they need to get data in front of people and get them excited about the potential of growing intelligence. Doing so can also fuel how organizations pivot together to respond to change as they motivate the “right” decisions, creating better and more flexible ways of getting work done and understanding operational opportunities and risks.

3. Run lean and agile DevOps
Keep data, tools, and processes flexible enough to allow data teams to exploit the top- and bottom-line promises of intelligent technologies such as artificial intelligence (AI), machine learning, blockchain, and the Internet of Things (IoT).

Without data, the latest applications have little to offer. This reality requires DevOps to leverage integrated enterprise-wide data sources across the entire IT infrastructure. Developers can then create leading-edge capabilities such as algorithm-driven models and statistical process controls. All of these capabilities are elements of improved employee and customer experience and include process automation, personalized interfaces, and responsive workflows.

4. Improve the quality of business intelligence
Blend operational data (O-data) and experience data (X-data) to support well-rounded decision-making and user experiences that adequately address business needs.

Data can be powerful when visually displayed in charts and graphics. However, data’s real value is best realized through machine learning models and predictive analytics tools. To take advantage of such technological intelligence, companies must ensure the right data is captured and processed in real time and made accessible to decision-makers anytime, anywhere – all within an integrated technology architecture.

5. Build a data infrastructure fit for a VUCA world
Run data on an integrated platform that supports and accelerates innovations suitable for a volatile, uncertain, complex, and ambiguous (VUCA) world.

Traditional bottom-up IT-development tactics usually extrapolate a two- or three-year view of how existing technology investments will evolve based on their software providers’ road maps. This approach no longer works. Knowing how quickly technology and markets change, planners need to think five years ahead and top-down. This requires starting from a desired future business state work backwards to find an IT roadmap that actually drives strategy in the years to come.

Bringing Purpose to Data for a More Competitive Future

Behind every company’s digital strategy is a sense of digital Darwinism. The right moves can help turn even the smallest industry players into dominant forces that can shake up entire industries. Although one misstep or miscalculation can bring a wave of disruption so perilous that recovery may seem impossible.

A big budget and a deep bench of resources and talent certainly make digital transformation easier.  The real magic happens when today’s explosion of real-time data is managed with a strategy that defines how business intelligence is orchestrated, scaled, integrated, quality-managed, and safeguarded.

Next week in the Enterprise Architecture and Landscape Strategy series, explore the emerging adoption of hyperscale environments and the value they can bring to your business.

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Kai Wussow is chief enterprise business architect at SAP.