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Shaping a successful data strategy for 2020 and beyond

The technology landscape is constantly in flux. As technology evolves, so does the customer—and their expectations are a moving target. Behind the scenes, data is quietly being collected than ever before across the enterprise, waiting to be translated into meaningful insights.

Data, properly managed and accurately assessed, can be an incredibly powerful asset, but only when organizations understand how to navigate and untangle its complexities. It can illuminate business inefficiencies and opportunities for cost savings and improved customer experiences. But before data can drive positive, transformational change, organizations must first understand what to do with it.

A well-informed, well-defined data strategy is the future-state blueprint—it’s essential for businesses as they prepare for 2020 and years to come.

Most companies know they need data, but they don’t know they need a data strategy. To be successful, a company’s data strategy needs to align with the broader goals of the business. Not only should the strategy be actionable but also comprehensive, considering the entire enterprise and not just one department.

For all companies, harnessing the power of data is a business imperative because of the opportunity it holds: competitive differentiation. When armed with the knowledge and capabilities to digest data and extrapolate meaningful insights, organizations are poised to make informed and strategic decisions about all areas of the business. In turn, these data-driven decisions can help save time and money, create new revenue streams, and exceed customer expectations.

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With the right data strategy in place, organizations set themselves up for success by creating efficiencies that drive increased time and cost savings, identifying new or adjacent market opportunities that generate new revenue streams, and strengthening relationships and loyalty with improved customer experiences.

So how does a data strategy take shape? To begin, organizations should take into consideration several key steps in the early stages of the process.

1. Identify top data priorities for the whole enterprise and clarify intentions for practical data usage

2. Establish a hypothesis, desired business outcomes and use cases (e.g., chatbot, natural language processing, machine learning)

3. Define the type of data needed, identify data sources and plan the approach to data collection, ingestion, management, storage, analysis and governance

Throughout data strategy development, collaboration across the organization is vital. IT, the chief data officer and all lines of business (e.g., marketing, finance, HR, sales and supply chain) need to work together. Collaboration ensures there is consistency and quality with the data, eliminates redundant data, and aligns the business to a single source of truth.

Before going too far, businesses can establish a proof of concept, demonstrating that solutions work before scaling. Regular gut checks and quality assurance can let a company know if they’re on the right track or if they need to revisit their strategy. Companies often design for perfection. But they need to be agile enough to cope and shift with the landscape.

There are several factors that must be taken into consideration when establishing a data strategy:

  • Business strategy: Data is a competitive advantage only when aligned to the business strategy as it evolves.
  • Security: Proper privacy and security measures are essential to maintain integrity and trust.
  • Data democratization: Gatekeepers create bottlenecks. A healthy strategy enables access and consumption of data across an organization.
  • Empowerment: Enable employees to take advantage of insights through self-service solutions.
  • Monetization: Identify opportunities through which data can enhance or extend products and services.

“Long term” in today’s world is one to three years. It’s critical to look at a data strategy as the business changes. Build a strategy for how to use data today, tomorrow and years down the line because a company may not use a particular set of data today, but they may need it in the future—and vice versa.

Putting together a data strategy is not a point-in-time activity to set and forget. It’s a very iterative process, and it’s critical to review on a consistent basis. Priorities change, tools change and technologies change. It’s an agile world—companies need to pivot and adjust to meet changing business goals.

If a company doesn’t have a data strategy, the new calendar year is an excellent opportunity to look at how it will transform business—not just systems and tools. A company needs to define what they want to accomplish with realistic goals and outcomes and talk about the problems they’re trying to solve—what do they want to predict in 2020 and beyond, and what do they wish they knew?

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