The large volume of data being generated every second explains in itself why it’s crucial to incorporate a robust enterprise data strategy and reap the benefits in terms of valuable business insights, accurate forecasts, and informed decision-making.
Time and again, data-driven decision-making has proven to be a competitive differentiator—whether you are using data to inform and refine product research, operations, or customer experiences. Still, organizations are slow to adopt a truly data-driven model and only 30% have developed a well-articulated data strategy.
How can your organization develop a robust data strategy and harness the power of data? You need to know what to include in your enterprise data strategy, but you also need to understand how to navigate data change management across the enterprise.
What is an Enterprise Data Strategy?
An enterprise data strategy is a collective vision and a roadmap for how your enterprise will collect, store, manage, and use data. An effective data strategy allows you to understand the changes you need to make within your organization to achieve the desired goals.
The strategy identifies data activities you need to carry out and how those activities will bring benefits, such as actionable insights, better profits, or data monetization. Put simply, a data strategy is a foundation for everything your enterprise does related to data.
The 5 Key Components of an Enterprise Data Strategy
A successful and scalable enterprise data strategy includes five core components that enable efficient data management and execution.
1. Data Collection
What are the data sources? Is the data structured or unstructured? No matter how valuable your data is, if it lacks proper definition, you won’t be able to utilize it effectively. Data collection and preparation of a data glossary (aka Data Dictionary) form the first component of an effective data strategy as you need to identify and measure all available data clearly to know where your enterprise stands.
2. Data Storage
Where do you store the collected data—a data warehouse, data lake, or a data lakehouse? Will the data reside on the cloud or within an on-premise system? Are the available resources enough to make data available across different systems? Identifying data storage needs is a necessary step. The goal here is to efficiently share and move data between systems.
3. Data Integration
Data integration consolidates data from disparate sources and systems. To generate meaningful reports and make important business decisions, business users across the enterprise need a unified view of data.
4. Data Provisioning
The next step is figuring out how to easily and securely share your data and make it accessible to specific users. Data provisioning is key for establishing an enterprise data culture as business units have a variety of uses and demands, but they all share one thing in common…they need access to accurate and up-to-date information in order to make impactful business decisions.
5. Data Governance
You want your teams to leverage data when making decisions, but you definitely won’t want your sensitive business data to get into the wrong hands. Data governance is the final and most crucial component of a well-defined data strategy as it helps you secure data and establish clear guidelines on sharing data.
While focusing on these key components helps you build an enterprise data strategy, the job is only half done if you want that strategy to be successful and scalable. Bringing the strategy into action demands enterprise-wide adoption. And, this task isn’t easy.
The 5 Key Elements of Successful Change Management
When pushing for data-driven decision-making across your enterprise, you’re actually suggesting a significant change in your company culture and internal processes. This change will likely invite resistance from teams, senior management, and even the C-suite.
So, how do you make everyone realize the potential of the change you’re suggesting and reach success? Through a data change management process. To successfully implement your enterprise data strategy, these are the steps you need to take for successful change management.
1. Prepare for the Change
Generate awareness about the data changes you’re about to bring to your enterprise. Discuss current challenges faced by your organization and how these challenges are driving the need to bring change. Highlight the tangible benefits of data-driven decision-making—include real-world examples and results—to get initial buy-in from teams and senior management.
2. Establish a Clear Vision
Once your organization is ready to embrace change, the next step is to identify clear goals that you want to achieve with this change. Define measurable goals and key performance indicators. Assign stakeholders responsible for tracking these goals and KPIs. Make sure to establish a clear project scope and the steps and actions that need to be taken within that defined scope.
3. Initiate Change
Once you’ve established a clear data roadmap, begin the change initiative. As a change manager, consider empowering teams with the necessary steps they need to take in order to achieve the desired objective. Identify and remove roadblocks along the way.
4. Scale the Change Initiative
With a successful change initiative, your focus should be on scaling the data changes across your organization’s culture and practices. Set up new organizational structures, reward systems, and proper controls so that the direction of the change is always moving forward.
5. Monitor Progress and Track Results
A change initiative isn’t complete without reviewing the process. By analyzing the change effort, you get necessary insights about how successful the initiative was and the steps you can take to course-correct your efforts.
Data change management is a slow and steady process, not an easy button. Start seeing change management as the other half of your enterprise data strategy, so you are able to move past resistance and reach success together.
