With all the modern advancements in data management, organizations stand in need of a rock-solid data strategy. According to a survey by NewVantage Partners, 99 percent of Fortune 1000 executives want to create a data-driven culture, while just 32 percent believe they have achieved this goal.
But what is a data strategy exactly, and how can you take steps to clarify and enhance your organization’s data strategy? We’ll discuss just that in this article.
Loosely defined, a data strategy is a list of high-level objectives and priorities for your enterprise data assets. This includes your plans for handling data at every stage of the pipeline, from collection and storage to integration, analysis, and collaboration.
While only you can decide for certain what your data strategy looks like, experts have chimed in with some solid advice. IT research and analysis firm Gartner has defined four crucial components of every organization’s data strategy:
- An overarching general business strategy.
- The current state of data management within the organization.
- The envisioned future state of data management that can align with and support the business strategy.
- A gap analysis between the current state of data management and the future state.
What’s more, Gartner has broken down enterprise data strategies according to four different aspects or axes:
- People: The members of the organization who will be affected by your data strategy: managers and executives, IT architects, DBAs, developers, business analysts, data scientists, etc.
- Process: The business workflows and IT processes that define your organization’s standard operating procedures (SOPs).
- Policy: The rules and regulations that control your organization’s behavior in terms of data strategy—for example, corporate governance policies or data privacy laws such as HIPAA.
- Technology: The technological assets and knowledge at your disposal. This may include data records, storage systems, integration platforms, business intelligence and analytics platforms, OLTP and OLAP databases, cloud services, and tools for AI and machine learning.
Regardless of how you break things down, your data strategy needs to be clear-cut rather than aspirational, easily understood by all relevant parties.
For example, stating goals such as “We will safeguard our information from cyber attacks and data breaches” is less helpful than stating intelligible, actionable policies such as “We protect data with AES-256 encryption, both in transit and at rest.”
The best data strategies think both short-term and long-term, outlining how the strategy will progress and evolve over time. In addition, your data strategy should contain metrics and KPIs to help assess its own success and, if necessary, reevaluate later on down the line.
To summarize, an effective data strategy should contain the following components:
- A comparison between your organization’s current state of data management and the ideal future state that you want to achieve.
- A long-term, multi-stage breakdown of concrete intermediate goals that you will attain before reaching this future state.
- A description of the people, processes, policies, and technologies that you have to draw on.
- A list of important metrics and KPIs for use in measuring the plan’s success.
Defining your data strategy is just one of the pillars of creating a strong data foundation. Want to read about the other three for yourself? Download my white paper “Build a Business Foundation on Trusted Data.”
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This blog post discusses the release of Oracle Analytics Cloud, a service that includes Essbase Cloud, Business Intelligence Cloud and Data Visualization Cloud.