Data Strategy Framework: How To Implement One & Scale It For Success

Imagine building a structure without a foundation or creating a company without a business plan. The same can be said of developing a data strategy. Many business leaders are realizing that merely aspiring to be data-driven doesnt always result in success. In fact, a recent NewVantage Partners survey found while nearly 99 percent of executives say their business strives to be data-driven, only 32 percent say they have achieved this goal. Success isnt possible without a clear framework that guides how people use data to support and inform their decisions.

Building and maintaining a data strategy requires both strategic and financial commitment from the entire organization. Therefore, before you build your data strategy, you need to secure support from leadership and your IT department, who will advocate and help orchestrate critical changes to scale the framework across the enterprise. Specifically for IT, the top priorities should be:

• Partnering with leaders to understand how business teams use data

• Helping to communicate and sell the vision for modern analytics across the organization

• Helping prioritize data sources by audience size and needs

• Influencing processes, policies, guidelines, and responsibilities around data access, content authoring, and compliance

• Installing, configuring, and maintaining a modern, governed analytics solution

Data-driven organizations dont draw a line between their business and IT strategy. They have a comprehensive data strategy that sits squarely in the middleone linking key initiatives and data, which addresses business goals, objectives, and your company mission, and doesnt treat data as a by-product. Your data strategy should be solid, but also flexible enough to adapt when business needs or operations change.

As you start implementing a successful data strategy for your enterprise, follow these critical steps to see a transformative impact:

Step 1

Define clear business objectives:

This requires an understanding of your organizations executive and downstream goals. Having clear business objectives ultimately helps you identify key performance indicators (KPIs) and metrics that influence decisions made from data. They also guide which data sources to curate and analyze.

Step 2

Capture the right data to support your objectives:

Your company is already collecting data, but are the sources cleaned and certified? If not, its like finding a needle in a haystack to uncover data that supports different use cases and advances business objectives. Successful companies opt for a defensive and offensive data strategy, which supports customer-focused business functions and legal, financial, compliance, and IT concerns, according toHarvard Business Review. As a result, everyone feels confident that reports seen and shared, internally or externally, reflect trusted, accurate data sources, follow standards, and have gone through a consistent, managed process.

Step 3

Modernize your data architecture:

One single data source cant answer all of your business questions. Your organization needs to connect to data where it exists and consider all of the ways data can be enriched when sources are combined. This means shifting from a traditional enterprise data warehouse, single-bucket mindset to a multi-bucket mindset that enables speed, agility, and high volumes of data.

Now that youve modernized data architecture, you need a way to get value out of your data sources. Each organization has different requirements and solutions for its data strategy so adopt a modern, self-service analytics solution that respects flexibility and choice for a variety of use cases. This solution should also have a way to certify data sources so people know what data to use in their analysis. Once they find insights in their analysis, there should be an easy way for them to share these insights so everyone in the organization can effectively answer questions, explore data further, and make better business decisions.

From sources of data to curated data sets, governance will be the foundation of your self-service analytics deployment. Alongside governance, businesses are leveraging data management toolslike data catalogsas they deal with broad, enterprise data access and implement a unified data strategy. Data curation encompasses how an organization captures, cleans, defines, and aligns disparate information; its a process that also creates a bridge between data and its real-world applications. A data catalog defines and exposes an organizations data terminology to support better comprehension by people and outlines user permissions, usage metrics, and lineage.

When data governance and data management are successfully balanced in your data strategy, youll experience important benefits. The right data will be in the hands of the right people, processes will be more transparent, documented, and understood, and data-driven decisions will occur repeatedly throughout the organization to create success.

After implementing and scaling your data strategy, youll need to institute some critical capabilities for your organization to grow and maintain a thriving Data Culture. As previously mentioned, youll need executive advocacy, as well as agility, data proficiency, and a broad, active community to ensure the mission, goals, and needs of your organization are metin process and technology. Businesses around the globe have successfully implemented a data strategy and reaped the rewards from deploying a modern, self-service analytics platform alongside these capabilities. Check out their stories.