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 doesn’t 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 isn’t 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
- Installing, configuring, and maintaining a modern, governed analytics solution
- Influencing processes, policies, guidelines, and responsibilities around data access, content authoring, and compliance
Considerations for your data strategy framework
Data-driven organizations don’t draw a line between their business and IT strategy. They have a comprehensive data strategy that sits squarely in the middle—one linking key initiatives and data, which addresses business goals, objectives, and your company mission, and doesn’t treat data as a by-product. Your data strategy should be solid, but also flexible enough to adapt when business needs or operations change.
How to implement an effective, enterprise data strategy?
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 organization’s 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, it’s 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 to Harvard 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 can’t 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.
Data management supports scale for an enterprise data strategy
Now that you’ve 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 tools—like data catalogs—as 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; it’s a process that also creates a bridge between data and its real-world applications. A data catalog defines and exposes an organization’s 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, you’ll 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.
Create beautiful visualizations with your data.
What success looks like with an effective data strategy
After implementing and scaling your data strategy, you’ll need to institute some critical capabilities for your organization to grow and maintain a thriving Data Culture. As previously mentioned, you’ll 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 met—in 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.
REI reduces time to insights with collaboration, improving the retail experience
Historically, REI analysts spent 80 percent of their time and effort performing data preparation tasks—and the rest of their time was spent finding the right way to convey information to stakeholders. They decided to implement a data strategy, prioritizing a more collaborative relationship between business and IT teams.
Domino’s becomes data-driven and transforms into the world’s modern pizza brand
Domino’s Pizza is one of the world’s largest pizza brands. But in 2010, they were losing considerable market share. By implementing a data-to-insights mentality for better customer engagement, Domino's became a data-driven brand, opening up new, innovative approaches to the market.
Charles Schwab advances its data culture with more than 16,000 employees
Banking and brokerage firm founded in the 1970s” doesn't align with the general public’s view of a “data company.” Charles Schwab proved otherwise, embracing modern self-service analytics and fostering an environment where people are interested in data and eager to learn how they can analyze it more effectively.
Considering these success stories, a growing number of organizations are also appointing a Chief Data Officer (CDO) to lead business process change, overcome cultural barriers, and communicate the value of analytics to all staff. To develop a highly-effective team under this strategic position, organizations are dedicating more money and resources. According to Gartner, 80 percent of large enterprises will have a CDO office fully implemented by next year. Stay tuned to see if this becomes reality, allowing IT and Chief Information Officers (CIOs) to have a more strategic focus on other areas like data security.