A data strategy and operating model for execution defines how an organization can:
- Achieve specific business goals through the strategic use of its data assets
- Support the overall business strategy by mapping data and data capabilities to business processes used to run day-to-day operations
- Utilize analytics to support decision-making
- Enable the technology solution architecture required for supporting operations and analytics
- Structure its people and teams in a way to ensure accountability for governing and managing data
- Design and mature specific data management processes that will better support their business systems and applications
Key driving factors that will decide the Data Strategy ownership and business adoption within an organization will include understanding the potential value of available data and what needs to happen to leverage it, how stakeholders across multiple departments will be engaged, and how the Data Strategy and Data Management Processes will be aligned with and support business priorities, strategies and plans. By the end of the development of a Data Strategy, an organization is typically in a much better position to address the business enablement and target-driven data strategy ownership.
“Think Big, Start Small’’
An enterprise data strategy is all about looking beyond “swim-lane optimization” to the most important business goals and the widest definition of business value. In this way, the data strategy must balance short-term goals and quick wins with longer term success and progress toward the most strategic business goals.
Every company wants to extract maximum value from their data, but most are trying to do it without disrupting the structure of the company. They want to avoid the political or structural challenges that come with breaking down silos, sharing data, and thinking about processes end-to-end. But that disruption is critical to extracting maximum value from data.
Data strategy applies both to any single project, such as better predicting customer conversion, as well as to enterprise-wide challenges like becoming a more customer centric organization. Some of the most successful data-driven companies approach it from both ends.
The idea is to apply sound data strategy principles to granular problems, while also building toward a wider data strategy and governance program that spreads across the enterprise.
Where an organization starts depends on the leaders in the company. If a company has a transformation-driving CEO or CFO, start with that person and get broad support to work with other groups to advance key corporate goals. If the CEO and CFO aren’t yet data advocates, find the right partner in another department, get wins, and gain broader support as people realize what success is being achieved. Either way, the key is to make sure the project-level initiatives all contribute to the bigger vision instead of pulling against it.
While an enterprise-wide data strategy is an important part of the endgame, you can’t start there. It’s too big a task; it’s far harder to get business partners to engage with you; and you haven’t yet built confidence in your organization’s ability to deliver value through a data strategy.
Informatica Professional Services has been very successful building and implementing Data Strategies by helping clients start small and win big victories and show business value very quickly. We accomplish this by:
- Starting with the most important business challenges that can best be addressed through more effective use of data. These will be best aligned with the strategic goals of the company. But some of these challenges are better starting places than others.
- Enabling a few quick wins: Nothing gets people on board faster than some quick, clear data strategy wins that deliver tangible value. Better prediction of customer churn. Reducing product returns. Accelerating the supply chain. Improving regulatory compliance. Helping with up and cross-sell. These are the kinds of wins that get attention and earn support for more investment in data strategy. But starting small doesn’t mean forgetting your bigger vision. Ideally, you want to find quick wins that move you toward your overarching enterprise data strategy. Consciously use these important early projects as building blocks for your bigger play.
- Data-driven digital transformation is hard. It’s asking people to change the way they work and the processes they understand. To make it through, it is critical to evangelize a clear vision of the end goal. Make the business partners understand the long-term roadmap—but the roadmap can’t be just the phasing of a big, vague project. To succeed, the roadmap needs to be a series of incremental business-value successes, each building on the one before. Lay out the vision as “wins,” not just “steps.”
- Identify the key, foundational data strategy components that need to be implemented along the roadmap in order to achieve the planned business value. Communicate often what investments [in people, skills, organization, tools/technologies, processes, and data] that need to be made and what is the expected planned value to be achieved along the way.
Scoping Your First Data Strategies to Find Your First Wins
Broadly, there are two ways to structure and scope a first data strategy initiative: by department, and by business capability. Informatica recommends that the scope of a data strategy is focused on a discrete business capability for the biggest wins. Why?
The ability to use data to improve business performance requires transparency across the end-to-end value chain of data use. Consequently, mapping data to the analytics used to make decisions and the business processes that support the execution of activities that impact business outcomes is a critical step to data strategy development.
It is important to understand what analytics are used to support decision-making related to the business goals, strategies, and plans. What data is used for analysis? What are the sources of the data? How is it consolidated, cleansed, and enriched? Which dashboards, reports, and machine-learning algorithms use the data? It also important to understand the current business data challenges and pain points.
Next, determine what business processes are related to the business goals. What data is used by these processes? What systems store the data? How does data flow between systems and processes? What are the dependencies and integrations? What parts of the processes are manual? How can better data help automate the processes?
Finding and prioritizing initial quick wins projects is not easy and always involves understanding and navigating complex political landscapes. Get a good sponsor who is a strong believer in the power of the enterprise’s data assets.
- Pros
- Targets whole systems
- Optimizes for outcomes, not silos
- Aligns naturally with highest business goals
- People become more proficient at performing some of the Data Strategy activities and can start doing this work for other departments/capabilities later
- Less initial impact on the IT and business department’s bandwidth
- Less expensive Data Strategy entry point
- Faster time to proving value
- Cons
- More complexity
- More data stores to integrate and rationalize
- More stakeholders to align
- Need staff with appropriate skill and experience at doing this type of data strategy work
Another key consideration is the need to understand where the starting current state is and what current capabilities are when it comes to your organization’s:
- Data architecture
- Solution Architecture
- ETL and Change Data Capture Architecture
- Processes, tools. technologies and people supporting these architectures
- Data management processes and enabling tools and the teams supporting and leveraging them