Does your Data Strategy Resonate with Business? Key Observations & Recommendations

This article is about my previous learning and references while reviewing few data strategies from different regions worldwide.

There are several patterns being observed while reviewing some of those data strategies and noticed that, regardless of the industry vertical and business domain, most of the data strategies look very similar. It is as though people developing data strategies have copied and pasted other’s documents!

Following are some of the key observations:

Observation 1: Most of those data strategies are very similar in nature even though their domain/business is very different. They have standard components such as data governance, data security, data quality, data catalog, records management, compliance, cyber threat etc.

Observation 2: They all have bold statements such as “treat data as an asset”, “data-driven decision”, “data to streamline delivery”, “data driven design”, “data as an innovation enabler”, “build data capabilities”, and so on. Strategy document also have a lot of fluffy words and technical jargon which does not resonate with all the staff across the organization.

Observation 3: Most of those data strategies follow a template. Start with principles, high level goals, strategic intent, program plan, governance structure.

Observation 4: Data culture should be nurtured to be a data-driven organization; Data mindset needs to be embedded in the people to use data for problem solving.

Observation 5: Data Catalog needs to be built to know what data we have, who has access to it, and also enables sharing.

Most of these data strategy documents have no connection to overall business strategies. These strategy documents have no connection to end users/clients/consumers they serve. They are not written in layman terms for common understanding! Thus, data strategies lack strong business focus. Sometimes data strategies dictate that business goals can be achieved with only use of data !

Many technical jargons we come across such as data lake, AI/ML, data warehouse, BI tools in those documents are also mentioned. Some organizations are attracted by technology and wish to adopt it quickly without doing an assessment on business fit, plan for long term, team skills, tech overheads and licensing. I believe that having these platforms mentioned in data strategy document positions themselves as they are part of adopting cutting-edge data technologies. Apart from being no business focus, those documents have exhibited a huge gap on execution of data strategies.

In a nutshell, it seems there is lack of resources and funding. However, to maximize the impact and implementing best practices, these projects have to be executed in tandem. In addition to this, strong engagement/communication plan is also important wrt building a robust data strategy. People in the organization need to be aware of progress of data strategies, success stories, failure stories, and so on.

In addition to this, many data strategies go into data governance and clears at least three level of hierarchy. I feel keeping the committees, councils lean is the best way to make quick decision and ensuring alignment with business goals. None of the data strategies have any success measures/KPIs attached to it! They are saying in all those data strategies that they want to be a data-driven organization. However, their data strategies do not have measures/metrics to monitor their progress!

Some quick recommendation pointers towards building a good data strategy aligned to a strong business focus:

1. Data strategies need to articulate how organizational staff can use the data (as one of the resource) to innovate in their business. This could be streamlining their business process to build a new business model for service transformation, new ways to experiment, getting a better clarity of process, customers, market trends, business behavior, cost etc.
2. Data management and governance are foundational activities for executing a good data strategy. Hence, these activities need to be tied to a business outcome and further needs to be built over a long-term.
3. Data strategy should have strong business focus and it should be aligned with achieving business strategy. It should clearly articulate how data offers a unique value proposition to the organization, how it helps internal stakeholders with their day-to-day operations, strategic planning, delivery; It should also make a connection to organization's customers/clients in terms of saving their time, saving cost, user experience, product experience etc.
4. The outcomes of those data strategies should be tied building internal stakeholder capabilities such as procuring platforms, offering training to staff, data literacy etc.
5. Business glossary is a key for an organization to be successful in executing a good data strategy as many business users will use data differently if the shared understanding of those business definition is missing.
6. Have KPIs/ Metrics for each strategic goal of data strategy; measure those KPIs every quarter, ensure actions are taken for reaching those KPI targets/metrics.
7. Concrete communication plan to let the organization know about success stories, failure stories, investment decision.
8. Ensure that your data strategy document is written in layman terms using commonly used business terms, policies or rules for better understanding.