Over the course of my career, much of my work has centered around data strategy, either helping conceptualize a strategy or executing the component parts therein. Many organizations are operating on a loose, informal patchwork of projects championed by various organizational units or leaders who feel that the rest of the organization is not adequately supporting them. Establishing a data strategy that acknowledges and incorporates these disparate projects’ challenges and benefits can make the difference in becoming a truly data-driven organization.
This article will look at the 5 W’s of a data strategy to help anyone interested in embarking on this journey understand how they should get started.
- What is a data strategy?
- Why is a data strategy important?
- Who is involved in planning, executing and measuring a data strategy?
- Where should you start with a data strategy?
- When will our data strategy start paying off?
What is a data strategy?
Google “data strategy,” and you find a plethora of different definitions. Some are concise, and some are too wordy. I’m not the most gifted wordsmith, but I am going to take a stab at defining a data strategy in my own words. May future readers be kind to my humble attempt.
A data strategy represents the comprehensive plans, means, and vision for the collecting, integrating, managing, and leveraging information assets utilized by an organization. The data strategy should align to, as well as inform, the overall business strategy.
I believe the Why, Who, Where, and When will add depth to that definition; so, let’s keep digging in.
Why is a data strategy important?
I have a hot take that some might not agree with. If you’re asking, “Why do we need a data strategy?” then you probably don’t. On the other hand, if there is a vision for data at your organization and people value the organization’s data as an organizational asset, why is likely self-evident.
And there it is; your “Why?” is because you value data. And if you value data, then you should have a strategy for managing it like anything else an organization values. More specifically, organizations value data because it makes financial sense to. Whether it is risk mitigation, opportunity identification, operational efficiency etc., it’s financially responsible to develop and execute a strategy around your organization’s data. In my opinion, organizations in 2021 without a data strategy are behind the curve.
Who is involved in planning, executing, and measuring a data strategy?
In short: Everyone! Data is the lifeblood of most organizations, and the vast majority of workers are involved in the creation, transformation, and analysis of data. But let’s be a bit more specific. I’ve narrowed it down to 3 broad categories for those involved:
- Business
- Technology
- Governance
The business is vital to any strategy, and increasingly this responsibility has been assigned to a Chief Data Officer. In many other cases, I’ve seen the “top dog” of data strategy ownership as the CFO, or less frequently the CIO. Further down the chain, there should be varying roles of “data owners” (as well as data stewards discussed more below); those folks are intimately familiar with the data in their line of business and act as the “gurus” people rely on to understand the intricacies therein. On the frontlines, everyone is responsible for data, and only an organization that has invested in the cultural shift to become data-driven will see full adoption of the necessary care from data owners.
When most technologists think about data, their mind immediately goes to the technology surrounding it. Sound technology infrastructure is vital to a data strategy, but I have often seen technology be the only facet an organization can conceptually wrap its head around. Ask any seasoned data and analytics professional, and they can probably tell you a story about an attempt at building an organizational data warehouse that failed. I’m willing to bet that in many of those cases, it’s because the data warehouse was built with little understanding of WHY it was needed; maybe it wasn’t even necessary! Still, the technology approach to a data strategy must align with the organization’s needs. At the very least, the nucleus will look the same:
- Data movement from source systems to centralized locations (ETL/ELT)
- Data centralization and integration (data warehouse, operational data store, data lake)
- Business intelligence & analytics
Which of the varying approaches taken in each category here are contingent on the overall need for data? A sound data strategy will need a team of technologists to implement and maintain the systems; and, more importantly, align the overall technology ecosystem with the strategy itself.
It’s vital to note that many organizations fail at implementing a data strategy because they focus on the technology, putting the cart before the horse. The business should be the driver. Technology should be the enabler.
Finally, that brings us to data governance. Often an overlooked piece of a data strategy. There is no “one size fits all” data governance framework for every organization. Still, like technology, there is a nucleus of capabilities and functions that are required for a successful data governance implementation:
- Data understanding (definitions, modeling, lineage)
- Data integrity (quality, master data management)
- Data security (access, compliance)
The people responsible for data governance (data stewards) may see overlap with both above groups (specifically, data owners), but not always. Some organizations have dedicated roles for data governance, while others fold it into people’s regular responsibilities.
To reiterate, an easy answer to “who?” is everyone. A shift in your organizational culture around data is equivalent to seeing a data strategy thrive and grow while merely heaping the required effort on the backs of a few data champions is a recipe for burnout and failure.
When will our data strategy start paying off? Where should you start with a data strategy?
The first question can be difficult to answer. Clearly, the investment in such a large undertaking would only be taken if it were deemed vital to the organization. Still, developing a time horizon for a data strategy implementation doesn’t have a secret formula and business leaders must balance short term valuable wins while working towards the vision. Which brings us to the where.
Knowing where to begin a data strategy is perhaps the most straightforward answer in this article. Start with the end in mind. Revisit your “why” and imagine the holistic landscape that takes that need in mind. After you’ve envisioned your future state, identify and articulate your current state. Take these needs and outline a series of incremental projects and programs to address them. Finally, order those in a logical way that prioritizes incremental wins, applies time estimates, and organizes them into a roadmap.
Performing this analysis and roadmapping with a third party can be hugely beneficial as an objective observer can see the “forest from the trees.” After completing a current state analysis, we find our clients are usually shocked to see extensive gaps in capabilities they thought they possessed or pockets of real innovation to be used as a model for the broader organization.
Looking over the many data projects I’ve been involved in over the years, I am most excited by data strategy work. There’s nothing quite as rewarding as witnessing a strategy’s transformative nature take root and permeate throughout the culture. If you’re starting on a data strategy, I wish you luck!
