Tips for Developing a Successful Data Analytics Strategy

Data has no value until it serves your organization, and without a data strategy, you’re basically operating an information slot machine—every now and then you’ll hit on insight, if you’re lucky. More often than not, you’re seeing random points spin past and unable to identify trends, analyze performance, predict shifts, or otherwise utilize your data.

Yet many organizations carry on with messy datasets and murky data strategies, with unfulfilled marketing potential, ill-formed objectives, and data governance that really isn’t. These organizations are missing out on insight (and ROI), and they’re leaving customers cold. Unless you’ve built a comprehensive data strategy and update it regularly, a lot of opportunity is slipping through your fingers.

The elements of a data strategy

A data strategy is essential, and it’s complex because it includes an array of concerns about how your data is ingested, stored, accessed, shared, visualized, kept secure, and applied to the complex questions you want your analytics to answer to move your organization forward along your business strategy. It encompasses these and other data capabilities:

For such a complex undertaking and one that yields such profound impact on your business strategy, small wonder building a data strategy requires—and deserves—careful planning and development. You need a clear destination and the roadmap that will take you there.

Important points to consider when building a data strategy

Data gets top billing, but a data strategy is a people-centered effort. Even the most well-considered strategy will fail to deliver business value if your teams aren’t involved from the start. Here are some of the essentials for ensuring you end up with the right strategy—and that it is embraced:

  • A successful data strategy is not a unilateral effort. It requires input and buy-in from leadership and stakeholders.
  • Data strategy affects the entire organization. Which means everyone should be involved, whether by answering questions about data usage in the discovery stage, collaborating on priorities, or helping to lead change through implementation.
  • Different departments speak different data languages. Spanning the systems, processes, and data cultures from team to team is critical for developing a strategy.
  • Setting expectations smooths your path forward. Although Data Problem X is the most important thing in the world to one team, it has nothing to do with another. Showing your teams a defined strategy lets them know they’ve been heard and their problem will be addressed.
  • An objective third party breaks through the politics of prioritizing. Building a data-driven culture takes work, and it inevitably requires delicate conversations around contentious issues. A neutral partner helps provide the objectivity to steer through obstacles.

Crafting a data strategy: Where to start, and where to go from there

The right time to build a data strategy varies, and there’s almost never a time that feels exactly right for any organization. (Which is why so many wait until they just can’t anymore.) Sometimes it’s because there’s a problem a company needs to solve and developing the data analytics to solve it necessitates a data strategy. Other times a large system implementation shines a light on the need for a compatible data strategy. And sometimes it’s just that organizations are overwhelmed by the volume of data coming at them and know they could do more with it—if only they knew where to start.

No matter the inspiration, a data strategy helps you lay the foundation for a sustainable, scalable, simple data program. And it always starts by figuring out where you stand right now through a strategic data assessment.

As Resultant Manager of Data Science Amrutha Pulikottil puts it: “The interpretability and quality of a data deliverable highly depends on the data quality of the input sources. Assessing what you have, acknowledging its shortcomings, identifying alternative sources, or curating existing ones to fill the gaps can help you tell more meaningful stories from your data.”

What an expert partner does for you

Data is especially tricky. It moves fast, and so do the innovations that help organizations utilize it. It’s a complex field with a lot of highly technical subspecialties. All of which adds up to: Don’t try to go it alone, and be careful about the partner you choose to help. A good one will:

Trim costs and time.

An expert team with a well-established protocol for developing data strategies saves you time and money—and almost certainly leaves you with a strategy that better serves your objectives.

Provide insight and expertise.

The expertise and objectivity you gain from a data partner focused entirely on building your strategy creates efficiencies that would be challenging to achieve with an in-house team learning along the way and pulled in various directions at the same time.

Increase data security.

The costs associated with data breaches reached an all-time high in 2021, and as data proliferates, those costs are likely to keep rising. The loss of trust that goes hand-in-hand with a data breach is immeasurable. Expert data security is essential.

Offer a third-party view.

A team who has deep expertise and a neutral perspective gives you more objective, innovative support for your strategy than an in-house team can deliver. Which not only shortens your time to value but gives your team more time to focus on day-to-day and business strategy activities.

To learn more about building the right data strategy for your organization, check out our whitepaper.

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