Six Data Resolutions To Get More From Your Data In 2022

Six Data Resolutions to Make 2022 an Exceptional Year

Maybe you’re not a resolutions kind of person. Maybe there’s no new-year-inspired urge toward mindfulness, a low-carb diet, or the perfectly organized closet that remains elusive. We support that mindset; you’re fantastic just as you are.  

But how’s your data?  

Our team dives deeply into data situations that run the gamut from “strong” to “hold up a minute.” No matter where you fall on that spectrum, there’s always room for improvement and optimization. The new year is as good a time as any to start getting your data ducks lined up. Our team of data experts suggests these six resolutions to help make your organization more secure and effective with your data in 2022.  

1. Prioritize data quality over quantity.  

You’ve probably been diligently collecting data because you know that data-driven organizations are much more likely to see their decision-making improve. But how much has your data helped this year? Chances are good that if you’ve yet to see benefits, your data quality isn’t quite up to snuff.  

Inaccurate data just can’t get you where you want to go. Basing decisions on bad data is like reading a map of Seattle to try to get around in Tuscaloosa. And forget machine learning: There’s no way to train a model sufficiently with unreliable data.  

Stale, inaccurate, or incomplete data and data arising from the stunning amount of nonhuman web traffic—none of it matters and all of it futzes with your results. You’re not stuck with subpar data, however. Strong data governance, centralized data management, and other improvements can take you from chaos to certainty. Consider refining (or creating) your data strategy in 2022 with a data assessment to figure out where you are and how you can get where you want to be.  

2. See what you’ve been missing with predictive analytics.  

Not just for statisticians anymore, predictive analytics is an important element of organizational growth thanks to important functions like fraud detection, risk reduction, and campaign optimization. Predictive analytics turns data into forecasts about behavior and trends.  

With machine learning and other analytics methods, risks can be identified and addressed, and opportunities come into focus. From estimating global product demand to determining equipment lifespan to finding out where a particular crime is likely to occur, predictive analytics turns what we know into reliable predictions about what will happen—and when. 

3. Get serious about data interoperability.  

A pan, a stove, and some pancake batter don’t do anybody any good until they can be used together. The same is true of the systems you use to propel your business: If they can’t work together to utilize data, you’re not getting your metaphorical breakfast.  

Data interoperability ensures your systems work together quickly and securely so that no matter how you’re accessing your data, you’re getting timely and reliable information. It’s a “greater than the sum of its parts” situation: The insight you glean from getting that full view, the ability to collaborate, and context for the information you’re collecting—that’s the stuff that propels an organization forward. 

4. Harness the power of measurable insight through operationalization.  

Making a data-driven decision is one thing; creating a replicable decision-making methodology is quite another. When you operationalize data analytics by crafting a repeatable process that’s relevant across situations—the benefits are exponentially more powerful than an insight here or there. You now have a method for achieving results. Over and over (like, say, for all of 2022 and beyond).  

Operationalization makes fuzzy concepts clear by meticulously defining each element. From there, you have the pieces in place for maintainable predictive and prescriptive models that help you arrive at insight. In short, you get highly effective data tools you can maintain and utilize through the change and growth you’re working toward.  

5. If you’re thinking M&A, think data integration first.  

There’s a reason we’re writing about data resolutions in the first place, and it’s that so many organizations have room for improvement regarding how they handle and utilize data. Throw in a second organization through a merger or acquisition, and you’ll often end up with about 100 times the challenge. Yet many M&A activities proceed with little thought to data integration. (And, sadly, a huge number of those fail.)  

What gifts and struggles will you inherit through M&A? What route will take you from separate to securely consolidated? Data assessments will help uncover potential problems and provide a clear, prioritized roadmap to data integration. All good things begin with strategy. 

6. Choose a DataOps approach to reach your best results from analytics projects.  

The idea that’s been lingering around the edges of data analytics for several years has come into its own, and organizations are becoming more responsive and adaptable because of it. With DataOps all grown up and having proven its value, using this approach helps build a data-driven culture and provides the tools that let you utilize your data better—faster and more accurately.  

And how does DataOps support your data utilization, exactly? Using an agile, process-based methodology, DataOps streamlines design, development, and maintenance of data analytics apps. It’s born from the idea that focusing on people and collaboration is the surest route to high-quality results, which sounds awfully familiar.  

If you’ve been putting off addressing your unique data woes, take heart.  

Trying to get your data under control so it can do all it can for your organization can be intimidating. You’re not alone there: Every day we talk to business leaders who feel overwhelmed by their data. But you don’t have to do it all at once, and not all of these data resolutions may apply to your situation. However, a strong 2022 begins with taking that first step toward developing and following a sound data strategy. Once you get started, you’ll be in a better place to grow.  

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