• by Nathan Bauer , Op-Ed Contributor, February 1, 2023

You know those anxiety-driven dreams where you find yourself in class without that big assignment that’s due? Don’t look now, but it’s 2023 – whether your marketing and data analytics plans are ready or not.

Deep breath; don’t panic: We’ve got some insights on making your way through what’s going to be another interesting year in the global data landscape.

Thoroughly Examine Your Data Strategies

Getting where you want to go begins with fully understanding where you’ve been and where you are.

Do you fully understand your business objectives, independent of what marketing is trying to achieve? It’s easy to let marketing goals drive the conversation, but remember: There are specific reasons you’re investing in this initiative – are they sales objectives, transaction objectives or community-driven objectives? Some forward-looking companies are also considering the “human metric,” the potential for a brand to improve the lives of real people, apart from the financial benefits to the brand. You need to know exactly what metrics are meaningful for driving those business outcomes in order to align the people, processes, technology, and data that you need to make better decisions.

Stay Current on Privacy and Data Rights and Sources

Even as we move toward a more cookie-free environment and a reduction in the amount of third-party data provided through web browsing, the number of laws and legislative debates over who owns and accesses data is increasing.

We could all probably stand to consider more deeply how these changes are going to impact our businesses and our reporting.

Revisit all the data sources you’ve been using, and make sure you understand the implications of these changes and potential shifts. Judging by the trends, every company in the world is going to have less first-party data to work with going forward – more power in consumers’ hands means less data for you – and it’s important to make sure your entire organization is aware of what you might be losing.

This places a renewed emphasis on training and paying attention to changes even when they’re outside of your arena. The GDPR European Union law on data protection and privacy was adopted nearly six years ago. Those who carefully charted this sea change were better prepared two years later when the similar CCPA was adopted in California. Stay ahead of the curve: With consumer privacy and data rights legislation on the books in more than a dozen states, it’s not going anywhere – and federal regulations may be coming soon as well.

Broadly speaking, existing and proposed rules require companies to let consumers know whether their personal data is being used or sold, and provide a means of opting out. But there’s a lot of variation in the details: Many apply only to companies of a certain size or income level. Some countries or states are aiming to provide exemptions for information collected as part of someone’s employment, for instance. Other locations may mandate a process for consumers to act if a company refuses to take action regarding their data.

Normalize Your Datasets and Expand Your Sources

If you’re not using a centralized location – a single source of truth – to store and process data from all across your organization – from Facebook advertising data to Google Analytics to The Trade Desk information, and even offline data like reports from billboard providers – sooner or later you’re going to miss something crucial. And not only do you need to bring all this information together in one location, but you also need to normalize the datasets so they mesh neatly and work cohesively to provide value.

Secondarily, you should explore a wider range of third-party data sources to expand your insight and empower even more informed decisions. For example, we have our Salesforce data and all of our marketing channels’ input in one data warehouse, and we’ve also pulled in publicly available census data to help us see certain household financial shifts in markets. This has enabled us to deliver new, actionable insights.

Insight Is Where It’s At

Understanding the difference between an observation and an insight is at the heart of getting value out of your data. Observation is simply retelling the result of a metric. Insight? That’s telling you the why and what comes next. And historically, there are a lot of analysts who are great at observations, but not so great at insight.

Paralysis by analysis has never been more real: Clients have C-suite managers and their teams breathing down their necks to build dashboards and reports, but data for its own sake is not valuable. Anything you’re analyzing and doing should be with a purpose: Why did we get that result? What do we need to do differently? How do we adapt to user privacy concerns?

And, how do we share the narrative that the data uncovers in order to drive our clients’ success?