Whether you work in the B2B or B2C industry, now is the time to ask yourself — am I doing right by my data?

It’s a complicated question. At many organizations, stakeholders know they need to improve data practices. Not only that, but they clearly see the relationship between data analytics and exponential value.

However, at too many organizations data considerations lack follow-through and stop after noting potential benefits. Although many organizations already use data as a gateway to more modern customer experiences and capabilities, fewer use data (and powerful data technologies) to increase the probability of success for these endeavors. The lack of reporting, validating and learning opportunities — especially at a time where over half of companies adopt big data analytics — opens companies up to unnecessary risk and stands in the way of optimal ROI.

Read More: Data and Creativity: Stronger Together

Define Your Data to Save Your Data

Unfortunately, many mid-market companies lack clarity about their data needs. Not too long ago, data analytics meant tracking website views, or maybe how long a browser visited a certain page. This is just the tip of the iceberg for what businesses can achieve via data analytics. With relatively simple, inexpensive integrations, companies can pursue virtually any data goal or program they can imagine. This includes data-driven operations like A/B testing homepage content, tracking attribution across channels, keyword optics and more.

This diversification of data capabilities is great for marketers, but it poses problems when companies fail to define their data needs early and bring on technologies that support a clearly defined business future.

On a very basic level, this starts with setting goals. It’s not enough to say you want your data analytics program to tell you more about customers and their desires. What customers? In what scenarios? And on which channels? You stand in the way of your own data program when you don’t define your metrics for success and the different factors that influence outcomes.

It’s important IT is aware of what data should be collected so it can pursue platforms that are capable of achieving those needs. How can you improve something you cannot evaluate?

Read More: Making Better Decisions with the Right Data

Share Your Data (And Data Program) Company Wide

Another main component of defining your data needs is determining who should access data, and in which ways.

Organizations that fail to disseminate information and resources about their data processes across the entire company cannot tap into all data sources. This makes it tough to pursue holistic data changes that factor in the intersectional nature of businesses and shoppers today.

A consumer interacting with your Twitter may also visit your website and follow up with a customer service representative via phone. An inability to put these data insights into conversation with one another — and to bring together employees responsible for this information — undermines the good work data can accomplish.

Let’s see this idea in action. Consider the special relationship between marketing and merchandising. For marketers, go-to metrics may be the number of downloads for a gated content asset, qualified leads earned via a social media push or the volume of unique monthly website visitors. Conversely, those in merchandising are likely more preoccupied with sales quotas, website conversions and product returns.

When these departments come together and define data with all needs in mind, it’s possible to track and analyze data in ways that help everyone involved. For example, sales information can inform data-driven marketing campaigns that better highlight the products shoppers prefer most.

Read More: People-Based Marketing is Missing a Critical Element: Accurate Data

Apply Your Data to Mitigate Creative Risks

Without data, marketers end up working harder, but not smarter.

Of course, that means the inverse is true when you allow data-driven insights to guide your creative endeavors, especially as it pertains to content. Too often, marketers are asked to start from scratch with content. Not only does this approach pose significant psychological hurdles (we’ve all experienced writer’s block from a blank page), but it also devalues the previous work you’ve done and the lessons learned therein.

Creativity and data may feel like opposites at first, but this cannot be the case for mid-market companies interested in driving sustainable, positive results. For example, the analytics platform or content management system (CMS) mentioned earlier pinpoints high-performing content (e.g., most views, conversion-driving assets). Once identified, your marketers can repurpose this content, lean on its themes for future ideas and generally speed up the content creation process by using existing colors, layouts, etc.

Try this exercise:

1. Find the image with the most views on your website.
2. Note key concepts/features from the image. What about the image resonates with you?
3. Explore how these concepts/features could translate to text or video (e.g., a new product page, style guide, blog post, customer testimonial).
4. Don’t stop there! Continue to incorporate concepts/features from your original image into unique campaign ideas across different marketing channels, like social or email.

In just four steps you have months’ worth of marketing fodder, all generated from one image. This activity is possible without data, of course, but that means you’d select the original image based on a gut feeling, not actual consumer behaviors. Sans data, there’s no way to guarantee relevant, engaging content at each click.

Once your organization develops the reflex to marry data and content (as well as other creative efforts), your marketing department can institute processes that track asset performance and reveal new insights over time. However, to earn iterative benefits, start with defining your data needs and positioning data analytics as a resource all employees care about and feel empowered to use.

Read More: Fix Don’t Stitch: CMOs, Beware of the Data Frankenstein Monster You’re Creating