Data Stack Evolution: 3 Changes Media Companies Should Make to Monetize Data

There has been a clear trend in the media space for companies to expand their revenue offerings by entering the data business. Across the sector, there has been talk of launching data products and about the need to bring in new types of staff, especially data scientists, to help make a run at generating new revenue from data. But if you’re going to make data a part of your culture, the first step is to recognize and adapt your tech stack to meet the data needs of customers. Let’s take a look at three of the biggest changes media companies need to make to in their “data stack” in order to make data a true revenue driver. Change 1: Data Alignment Can No Longer Just Be A Goal Let’s face it: For years, media companies have talked about the challenges of their disparate databases and how bringing these data sources together is a key to future growth. This challenge brought about the need for data warehouses, data lakes, and digital management platforms (DMPs). But despite all the hype, most media companies still have a fractured data architecture. Webinar platforms live in one area. Event data sits in another. Website and email data in another. If companies have made some moves to unify their data, these disparate databases flow into a single database. However, often that database has poor visualization tools needed to make the data actionable. If you’re going to be a true data player, it’s time to remove the fractures and come up with a true structure for developing a unified data lake or data warehouse that brings data together. So what’s the difference between a data lake and a data warehouse? A data warehouse brings together structured data while a data lake brings in raw data that can be ultimately mined. No matter what data storage structure you choose, the key is to get a structure in place. If data is not brought together, then there is no way that is can be mined and turned into a valuable product offering for customer. Change 2: Use Third-Party Data to Make Your First-Party Data Stronger Over the last year, I’ve heard many colleagues talk about the benefit that B2B media companies have in the data arena. With many media companies still conducting audits, there is a feeling that there is a rich first-party dataset sitting within a media company that can be sold to customers to make money. Here’s the problems with this thinking: First, we cannot forget that an audit is taken at a moment in time. What a user said at the time of an audit may not reflect their content and buying needs today. Second, many media companies have turned to short-form audits when qualifying users for their pub. This type of audit provides a basic set of information that can be used for audit purposes but falls short in updating key demographic area, thus making your data less accurate. The final problem with the mentality above is that it does not take into account the additional data that can be obtained on users through outside sources. We all know that the problems at Facebook this year have made third-party data a bit less valuable. There are, however, still plenty of ways that third-party data can help. Here’s a classic example: At Edgell Communications, we had two people from Nike download two distinct reports in a 24-hour period. One listed the company as a retailer. The other listed Nike as a manufacturer. Both offered different revenue numbers. By using third-party data, we could bucket those used under Nike as an apparel/footwear/ manufacturer/retailer with the same revenue number. In the end, this type of work was extremely powerful for Edgell’s data business because when we handed that lead over to the customer, we could show how the lead was aligned with a major retailer while also providing the title, contact information, and area of interest the user was working in. Ultimately, by using third-party data, we made the lead more valuable to the company. There are tools available that allow you to append first-party data on top of your data to add value. For example, tools like LiveRamp allow you to take your offline data and align it with a unified digital ID. Then, this data can be tracked around the web so that you can gather additional first-party data on the user. And let’s not forget remarketing/retargeting. These are great ways to take your data and extend the reach of that data on the web to better reach your audience. The key here is that media companies need to understand that their data is not the only data on their users. We have to understand that our data is a starting point and that it is key to layer additional data on top of our data to add value for our customers. Change 3: Improve How You Ingest Customer Data Probably one of the biggest changes in the data stack surrounds how media companies align their data with client data. In the past, media companies have offered various data products and sent data to the customer for additional processing and number crunching. But as marketing budgets have thinned and clients are looking for better ROI, there has been a push by clients to have media partners ingest their data and layer it on the media company’s data in order to extract unique insights for the customer. Here’s a good example: Let’s say that a client has a set of leads in nurturing. By layering those leads on top of the media company’s data, the client can get a better look at the prospects’ habits and better target with a more personalized marketing campaign for that group of prospects. Don’t Forget Visualization The key to a successful data business goes beyond simply getting the data right. All too often, media companies get their data in place but need the IT team or data scientists to run reports that can be leveraged by the sales team. For years, many have used Tableau to handle data visualization. But if you ask the average user, Tableau is hard to configure and use and is often a key tool only for power users. Fortunately, a new set of tools are coming out to help bridge that gap. Tools like GoodData and Looker have arrived that are starting to put the power of visualization in the hands of business users. If they deliver on this promise, this will only bode well for a more mass rollout of data products for media companies.

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Data Stack Evolution: 3 Changes Media Companies Should Make to Monetize Data

Rob Keenan

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Rob Keenan