As a media company, each quarter brings new opportunity to grow your audience and your yield. To seize those opportunities, you need to collect, centralize and analyze data. Lots of data.
What’s the best approach to do that? Should you purchase a purpose-built solution that offers low maintenance and ready-to-use KPIs? Or should you invest the time to develop your internal analytic capabilities and chart your own course?
The question of buy vs. build is very different in the data analytics space than it is for transactional systems, such as ad servers, CRM, or order management systems. If you want to build a transactional system, you need to start from scratch since there isn’t a base infrastructure readily available. But data, well that's another matter entirely.
Most business users want to two things:
Off the shelf functionality that makes it easy to start working. (AKA ‘best practices built-in’)
Configuration and customization to the unique contours of their business
Many media analytics solutions deliver on #1 fairly well. They have spent time studying an industry problem and have built a scalable solution. As to #2, there’s more of a mixed scorecard. Most systems DO provide standard design and filtering capabilities and many offer the ability to do some categorization. Problems arise when users get more sophisticated and want to drill deeper or the industry and/or their business changes. Now the old design doesn’t fit as well. And user find themselves asking the vendor for features and lobbying to get on the roadmap. If you’re a smaller company, you know the feeling that the big guys are dictating the roadmap and you’re not getting the love. But if you’re a big fish, you’ve also felt, why am I not as influential as I think I should be? No one likes waiting in line.
Alternatively, there are several massive data-platform players that provide a full ecosystem on which you can build your custom data analytics solutions. Amazon Web Services (AWS) and the Microsoft ecosystem (SQL Server, Azure, Power BI) are top players and offer power and affordability. These systems were designed to support your company’s unique data needs and make it quite easy for you to chart your own analytics course. Tools such as Tableau and Qlik are also empowering end users previously struggling with the limits of Excel.
As to cost, that can vary from less to more than a packaged solution, but usually less since the platforms are affordable. Chances are you already have at least one of these analytics platform implemented within your enterprise, so what’s the point of purchasing redundant functionality? Besides which, every SAAS company out there today calls itself a platform. Platforms are all the rage. But here’s the deal, they’re not whole ecosystems capable of managing all of your data needs; they’re point solutions. Ultimately, those solutions will need to be integrated back into the larger internal data stack so the idea of maintenance-free usage is somewhat of a mirage.
So what’s the best approach: buy or build? Honestly, we believe maybe a bit of both. Certainly there are times when managing a huge overhead of say, hundreds of data source connections like Ad-Juster does, offers value. But taking the broad view, there’s a lot to be said for an analytics stack that’s independent and allows you to use data to fit your company strategy. And equally important, KPIs and forecasting are sensitive IP that should be owned and actively managed - it shouldn't be a black box.
If you’re an analyst, you got into this because you like answering critical questions for your company. You didn’t sign up to be in the IT group. And you aren’t interested in reinventing the wheel. To quote the Henry Fonda in The Wild Angels : “We wanna be free to ride our machines without being hassled by The Man!” I guess that sums it up: whatever technology and relationship gives you the maximum power and freedom, that’s the one for you.