Common Myths Tied to Data Monetization

Bigfoot, Loch Ness, Chupacabra. We’ve all heard them: these stories gain traction because of their folklore element. Someone, somewhere, saw something that they couldn’t explain, and in the course of investigating, a fantastical tale emerged that captured the minds of many.   The same can be said of the business world. All too often you hear of “industry standards” and “best practices”.  It’s hard to pin down where they started, and often even harder to figure out why they’re perpetuated. Most frightening is that many of these standards or practices have actually morphed into myths. What may have once originated from someone, somewhere trying to help a specific customer in a specific situation has now seen so many iterations that it simply no longer holds true. When it comes to what works best when monetizing data, we’re finding many a myth that needs busting.

As a product manager, you’re probably facing growing pressure to package or “productize” your data. Your organization may be in search of greater return on their Big Data investment or could be looking to add incremental value to an existing product. No matter the situation, let’s go through some of the most common myths concerning the creation of data products.

Myth: No One’s Asked for It

Fact: While members of your organization may not be asking you for a data product explicitly, they might be saying it indirectly.  Their requests could be hidden in questions posed to you, your sales or support teams. Questions such as:

  • How do I compare to others?

  • How do I compare to the industry average?

  • Can I get more frequent access to my data?

  • Can others in my organization get access?

  • Does a summary version of this data exist for my boss?

Don’t wait for a specific product request, but listen to what they are asking the data to do. Don’t be surprised if more than one request is identified.

Myth: You Can’t Monetize Data That’s Already Owned

Fact: The value isn’t in the ownership of the data itself, it’s in the value add of the industry-specific metrics, customer benchmarks, and recommendations. The data itself is not what’s being sold, but the insights, metrics, algorithms, display, etc. that’s baked into the analysis. Remember all those questions in the previous myth? Providing thoughtful, easy to navigate visualizations that guide others to those answers is the key to monetizing data. Don’t position a data product as easy access to raw data, but rather as a solution that solves a problem.

Myth: More is Better

Fact: Not really.  This is probably the most common myth encountered and can often be one of the hardest to overcome. Those asking for data products often think that more is better – more data fields, more ways to “slice and dice” results, more metrics, more dimensions, more chart views. In their minds, they’re asking for flexibility to manipulate the data. The reality is that these requests almost always stem from uncertainty. They’re unsure what exactly to do with the data, so they figure they might as well as ask for all of it.

Our experience suggests that most users want to be guided to their answers. They want the data presented to remove uncertainty -- not just raise more questions. Users, particularly non-analytical ones, don’t invest more than a few minutes using data trying to answer their questions. Sure, creating the uber - report with lots of filters, a date range selector and the ability to download the report is pretty easy to implement.  However, if the member of your organization can’t easily derive value, then they won’t use what you’ve given them and even minimal efforts on a simple report download interface are wasted.   

For example, compare the two reports below. In the first, the user is confronted with so many filters, columns and data points that making an informed decision from this information would be extremely time consuming. In the second, the user is immediately drawn to the key pieces on data that are needed to quickly understand the important details of what is happening and what’s driving the information.  Investing time on designing for the data consumer and providing them a clear path of guided exploration is the way to go.

Assembla filtered view
Slice campaign example

So tell us, what’s the most outrageous request you got for a “more is better” data product or visualization report? Send in your stories to info@juiceanalytics.com with “more is better” in the subject line and we’ll pick our favorite. The winner will get to spend two hour with us and together we’ll turn that unwieldy request into something functional, informative and cool. Bonus points if there’s a snapshot of the current report attached to your story!  

Think you’ve run into a data product myth that is more mysterious or pervasive? Drop us a note at twitter.com/juiceanalytics.