Written by GoodData Author |
Over the years, I’ve worked with many successful companies who have given away something incredibly valuable: their data.
Here’s how it usually goes: Say you have a product or a business line that generates data. Before too long, your customers or partners ask for access to your data, explaining that they believe they could put it to good use.
Typically, I find that companies then simply share their data, instructing their data and business intelligence team to spin out a warehouse and give access to each customer. At that point, they ask the customer to do their own business intelligence work on the data, and settle in to pay for just the management and hosting costs.
You might be tempted to think that your job is now done, but exporting data is only the first step on the analytics maturity curve. There’s a lot more you can do to deliver additional value and, ultimately, monetize your data.
Start by figuring out what value a customer is finding in your data
As soon as your customers or partners ask you to share some of your data, you need to figure out why. Though it may appear to be a simple request, there’s clearly something in your data that is valuable—and could potentially be packaged into a product that can serve as another revenue stream.
Start digging into your data and consider which of your customers have asked for access. Why might they be interested? If you can’t figure it out, reach out to your customers or partners or invite researchers and start uncovering the hidden needs of your data consumers. Don’t sit idly by because it’s not clear at first glance what there is to be found in your data. (After all, we all know what happened to Blockbuster.)
Add semantics to make it easier to interpret the data
There’s a reason why providing raw data in a database or Excel file is just the first step on the analytics maturity curve. Without a description of how to interpret the data, your customers and partners can easily get lost.
To help give data meaning, you need metadata and semantics. And to figure out what kind of metadata and semantics to add, you need to know how your customers are using the data.
Based on what you learned about why your customers were interested in your data, figure out which decisions that data would ultimately support. Are they frequent operational decisions? Or are they more tactical or strategic decisions, the kind that are only made a few times per quarter? Finally, who makes those decisions?
Armed with this information, you can add the right kind of metadata and semantics to serve as “signposts” for your customers, helping them figure out how to interpret and apply the data for their own use.
Package your data into a product and start your insight delivery journey
Finally, once you know what your customers can do with your data and how you can help them interpret it in a meaningful way, you can start to package it all together into a product.
If you’re starting from scratch, you may want to first build a dashboard and test it out on a few select customers. If you already have a product, you can start thinking about how you can apply what you’ve learned about your customers to successfully embed analytics and insights into your application. Or if you lie somewhere in the middle—perhaps you use dashboards but also continue to share raw data—then consider whether there are any ad-hoc data discovery capabilities missing from your current product. (Hint: there probably are.)
Regardless of what you’re interested in doing with your data, it’s a definite step up from exporting and sharing. If you need help figuring out which route you’d like to take, feel free to reach out to our team for insight and support. Let us help you uncover the best way to get value from your data in your business relationships.
Written by GoodData Author |
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