Over the last few weeks I have laid out some key concepts of mind+machine analytics, so let’s now take a look at some trends in the world around mind+machine analytics, in particular the nature of the relationship between the service provider and client.
For most things in life, success means getting the right outcome. If the result is not what you expect, you probably won’t be satisfied. Why should analytics use cases be any different?
Decision makers want their invested resources to produce insights, not just gather a collection of data scientists, big data tools and AI algorithms. And yet, 99% of all analytics delivered still relies on input-based pricing, where the input is defined regardless of output.
Input might be defined by salary or hourly rate, whereas output is defined as a product delivered—for example, a qualified early-stage sales opportunity, a pitch book for investments, or a patent landscape. In mind+machine analytics the direction of travel seems to be towards output-based models, where the ‘units’ and the corresponding prices can be clearly defined.
Where does this trend come from? Clearly, cloud-based offerings in various domains have created demand for such models in analytics as well. For example, Microsoft Office is now sold on a pay-as-you-go (PAYG) model. It’s logical to apply the same model to analytics as well.
What are the drivers for output-based, PAYG models in cloud or enterprise mode, and what are the benefits for the customers?
Of course, there is also the vendor’s perspective, be it an internal ‘vendor’ like a central data analytics team or an external vendor like InsightBee. There are advantages and disadvantages.
Let’s analyze the advantages first:
Let’s not forget to consider the disadvantages:
This model has worked extremely well for software providers thus far. In the mind+machine market, the first few models are becoming available now: InsightBee for Sales Intelligence or the Banker’s Studio for investment banking pitch books.
Of course, not all services will become output-based, but the philosophy of analytics use cases forces everyone to focus on the output rather than the input. With the predicted explosion of analytics use cases, output-based thinking will be essential if you want to keep costs from going through the roof.
In my new book, Mind+Machine: A Decision Model for Optimizing and Implementing Analytics, I provide more detail about how engagement models are changing and other trends that will affect data analytics in the near future.