Products are created for customers and sales. That applies to data products as well. Data Product toolkit helps you to focus on defining the value proposition for your customer and think about who is the primary customer. You must have a target persona, a stereotypical customer for whom you are offering data products. That persona can initially be fictional, but later on, you should aim for data-driven customer personas.
80% are data scientists
Different data products might have a slightly different customer profile, but in 80% of the cases, the customer is a data scientist. Keep in mind that traditional application developers are data product consumers too as well as their managers. Thus your data product-related material and communication must serve both technically savvy and business-oriented people. At the moment data scientists are the sweet spot as the customers for data products.
Data scientists are a new breed of analytical data expert who have the technical skills to solve complex problems – and the curiosity to explore what problems need to be solved. They're part mathematician, part computer scientist and part trend-spotter.Products are created for customers and sales. That applies to data products as well.
Data Product toolkit helps you to focus on defining the value proposition for your customer and think about who is the primary customer.
You must have a target persona, a stereotypical customer for whom you are offering data products. That persona can initially be fictional, but later on, you should aim for data-driven customer personas.
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Support customer’s existing tool stack
This in something you should keep in mind while drafting data product plans. You need to support the stack consumers are already using. You need to choose which tools and environments your data product needs to support out of the box. Luckily Data Product Toolkit forces you to consider data output channels thoroughly.
If your data scientist customers are hard core PowerBI users, then you must optimize data products for that platform. If they want to use your data in Unreal game engines in order to create XR applications for industry, then make sure data product is found by the Unreal game engine users. You get the picture, right? You should not expect costumers to adapt new tools just to utilize your data products.
Supporting tools and platform used by the customers, reduces the friction to become your customer. It lowers also the barrier and learning curve. Keep in mind that not all customers want to fiddle with huge code stack, but prefer to use low-code or even no-code platforms.
Keeping your eyes on the instant usage of data products is also building capabilities for the future needs.
Quick results with minimal code development
Application programming interfaces also known as APIs are common technical solution to offer access to data products. Even if the stereotypical customer is technologically talented, you should keep in mind that not all want to use APIs and write code to consume your data products. That is because they might not have time, they just need quickly build proof of concept or use existing graphical user interface driven platforms and frameworks. In those cases you need to consider nocode and lowcode platforms as the most likely usage environment for your data products.
Data product customer profile is changing
Future is changing the setting for data products too. To be able to use data products in own apps or environments in minutes without writing any code is becoming the winning feature. Why? Currently we are witnessing lack of developers around the world. Despite of the efforts like training more developers, the situation is not expected to get much better. We will have shortage of software developers in the future. The same is happening with the data scientists as well.
The remedy is to drop the barrier to use data by other people than just hard core developers or data scientist. We - the average business developers - are in the spotlight soon. We are expected to build simple apps and consume data products to create value internally and externally by increasing sales, customer experience and efficiency.
We business people must become “DIY data scientists”. We must be able to use ready-made tools and data products to create quick and dirty solutions for business needs. Thus nocode and low-code solutions are deemed to rise as is expected to happen on the more tradition application development where APIs now reign.
Business oriented segment becomes more important
As I mentioned in the beginning you have two personas among the customers - technically oriented developers and business oriented managers. Nowadays the developer persona is in the focus.
It seems that in the future you might want to focus more on the managers since they are destined to be your data product consumers. The size of the markets is also bigger among the not so technical business people.