You have a customer in need, you have valuable data and you have the ability of finding the solution from that data. Now it is time to put the cherry on top by building a data product!

A data product is built to addresses a particular type of a problem and it enables an end goal through the use of data. It is not just about analyzing and delivering data, it is a product that delivers results based on data. Most of the services we are all familiar with are big data products or big groups of many data products- such as Google, Netflix, LinkedIn and Facebook. Indeed, LinkedIn’s People `You May Know` function is a data product viewed by millions of customers, and it’s based on the complex interactions of the customers themselves.

Recently, data-driven companies started considering new paths to generate revenue (because let`s face it, who doesn’t have big data now?) and building data products to deliver as a service to customers. A powerful data product can generate new revenue sources, enhance customer relationship, offer a different solution to the market and even revolutionize entire industries.

A successful data product lies at the intersection of key points mentioned below:

Determining your Objective

A good data product needs to be a solution of a specific problem. Therefore the first step is to pinpoint your or your customer`s need and identify how you can solve this problem. Find out where you need more information to perform your job better and look for the insights and unique solutions that will lead you to make better decisions. With an objective and focus, you will have a definite foundation of your data product.

Determining the Characteristics of your Data

Customers don’t need a big pile of data, they need solutions. Therefore the foundation of your data product should contain a differentiating content containing various components that will bring value to your solution.

The characteristics of your data should contain:

Depth: You need to reach data deeply, from each individual source to find correlations that weren`t visible before

You need to reach data deeply, from each individual source to find correlations that weren`t visible before Extensive: Your data has to have appearance across an entire segment or industry etc.

Your data has to have appearance across an entire segment or industry etc. Multiple Data Perspectives : It is very important for you to be able to compound data from different sources across industries. This will bring you various perspectives on your subject.

: It is very important for you to be able to compound data from different sources across industries. This will bring you various perspectives on your subject. Distinct: The visual representations that reveal patterns have to make sense, be understandable, readable and be presentable.

Creating a Collaborative and Automated Platform

You should have a single platform that enables you to build an entire data product from start to end. All of the components in the process should work together, instead of solving pieces of problems separately which will make the management harder.

A data product which requires manual execution at multiple points is not effective. An ideal data product should require minimum human interposition throughout the process.

Testing in Detail

In addition to “What should we build?, you also have to consider “What should we test?” and build your product roadmap considering all of the objectives including measuring the steps. This helps you to have an idea of what sort of results you should expect and leads you to have a list of future requests.

Product Usability

A data product should be comprehensible and useful for every team in your organization. It is important to integrate your data product to your existing software and tools. This will allow your teams to turn insights into action.

Getting the Most out of Subject Matter Experts

Building a powerful data product requires a great collaborative effort from multiple industry experts –business analysts, platform and data engineers, data scientists and the right product manager. However, building a data product doesn’t just end at the product launch. You also need to have experts in your sales team, marketing team, stakeholders and legal team as well, to bring out the best practices.

With developing a successful data product, your company can benefit either from Direct Revenue where you charge customers, or Indirect Revenue where you enrich your existing services and drive customer loyalty, generate cost savings etc.