Imagine you have a customer with a problem. Secondly, you have significant data. And you also have great capabilities to find a solution to that problem from the available data. Say Hello to creating a data product.
A product created to address a certain type of issues and enable it to reach its end goal by using the data available at the problem source is called a data product. It is not any simple tool that can help in analyzing the problem and finding the results. It is that product that gives results based on the information available.
Various types of data products
There is a wide range of data products available in different industries. Even by filtering them from various fields to those that meet the criteria of our definition, there are still many products that fit the range. We can segment these into five broad classifications.
- Raw Data: These types of data are mostly used as it comes. Maybe a little bit of cleansing and processing will be done if required but mostly made available as such. The user can select the appropriate data. In raw data products, the work is done mostly by the user.
- Derived Data: The derived data products are those where the processing is done from the provider side. For example, if we are providing customer data, segmenting the customers accordingly, proving the customer areas of interest, their familiarity with an ad or purchasing a product will also be provided.
- Algorithms: In this segment of data product, the data is run through an algorithm. It is either machine learned or otherwise and then return insightful information. Like how a google image works. The picture uploaded by the user helps the search to load similar results. At the back, the product absorbs the feature of an image, segments it, matches the image history and returns with most similar data.
- Decision support: This kind of data products help the user to make the right decision but refrains themselves from making one. The best example could be Google Analytics, WGSN or flurry. Most of the weighted job is done by these data products and provide the user with insightful information in an easily understandable way such that the decision making becomes effortless. The user in this case is still the controller of how to interpret the data and act or in other cases not to act upon the results.
- Automated: Here the intelligence is outsourced within the limitations of a provided domain. The recommendations provided on Netflix or Amazon prime are few examples of these Automated Decision-making data products. This closed-loop has more manifestations physically in the cases of a self-driven car or an automated drone. The algorithms do the entire process and give the user their desired results like the final outputs. In few cases, the AI explains why it has chosen that particular method of action and in other cases, it is completely opaque.
This classification is done based on the complexities internally. Simply a data product should be able to compute and make decisions all by itself with minimal user interaction. The user group of these data products fall under two categories either technical or non-technical. The decision making and automated group are more balanced with a mix of both but the first three categories fall under complete technical users.
The Interfaces of data products
We have gained knowledge on various functional types of data products. But there are again multiple ways to present these data products to a user. With clarity of implications in their designs. What interfaces are these?
- API’s: In this interface, the data is integrated from all the sources and made available across the channel of operations. Good practices of the product are ensured such that this interface can give well documented, intuitive information that is required by the user to work.
- Visualizations & Dashboards: This interaction is all about dealing with numbers for deriving statistical literacy and competence. Like doing the tough job behind the scenes to provide the user with a clear picture presentation of what the action course should be.
- Web elements: This interface is still new to the users. It has been in extensive use for the last 5 years for designing data products. Most recently they started including augmented reality, robotics, voice and other things like AI. The design details differ from each other. But they are still in the considerable region, as an interface that can decide for the user and explain why.
Tips to design extraordinary data products
- Setting an objective is the first point to design a great data product. A good data product is only justified when it can solve the problem for which it has been chosen.
- The data should be possessing great characteristics. The customer is never interested in volumes of data you’re working with but needs the solution for their problem. The key characteristics of the finest data are depth, distinct, extensive, adaptable to multiple perspectives.
- Create a platform where you can build the data product from beginning to ending. The project and the product will function effectively when all the components of the interface and design work together. Doing it in small pieces and gathering them together will be very tough to manage and affects the quality. A product made with less human interposition is the ideal one we are talking about.
- It is not about what to build and how to design, but how effectively you are planning to test is crucial. It helps the design to show your future possibilities thereby creating a road map of what to expect in this journey of designing a data product.
- A data product is not just about solving an issue. A great data product is something comprehensible and useful for every sector of your business. It is very important to integrate the existing tools into the new product to convert insights into a process of action.
- Bring in the subject matter experts at every phase of designing a data product. It ensures the high quality of the design with minimal possibility of errors. Because to create an accurate and commendable data product you need a team. A team of data engineers, platform engineers, business analysts, product manager, data scientists. And, it just does not end there, by launching a product. You need a great marketing team, sales group, legal mentors, stakeholders and so on. So collaboration works wonders.
Understanding what is a data product, the types of them and the interfaces that are used in designing it helps to create a great product. All that you need to ensure are the tips given on how to create successful data products. Such products can generate direct revenue to your organization as you charge your customer, or indirect revenue by enriching your current services. Either way, you are going to see visible and possible growth in your journey.
