The Nuances of Data Product Design - Pragmatyc - Digital Product Engineering | Enterprise Solutions

The Nuances of Data Product Design

07 Oct, 2021

The journey of any product today needs to be driven by the latest trends in the market. Today, data is one of the biggest trends sweeping the business world. But relying on “expert” data scientists and analysts every time to make sense of critical data and fuel important business decisions isn’t always feasible. Not only does this delay the decision-making process, it also demotivates the non-technical users, as they cannot make decisions on their own. It is for this reason that data products are becoming extremely popular: these are tools or applications that constantly (and inherently) use data to help every day, non-data scientists make decisions. But what do data products really mean for product design teams? Read on to find out!

Data products are becoming extremely popular across sectors 

Offering user-friendly interfaces, data products allow everyday users to leverage modern data science capabilities to make informed decisions and achieve business objectives. By empowering non-data scientists to analyze data on their own, they help create competitive advantages that eventually add significant economic value. There are several reasons why data products are becoming extremely popular across sectors

  • Data products allow organizations to reach their business goals faster, through informed decision-making.
  • They provide non-data users with the insights they need to improve processes, enhance products, and boost customer experience.
  • They allow every user to become a data expert, analyzing data, uncovering insights, and converting insights into action.
  • They reduce the time organizations spend in gathering, cleansing, and processing data, accelerating time-to-value

Things design teams have to keep in mind

Delivering data products to the workforce is a great way to improve business decision-making. But building these data-driven products requires careful planning and thorough detailing.
Here are certain things design teams looking to build data products must keep in mind:

1. Make way for embedded analytics

One of the first steps in building a data product is to ensure it makes intelligence and analysis results easily accessible by users. Instead of integrating data products with a standalone analytics solution, use embedded analytics as an integral part of the data product – so business decision-makers can spend less time processing data and more time analyzing results. By incorporating analytics within the product itself, design teams can help users work smarter and solve high-value business problems more efficiently using data visualizations, interactive reports, ad hoc querying, and visual workflows.

2. Have an API-driven mindset 

Having an open architecture and building API-driven products can further enhance the outcomes of data products. Such a mindset allows for APIs to form the basis of the software architecture, ensuring the product is compatible with and accessible to other products and systems via APIs. API-driven products also help in ensuring a modular framework, allowing design teams to break the product into different modules and deploy them across a distributed architecture – for greater adoption and use across different use cases.

3. Offer a simple user interface

Unlike complex data analytics platforms that are mostly used by data analysts and scientists, data products are used by non-data experts who have little or no experience working around complex data algorithms. For this reason, the user interface has to be extremely simple, intuitive, and easy to maneuver. It should allow users to get access to the data and tools they need for quick analysis, as well as provide them with insights via dynamic dashboards and reports for improved decision-making.

4. Aim for continuous modernization:

Modernizing existing data products by incorporating new innovations is vital to stay relevant and continue winning. Since new features are continuously being demanded, new data sources are emerging, and new API versions are releasing, the analytics functionality of data products must keep up, too. Taking an agile approach to product modernization can enable rapid integration of new data sources, the addition of new analysis functions, and the development of the latest dashboards and visualizations.

5. Constantly incorporate user feedback

In addition to modernizing data products, constantly incorporating user feedback into the design of the product is also critical. Since users use these products or applications on a day-to-day basis, they are the best judge of what’s working and what isn’t. This feedback can be used to make necessary tweaks to further improve the functionality and performance of data products. it can also help in ensuring that the product is up-to-date and continues to meet the evolving needs of non-technical users.

As data becomes the new oil, building and deploying cutting-edge data products is quickly becoming a popular way to enhance business decision-making. By integrating embedded analytics, offering a simple user interface, having an API-driven mindset, aiming for continuous modernization, and incorporating user feedback, you can build new-age data products that enable non-data scientists to carry out advanced analytics – without having to navigate their way through complex logic and algorithms.

Written by Rahul Bajait

Co-founder & Chief Strategy Officer

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