Why data thinking is integral to product thinking? – Part 1

Data thinking should form the core of product thinking

Data thinking should form the core of product thinking


Increasingly, product managers are looking at big data and data analytics to review product performance. Technology has enabled the access to gather real-time insights from millions of data points, at a scale never imagined before. Smartphones have been at the forefront of this evolution.

But mostly, product managers use the data for reviewing performance of market products. Employing data science before product is market ready is still a nascent behavior. This happens because product data sciences came much later into the game than product management. And to an extent, many still see data sciences as an extended tool to accelerate and increase the scale of market research.

While that is still a useful strategy, arguably, it is a little too late for considering path breaking results from data sciences in changing product fortunes. And that is exactly the reason why companies such as Facebook, WeChat and Google are able to innovate so disruptively compared to the competition. Sitting on tonnes of user behavior data, these firms use data for product innovation too. Successful firms have realized that data thinking is as important to product management (thinking) as any other element – and it deserves a spot from day 0 of the product life cycle.

Is it the right product? Is it the right method to build the product? Is the user ready for the product? Is the product robust? Some will argue these questions are subjective and do not necessarily need an intervention from data sciences for decision-making. But the turf of businesses and products isn’t the same as ten years ago. Two important developments have been made. Firstly, businesses have moved to platforms (thinking). Platforms means tonnes of data that can be analysed, optimized and designed for success. Secondly, data is coming in ways that was considered probably impossible before. The scale at which data is available right now – is unparalleled in the history of the world.

Re-imagining product thinking by syncing data thinking into it is the key. For convenience, I have recreated the product management framework below – adding key elements of data thinking into it.

Data thinking for product management

Data thinking for product management

Arguably, data driven thinking shouldn’t be limited to assess merely effectiveness of marketing campaigns and consumer targeting. Albeit, if there is something to be focused upon, it should be user needs and habits. I will discuss the impact of assessing user habits in a subsequent article in this series. For now, it is important to understand that every stage of the product management framework is helped by data driven decisioning. Think of data as additional ears that the product manager now has.

Thinking data doesn’t mean seeking non existent trends from available data. Product thinkers must employ a robust framework while using the help of data to make useful analysis. I call such a process to be Need-Search-Deduce (NSD). First step is to identify the data Need. I say it because often, decision makers fall prey to available data and trying to make some sense out of it. Such analysis can be misleading. Second step is to Search if that data or its analysis is available. Two things should follow. If it is available, whet if indeed the data is conclusive. If not, explore if the data can be accessed or tracked. That opens up new channels of innovation for common good. Third step is to Deduce the data insight. Importantly, deductions should be made on the premise or hypothesis and not otherwise. Also, deductions should be actionable. Making deductions actionable should be the project manager’s responsibility.

In subsequent articles, I will further delve deeper on the topic.

Anubhav, a data scientist, writes about new developments and future trends in the machine learning and data analytics domain.
He can be reached at anubhav@thinkbigdata.in
Follow him on Twitter at: https://twitter.com/think_bigdata


  1. Rahul Ranjan December 25, 2016
    • Anubhav Srivastava January 1, 2017
      • Rahul Ranjan February 1, 2017

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