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#14 Using the Data Advantage Matrix to think about data strategy

As we all seem to be planning for the year ahead, one question that we’ve been hearing in a lot of office hours lately is about data strategy. Some companies already have a framework in place but want to re-evaluate them, while others are taking first steps with their data strategy and want to understand how to prioritize data initiatives.

In this week’s digest, I wanted to introduce the “Data Strategy Matrix” as a tool to help you design your data strategy. Some time back, Prukalpa wrote an in-depth article about the Data Advantage Matrix and how to use that to find your path to creating a data strategy. Here’s the article: Link

The key takeaways from before we deep-dive into the Data Advantage Matrix are:

💙 There’s no one path to creating a data strategy. Remember that your business is unique, so the data moats and advantages that you create for yourself will be unique from every other company in the world.

💡 When you think about prioritizing possible initiatives, start with three fundamental questions:

  • What kind of data advantage will this create?
  • How could this initiative impact our company?
  • How much effort will it take for us to get there?

Great, now let’s briefly look at how the Data Advantage Matrix works.

The rows represent the type of data advantages that companies can create, and the columns are the three stages for each of those advantages.

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Broadly, there are four types of data advantages:

  • Operational: This is about understanding the levers that drive your business, then using them to improve operations. A key aspect is making data available and understandable to those who are making daily decisions. An example is Gojek’s daily updates about key metrics, which its first CEO Nadiem Makarim used to form an intuitive sense of what was breaking.
  • Strategic: Every company makes a few critical strategic decisions each year. The more data-driven these decisions are, the more likely that they will jumpstart growth or success. An example is the Government of India using geo-clustering to open 10,000 new LPG centers.
  • Product: This is when companies leverage data to drive a core product advantage, one that separates them from competitors. An example is Google’s “smart compose” auto-completion feature.
  • Business opportunity: This involves using company data to find and create new business opportunities. An example is Netflix Originals, where Netflix started to produce its own TV shows and movies based on its data about what people want to watch.

Next, we’ve got the three stages of each data advantage:

  • Stage 1 (Basic): This is a quick-and-dirty MVP that uses basic tools (e.g. SAAS products, Google Sheets, Zapier) and no data specialists. This tier is great for quickly deploying and assessing new initiatives.
  • Stage 2 (Intermediate): This tier includes investments in data platform tooling and data specialists or teams.
  • Stage 3 (Advanced): These are best-in-class data initiatives with specialized teams for each use case or project. When you get to this tier, you’re basically a case study for what it means to be a data-driven company.

If you want to know more, here’s the link to the detailed article, where Prukalpa shows how to use the matrix to prioritize data initiatives for a fictional SAAS software startup and a cab aggregator. If you have any questions or want to discuss this in detail, shoot me an email. Always happy to help over an office hours session! Speak soon, Nandini

P.S. The purpose of these emails is to share learnings and best practices to empower our community of DataOps Leaders. All the previous editions of these weekly digests can be found on our community website. Though we have put a lot of thought into curating the most relevant content, if you do not wish to have access to this, you can choose to opt-out by emailing me here: opt-out 🙂