min read

Are you getting the full value from your data? Chances are, you’re leaving something on the table.

Despite all the time, energy, and money invested in data, companies are still likely missing out on the full potential of their data.
Written by
Alec Whitten
Published on
17 January 2022

So you’ve spent a lot of effort collecting data. You’ve likely also spent a lot of effort cleaning it and preparing it. But after all that time, energy, and money invested, it’s likely still stuck within the four walls of your company, causing you to miss out on the external opportunity. In this post, we’ll look at the data-value maturity curve and showcase why a Data as a Product approach could unlock the full value in your data.

Level 1: It starts with the dashboard

We all love a good dashboard - charts, metrics, and trendlines. For many teams, this is the final output of their data initiatives. After all, in most cases, just being able to answer the question “What’s my current revenue?” is good enough.

In more recent times, descriptive analytics has been up-leveled thanks to the rise of Machine Learning and other statistical means to start answering predictive and prescriptive questions: “What is my revenue predicted for this quarter?” and “Which accounts should we target to drive the biggest impact?” These efforts have been transformational for companies and have automated mundane tasks, elevating the role of the analyst.

Level 2: Data-driven operations

Building on these advantages, companies further along the data-value maturity curve have begin to empower machines to take autonomous actions and decisions using these predictions. We’ve seen this with the rise of Decision Intelligence where companies combine real-time analysis with autonomous decision-making: “Predict where we have the biggest demand shortages and automatically route excess inventory to the nearest warehouse.”

This has taken internal operations to the forefront of efficiency, with machines and models being able to take actions at a pace and scale that humans can’t. Once again, the main goal here is to optimize internal operations - better, faster, cheaper.

Level 3: The emergence of Data Product Applications

Here we see the first examples of companies beginning to use data to open brand new value streams. Companies realized they could use their data to create new products that could never have previously existed. Take the example of Stripe Radar, which applies Machine Learning to it’s vast network of transaction history to create a real-time fraud detection / prevention application. Leveraging the data from its core payments business, Stripe was able to create a brand new product (and in turn, revenue channel) to bring to market.

Data Products hit the stage as a powerful new class of applications, serving as core applications for new startups and add-on revenue streams for existing companies. Even applications you would otherwise not consider to be Data Products like Netflix and Spotify integrated their data into their products to generate recommendations and keep you binging shows well into the night.

Level X: The final frontier, Data as a Product

So far, we’ve shown how data has been used to answer internal questions, improve internal operations, and even create new applications. But this begs the question, if data is so valuable, wouldn't other companies also want to use the same data? Other companies want data to answer their internal questions, improve their operations, and feed into their applications, so why should you not benefit? Here's how leading companies are starting to share their Data as a Product externally to create new value streams:

  • Unlocking analytics for your customers - Today many software products have an analytics module, but customers are increasingly demanding access to the underlying data itself. They want to analyze the data on their terms, join it with other data sets, and use it to power their own applications. Software providers are beginning to embrace this with external-facing APIs and now even zero-ETL data delivery (see Salesforce’s recent announcement). Providers that do this have been shown to increase revenue and improve customer stickiness.
  • Data marketing and partnerships - If data is the new “oil” then it goes without saying that your partners need your data to be effective partners. Leading marketing teams are using their company’s data as PR fodder by sharing aggregated and anonymized data with the press and media. Additionally, Business Development teams are using their company’s data to sign partnerships (like Carta and Sequoia’s compensation partnership) that are both beneficial for their mutual clients and open new customer acquisition channels.
  • Data monetization - SaaS vendors are selling an aggregated and anonymized version of their data as a new product SKU. With the rise of AI/ML, businesses are looking externally for new and proprietary datasets and SaaS vendors are sitting on one of the biggest untapped goldmines. We’ve seen from our clients that these datasets are in high demand from hedge funds, banks, and other research firms, especially in industries like Supply Chain and Retail.

…And this is just the beginning! As more companies re-evaluate the role data plays in their business, more new use-cases will emerge. For now, it’s clear though that data should not just be viewed as an internal asset but one that can open new and previously impossible external value streams.

If this is interesting, please reach out, we’d love to share more about how we’re seeing companies treat their Data as a Product and help you unlock the value in your data.

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