The Race to Data Monetization

The race for data monetization is on. As businesses collect more and more data, they are looking for ways to turn the expense of storing raw data into valuable insights and new revenue streams. With the rise of Artificial Intelligence (#AI), companies can now extract insights and create value from their data in previously impossible ways. However, success doesn't come simply by plugging your data into a ChatGPT prompt (yet).?

A common problem with new data initiatives is the "Field of Dreams" fallacy -- believing that by simply building a shiny tech product, customers will clamor to buy it. Unfortunately, expensive 'big bet' data programs fall into this trap by approaching their strategy with this three-step process:

1. Establish a data platform?- gather data from various sources and store it in a central location (e.g., Data Lake).
2. Generate meaningful insights?- use data analytics tools to identify patterns and trends in the data.
3. Take actions based on insights?- use the insights to make decisions about how to improve business processes or operations or build new products based on available analytics.

IT leaders often attempt to move through these steps sequentially, but that inevitably fails because customers (and CFOs) are demanding faster results than this approach offers. Additionally, it's high risk. All too often, data teams build a "data palace" that has all the trappings of modern architecture (e.g., APIs, data mesh, cloud auto-scaling) built for "everything" but not optimized for anything. As a result, projects run over budget and extend past timelines. The sales team is left without anything clients want to buy, and 'the business' becomes skeptical of the actual value of data.

This scenario is the "data race," in which businesses try to get the most value out of their data as quickly as possible. Based on popular media articles, racing to generate value from data may seem easy. The reality is many obstacles can hamper progress along the way.

Data Monetization Challenges

• Data quality issues. Data can be inaccurate or incomplete, which can make it difficult to generate meaningful insights that scale beyond a proof of concept ("POC")
• Lack of skilled resources. Data analytics is a complex field, and businesses often lack the skilled resources to develop, implement, and run (i.e., operate in production) data initiatives. POCs are often tested with vendors or consultants (which isn't necessarily a problem), but emerging data organizations often struggle with bringing concepts into production.
• Lack of support from senior management. Data initiatives can be expensive, and senior management may not be willing to invest in them unless they can see a clear return on investment.?

Despite these challenges, data races can be a valuable way for businesses to gain a competitive edge. By quickly identifying patterns and trends in the data, companies can make better decisions about improving their operations.

Here are some tips for?winning a data race:

1. Start with a clear goal. Before you start any data initiative, it's essential to have a clear goal in mind. What do you want to achieve with your data? Once you know your goal, you can work backward to develop a plan to achieve it.
2. Get the right people on board. Data initiatives require a team of skilled professionals. Ensure you have the right people in place before starting your project. Explore 3rd party solution providers, but having a core team in place to vet solutions across modeling and engineering will help avoid headaches down the line.
3. Think lean &?agile?- your most significant advantage as an emerging data organization is speed. Early on in your journey, derisk the likelihood of terminal failure through build-measure-learn feedback loops. Clarify your assumptions about the data, predictions, and end-user experience so you can test and validate them early on. Don't expect one smooth lap around the track in the race for data monetization. Keep your objectives locked in, but think like a startup and experiment with the optimal mix of data, insights, and action, ensuring your 'pit crew' (data science & engineering team) is supporting you each lap.
4. Get the right tools. There are a variety of data analytics tools available. While it's fun to shop for the shiniest tools available, remember to keep the long-term value in mind. Suppose an easily integrated low-cost solution supported works. In that case, it may be more efficient long-term than the higher-performing tool that requires an army of support.
5. Place bets. Data races can be a long-term process. Don't expect to see results overnight with one unicorn solution. Before writing a line of code, you should have a stable of at least ten use cases split across quick wins and moonshot bets. Then, you can evaluate complexity vs. payoff to sequence the work intelligently. This portfolio-based approach will also help the data and engineering teams build a data platform built to spec vs. generic and "over-built."

Data monetization can be a complex and challenging process, but it can also be very rewarding. By following these five basic steps, you can build a successful data monetization plan that drives tangible results for your organization. Don't build a "field of dreams;" instead, create a lean data factory optimized to solve customer problems at scale.