Are you a data company? If you answered, “No”, you are about to become obsolete. Harsh words perhaps, but that is in fact how the economy is evolving. You might even be thinking, “but we just realized that we need to be a technology company, and everyone is now a technology company.” True, but late to the game. Now you have to be good at technology and data (and right around the proverbial corner is machine learning and AI). The question is not only what, but how.
You need to be doing two things with your data right now to remain relevant in the so called digital economy: Monetizing and Commercializing.
Get good at finding the opportunities to monetize your data, and even better at operationalizing it. Only then can you remain competitive. Figure out how you can commercialize your data assets, and you might just invent a whole new revenue stream and business model thus catapulting to front the of economic generator for your industry. This sounds grandiose, but it’s also not only possible, even likely.
What’s the difference between monetizing and commercialization, you ask? Let’s break it down.
Monetizing data is using it internally to create financial value for your company. For example, analyzing the data on your products, customers, and transactions to uncover the best or most likely demographic to need various things you offer, and then targeting them with spot-on messaging to drive new revenue opportunities. If you are not already doing this, I’ve got some bad news for you: your competitors are, and they’re using this capability to steal market share from you. This is why I wrote up front that you need to be here to not be obsolete.
There are two games to be played here: status quo/catch up in the market, and creating new monetization opportunities. As you might have guessed, machine learning (and of course, AI) play a role here. The example above is now the status quo. If you’re not already there, then you are playing catch up (hurry up!). This allows you to continue to compete effectively in the market and not get left behind. It’s necessary, but not where the real action is. The creation of competitive advantage comes from monetizing your data in new ways that your industry hasn’t figured out yet or that allow you to create new revenue sources. I’d love to give some concrete examples here, but I’m not sure how to do it without bending some NDAs, so you’ll have to use your imagination. What data do you have available, how could you use it to figure out a new product or service offering in your current market, or create value in a new market segment that customers would pay you for?
Commercializing data is using the data you have to create new direct revenue streams. It might be selling some form of the data, such as aggregated market information to your partners or other ecosystem players. Or, it might be creating analytical products on top of the data and selling those into the market. Benchmarks, trends, and predictions, for example. The approach you take depends on how proprietary the data is, where the value to the market it, the sensitivity (PII/PHI) of the data, and a whole host of other factors. Yep, machine learning and AI rear their heads here as well. Have some interesting data on which you can train a model and then sell in a “as a Service” model (AWS, Google, and others already do this)?
Here is a made-up and oversimplified example to get your ideas motor running:
Imagine that you’ve been collecting data on a topic for some time. You’ve got one of the most robust data sets on this topic available anywhere in the world as far as you know. It’s gone through a lot of changes over the years, you’ve added data points, stopped collecting others, changed the data model, and maybe it’s not even all in one place. None the less, there is unfathomable value locked up in there. You’ve got some work to do for sure, but after an initial analysis, you’ve figured out that you have what most of your industry cares about and would pay good money to have access to.
But what’s the business model? Do you want to allow customers direct access to the data (hint: probably not) or provide them access to aggregated results or a data enrichment capability to their own data? It may even be possible that you only want to allow customers access to processed results, say from your ML model or other analytical capabilities. The possibilities here are as varied as there are needs and business models you can think of. I have personally seen successful data commercialization efforts in life sciences, healthcare, mission oriented non-profits, media, insurance, energy, and telecom. If you think this doesn’t apply to your industry, get more creative about it, I assure you that it will.
Becoming Data and Beyond
It’s ironic that many organizations used to look at the cost of storing and archiving data as a burden and liability. Today, it’s more costly to destroy data than to keep it around so you can use it to create new financial value. The key, just as with the last wave of become adept at managing and operating technology, is to become adept at recognizing where the value in your data lives and how to extract it. This requires looking at all your data as an asset to be leveraged for competitive advantage (monetization) and new revenue streams (commercialization).
But the journey does not stop there, nor is it even a discreet journey. The time to incorporate analytical capabilities, machine learning competencies, and even artificial intelligence into how you think about the business, is now. Depending on how big, or slow, your firm is, it could take years to make data and machine learning a core competency. Collecting and organizing data is the easiest and cheapest it’s ever been. Using this data as a revenue generator is the preeminent emergent business model of current times. Don’t wait around for that little startup somewhere half a world away to eat your lunch. Start looking at what you have for monetization and commercialization opportunities now.
Next week I plan to write about patterns of data usage. Specifically socially responsible data usage vs. dark patterns of exploitation.
Tags: data strategy