More businesses are recognizing the opportunities for growth and improvement hidden within the data they collect. Still, too many organizations are not leveraging data analysis to its full potential.
This is by no means trivial, but the ROI on reaching data maturity is worth the investment.
What data should an organization collect?
Businesses that are serious about maximizing data maturity cannot do it ad hoc. It needs a strategy and implementation. The first step is understanding the data your organization has access to and can collect and acquire.
However, organizations often incorporate a data maturity strategy too late. When building MVPs, teams tend to choose the “we will add data collection at a later stage” path. Then, when their MVP becomes a successful product, they must cope with the fact that bits of historical data are missing because they were never produced in the first place, let alone collected and analyzed.
Data collection should be the level 0 feature of any information system. It doesn’t matter if you don’t know exactly what you will do with it. Collect it from the get-go, store it somewhere — storage is cheap these days — and leave it to your future self to discover which insights to pull from the data.
Let’s say your business is a food delivery platform that connects hungry people with their favorite restaurants. The system should track the following data:
- Each customer’s most common food choices (so you can recommend similar interesting dishes)
- Times of the day when customers make orders (so you can predict and offer deals before the user makes the same order again)
- The locations from which customers make orders (so it could recommend restaurants nearby)
The business scenarios for leveraging data potential are practically infinite. For modern businesses, the importance of data maturity is not negotiable.
Once the organization understands this and wants to transform its current approach into a real data-driven culture, it should put a data strategy in place. This is where the data maturity model can help. There are different definitions for data maturity models, but in general, they all do the same thing: help organizations structure their data capabilities.
* Data maturity model created by Gartner
A data maturity model should help an organization do the following:
- Create its data vision and communicate it to everyone.
- Define the goals it wants to achieve.
- Educate people on all levels of the hierarchy.
- Measure and monitor its data capabilities.
Data maturity success stories
Take TaxJar, for example. This SaaS platform helps customers (e-commerce) with their taxes drilled down into transaction data and realized their pricing model was losing them money when it came to accounts with a large volume of transactions.
Once TaxJar identified the issue, it fixed it by creating pricing tiers and offering custom prices for enterprises. This adjustment led to revenue tripling in one year.
The retail industry is using any kind of data it can collect to satisfy customers with better deals. There’s a big opportunity to know your customer personally and understand their behavior and needs. After all, 71% of the customers prefer ads tailored specifically to them. Some brands, such as Very.co.uk, have gone as far as personalizing their homepage for every customer.
Netflix is another example of a company that makes the most of its user data. The streaming giant measures whether certain shows are watched on specific devices, whether the credits are skipped, and whether the show is paused at some point. All of that data is used as a foundation for building personalized show recommendations and improving customer retention.
How does an organization know it has reached ‘data maturity’?
Data maturity means that an organization understands the data it possesses and can acquire. The organization also understands that successful utilization of data will have a positive impact on the growth and overall health of the business.
Only a few companies are at the “mature” stage of the data maturity model. It’s no surprise that those companies are Facebook (Meta platforms), Amazon, Apple, Netflix, and Google, along with some financial institutions and retail organizations.
Getting to a high level of data maturity takes time — which is a resource lacking in most modern businesses. So, the best thing every company can do is build its services, products, and platforms with data analytics in mind from the get-go.
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