Saying “data is the new oil” is an old story, very well known. Some businesses recognized the opportunities for growth and improvement hidden beneath the vastness of data they collect. However, there are still many many organizations that do not understand or just don’t leverage to the fullest the potential of the information age we live in. Steps to get there are by no means trivial, but the ROI always proves the worth of the investment.
Success stories of harnessing the data potential are numerous.
For example, TaxJar, a SaaS platform helping customers (e-commerce) with their taxes, drilled down into transaction data and realized their pricing model was losing them money when it comes to the accounts with large numbers of transactions. Once they identified the issue, it was straightforward to fix by creating pricing tiers and offering custom prices for enterprises. This resulted in revenue tripling in a year.
The retail industry is already using any kind of data it can acquire to satisfy its customers and offer them better deals. However, knowing that 71% of the customers prefer ads tailored specifically to them, we can safely say there is still a big opportunity in knowing your customer personally and understanding their behavior and needs. Maybe that opportunity lies in matching the weather forecast with your webshops’ latest arrivals like very.co.uk did some time ago.
Or, there is always an interesting example of how Netflix uses data collected from user searches and ratings, for example. One specific data measure is if certain shows are watched on specific devices, or if the credits are skipped, or the show was paused at some moment… All of that is used as a foundation for building a personalized show recommender system and improving customer retention.
Businesses that want to tackle data potential with a high level of seriousness certainly cannot do it ad hoc. It needs strategy and implementation. The first step is understanding the data your organization has access to and can collect and acquire.
This question shows up often too late. When building MVPs, teams often choose the “we will add data collection at a later stage” path. Then, when their MVP becomes a full-blown and successful product, they have to cope with the fact that bits of historical data are missing because they were never produced in the first place, not to mention collected and analyzed.
Data collection should be the level 0 feature of any information system in this day and age. It doesn’t matter if you don’t know exactly what you are going to 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 find out to which insights it will lead you.
One may ask, “But, what data should my platform collect?”.
The answer looks pretty simple, “Every kind at your disposal.”
If your business is a food delivery platform, connecting hungry people with their favorite restaurants, the system could (and should) track each customer’s most common food choices (so it could recommend similar interesting dishes), times of the day when orders were made (so it could predict and offer deals before the user makes the same dull order again), locations from which the orders are made (so it could recommend restaurants nearby)…
Or… if your business is casual gaming, the gaming platform could track what games people of similar user profiles play (we already assume that the platform contains information to build user profiles based on their gaming history and behavior) and offer them other fun games. This kind of organization would literally sleep on tons of data you may need when deciding if it makes business sense to spin out a design studio company or not.
I believe it is clear enough that possible scenarios leveraging data potential are almost indefinite and that, for modern businesses, the importance of data is not negotiable.
Once the organization understands this and wants to transform its current ways into a real data-driven culture, a data strategy should be put in place. This is where the data maturity model can help.
There are different definitions of the data maturity model, but in general, they all do the same: help organizations structure their data capabilities.
What does this mean? It means that the organization that took this path should:
- create its data vision and communicate it to everyone
- define goals it wants to achieve
- educate people on all levels of hierarchy
- measure and monitor its data capability
simplified view on data maturity model by Gartner, original source: link
What does it mean for organizations?
It means that understanding of data they possess and can acquire, along with successful utilization of that intelligence, can have only a positive impact on the growth and overall health of the business.
Only a few companies are considered to be at the “mature” stage when it comes to the data maturity model. It is not a surprise those are some of the most powerful ones, such as Internet giants of the FAANG group, for example, along with some financial institutions and retail organizations.
To get to that level takes time — which is a resource modern businesses do not have in abundance. Quite opposite. So the best thing every company can do is build its services, products and platforms with data analytics in mind, from the very get go.
Author: Dusan Zamurovic,Software Architect at HTEC Group