3 Things You Can Do to Mature Your Analytics Practice
With the pandemic disrupting business in unparalleled ways, no company has been spared the imperative of quick decision-making and operational changes. The question is, amidst a volatile market, have your decisions been driven by data? That’s an easy “yes” if a sophisticated data and analytics practice was built into your business model prior to Covid-19. But what if, like many, this competency has been idling on your organization’s back burner?
It’s simple: now is the time to take stock of your approach to data.
If you need convincing, consider that we have been catapulted five years forward in digital adoption. Five years! The nationwide lockdown in the spring made digital nonnegotiable almost overnight, forcing many to adopt digital practices that they may have otherwise avoided. Consumers suddenly required an ability to makes purchases, do business and receive healthcare online, and companies needed to respond quickly. Business models were therefore flipped on their heads, with more revenue being driven by ecommerce and online services than by offline – affirming that digital is, in fact, a mature revenue-driving channel. And what’s more, it’s highly measurable. But with that, analytics adoption is required.
If your organization wants to keep pace with a changing and, as we’ve seen, unpredictable marketplace, the strongest move you can make is to be able to capture and analyze quality data. This means that advancing your data and analytics practices is paramount.
Here are three things you can do right now:
1. First, embrace that “disruptors” will only keep coming
We will all certainly remember Covid-19 as the major disruptor of 2020. And seeing that the last pandemic was over 100 years ago, you might be tempted to write it off as an anomaly. But the reality is that disruptors will only keep coming. For instance, think about data privacy efforts like the California Privacy Rights Act (CPRA) in California, which will certainly disrupt business when it goes into effect in 2023. Just like the coronavirus, other disruptors will force you to adjust and adapt your business model, and having quality data and analytics to inform those changes will help you to respond nimbly and strategically. It really isn’t a matter of “if,” but “when.” Whatever time and resources you invest into data and analytics now might just save the day in the long run.
2. Determine where your company is in the data maturity model
You can’t solve a problem without diagnosing it, so gaining a clear understanding of your company’s data maturity is critical. There’s more than one way to accomplish this, but a good starting point is Gartner’s model for the maturity of data and analytics in a company. This particular model walks through five levels of data maturity:
Level 1: Basic
At this level, data and analytics are managed in silos, so instead of there being one ‘source of truth’ for the organization, people argue about whose data is correct. Data is used but not capitalized on and analysis happens in an ad hoc fashion.
Level 2: Opportunistic
At this level, an organization is making an effort to advance data quality and efforts, but it is still managed in silos. Although a strategy is in place, it is long and cumbersome and leadership is not clear.
Level 3: Systematic
At this level, the strategy and vision for data and analytics have been articulated clearly and succinctly. Although different content types are being treated differently, company executives are champions of data and analytics.
Level 4: Differentiating
At this level, data and analytics is linked across programs and is seen as the primary fuel of innovation and performance. Executives champion and communicate best practices related to data and analytics, there may even be a Chief Data Officer, and data informs return on investment.
Level 5: Transformational
At this level, data and analytics is central to a company’s business strategy. There is a Chief Data Officer in place who sits on the company’s board, and strategy and execution are aligned and continually improved.
At lower levels of maturity, data analytics is set up to provide hindsight and insight, answering questions like “What happened?” and “Why did it happen?” However, at higher levels of maturity, data analytics offers foresight, answering questions like “What will happen?” and “How can we make it happen?” At these higher levels of maturity, companies are leveraging predictive modeling to optimize performance and anticipate outcomes. While it’s not easy to get to that point, it’s not difficult to imagine why the time and energy is ultimately worth it.
3. Work on getting to the next level of data and analytics maturity
Once you have determined your company’s level of data and analytics maturity, it’s time to dig in. But first, set realistic expectations. As with any model that reflects a developmental process, there aren’t any shortcuts. Moving from level to level will come with growing pains, through which you simply have to persevere. Something like Covid-19 might force or speed up the process, but it will still be a process. Keep your long-range goals in view, but plan an incremental approach to moving from one level to the next.
This will look different for everyone, but here are a few thought starters:
Think about making investment hires with people who have data backgrounds. Hiring non-traditional marketers or analysts to support your marketing teams will provide in-house expertise and provide a unique point of view on how to use data to develop your strategy or better define audiences.
Find partners within your organization who are data-driven. There could very well already be larger initiatives in progress that are aggregating the data you need to make informed decisions. Working to find these partners will save you the effort of collecting and pulling the data yourself.
Finally, understand how to integrate advanced analytics into your decision-making process. With machine learning and AI on the rise, take the time now to learn what those platforms can and cannot inform.
Although this work may be daunting, you will be thankful you didn’t bury your head in the sand. Choosing not to advance your data and analytics practices might save you time, money and effort in the short run, but you can all but be sure you will pay for it in the long-run. As the marketplace disruptors keep coming, if you don’t become data-driven, you might just be driven out of business.