Is Your Company Healthy? Diagnose with the Data Analytics Maturity Model

How your marketing organization ranks in sophistication within the analytics capability maturity model is largely dependent on the level of investment from the business in people, process, and product. As you review the people (marketers, analysts, data scientists, etc.), there are other factors besides job performance to consider as you strategize on the best path for improving your organizations’ analytics maturity. What happens when you do no not have enough representation from any one of these people roles? The obvious answer is that your progression to analytics “Quant-dom” will be hampered. To make up the difference, someone or something has to step up and fulfill the function of the lacking role.

What we see across the industry and among our customer set is that executives are working to drive analytical maturity and sophistication throughout their businesses. Most companies have the ability to do some descriptive and diagnostic analysis, but few are able to do predictive or prescriptive analysis. The chasm between the demand and supply of data scientists is a well-studied and documented current and future problem. You can’t hire enough data scientists, despite your CMO’s budget. Our strategy? Empower digital analyst to do data scientist work and naturally teach the analyst how to become a scientist. To better understand this problem, the following represents what I hope to be a useful analogy.

Think of your company as a person who might not be as healthy as possible. Let’s call your company “Al.” In this scenario, Al’s friends (or in the case of your company, digital marketers) notice he’s not as energetic as he usually is. After much prodding by Al’s friends, Al decides to visit a clinic. At the clinic, Al tells the nurse (an analyst) what’s ailing him—he describes his symptoms. The nurse takes his vitals to confirm something is wrong.

At this point, Al is triaged and put into an examination room where another nurse asks more detailed questions in an attempt to isolate Al’s health issue. The doctor (data scientist) arrives, asks a few more questions, and writes a prescription. Al’s health concern is addressed and (hopefully) cured. All three participants (friends, nurses, and doctor) worked together to get Al back on his feet.

So what happens when there aren’t enough doctors? More to the point: What happens when there are not enough data scientist to fill their role in the data analytics maturity model? This is where quants become data nurse practitioners

There’s no way to avoid going through this process if Al is going to get healthy. You can’t just look up his symptoms on and then hope that the right prescriptions will magically appear on his doorstep. Just as WebMD is a fantastic tool for assessing one’s personal health, the Adobe Analytics Capability-Maturity Assessment Tool (CMAT) is only a tool for diagnosing a problem and for providing suggested next-step recommendations. Similar to how WebMD cannot provide you with prescriptions or surgery, Adobe’s CMAT will not suddenly inject your business with “data science” so you can increase your analytics maturity. No, there still has to be a hands-on professional involved.

It has been predicted that in the next 2 years there will be a need to hire as many as 4 million individuals to support data analytics. While that sounds like good news, there is a problem; there are not enough students in the pipeline to fill all these positions. The end result will be analysts and marketers taking on an expanded role in the future. While you may be experiencing a slight increase in blood pressure or anxiety at the thought of expanding your role in this way, I hope you know that this is not a bad thing but an opportunity. There is now room for quants, individuals with expanded roles and skill sets, to bloom and gain recognition through performance.

Going back to the medical field, it is fairly easy to draw a few analogies from the challenges medicine has experienced and where the future of analytics be over the next few years. The role of the nurse has expanded quite a bit in the last few years. Clinics and hospitals rely on nurses to pre-diagnose patients, apply treatment, and even triage patients. Doctors average less and less time with the patient every year; there just are not enough doctors to go around. What’s also interesting are the increased number of nurse practitioners available these days. A nurse practitioner is highly trained and skilled in their field. These individuals could even be called “super nurses,” as they are not quite doctors, yet still do much of the work traditionally performed only by doctors. While it might be considered an oxymoron to place “efficiency” and “medical practices” in the same sentence, nurse practitioners have significantly increased a medical office’s overall efficiency and ensure the doctors are being as effective as possible with each minute they spend in the office.

Much like nurse practitioners, the analyst of the future will have to become a “super analyst” performing many of the tasks a data scientist would. This also means that marketers will have to step up their game as well. Marketers should be familiar with the analytics capability maturity model and where their company stands within it, as well as what they can do to improve their company’s sophistication.

Think of this expanded role as the marketer being a parent who tends their child’s medical needs. Marketers must have some familiarity with the condition of their company and know how to spot the early warning signs of when something is not right. For example, the parent of a child with chronic allergies knows how to spot the early warning signs of a severe allergic reaction and how to administer a hydrocortisone shot or if their child needs to be taken to an urgent care clinic or ER right away. In most instances, I would even go so far as to say that it will have to be up to the data analyst to train their marketing department on how to spot issues early and how to more effectively self-serve using the tools at their disposal (e.g., Adobe Analytics).

Much like nurses and nurse practitioners do an outstanding job in leveraging a doctor’s time in the office or clinic, the analyst of the future will have to balance time, data, and output in ways that will be critical to a company’s success. The future analyst will have to triage multiple issues, gather information accurately and efficiently, and relay their findings in such a way that nontechnical people can understand and react appropriately. Training key personnel within your organization, as well as understanding their specific needs, is crucial to leveraging time with success.

For many of you in the analytics world, the future is now. For the rest of you, it is coming. Having the right tools, training, knowledge, and preparedness in place is essential. New technologies and processes are here and even more are on the horizon. What’s the first step into the future? Knowing your company’s score of the data analytics maturity model gives you a great starting point.