Data Analytics and Organization Maturity

Data Analytics and Organization Maturity
Data Analytics and Organization Maturity

I was in a conference call with a mid-size company and their leadership was curious to learn about the stage there analytics capability is in. Sure, it is a maturity model problem. Maturity model presents a great framework on 5-7 stages of evaluation. You enter at one stage and exit at top stage. Some rank it as a journey from Chaotic to Predictive; some pull it as Emerging to Leading etc. You could skin the cat in any way you want. More often these maturity models are complicated to understand and require some groundwork before you could use it in gauging your organizational maturity. Most of the businesses are different, and so is their data analytics journey, capability and maturity. So, why not work on something which is simpler and provides great litmus test to understand where your capability as an analytics driven company resides?

Data Analytics maturity is a capability, which is synonymous to the culture of the analytics team. Maturity is closer to human evolution than we think. What better way than to learn evolution from a cycle that we all are accustomed to and related to, human maturity. No, I will not go into much biological digs and rate all 12/17/19 stages of maturity. Let’s keep it simple and put it into 5 wider containers. The objective is to give something which is easy to visualize and map your organizational analytics capability against. This should be relatively faster test giving you a quick perspective and direction for further investigation.

Infancy: Yes, the most chaotic stage of em all. You know analytics is important but you are all over the place. Not much synchronization, too much randomness and repeat work. This is a stage which is mostly present at initially when you are building analytics capabilities. The good thing is that, just like for humans, it is a short-lived stage. You get over this and start doing things right which is required for your survival.

Childhood: As suggested above, this is a stage when your survival instincts get sharper than your infant stage. You know what are the few things important for you, you do it right and rest is still a random chaos. This is where most of the ignorant and non-analytics driven businesses lie. They do barebones analytics just to get through their daily chores.

Adolescent: The fun age, as we all know it. It is an age with a lot of confusion, energy, friends, collaboration. This is a good time in human growth as well as in data analytics maturity. You rock your daily chores, and you take out time to explore more avenues. You are open to risks and start making calculated risk and bold moves. This is the fun and aspiring age in analytics maturity as well. I am sure you must be craving for your teen ages as well. This is exactly the reason why. A good data analytics driven business wants to stay in this age for maximizing the ROI on their analytics spend.

Youth: This is a typical analytics driven company. You mastered the art of survival, you can prioritize best, and you are less open to risks. You have sharper biases and likings. This is a stage which slows things down for businesses. You want to embrace change but you find it difficult. Good thing is that you still possess some traits of your adolescent which makes things spicy for you. You could be more experimental and change friendly if you want to be.

Adulthood: This is 800 pound gorilla problem. It is the age with all wisdom, barely any appetite for change, risk, agility. This is the stage where most of the big businesses are stuck. It is a stage with very long turnaround cycle, least risk friendly, slow in moving, slow in adopting. This is the stage which most of businesses aspire to avoid. They acquire, embrace churn, hire new talents just to keep its strategies fresh and change friendly. Analytics should never be in this maturity level.

BTW This is not an enter at first stage and leave at last maturity progression. These are 5 containers you find your analytics capability in. The journey is to find a way to container that fits your competitive landscape and find a way to that container.

From the 5 containers it is not difficult to see that data analytics capability should always stay in its adolescent. Teams, processes and logistics should always embrace agility, change, adoption, scale and risk. This is what will open new horizons for any businesses and specially data driven ones. One good thing is that every business possesses the ability to swing to its adolescent stage, all it requires is a change in mindset which could happen slowly and gradually at the worst.

Originally Posted at: Data Analytics and Organization Maturity

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