Where does your organization lie on the analytics maturity curve?
This question is functionally equivalent to:
- What is the state of data analytics at your organization?
- How do we compare to others in the areas of business intelligence, data, and analytics?
- With regards to analytics, what the heck are we doing right and wrong?
If Google, Facebook, Uber, Amazon, and Netflix are at the top of the “analytics maturity curve”, where does your organization lie?
Over the next several posts, I will be describing the various stages of the analytics maturity curve. I’ll discuss the distinctive features of the stage, the technology employed at the stage, the types of questions asked, and the general skills required at each stage of analytics maturity.
Arguably the seminal work when it comes to discussing an organization’s analytics maturity, Tom Davenport’s Competing on Analytics: The New Science of Winning provides the foundation for much of the content in the coming posts. I highly recommend Davenport’s book as a fantastic resource for all things analytics.
Stage 1: Analytically Impaired
Stage one. The floor of the analytics maturity curve. You’d better act quick if you expect your organization to survive long at this level of analytics maturity.
Why? Because you are driving with a blindfold on at this stage.
Enterprise analytics – even quality operational reporting – is borderline non-existent. Excel is your greatest analytics tool and you struggle with gaining anything beyond limited insight into your operations.
At best, you might be asking vague questions like “what happened last month?” None of your questions align with the organization’s KPIs – in fact, you might not even have any defined KPIs! Answering even simple questions about the state of your business is a challenge littered with inconsistency and uncertainty. If I asked “how many customers/members do you have?” to several different areas of the organization, I would, undoubtedly, get multiple answers back.
Your primary source of analytics is derived from pre-built, standard reports from application-specific reporting solutions. Data integration is likely only a figment of your imagination thus making it nearly impossible to gain a 360-degree of your business, its customers/members, and their interactions.
At this stage, your organization “analytics” hinges on how many times your VLOOKUPs break in your Excel files. Skills in things like SQL, ETL development, data architecting or data visualization are non-existent or simply un-utilized.
The good news? There is nowhere to go but up. And, to all you CFOs reading this, an organization at this stage has one of the strongest opportunities to yield a near-immediate ROI. Why? Analytics, especially for Stage 1 organizations, represents an untapped oil field of new opportunities and efficiencies (just read a previous post “5 Reasons to Invest in Data and Analytics in 2016” to learn more about these opportunities).
How can you start to move out of the “Analytically Impaired” stage and into the second stage of the analytics maturity curve? I’d strongly urge you to take a step back and honestly assess the current state of analytics at your organization. Create a data strategy and develop a plan for how you can navigate your way up the analytics maturity curve.
Stay tuned for our next post to learn about what analytics looks like in an organization at the second stage of the analytics maturity curve.
Subscribe to have new content sent directly to your email!
[mc4wp_form]
Trackbacks & Pingbacks
[…] can be gained in most markets/regions through a greater maturity with analytics. However, as the analytics maturity of the industry rises, the value of the competitive advantage […]
[…] my last post, I talked about some of the defining traits of an organization at the lowest, first, stage of the “Analytics Maturity …. The second stage of the “Analytics Maturity Curve” is what Tom Davenport refers to as […]
Comments are closed.