WRT #digitaltransformation success
In a world of rapid innovation around new cloud software and the reality of data gravity, how can businesses realistically improve their analytics maturity?
I think this is an important question.
Consider the ability to provision almost anything a company needs today to get a job done — there are many options, AI and otherwise.
Also consider that data gravity states that where most of a business’s data lives, is where the analytics work happens. Applications and information architecture are of course related and tied, but totally distinct from one another.
The reason to “improve analytics maturity” is because it’s a fundamental part of any successful digital transformation.
To resolve all of this starting with the C-suite, I think it’s healthy to look at the world like this:
- Best in class companies will figure out how to manage all their data and do so as an asset. Not any one type of data, but all relevant data.
- Best in class companies will develop plans to leverage this data using a use case roadmap that prioritizes the highest value and most quickly tested projects. I’m not speaking about BI, reporting and analysts here, but rather use cases aimed at improving the business.
- These projects may need to operate across the innovative cloud software layer and your information architecture. The smarts may live with the cloud software and the data it creates and manages or live nearer the data layer and be completely separate but require integration with the software. Either way, all of this needs to be easily activated and governed.
What I have described here is the necessity to ensure collaboration — not separation — between the line of business management and the IT or data management group. Both need to execute in unison on an agreed-upon use case strategy that is endorsed by the CDO, CEO or other executives, who are actively engaged in what’s happening.
This is totally uncommon but is totally necessary for #digitaltransformation to succeed.
DISCUSSION QUESTIONS: How should retailers and brands be looking to modernize their analytic infrastructure to reach higher levels of analytical maturity? What behavioral changes may be required?