I recently moderated a lively panel discussion in Nashville around the challenges and opportunities for using data to create better customer and employee experiences.
The panelists included:
Gerry Gorman, Chief Information Officer at Caterpillar Financial Services Corporation
Jim Pierson, Data & Analytics Leader at Louisiana-Pacific
Douglas McDowell, Chief Strategy Officer at SentryOne
Here are seven key takeaways from the discussion:
- Implementing new data and analytics solutions requires organizational change management. As with many forms of innovation, it can threaten culture and cause stress in the workplace. It’s important to recognize these potential issues and act to address them before they obstruct the solutions.
- One size does not fit all. Systems are important, but the people, and your ability to help them understand the value of the system, are more important than a technology solution.
- Partners are important. Talent shortage is a major issue for these initiatives. Early in your project, consider your team's specific expertise and bandwidth, then choose an implementation partner to fill those holes on your team. Do this before you start the implementation and consider not only the partner's track record, but past relationship successes.
- Analytics work is never done. No matter how successful the implementation, analytics must adapt to changing business needs just like any other living system. And no matter how good your team is, it’s incumbent on management to provide strong oversight and investment to guide that development without creating a top-down system.
- No matter where you are in your journey, you have data analytics. You may be getting just descriptive data from spreadsheets or predictive modeling results as ranking scores, at some level everyone is doing this. Regardless of where you stand on the journey of descriptive to prescriptive analytics, there is value at each stage and a valuable use for each type of analytics. And, no matter what, integrate business and IT to make your journey more effective.
- Never throw any data away. That’s what data warehouses are for. The journey never ends.
- Don’t hire a data scientist as the first step. It’s not about an individual, it’s about building a competency. Data scientists are best used when you have good data, competency at collecting it and need to do more with predictively. It's not necessarily the first hire.
Jim Pierson summed it up like this:
"Data and analytics solutions are not one-size-fits-all. And, most importantly, doing this right is as much about people and relationships as it is about systems."
