A wise speaker I heard recently said, “We are human, which means we make emotional decisions first, then justify them with logic.”
Do not underestimate the value of people liking you before they will actually believe your data and take action on it. We think we are rational about data but we are not. We emotionally decide what to believe.
To get your message across, to have a real impact in an organization, the feeling must be, “Allison’s got my back. I may not understand this data completely but I trust her and her judgment.”
In order to win, you must be seen as a brilliant advisor, not the nerd in the corner. When setting your data team up for success, remember that we all need a lot more EQ to go with the IQ.
And here are some ideas that you can implement to truly take your data analytics to the next level for your business:
It is hard to get a bigger budget when your department is seen as a cost center. At a recent conference I attended a speaker calculated that it took more than three years to build up a sense of value for the data division. The process included these steps:
- Demonstrate value in specific projects and use the ROI to help you get more funds for the team.
- Create scalable infrastructure that can be reused.
- Demonstrate value across multiple use cases by leveraging infrastructure.
- Create new businesses (new P&L owners, data monetized, billion dollar value).
Another speaker led a cross-functional center of excellence to examine what was needed to deliver value to the organization. When asked if he was required to redeploying existing teams, he said he addressed this specifically in the ask. “We couldn’t bring existing people into this function because those people already were used for other work. We needed their knowledge and we wanted to leave them alone. All of our ask was for net new resources. And we got it.”
Everyone wants to know how to measure the ROI of their data analytics team. This question is often muddied by mixing the ROI of project outcomes vs. the ROI of operational purchases. The latter should be seen as a sunk cost. It is necessary just to get in the game. For project ROI, it gets back to revenue, cost, risk, balance sheet, human capital, and your contribution to this. Quantify the upside.
However, that is not easy if your analytics team is decentralized. Organizations often do not give the data analytics person enough real estate to operate. If the CEO were the buyer, analytics teams would get access to every data warehouse in the company. Rarely do they have universal access. This, in turn, gates how long the journey takes as well as how easily you can connect to ROI.
Another way to look at ROI is to model impact instead. For example, run discrete event simulations to optimize pre/post then attach impact assessment. It might not be revenue, it might be cost optimization. As the bar of analytics continues to rise, the team must be less about shiny dashboards, and more about impact.
The Rise of the Data Product Manager.
The chief analytics officers and data teams that are cranking out the best results are veraciously building on top of their data platforms. Apps, tools, products of all sizes and shapes to both answer worthy business questions (based on use case) and expedite the easy adoption of data.
The best teams actually bring a product manager on to do this. Separate product management teams can also help you protect longer-term “build work” from urgent short-term “project work”.
Here are a few examples of these product portfolios from the healthcare space: mortality reduction, capacity management, readmission prediction, cost analytics, service line analytics, healthcare utilization explorer, clinical variation (how are we treating similar patients differently), risk segmentation and throughput simulation (to move efficiently in physical spaces).
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