Seven fundamentals to set an analytics team up for success

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The value of data for analytics is undisputed in today's business environment, but not all companies use it well.

How do leaders create factories of analytics and positive ROI? First, they nail the basics, then they build up the case for budget through ROI.

Here are the seven fundamentals that get you there, and some new ideas that will truly bring you forward.

1. No one wants to know how the sausage is made.

Data scientists need to be able to tell a story without explaining what a p-value is or the difference between a prop and an evar. No jargon. Ever. I actually test my big presentations in front of my nine-year-old. If he gets it then I’m good.

2. Data Miners vs. Subject Matter Experts (SMEs).

Data miners tend to interpret the data without enough SME context. This means they may not know what’s reasonable. The best case is either training SMEs to become data miners or embedding your data miners in the business. There is a third option – partnering - but this is a much weaker choice.

3. Buy, Sell or Hold.

Just tell your audience what to do. Scoring systems or raw metrics might be meaningful to analytics teams, but stymie decision makers. Don’t play games. Just take a stand and tell your stakeholders what to do.

4. Single Source of Truth.

This applies both in data sources and with formulas. People will rework the data to display what they think is correct (and what makes them look good) until they cannot. Therefore, you need a tech to pull the data together, business SMEs to refine it, and ideally, the CFO’s office to bless any connect to cost or revenue if you’re going to get real impact.

5. Start with Business Value.

Where value is lower costs through optimization or efficiency, increase growth by finding rich new types of customers or reduce risk through stronger predictive modeling. Problems worth solving trace their roots back to these fundamental business drivers. But understand, this is an iterative process of learning and trying based on data.

6. Democratize the Data. Carefully.

Giving stakeholders access to data with multiple sources of the truth is like spilling a bag of sugar in a sandbox of toddlers. No one will be happy with the end result. Instead, bake your data into business-ready cookies and then monitor how much they eat. Consider teaching some more advanced skills and certifying higher levels of access.

7. Message Your Data.

Do this before someone else does. This means creating a data dictionary or user guide to explain the subtle nuances known about the data such as the best dates to use, fields that might go together, what’s rolled up into what. This is particularly relevant for companies who are required to share data publically. Get ahead of the story and protect the data.

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