THE ADVANTAGES OF DATA-DRIVEN DECISION-MAKING updated for 2020

“Are you interested in learning how data-driven decision-making can enable you to be a more effective entrepreneur or member of your organization? Below is information about the benefits of becoming more data-driven, as well as a number of steps you can take to become more analytical in your processes.”

WHAT IS DATA-DRIVEN DECISION-MAKING?

Data-driven decision-making (sometimes abbreviated as DDDM) is the process of using data to inform your decision-making process and validate a course of action before committing to it.

While DDDM works well it needs a lot of ‘historic data’ or what Clayton Christensen called ‘data of the past’ to work.

The problem with the below statements are many. In 2020, better ways to gather data exist, and if you do the below items, make sure you have a checklist on planning what you collect and how you collect it.

In business, this is seen in many forms. For example, a company might:

Collect survey responses to identify products, services, and features their customers would like

(ISSUE: survey data, if you pick the wrong markets, wrong personalities, your sample can be way off. Example: there are likely 20+ personality trait patterns of Vitamin buyers. So, your sample needs to collect the right people, in the right locations with the personality traits that you are targeting. If you blend people, you get averaged results).

  • Conduct user testing to observe how customers are inclined to use their product or services and to identify potential issues that should be resolved prior to a full release

(ISSUE: user testing data, if you pick the wrong markets, wrong personalities, your sample can be way off. Example: there are likely 20+ personality trait patterns of Vitamin buyers. So, your sample needs to collect the right people, in the right locations with the personality traits that you are targeting. If you blend people, you get averaged results. The same problem as above).

  • Launch a new product or service in a test market in order to test the waters and understand how a product might perform in the market

(ISSUE: the test market should be defined in a careful way so you know your market and customers well. You have to remove adverse selection and solve the asymmetric data problem you have right now. Pick the user testing data, if you pick the wrong markets, wrong personalities, your sample can be way off. Example: there are likely 20+ personality trait patterns of Vitamin buyers. So, your sample needs to collect the right people, in the right locations with the personality traits that you are targeting. If you blend people, you get averaged results. The same problem as above).

  • Analyze shifts in demographic data to determine business opportunities or threats

(ISSUE: Netflix tosses demographics in the data garbage can – for real. Demographics is a dated concept. While you can find correlations to demographics you are looking for causal reasons why people buy. Think about this: You have likely heard that ‘all sales are emotional’ – well, measure it!

A suggested checklist:

Data-driven decision making needs to be a mix of hypothesis-based, look-ahead data AND historic data. Think about driving a car: You need 3 forms of data a) predictive (I have a better way to get to the place) b) real-time data (I have fuel and tires are good) and c) historic data.

Add in ways to collect hard to measure data – likely more valuable than the easy stuff. That is a classic IBM philosophy.

Add in a reason ‘why people buy’. Whether B2B or B2C, ‘decisions are emotional’ – I say they are personality trait-based. Look for causal reasons by decisions are made. Can’t find them, ask me – I may have them.

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