Insight > Adaptation > Action

Critical thinking may be needed to turn insightful findings into actions. For example, a lack of parking on campus may be best solved by working with the city council to improve public transport options. Or, to give a positive example, if students say that they love the environment and the campus, the action could be to use this finding in the marketing material to attract students who care about this.

Insight > No Action Required

While it's important to not be bogged down in measuring everything, data analysis can validate one's assumptions. This can lead to insights that aren't necessary actionable but just as vital. For example, you may think that class size is a key issue, but if students don't mention it, no action to fix it is needed.

Insight > Rethink strategy

Data analysis can also help validate if a strategy implementation is working or not. Let's assume, last quarter students complained that university staff weren't helpful. After taking measures to change that, this quarter's results should demonstrate if the measures worked or need further thought.

Can today's data analysis software deliver actionable insights?

In my opinion, despite all the promises, none of the today's solutions can ingest data and spit out actionable insights. Why? Because separating Actionable and Insightful findings from those in the other three quadrants would require two types of knowledge:

b) objective knowledge of difficulties associated with different actions,

a) subjective knowledge of what's old news and what's truly insightful.

The ultimate AI agent may be able to learn the objective knowledge by reading materials published by people over the years. And this agent may also be able to build up the subjective knowledge over time by working alongside its user. But unfortunately, we are still far from what's invented in science fiction. 

What data analysis can deliver today is the ability to sift through the data more efficiently. When it comes to making sense of people's comments, Natural Language Processing algorithms can turn people's comments into themes that can be analysed just like numbers. And when it comes to making sense of numbers, data visualisations help understand differences, uncover correlations and detect trends.

When evaluating a solution that claims to deliver actionable insights ask the following questions:

• Will this solution tell you things about your business that you don't already know?
• How easily will you be able to separate signal from noise?
• Will it be able to identify trends in data without having to specify them in advance?

At Thematic, we help companies to find insights by digesting customer and user feedback into easy to use data visualisations. Get in touch if you'd like to see how.

src: linkedin.com