Stand Out From the Crowd with Analytical Thinking

As a consultant, I spend time with different clients and teams every week and I’ve noticed a couple of trends recently.

1. Work is being automated

Obvious, I know. RPA is automating the routine and manual tasks being carried out by humans in functions like Finance and Operations. Analytics is doing the same, but automating the production of insight. Slightly more emerging, but machine learning is well underway to start taking decisions away from human workers (or at least taking away the research / time intensive part of a decision).

2. Analytics specialists aren’t just producers anymore

The second isn’t a well reported trend, but something I’ve become more aware of recently. An analytics team, similar to the one I currently lead, is commonly given questions to answer by a business team (the ‘consumers’). E.g. ‘we think users are bypassing this part of the process, can you show us what is really happening‘, or ‘there was a recent fraud, here is roughly what happened, can you show us if this is happening elsewhere‘.

Historically the analytics team would then capture some data, do analysis, produce dashboards to provide back to the business and communicate some observations. The dashboards would provide the business with a platform to interpret findings and draw their own conclusions, which they would then take forward as actions.

Over time, we realised that our consumers weren’t always concluding with hard hitting findings and recommendations. Why? It was possibly a combination of dashboard design, lack of time for the consumers to look at the results and lack of experience working with data. In my opinion, in many cases, it was a lack of experience applying analytical thinking (researching, analysing, challenging, communicating, reporting) and the supporting basic data skills.

As an analytics team, we changed our way of working. Rather than just providing consumers with dashboards to explorer results, we started to produce findings packs. The packs would summarise our own findings, observations and recommendations.

This turned out great for us. Over time, as our team became experienced in the various domains (the business, their challenges) we were driving the hard hitting recommendations and actions. Rather than facing off to our usual consumers, we were quickly starting to be brought in to present to Global Heads and the C suite.

What’s the problem?

For businesses, this is a problem because there is an increasing demand for specialist analytics teams and they are starting to become a bottleneck.

A business will probably be more likely to advance if their relatively small group of analytics and data science specialists are focusing on the complex analysis and their large groups of business staff are thinking analytically.

Check out how M&S are tackling this, via their Data Science Academy, presumably aimed at their non-techy staff.

“Transformation of our business is key to survival and a huge part of this lies with our colleagues. We need to change their digital behaviours, mindsets and our culture to make the business fit for the digital age…”, M&S CEO

For employees, who are not analytics specialists, this is a career risk. Straight forward tasks are going to be automated, and the complex questions are starting to be answered by the analytics specialists.

Do we just need more data scientists?

I think the solution has to go beyond waiting for future troops of data analytics and scientists to be trained. And think of all of the domain knowledge in the brains of your current business staff.

Everyone who’s job currently involves sitting at a computer needs to become an expert analytical thinker, with the skills and confidence to explore data. Imagine the impact on the business and your career. I see it every day in the Consulting world — the people who stand out from the crowd are those that can take a question and answer it using data.

For most clients I work with, there are always one or two business people I spot who are high-flying. Here’s what differentiates them:

  • They know the art of the possible with analytics: They’re asking great business questions, know what is technically feasible, and know what can be done themselves and what will require an analytics or data science specialist.
  • They can use the results provided by specialists: They don’t need to be spoon fed answers. They work with the technical specialists to understand results, do their own analysis of the results, and extract hard hitting findings which they can present to leadership themselves.

So how can you upgrade to remain competitive? Here are some suggestions for some self-learning to help you:

  • Excel Basics to Advanced: Upgrade your Excel skills
  • Analysis & Presentation Skills: This is all about asking the right questions and telling a story with data
  • Business Analytics: A longer course via Coursera, and a bit more $ to get the certificate, but it has a great syllabus. Getting hands on with data including a number of case studies, so this will really help to get into the habit of asking questions. This could differentiate you.

And even more importantly, start to apply analytical thinking to your job:

  • Providing a view to your bosses about an area of improvement? Have a think about how this could be backed up with data, allowing you to tell a story.
  • Do you have analytics specialists involved in your area of business? Get involved by checking out what they’re producing, get a copy of the output (whether it’s a dataset or a dashboard) and see what insight you can draw from it.

Data Analytics & Science Leader, with a focus on Risk & Fraud. Big 4 Director.