Developing Expertise as a Data Analyst

Introducing the Data Expertise Triad

Hello everyone and welcome back to Data Patterns. I know it has been a while since my last newsletter.

I only wanted to take a short break over the holidays but here we are 3 months later. Don’t worry; everything is fine and I’m ready to fire on all cylinders again!

I’ve got an interesting post and an announcement for you today. So without further ado let’s get into it.

The expert data analyst

When you think about expertise and improving your skills as a data professional what’s the first thing that comes to mind?

If you’re like most people, I bet the answer is “technical skills.” Learn a new programming language (like SQL, Python, R, etc.); learn a new technique or methodology (AI is pretty hot these days, right?) and so on.

But expertise doesn’t work that way.

Experts are valued for knowing the stuff that cannot be easily googled or prompted into an LLM. This is known as tacit (intuitive) knowledge. What makes experts rare and valuable are their intuitions about their domain.

And that can only be learned from experience. If it can be googled or prompted it might be valuable but it’s not rare enough. So you can learn new technologies all day long but never become an expert.

Ok, Ergest how do I develop rare and valuable skills?

The data expertise triad

First we need to understand our objective. As a data professional what do you want out of your career in data?

Ideally you want to have a successful, enjoyable and long lasting career. Which means:

• Making meaningful contributions to the organization with your skills

• Carving out an important role for yourself (and your team) in the organization

• Creating leverage for yourself (and your team) that can be parlayed into promotions, raises and future opportunities

Pretty good right? So how do we get there?

Since I love triads so much, I now present to you the Data Expertise Triad.

The data expertise triad consists of:

• Developing skills in mapping and improving business systems with data

• Developing math and statistics skills that allow you to model business systems with data

• Developing technical skills such as data modeling, data manipulation and transformation, data analysis, etc.

As you can see, technical data skills and math/stats skills MUST be combined with understanding the business and figuring out how to improve it with data. That combination cannot be easily googled or prompted and that’s what makes you a rare and valuable expert.

Out of these three, business mapping with data is the only one that doesn’t usually get taught in courses or books and it’s the one of those skills you can hone over a long career. I also can’t promise that I can teach you everything about it but I can get you started.

Which leads me to my announcement!

Announcement: Metric Trees course

I’m working on a new project (most likely a course) on metric trees with Timo Dechau of DeepSkyData and I’m really excited about it. As you all know I love metric trees, and so does Timo.

We have both seen first hand the power this simple diagram has in communicating and aligning everyone in the organization and we believe every data professional should learn how to build them.

On my last newsletter I wrote about an interview with a data leader who implemented metric trees in her company and saw tremendous benefits.

Metric trees helped her:

• Drastically reduce the number of ad-hoc requests on the data team

• Drastically reduce the number of dashboards the team supported

• Significantly increase data team’s impact on the business

• Easily prioritize data questions from stakeholders

• Boost date team’s impact by shifting their role to internal consultants

• Boost data team’s morale by empowering them to own outcomes

Now she got all those benefits by implementing the metric tree for her company but you don’t have to implement it to reap good benefits.

The amazing thing is that even a static diagram of a company’s core metrics / KPIs is by itself a useful tool for mapping and figuring out how to improve organizations.

Are you interested? If so, what would you like to learn? Let me know by replying to this email or posting a comment onn Substack.

Until next time.