Power BI Data Analyst Job Role
Power Bi Data Analyst Job Role

In this presentation, we share our thoughts on what it’s like to work as a Power BI data analyst and the skills and temperament best suited to the data analyst job role. We look at some of the challenges and rewards of the Power BI data analyst role, painting a picture that is fairly typical of most workplace environments. If you are interested in becoming a Power BI data analyst or want to find out what the role involves, this course is a good starting point.

Learning Objectives

  • See if the Power BI data analyst role interests you
  • Find out if you have the temperament to be a Power BI data analyst
  • Get an idea of the skills and aptitude required to start on the journey to becoming a Power BI data analyst

Intended Audience

This course is intended for anyone who is not familiar with the data analyst role and is contemplating investing time and energy to upskill themselves to work as a Power BI data analyst.


No prior knowledge is required to take this course. 



Hi, my name is Hallam Webber, and I want to talk to you today about what it is to be a Power BI data analyst. The data analyst role is often described as "turning data into information," and that four-word summary encapsulates the most important aspect of the job. But as with most things in life, and especially in technical roles, the devil is in the detail. Unlike purely technical roles, such as network administrator or coder, the data analyst role bridges technical and business domains. A data analyst is a little bit like an interpreter, translating raw or operational data into information that speaks to the business. When I say "speaks to the business," I don't mean just plotting graphs. I'm talking about presenting the information in a way that makes sense to the target audience.

Knowing the target audience for your information, findings and reports is the key soft skill a data analyst needs. Depending on the size of the organization you work in, you may take instruction directly from end-users who are interested in learning about some aspect of the business or the market it operates in. You'll need to listen carefully and ask questions to fully understand what they want to know. Your audiences aren't just IT and everyone else. Sales, marketing, operations, and management have their own perspectives on the business, and this is often accompanied by words and terms for what is essentially the same thing. 

Sometimes "the same thing" isn't exactly the same thing but has a crucial difference that will impact how you source the raw data and package it for the respective audiences. The ability to interpret what's required and map the requirements onto the raw data is vitally important. If the organization is large or the data analyst role has to get a dba to pull the data, you need to specify the requirements accurately. Measure twice, cut once. The ability to communicate effectively across business units and disciplines extends to how to package the reports, including the types of visualizations and terminology. 

Several years ago, I built a data processing system that ingested daily data from banks and produced reports. My company was under the impression we were interfacing with the banks' automated systems. This was the case except for one. One bank manually prepared their data and had a staffing change where a new person took over the preparation. As often happens, the new person decided to do things differently, not understanding that they were interfacing with an automated system. After some exasperating back and forth, the bank liaison project manager assured us that they would perform a four-eyed check before sending the data. A four-eyed check? I'd never heard this term before. Did the person need glasses and they were going to wear them? Were they going to have two people look at the data, or were they employing four specialist data cyclopes? 

This story illustrates some of the day-to-day issues a data analyst faces in a commercial environment. First and foremost, the source data can't be assumed to be correct all the time. 

As with software and system development in general, you need to build fault tolerance into report processes. One of the benefits of Power BI, as opposed to traditional report generation, is you can build a dynamic solution that has the potential to evolve and adapt to changing data and reporting requirements. From the data analyst's point of view, this is a big leap forward, meaning more time can be spent on advanced and sophisticated solutions rather than tweaking graphics or other menial tasks.

As a Power BI data analyst, you are working with one of the best tools in the market. The analytics capabilities are quite frankly astonishing. Combined with a slick user interface, you can build insightful and compelling solutions backed up with statistical rigor. With great power comes great responsibility, and when implementing analytical solutions, you need to have an appreciation for the end result. While a data analyst doesn't need to be a mathematical genius, numeracy and basic knowledge of statistics are essential.

Often Power BI solutions are databases and reports rolled into one, requiring you to understand relationships within the data to produce fast and efficient reports. The Power BI database engine called Vertipaq is fast and efficient, but as with all databases, the bigger it is, the slower it runs. Understanding your data so that you can build the most efficient and responsive solution is key to being successful. In most cases, your Power BI dataset will be regularly refreshed from external sources. Depending on the organization, this task may end up being the responsibility of the data analyst, requiring knowledge of data sources other than Power BI datasets. Wrangling external data isn't always straightforward, so persistence combined with logical and deductive problem-solving skills goes a long way.

So what's it like being a Power BI data analyst? Well, it's not all databases and report designing. You may spend a significant amount of time with business stakeholders learning about their domains. This will enable you to build solutions to help them understand their "world" or gain insights into specific problems. Distilling previously impenetrable data down to actionable insights is highly rewarding and gives you a sense of achievement. In my experience, non-technical people who have neither the time nor the skills are very appreciative when you give them the tools to find the information needle in the data haystack.

It's not uncommon for a data analyst to become a subject matter expert across several domains or become the go-to specialist in one area. Data analysis skills in one domain are transferable between organizations most of the time. You might develop dashboards at one company that helps improve productivity in a particular area. You learn about that aspect of the business in the process, resulting in an expanded skill set and experience beyond data analysis.

Being a Power BI data analyst is first and foremost about the technical skillset, so in that respect, you've come to the right place. If this sounds like a bit of you, let's get you started on becoming a Power BI data analyst.

About the Author
Learning Paths

Hallam is a software architect with over 20 years experience across a wide range of industries. He began his software career as a  Delphi/Interbase disciple but changed his allegiance to Microsoft with its deep and broad ecosystem. While Hallam has designed and crafted custom software utilizing web, mobile and desktop technologies, good quality reliable data is the key to a successful solution. The challenge of quickly turning data into useful information for digestion by humans and machines has led Hallam to specialize in database design and process automation. Showing customers how leverage new technology to change and improve their business processes is one of the key drivers keeping Hallam coming back to the keyboard.