Corporate IT Technologists Can Make Good Data Analysts

Data Analysts are more important than ever for your Data Science Projects.

There’s a myth in the Data Science community about that full-stack Data Scientist who is going to swoop in and magically transform your data science ventures into a profitable one overnight. Don’t get me wrong, if I ever meet a full-stack Data Scientist, I will bow in deep respect. This person has immense experience and technical knowledge. With the right business sponsors, this person can start a data science venture in the right direction and lead the project as it grows. However, as your data science initiatives become more clear, you will likely need a team of technologists to take your project to a new level. This team can consist of a Machine Learning Engineer, a Data Scientist, a Statistician, and a few Software Engineers. But, more likely, your team will also include one or more Data Analysts.

If you are wondering what a Data Analyst does, here’s a great article that explains the work of a data analyst.

After I read the above article, I realized that the “Corporate IT” technologist who works with systems centered around data have great hybrid skills such as programming, systems development, systems support, business support, and cross-functional teamwork skills that make them ideal candidates to step into a Data Analyst role. Particularly, a mid-level developer who has some business knowledge and knows their way around the various technologies to transform and house data is ideal for such a role. Having spent my career in Corporate IT as a technologist, I just had to write this article to share my point of view. If you’ve never worked in Corporate IT as a technologist, you can check out my article about what it’s like to work in Corporate IT.

Disclaimer: Please note that all opinions are my own.

Data Analysts are the go-between for all of your specialists.

On a data science team, a data analyst can serve as the person who brings together all the specialists. The data analyst can often point to a direction of investigation for the data scientist or the statistician where the hypothesis can be defined for further analysis. The data analyst can work with the ML engineer and software developers to prototype and develop full-scale data science projects for an organization. A data analyst can present an initial analysis using data visualization to kick off an iteration of the data science journey.

A middle-level or a senior-level Corporate IT technologist likely worked cross-functionally for some time. Cross-functional teamwork coupled with a software engineering background allows the Corporate IT technologist to work well with different types of specialists.

Data Analysts know the business and have intuitions about data.

A data analyst should be someone who already has experience working with the data from either the technology side or the business side. Often, having worked with this data to produce insights for business, the data analyst developed an intuition about the data. Often, having worked with the business before the data science project allows the data analyst to anticipate where business insights may lie. Honing the intuition and developing it around the business can save a lot of time for any data science project.

The Corporate IT technologist is working around “data” all the time. With the Big Data movement, in IT, most software development projects center around data. Developing system solutions for business allows the technologist to learn business knowledge. Supporting these systems by working with issues such as data integrity, data warehousing, and system performance allows the technologist to develop an intuition about both data as well as issues around data integrity.

Data Analysts have a level of curiosity that rivals specialists and possess the kind of persistence that allow for follow-throughs.

Data science is an iterative process. Often, generating business insights means looking for needles in haystacks. It can take months of investigating the data to finally develop a viable path. Persistence and uncompromising curiosity is called for.

Corporate IT technologists deal with frustrations day in and day out. Dealing with legacy system codebase, boredom from working on the same systems for years, bureaucratic hierarchy, and complexities introduced by huge amounts of data are all exercises of persistence. In such environments, good engineers who don’t quit, persist to find creative solutions are the best candidates for a data analyst position.

Data Analysts know the way around the process of housing and retrieving data.

In business intelligence, historically, there are many data warehousing techniques used to house Big Data. On top of that, historically, the business relied on data mining tools that can generate insights on the fly. Data Analysts who know their way around data warehousing and data mining technologies learn the shortest routes to generate “clean” datasets needed for analysis.

Corporate IT technologists who have supported their business units for years not only have system development skills but also have skills in data warehousing, data mining and data cleaning. These skills are valuable. They can help to focus the specialists such as the machine learning engineer and data scientist on their work of modeling and data science.

Data Analysts are first-line investigators of data.

Data Analysts are often the first line investigators of data. Not only do they organize and clean the data, they can hack their way through datasets to point to the most important pieces of information. They rely on their intuition about the business to hone in on the most important information.

One of the best skills that the Corporate IT technologist possesses is “investigative skill”. Dealing with many legacy systems, refactoring code bases that often gets out of hand, and optimizing for performance, Corporate IT technologists are masters of debugging. They possess cross-system debugging skills that allow them to tag, trace, and follow data to figure out system problems. This investigative skill can come in handy for the data analyst who has to “hack” their way across datasets to figure out directions to take in data science projects.

Data Analysts have the “big picture”.

Data analysts are by nature “big picture” people who are skilled at working with specialists. The nature of their work that brings together specialists’ work helps them to keep the “big picture” in mind. If their skills are developed properly, the data analysts can learn from management the important pieces of information for the business. Eventually, they will be able to work independently to hone in on insights.

Unlike software engineers in technology firms and technology startups, Corporate IT developers are often more focused on the “big picture”. They are not spending most of their time scrutinizing details for one piece of software, but rather they focus on developing solutions that might run across many components of one system or multiple systems. The open-mindedness of these types of Corporate IT developers make them ideal candidates for a data analyst role in a data science project.

Data Analysts have technical skills across many programming languages and different types of data warehousing solutions.

Since data analysts have to hack their way across data, assist in cleaning the data, visualize the data, good data analysts will be skilled in programming languages such as R, Python; data visualization concepts and practices; and data extraction techniques using SQL and NoSQL databases. Often, a good data analyst might also have experience working with Tableau, or SAS. They may not possess data science experiences in algorithms, statistics, and analysis, but they have worked with tools used to write algorithms in the technical capacity.

Corporate IT developers work with many different programming languages. Mid-level to senior-level developers that have more than 5 years of experience are skilled in developing system solutions in both the SQL and NoSQL environments. Some of them even come from the C++, Java world. Dealing with complexity and learning new programming languages is not a problem for these developers. These developers frequently have skills in scripting languages such as Perl, Awk. If they don’t already know Python in the objective-oriented programming capacity, it is easy for them to pick up either R or Python in data science projects.