• Data-driven business transformation initiatives, i.e., digital transformation ones, require specific skill sets that are hard to find
• These skillsets, and therefore the associated profiles, evolve throughout the course of implementation; and they may not always be necessary long term.
• In most cases, up to 80% of a data analytics project is going to be about consolidating and preparing the data.

What types of profiles are required for a typical data analytics project?

The level of complexity in data analytics from a team, skills, and knowledge perspective vary per project, but, as our experience shows, often leads to recurring patterns of needs.  

Through our many engagements, we have been able to build a set of standard patterns of operational excellence to serve our customers best. One of these patterns is the type of roles necessary throughout a project, depending on its stage.

The 6 stages of a data analytics project

Within the CI/CD (continuous integration/continuous deployment) process mentioned above, a technical team typically goes through 6 stages in supporting the digital transformation journey outlined above.

Depending on the stage in the CI/CD process, the team composition needs to be adapted. The diagram below demonstrates when each role will play a role at any given stage. In addition to “when” (at which stage), there is also a “how much” element that could not be depicted in this simple diagram. Some projects may require only 50% of a data scientist time, while another may require a team of three or more.

To illustrate how the team composition will evolve, let’s look at the business analyst role for example. It will be critical and will represent the bulk of the work in the initial phase, but its contribution will decrease, compared to the project manager or data engineers, when the project moves on to the data pipeline management stage.

Similarly, data scientists will start to engage in the project when the data pipeline management step is already well underway but will ramp up to carry most of the project load in the data modeling phase.

Below is the typical analytics project lifecycle and its resources aligned by stage:

Defining the roles needed in a data analytics project

As described above, a wide range of roles, therefore, competencies, are required to deliver a data analytics project successfully. Let’s review these roles at a high-level.

What is the business analyst’s role?

Typically, within this type of project, a business analyst will combine the understanding of business processes and goals as well as knowledge of the possible technical solutions at a functional level. This senior-level profile showcases a substantial business experience, most likely a recognized business degree such as an MBA or equivalent experience, and is technology savvy.  

They will drive the original ideation process, to the scenario selection and the final data analysis and overall project reporting to the customer.

What is the architect’s role?

Architects define how the different pieces of software fit, work, and function together within the scope of a technology project. They typically have a background in development, and most likely, a recognized technology degree such as a degree in computer science.

What is the project manager’s role?

Familiar with Agile software development methodologies with both a business and technical background, the project manager will drive the planning, forecast, and ultimate delivery outcome of the project. The project manager is the glue that will remain the driving force throughout the project as other team members ramp up and down.

Depending on the project complexity, a project manager typically manages no more than three to five projects in parallel. For large projects, the project manager can be occupied full time and even require the help of more junior project managers.

What is a database administrator’s role?

Database administrators provide the backbone of systems functionality within databases. They ensure databases run smoothly, optimize and tune systems, and assist when issues occur by troubleshooting and bug-fixing problems with database systems.

What is a data engineer’s role?

Data Engineers are developers focused on data movement and transformation tasks, as well as the tools required to make these movements and transformations happen. Frequently, data engineers also engage in database specification, creating entity-relationship models or other specifications to create databases. Data engineers typically have a background in data development, knowledge of SQL, and a deep understanding of how data moves and interacts through different systems and processes.

What is a database developer’s role?

Database developers are similar to data engineers, although they often have more specialized skillsets around a particular type of database development. Where data engineers are often generalists specializing in tools like Java, Python, Scala, or cloud-native tools, Database Developers frequently specialize in specific database types, like Oracle, Microsoft SQL, or others.

Otherwise, these roles are similar, with a deep focus on data development, pipeline creation, and modeling databases relationally.

What is a data scientist’s role?

A data scientist will transform data into insight. Using a portfolio of data analytics, machine learning, and other advanced AI techniques, the data scientist will build the knowledge engine that will power the transformation process.

What is an application developer’s role?

The application developer’s role is to convert the insights coming from the data scientists into actions. By integrating these insights into dedicated or existing applications, whether these applications are part of an end-user workflow or whether they control a business process (e.g., an ERP).

In some situations, the app developer will build data visualization dashboards, e.g., in Microsoft PowerBI, that end-users will use to help make business decisions.

What is an IT system administrator’s role?

IT Systems Administrators are the generalists who keep an organization functioning. They’ve typically got a little bit of knowledge of all of the systems in a company and have the skillset and tools to figure out anything themselves with an internet connection. These resources are often tasked with keeping the lights on, cloud management, security, office, email management, or other server-side admin functions. They typically have learned PowerShell or Linux and at least one other scripting language.

What is a platform engineer’s role?

Platform Engineers are IT Systems Administrators who’ve expanded their systems knowledge into the cloud. In addition to the skills described in the IT Systems Administrator job description, Platform Engineers bring knowledge of how to scale up their automation to a multi-machine level, using tools like Jenkins, Docker, VMWare, AWS, and Azure. Their specialized knowledge of platform systems enables them to harness the power of whatever platform they’re using for massively distributed computing.

Conclusion

Although the scope and variety of skill sets required may appear daunting there are many options for companies that want to get started. Neal Analytics offers multiple engagement options that can adapt to virtually any project size and any pre-existing data analytics team size and composition. Contact us to learn more about how Neal Analytics can help you build and deploy successful data projects at your company.

For those interested in learning more about managing data projects, here are a few relevant articles:

MSDN Blog post from Buck Woody on the Team Data Science process

Work in the purview of a Data Scientist typically falls into one of two categories, with most Data Science jobs leaning one way or the other, and this lifecycle is referred to as experimentation versus operationalization by Gartner.

We also suggest reading Gartner’s associated detailed article and blog post.

Interested to learn more? Download a copy of our e-book: “How to Staff a Data Analytics Team”