Defining data professionals – who does what?

Do you know the difference when it comes to data professionals? To become a data driven business, your entire team needs to be data focussed. Data is important at every level, from executive to C-suite. If you want to find out more on the value of data at C-suite level, take a look at our blogs on why kleene data matters to the CFO, CEO and CTO

When you want to get serious about data, you might look to find a data solution or build a data team. But do you know your Data Analyst from your Data Engineer? And what exactly is a Data Scientist? Understanding these roles, what data professionals do and how they work within a business, will help you to understand the data solution you need. The answer might not be as complicated as you think!

Data professionals

Data Analyst

Tasked with answering business questions through data (primarily using SQL). Data Analysts are focussed on gathering insights to produce reports and Business Intelligence in order to achieve business goals.

Data Scientist

Specialises in building machine learning/AI models (primarily using Python). A Data Scientist will combine science and creativity to ask new questions and make predictions.

Data Engineer

Responsible for building and maintaining data pipelines, managing data integrity and security (using SQL, Python, Spark, Go). A Data Engineer solves problems with technology, setting up the foundation for the Analysts and Scientists to build on.

Data problems 

Businesses face four hard data problems. Solving these problems requires either a data team or a technology solution.

Discover the key data problems and who would typically solve them within a data team:

  1. Extract and Load – The extract and load process means moving data from siloed sources around your business into a single source of truth cloud data warehouse. This activity would typically be the responsibility of an Engineer. 
  2. Building the logic layer – To apply your business logic, you need to layer up SQL scripts. This would usually be carried out by an Analyst. 
  3. Orchestrating the logic layer – Automating and orchestrating the order that scripts need to run, in order to be effective, requires an Engineer. 
  4. Analysis & visualisation – Answering critical questions and presenting the data in a way that business users can understand and utilise. This is a job for an Analyst.

All of these data professionals have a specific and valuable role within a business, but they aren’t always all necessary in a scaling business. Hiring a data team can be time consuming and expensive. If you don’t have the resources to build a data team, technology can provide the solution.

kleene can provide the answer, solving all four of these problems.

A Data Analyst drives the most business return and will add the most value to a scale-up, but needs the tools to be able to do so.

Our software solves problems one and three, whilst problems two and four are resolved by our professional services offering. Supply a Data Analyst with kleene to discover your single source of truth and find the answers to your business questions. You can choose our Analytics as a Service offering for a dedicated Analyst resource to help guide your business with powerful data insight, or hire your own.

Want to start building a data-driven environment? Book a demo to see what kleene.ai can do for you.