The Data Scientist vs. The Data Analyst: What’s the Difference?

Data - it’s been called the oil of the 21st century. Without it, organisations are essentially running blind, hoping that their efforts will pay off and translate into business wins. As Jim Bergeson famously said, “Data will talk to you if you’re willing to listen”.

Many employees and departmental heads start the journey of data management but end up backing out, believing that it’s too complicated and requires a lot of tech expertise. They then pass off the entire process, thinking that only data scientists can work with data meaningfully.

While it’s a good idea to trust the experts when it comes to data, this doesn’t mean that the process is just reserved for data scientists and no one else can add value to data management. Data analysts play an equally important role in extracting value from organisational data.?

Data Scientists vs. Data Analysts: Is There a Difference?

Yes. It might be tempting to lump data scientists and data analysts in the same category due to the data focus both positions take.?

Very often, the roles of a data scientist and data analyst may even overlap, depending on a particular organisation’s data requirements. But don’t be fooled - they’re not the same - and that’s a good thing.?

A data scientist designs and builds processes, models, and systems to collect and interpret data. They work at a more macro level to design new ways of capturing, storing and using data to answer important questions about the organisation and make predictions for what could happen in the future.?

A data analyst, on the other hand, spends more time assessing and analysing the captured data to make sense of what the data is demonstrating. Their key focus is less on how to capture the data and more on looking through the data to identify trends and extract the story behind the numbers and statistics.?

The Role of the Data Scientist

Data scientists spend a good part of their time dealing with the unknown. They aim to make the unknown known by making data accessible and actionable.?

There are several ways they can do this by automating machine learning algorithms or designing predictive modelling processes, both of which use structured and unstructured data to generate results.

While data scientists have a clear objective that they aim to achieve, they’re involved in multiple underlying data procedures that help them meet their goals. Some of these day-to-day processes include:

• collecting, cleaning and processing raw and unstructured data.
• Designing and developing machine learning algorithms and predictive models capable of mining big data sets.?
• Using tools and implementing processes to monitor and analyse data accuracy.
• Building dashboards and data visualisation tools for data reporting.
• Writing programmes that automate data capturing, collection and processing.

The Role of the Data Analyst?

Data analysts focus more on the “why” instead of the “how” of data. They typically work with structured data to solve business problems or answer questions organisations might have surrounding a particular operation or process.?

For instance, a data analyst may work with available data to figure out why something happened, such as why sales dropped during a particular period or they may work to represent identified trends or shifts using dashboards and advanced reporting. Certain tasks that data analysts will undertake include:

• meeting and collaborating with business heads to identify key questions, pain points or other informational needs.
• Getting the necessary data from primary and secondary sources.
• Cleaning and arranging the data for analysis.
• Assessing the data to identify trends and patterns that build up the story behind the data.
• Translating the data set findings into actionable insights.
• Presenting the data findings in an accessible, easy-to-understand way that can inform the decision-making process.??

Skills and Tools Used by Data Scientists and Data Analysts

Although analysts and scientists both work with data, we can see their roles are very different, each requiring a unique set of skills and tools to do the job.?

A data scientist needs to have a background in mathematics, statistics and machine learning algorithms. They also need to be proficient in handling tools and systems like Python, SAS, Hadoop, TensorFlow, R and Spark.

A data analyst should have a high-level understanding of statistics as well as basic problem-solving abilities. The tools they primarily work with are SQL, Excel, Tableau and, in some cases, R.

Conclusion?

Data management is a comprehensive process that requires the skills and abilities of different people who work together to help develop data-driven solutions and strategies for businesses.?

Even if you’re not a data scientist, you can play a role in the data space for your organisation by assisting in one or many of the multiple processes that help to create meaningful data insights.