Data analysts and data scientists – they’re basically the same thing, right?
Honestly? No, they’re not.
After spending 20+ years in the Big Data and analytics world, one of the most common mistakes I have seen is business leaders using data analysts and data scientists as interchangeable terms. The fact is, each of these disciplines require very different skillsets, and also offer very distinct values to organizations. This is why interchanging the two terms can be dangerous – if you place people with certain skillsets in the wrong roles, your organization will never experience the true lift that Big Data has to offer.
However, just because the two disciplines are different doesn’t mean they are separated. Data science and data analytics need to work together in the era of Big Data. They just need to be recognized as different sides of the same coin. But to be able to do this and reap the benefits, you need to understand the differences between the two roles.
So, what is a data analyst?
The responsibility of a data analyst can vary from company to company and industry to industry. However, fundamentally, data analysts deliver value to their companies by taking data, using it to answer questions, developing reports and visualizations to showcase the data and help make better business decisions. They typically analyze well-defined sets of data using a data delivery tool, like Power BI, Tableau, or Qlik, to answer tangible business needs. What sales markets are faring better than others? What is the company’s outstanding receivables? What is driving sales growth?
Common tasks done by data analysts include data cleaning, reconciling, analysis, and creating data visualizations. These all play a role in answering those critical LOB questions.
Sometimes, data analysts go by a different title: Business Analyst, Business Intelligence Analyst, Operations Analyst, Database Analyst, just to name a few. Regardless of their title, data analysts are generalists who can fit into many roles and teams to help others make better data-driven decisions.
The power of a data analysts is revealed in automating data and data visualizations, creating a story that gives the user the ability to answer any number of questions. Often, the collateral benefit comes in the form of process optimization. Working with product and technical engineers, a data analyst can automate manual activities in data collection and reporting. This type of activity allows business users the ability to spend more time analyzing data rather than collecting it.
Here are some examples of Data Analyst responsibilities:
- Designing and maintaining data systems and data models
- Preparing reports and dashboards for business customers that effectively communicate relevant facts and trends in relevant data.
- Collaboration with product owners, software engineers and business customers to identify opportunities for reporting and analytics
- Mining data from data sources; Organizing data in a format that can be easily read by either human or machine
An effective data analyst takes the guesswork out of business decisions by delivering facts supported by data. They work across different teams by analyzing new data, combining different data sources, reports, and translating the outcomes. To sum it all up, a data analyst is key to unlocking the answers that hide within your data; they create process efficiency and are integral in creating a data-driven culture.
On the flip side, data scientists are experts at interpreting data, like data analysts. But data scientists tend to have coding and mathematical modeling expertise.
Most data scientists hold advanced degrees – many people start as a data analyst and move to a data scientist role. They are hands-on in machine learning, skilled with advanced programming, and can create new processes for data modeling. This includes working with algorithms, predictive models, and more.
Data Scientists are future focused – identifying the hidden patterns and secrets within an organization’s vast volume of data. Data Scientists tend to go after the more open-ended questions the organizations have by leveraging their knowledge of advanced statistics and algorithms.
All businesses and organizations can learn and benefit from data – and today, it is more critical than ever to do so. Data keeps you ahead of the game – it helps you survive amidst vicious competition. This is why data scientists are so important. Not only do data scientists leverage some of the same abilities as a data analyst, but a data scientist also has expertise in statistics, data science, Big Data, R programming, Python, and SAS. Expertise in these areas leads into AI, which helps data scientists build more insightful data models, giving you better results.
Here are some examples of Data Scientist responsibilities:
- Define and develop programs for modeling, data creation, metrics definition used for reporting
- Design and building data models and visualizations to summarize the conclusion of an advanced analysis.
- Define statistical models to determine the validity of analyses.
- Define and build machine learning to build better predictive algorithms.
- Testing and improve the accuracy of machine learning models.
At the end of the day, data scientists bring an entirely new approach and perspective to understanding data. These professionals typically interpret larger, more complex datasets, that include both structured and unstructured data. While an analyst may be able to describe trends and translate those results into business terms, the scientist will raise new questions and be able to build models to make predictions based on new data.
Where LOB Users Find Value in the Two Roles
When thinking of these two disciplines, we shouldn’t look at them as data science vs data analytics. Instead, we should see them as vital parts of an organization that help us understand not just the information we have, but how to better analyze and review it. This is key if you want data to be able to tell you where your business is going, and what you can do to improve overall.
For companies that truly want to create a data-driven culture, to mine the mass amounts of data they have, adopting data analytics and data scientist is a big step in that direction. These roles have the skills, knowledge, and ability to transform a traditional business into a data-driven empire.
Insights with CORTAC Group
At CORTAC Group, our team of data analysts help clients increase visibility across their organizations. With us, you get self-service, interactive dashboards that allow for more efficiency, transparency, and agility. Empower yourself with the ability to make fact-based business decisions faster, increasing strategic analysis in today’s fast-moving environment. Learn more about our Insights service here.