Analyst, scientist, or specialist? Choosing your data job title

In The Future of Work, we explore how companies are transforming to stay competitive as global collaboration becomes vital.

We live in a constantly-evolving world of data. That means that jobs in data big data and data analytics abound. The wide variety of data titles can be dizzying and confusing! The good news is that if you are passionate about data and have the skills to back it up, you can probably find a challenging, rewarding role. The growth in the range of data job titles is a testament to the value that these experts bring to their organizations.

If you are looking to start or transition to a career dealing with data, there are several possible data job titles you may want to include in your searches, such as Data Scientist, Data Analyst, Business Analyst, and Data Specialist. It’s important to find one (at least to start with) that matches your capabilities and aspirations while understanding that constant education is a necessary part of staying competitive as a data worker. Be sure to study each job listing during your search so that you can understand the finer details of what makes them each unique and how the company in question is using the specific job title (data analyst vs data specialist, etc.).

Again: Different companies use the same data job titles in different ways, sometimes even interchangeably. When applying for a role, worry less about the title and more about what the job role entails.

Data Scientist

Often called “unicorns,” people with all of the requisite skills to fill this role are rare indeed. Data scientists usually build models for data-driven decisions asking challenging questions that only complex calculations can try to answer and creating new solutions where necessary.

Besides strong technical skills (for instance, use of Hadoop, programming in R and Python, math, statistics), data scientists should also be able to tackle open-ended questions and undirected research in ways that bring measurable business benefits to their organization. If you’re inquisitive by nature and can relate simultaneously to data, organizational needs, and the business audience that needs to hear about their results all at once, you might be a good candidate for a data scientist.

As you can imagine, data scientists with all these aptitudes can be of considerable value to their employers. They are the link between the data resources available to an enterprise and executives looking for opportunities to make the business better, faster, and stronger. Depending on the size and objectives of the enterprise, they may report to a Chief Scientist, CTO (Chief Technical Officer), CMO (Chief Marketing Officer) or even directly to the CEO.

Data Analyst

One of the most common data job titles, data analysts use existing tools and algorithms to solve data-related problems (instead of inventing new ones like data scientists might do. Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization. They also put together custom database queries to answer the questions of business users, implement new metrics from existing data, strive to improve data quality, and contribute to correct acquisition of new data.

Unfortunately, there’s often no absolute rule about the use of this analyst job title. Data analysts in one organization might be called data scientists or statisticians in another. They are also often expected to combine technical know-how with industry knowledge, overlapping with the business analysts we discuss below. Overall, however, what often characterizes them is a focus on data collection, manipulation, and analysis, using standard formulas and methods, and acting as gatekeepers of an organization’s data.

Data analysts might report to a CIO, a Chief Data Officer (CDO), or possibly to a data scientist or business analyst team leader. While salaries for data analysts are often reasonably high, salaries for data scientists may be higher still. This may reflect the requirement on data scientists to create models to improve the future, compared to the role of data analysts to use data to describe the past and the present instead.

Business Analyst

Usually experts in their industry, business analysts must also have reasonable knowledge of manipulating data and specifying systems, while being able to communicate well at different levels. In a general sense, the starting point for business analysts is the assessment of the operational and functional needs of their organization. They then translate those needs into system specifications and look for the most attractive financing options for such systems.

Database design is often an important part of the business analyst role. This includes database modeling, metrics definition, dashboard design, and creating and publishing executive reports.

ROI (return on investment) is also a key concern, as business analysts apply their data-related activities to finance, marketing, and risk management, for instance.

Business analysts may work together with data scientists and data analysts in areas such as metric definition and database design. The distinction between all three categories can become blurred, for example if a business analyst also provides code for new business systems and applications. Business analysts often work within matrix organizations, reporting to a line manager like a CIO or CFO, for instance, and to a functional manager like a project leader.

Database Specialist

As their name suggests, database specialists possess in-depth knowledge of databases. People with this data job title work with information security software to prevent data breaches and assist business operations by organizing volumes of data. They make it’s correctly stored, protected, cleaned, transformed, and aggregated to meet business requirements (for instance, to go into a data model for self-service analyses, to be embedded into products, etc.). Other duties include compiling and installing database systems, scaling to multiple machines, and implementing disaster recovery plans.

Combining datasets is key to unlocking more advanced insights. Database specialists may be charged with looking after other data repositories used by the organization, such as data stores, marts, warehouses, and lakes. Besides data management skills and optimization of the data architecture to meet business requirements, they provide a robust platform for data analysts and data scientists to obtain the data they need for their own models and investigations. With strong technical abilities, database specialists are likely to be at ease with both SQL databases like MySQL and PostgreSQL, and NoSQL technologies such as MongoDB and Redis.

Frequently part of the IT department, database specialists may report to an IT team leader or to the CIO. Salaries can be comparable to either those of data analysts or data scientists. Those with stronger software engineering skills may warrant higher payscales.

Data analyst vs. data specialist

Sometimes, being a pioneer is fraught with challenges. For instance, the proliferation of data job titles can sometimes make it confusing to look for what you really want out of a role. Oftentimes, different companies will use either of these names to mean roughly the same thing (analyst vs specialist), not to be confused with a database specialists above. If you are excited by tackling complex challenges using self-service tools and maybe some SQL, then either of these data job titles could suit you.

However, if you are more interested in living in databases and dealing more with data and the backend, talking to other data experts as opposed to interacting with other frontline users and business experts, then database specialist would be more your focus. Again: Titles vary from place to place, just check the job duties and make sure whatever they’re asking for, you could see yourself doing it day after day for a long time! There’s only going to be more and more data as time goes on, so companies will need more data experts of all kinds to tackle the wide array of challenges and ultimately shape the world.

Data job titles: Bridges between roles

All the roles listed above intersect in multiple places. While data scientists require modeling, storytelling, visualization, and statistics skills, compared to a database specialist’s expertise in system implementation, data storage, and database administration, they overlap in areas like programming and math. Likewise, a data analyst may focus on standard SQL data stores, analytics, statistics, and business intelligence functions, compared to a data scientist involved in new data acquisition and manipulation with advanced statistics, but they both typically share a curiosity about data, a desire to obtain insights, and an ability to “tell a story” to business audiences about data.

It may, therefore, be possible to transfer from one role from another and to change “internal customers.” The platform you or your organization uses for preparing and managing data, modeling and designing algorithms for analytics, and visualizing both data and the results of the analyses can contribute to this process. For instance, Sisense accelerates and facilitates all these functions, making it easier for data analysts and database specialists to “grow” into the role of business analyst or data scientist if they want to do so.

Jack Cieslak is a 10-year veteran of the tech world. He’s written for Amazon, CB Insights, and others, on topics ranging from ecommerce and VC investments to crazy product launches and top-secret startup projects.