Analyst, Scientist, or Specialist? Choosing Your Data Job Title
There’s a world of job opportunities in data, big data, data analytics, and the business improvement they can bring. A prediction for the United States in 2018 from the McKinsey Global Institute showed a shortfall of deep analytic skills in the amount of more than 140,000 people, not to mention the 1.5 million people needed with the ability to interpret data analysis to make data-driven business decisions. If the numbers are right, it looks like demand will clearly exceed supply.
There are several possible data job titles involved, including Data Scientist, Data Analyst, Business Analyst, and Data Specialist. It’s important to hone in on the one that best matches your capabilities and aspirations, possibly leaving the door open for further career development later. However, be aware that people may use titles in different ways, sometimes even interchangeably. So, look at the description for each title below to make sure that at your next performance review or interview, you agree on what the job role entails, as well as its name.
Often called “unicorns,” people with all of the requisite skills to fill this role are rare indeed. Essentially, a data scientist makes models for data-driven decisions, looking to the future and demonstrating initiative and innovation to build new solutions where necessary.
Besides strong technical skills (for instance, use of Hadoop, programming in Rand 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. Ideally, they are inquisitive by nature, and can relate simultaneously to data, to organizational needs, and to the business audience that needs to hear about their results.
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 Office), CMO (Chief Marketing Officer) or even directly to the CEO.
Data analysts use existing tools and algorithms to solve data-related problems, rather than 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.
There is 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 knowhow 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.
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.
As their name suggests, database specialists possess in-depth knowledge of databases. They work with information security software to prevent data breaches, and assist business operations by organizing whatever volume of data is needed. They make sure data is correctly stored, protected, cleaned, transformed, and aggregated to meet business requirements. This includes compiling and installing database systems, scaling to multiple machines, and implementing disaster recovery plans.
Database specialists may also look 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, with stronger software engineering skills tending to lead to higher levels of remuneration.
One Data Job Title Today Can Bridge to Another Tomorrow
The roles above intersect in several places. For example, although a data scientist offers modeling, storytelling, visualization, and statistics skills, compared to a database specialist’s know-how in system implementation, data storage, and database administration, they overlap in areas of 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,” for example from a more technical audience to a more business-centric one. 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. But if you think you’d like to carve out an entirely original data job title for yourself instead, try the Sisense data job title generator and see a whole new range of possibilities!
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