Data science is a broad field in which scientific systems, algorithms, processes, and methods are used to extract insights and knowledge from unstructured, structured, and noisy data. This broad field houses various data experts who apply actionable insights and knowledge from analysed data to improve business processes and operations.
Typically, data science has brought radical changes in the way businesses operate, from government institutions to private companies.
And while data science is a crucial aspect of most organisations, it’s somehow complex, especially in understanding the different data roles associated with it. This is because most of the roles seem interlinked. If you’re looking forward to joining the world of data science but don’t know where to start, you’re in the right place.
In this article, we’ll look at the over-arching responsibilities and differences between the common data roles. Let’s dive in!
- 1. Data Analysts
Data analyst is one of the popular roles in the data science world. Data analysts perform various tasks, including processing, munging, and visualisation of large amounts of data. They also query databases once in a while.
To be a successful data analyst, you must possess the skills and curiosity to investigate from different angles. You must also be able to perform data transformation and cleaning operations to look for patterns and trends.
Typically, data analysts identify, gather, and analyse data then make their findings accessible to decision makers through easy-to-understand reports, tables, charts, and graphs.
Roles and Responsibilities
The roles and responsibilities of data analysts go beyond gathering and analysing data. They also perform the following duties:
- Extract information from analysed data through data mining to identify patterns and correlations
- Transform and organise information to make it comprehensible
- Predict trends using data
- Statistically analyse data then use special techniques and tools to visualise data and deliver it in a format that’s easy to understand
- Remove corrupt data and monitor data quality
- Prepare and present reports to stakeholders
- Identify and recommend new ways that can be used to streamline business operations and save money
To be a successful data analyst, you must have both leadership and technical skills. Other vital skills for this role include:
- Strong computational/mathematical skills to help in data collection, measurement, analysis, and presentation
- Knowledge of Python, MATLAB, R, Oracle, SQL, and other programming languages.
- Proficiency in data models, database design, data segmentation, and data mining
- Proficiency in SAS, SPSS, Excel, and other statistical packages
- Excellent communication skills
- Attention to detail
- Working knowledge of Qlik, Tableau, and other data visualisation software
- Business Intelligence Analyst
Business intelligence analyst (BI analyst) is one of the data roles that’s becoming increasingly popular as most organisations seek to capitalise on the amount and quality of data collected.
BI analysts use data analytics, visualisation, and modelling tools and techniques to transform data into meaningful insights that improve business operations.
Among other activities like building dashboards, BI analysts also identify areas of revenue loss in a business and advise on improvements needed to increase profits and operational efficiencies. They use BI software tools to mine complex data, compare it to competitors and general industry trends, and employ visualisation tools to communicate it to decision makers.
Roles and responsibilities
Here’s a rundown of the basic roles and responsibilities of a BI analyst:
- Review and validate collected customer data
- Review customer files to foster integrity in data collection and utilisation
- Develop procedures for data collection and analysis
- Oversee successful deployment of the collected data to data warehouses
- Team up with the IT department to make the necessary hardware and software upgrades to help leverage the collection and analysis of large volumes of data
- Implement new data analysis techniques
- Monitor data analytics and metrics
BI analysts should possess the following skills:
- Strong leadership and management skills
- Working knowledge of SQL, Python, Hadoop, and C#
- Proficiency in data visualisation tools such as Tableau
- Working knowledge of business regulations and policies
- Excellent analytical and problem-solving skills
- Ability to work in a team or independently
- Excellent communication, listening, presentation, and interpersonal skills
- Background in mathematics and computers
- Data modelling skills
- Insight Analyst
Customers are the main focus of every business, explaining why consumer insights are key in gaining an edge against competitors. Insight analysts are experts in studying consumer behaviours, needs, and preferences to develop customer-centric operational and marketing strategies.
Insight analysts must have the relevant expertise and skills to analyse both qualitative and quantitative data to learn what motivates the customers and leverage this data to support business interests.
Roles and Responsibilities
The roles and responsibilities of Insight Analysts include:
- Gather and analyse data
- Find trends and patterns in analysed data
- Data interpretation
- Submit reports to the top management and department heads
- Design procedural manuals used in all departments
- Assist the top management in developing business needs and goals
Here are the vital skills for Insight Analysts :
- Report writing skills
- Presentation skills
- Strong written and oral communication skills
- Strong mathematical background and the ability to simplify complex mathematical information to make it easy to understand
- Leadership and management skills
- Strong analytical skills
- Data mining skills
- 4. Research Analyst
Research analysts or data crunchers are professionals who work with data in both public and private enterprises. Research analysts gather data, analyse it and use the results to improve the savings, profitability, and efficiency of an organisation. As a research analyst you must have excellent communication and data presentation skills to help you present analysed data to decision-makers efficiently.
Roles and Responsibilities
Generally, a research analyst performs the following responsibilities:
- Use mathematical techniques to come up with objective answers to questions that arise from historical data
- Research to come up with recommendations to investors, brokerages, or money managers.
- Use statistical software to analyse data
- Identify trends and patterns in data and make recommendations based on these trends
- Create easy-to-understand presentations using graphs and charts
- Develop long-term recommendations for the business
A research analyst must possess the skills listed below to perform the assigned duties effectively:
- Research and analysis skills – must know how to use computers and statistical software.
- Communication skills to present the results of data analysis. Must also be able to communicate well with vendors, clients, managers, and other co-workers.
- Analytical skills
- Mathematical skills
- Working knowledge of statistics and must know how to use statistical packages
- Data Engineer
Data engineers are Information Technology (IT) specialists who prepare data for operational and analytical uses. Data engineers build data pipelines that bring together data from different sources. These engineers structure, cleanse, consolidate, and integrate data to be used in analytic applications.
Typically, data engineers focus on preparing data for use by data analysts and scientists. There are three categories of data engineers: generalists, pipeline-centric engineers, and database-centric engineers.
Roles and Responsibilities
The following are the roles and responsibilities of data engineers:
- Assemble complex, large data sets that meet both functional and non-functional requirements of a business
- Update existing systems with newer versions to boost operational efficiency
- Identify, design, and implement internal processes to improve business operations, including automating processes, optimising the delivery of data, and re-designing infrastructure for better scalability
- Build the necessary infrastructure to optimise loading, transformation, and extraction of data from different sources using SQL and AWS technologies
- Work with stakeholders to support data infrastructural needs and assist with data-related issues
- Build analytical tools to provide actionable solutions to business problems
The skills required to perform the above duties include:
- Process building skills that can enable the data engineer to build processes that support data structures, workload management, data transformation, metadata, and dependency
- Analytic skills and the ability to handle unstructured datasets
- Ability to identify areas that need improvements in the organisation
- Root cause analysis skills on both internal and external processes
- Data modelling and ETL tools
- Data warehousing solutions
- Knowledge of programming languages such as SQL, Matlab, SAS, Python, SPSS, Java, Hive, Pig, and C++, to mention a few
- Data Scientist
A data scientist is an expert who understands the business challenges and offers practical solutions using data processing and data analysis. For example, a data scientist is expected to perform predictive analysis and use disorganised/unstructured data to offer meaningful insights. In the course of their work, data scientists should be able to identify patterns and trends that can help with decision-making.
Roles and Responsibilities
The roles and responsibilities of data scientists in an organisation include:
- Data collection
- Correlate similar data to find practical solutions
- Create reports and present them for use in the organisation
- Search through data to find useful information
- Build tools to help automate data collection
- Create new frameworks for data collection
- To transform data to make it appropriate for analysis
Data Scientist must possess skills such as:
- Exceptional attention to detail
- Predictive modelling
- Distributed computing
- Knowledge in Matlab, Python, SQL, R, and other complementary languages
- Ability to use data management tools
- Experience in Artificial Intelligence (AI) and machine learning
- Problem-solving and analytical skills
- Data visualisation skills
- Ability to simplify complex data
As the field of data science continues to grow, more and more data roles emerge, and the demand for data experts continues to increase as businesses create new roles to help them meet the ever-increasing industry demands. The wide range of data roles causes overlapping responsibilities, making it hard to choose your ideal career path.
However, with the information discussed above, you’ll clearly understand what each data role demands and the skills needed for each.
As you begin your journey in the data science world, consider joining Brijj, a platform that makes it easy for data & insight teams to work with their business customers.