A Big Data Analyst or Functional Analyst uses their ability to focus on details and patterns to analyse big data and models. They provide reports and visualizations to demonstrate insights that can be drawn from raw data, they inform decisions, and use mathematical modelling techniques to solve problems in financial markets. This role requires a strong affinity with mathematics, an understanding of data and an ability to draw key learnings from data.
A Big Data Analyst or Functional Analyst uses their ability to focus on details and patterns to analyse big data and models. They provide reports and visualizations to demonstrate insights that can be drawn from raw data, they inform decisions, and use mathematical modelling techniques to solve problems in financial markets. This role requires a strong affinity with mathematics, an understanding of data and an ability to draw key learnings from data.
The Big Data Analyst needs to be highly organised and have the ability to focus on detail. Strong communication skills and the ability to draw meaningful insights from statistics and data, and to represent these learnings in a way that makes sense to others, is the foundation of a successful analyst. They need to both understand the data, and be able to provide insight and analysis through clear visual, written and verbal communication. Data Analysts are expected to analyse various scenarios, leveraging intelligent inferences for the company and in so doing, helping the company to develop strategies for doing business in the given scenario.
Fundamentally, the Big Data Analyst answers the questions, “What happened?” ; “Why did it happen?” ; “What is likely to happen?” ; and “What action should be taken?.
At its core, the role of the data analyst is to comb through facts, statistics and other raw data gathered from various sources to build a holistic view of the scenario which they have been tasked to model. This scenario provides the basis for further insights and analysis, in order to propose various solutions to the given challenges, and predicting their likely outcomes, thus aiding the company in developing effective strategies.
This eventual output involves a myriad steps and competencies. You will produce and track key performance indicators and work with IT teams, management and data scientists to determine organizational goals. Once you have established your goals, you will be responsible for developing records management processes and policies, setting up and maintaining automated data processes, as well as gathering, understanding and documenting detailed business requirements using appropriate tools and techniques.
You will mine data from primary and secondary sources, analysing large datasets to draw valid inferences. Data quality must be audited and maintained, through efficient automation processes. Tools to support data validation and cleansing must be developed in order to retain only relevant information.
Once the data is gathered and qualified, you will develop and support reporting processes. You will liaise with internal and external clients to fully understand data content while manipulating, analysing and interpreting complex data sets relating to your employer’s business, using statistical tools and techniques.
Your learnings must then be represented in reports for internal and external audiences using business analytics reporting tools, data dashboards, graphs and visualisations. You will provide sector and competitor benchmarking, pinpoint trends, correlations and patterns, and provide concise data reports and clear data visualisations for management.
All the while, you will be improving processes by designing, creating and maintaining relational databases and data systems and working through code problems and data-related issues, all to ensure the most accurate information is available, and that the most valuable insights and strategies are presented to management.
As a Big Data Analyst, you are most likely to work in an office-based corporate environment. Most industries can make use of data analysts, from medical, to media to FMCG. No matter the industry you choose to work in or the type of data you analyse, you will be required to liaise with various stakeholders throughout the company, from IT to Exco, on a regular basis. As such, you will need to be comfortable communicating across cultures, age groups and competencies. Depending on experience, a data analyst can earn between R12 000/month (at junior level) and R36 000/month
You can study any
of the following degrees
These degrees are geared towards developing skills and knowledge that are vital to success in this field.
Should you choose to focus on Mathematics, you will learn numerical and analytical skills, applied Mathematics , Mathematical and risk modelling, statistical methodologies and data analysis techniques.
For the technologically inclined, you will learn about VBA and SQL Servers, APIs, and XML. Business intelligence and analytics platforms are key, as are statistical programmes. Programming languages such as R, Python and MATLAB will provide a basis for data gathering and analytics.
Finally, if finance catches your interest, you will develop your knowledge of accounting procedures and financial reporting practices, financial modelling, and your knowledge of economics in order to analyse current economic condition and predictions for future economic indicators.
It is important to note that data analysis is not a purely scientific field. A data analyst needs to be able to properly communicate with internal and external stakeholders to inform their analysis, and present complicated scientific and technical findings in a way that makes sense to laymen. As such, a data analyst needs an excellent command over English and strong conversational skills. It also requires the ability to present findings in easy-to-consume reports and presentations. They need strong organisational and co-ordination ability, patience and an ability to spend long periods of time working on their own, trawling through code and spreadsheets on a computer screen. A data analyst employs best practice techniques to analyse large amounts of data whilst maintaining intense attention to detail. They require a deep understanding of the industry, of current data collection and analysis techniques, as well as the ability to question existing practice and develop new practices.
