Part 3 of 9
In this blog, we’re unpacking the Data Analyst role. Sometimes referred to as “data scientists in training”, these analysts are usually limited to tackling specific business tasks using existing data sets, tools and systems, rather than being given the authority to create their own projects.
Data Analyst is an umbrella term that in many cases doubles for a variety of other job titles, including Marketing Analysts, Quantitative Analysts, Business Analysts, Data Warehouse Analysts or Business Systems Analysts.
Data Analysts are required to discover, through the collection, processing and statistical analysis of data, how data can be used to answer questions and solve problems. The complexity of the environment and the skill and experience of the individual would determine the scope of role. Their key areas of responsibility typically include:
Mining data from primary and secondary sources
Cleaning data to discard irrelevant information
Analysing and interpreting results using standard tools and techniques
Pinpointing trends, patterns and correlations in complicated data sets
Identifying new opportunities for process improvement
Presenting data reports and clear data visualization for management to make more effective decisions
Designing, creating and maintaining relational databases and data systems
Working with IT teams and management to determine organisational goals and monitoring metrics
Due to the analytical nature of this job, most individuals at entry level are required to hold a degree in mathematics, information management or economics. In each instance, the degree should carry subjects with a heavy emphasis on statistical and analytical skills.
Employing best practice to analyse large amounts of data whilst maintaining intense attention to detail.
In a dynamic environment such as this, technical skill competence can change rapidly, and this therefore serves as a guideline.
Statistical methods and packages (e.g. SPSS)
R and/or SAS languages
Data warehousing and business intelligence platforms
SQL databases and database querying languages
Data cleaning and munging
Data visualization and reporting techniques
Working knowledge of Hadoop & MapReduce
Machine learning techniques
Ability to translate technical concepts into pragmatic solutions that management, and other staff members, understand and can buy-into.
Willingness and ability to question established business practices and lead brainstorming for new approaches to data analysis.
Knowledge and understanding of what drives business in the chosen sector/industry and how effective analysis of key data can contribute to the success of the company’s strategy.
Depending on the environment, the Data Analyst may have a range of career options available, most obviously promotion to Data Scientist. For those analysts who grow weary of number crunching, there may be avenues to shift into data construction roles such as Data Engineer, Data Architect or Hadoop Developer, for example.
Remuneration is directly linked to job responsibilities. A senior Data Analyst with the skills of a Data Scientist can command a high price, particularly in today’s skills-short market. The addition of technical skills, including additional programmes and languages, can improve earnings potential. At entry-level, Data Analysts would be earning R200k per annum with those carrying greater levels of experience and skill as much as R600k per annum.
Outlook: Future of Work
Change is already here with increasing numbers of self-service business intelligence software becoming available to operations and management teams. These systems now automate many of the regular tasks that previously would have been required to be completed by Data Analysts.
Today, many managers and operational individuals can, at the touch of a button, generate data reports, build dashboards and monitor numbers to assist them in managing their desks or departments in real-time. As a result, the need for analysts to extract information and prepare it into a readable format is rapidly decreasing.
The rapid rise in sheer volume of data, especially that in unstructured formats (such as from social media feeds) means that Data Analysts are required to clean, filer and convert billions of diverse data points. To do this, they are expected to employ complex modelling and predictive analytics techniques to generate useful insights and actions and then to explain to their colleagues, in laymen’s terms, how this can be used to the benefit of the business. In a nutshell, Data Analysts need to acquire the skills to perform as Data Scientists.
Our Data & Analytics Expertise
Look out for the other installments in this 9-part series, where we unpack each of the 8 core roles within the Data & Analytics environment. Visit www.tsrecruitment.co.za to view our blog and make sure you follow us on our TSR social media platforms to access each part in the series.
If you’re looking for an opportunity within the Data & Analytics space and would like to have a confidential career conversation, please get in touch with our expert recruiter, Sheila Mtakwa (firstname.lastname@example.org) And if you’re looking to enhance your organisation’s data or analytical capability, look no further. We’d be delighted to meet with you for a no-obligation consultation on how to boost your chances of securing the best talent available in the market.