Find Out What Data Analysts Do & See if it Fits You

Check out the Data Analysts’ Career so that You can Decide if You want to Study in Malaysia to Work in this Field

Data is getting generated at a massive rate, by the minute.In fact, the amount of digital data that exists is growing at a rapid rate—in fact, more than 2.7 zettabytes of data exist in today’s digital universe, and that is projected to grow to 180 zettabytes in 2025.

All this data—from your photos to the Fortune 500’s financials—has only recently begun to be analysed to tease out insights that can help organisations improve their business. That’s why more organisations are seeking professionals who can make sense of all the data.

Organisations, on the other hand, are trying to explore every opportunity to make sense of this data. Data Analysts sift through large data sets collected through online surveys, users’ purchasing habits, social media posts and other sources, and turn them into useful information. This is where Data analytics has become crucial in running a business successfully. It is commonly used in companies to drive profit and business growth.

Skilled data analysts are some of the most sought-after professionals in the world. Employed across industries from insurance companies and technology firms to finance and retail. Any company that uses data needs data analysts to analyse it. Some of the top jobs in data analysis involve using data to make investment decisions, target customers, assess risks, or decide on capital allocations. Because the demand is so strong, and the supply of people who can truly do this job well is so limited, data analysts command huge salaries in Malaysia and globally.

Data Analysts use maths and statistical models to study data programs and data flows, extracting key bits of information. Students interested in this career should be organised, analytical, and have a keen eye for detail. Read on to find out more about the Career of a Data Analyst to see if it is the right choice for you to study after secondary school or Pre-University.

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What is Data Analytics?

Most companies are collecting thousands or millions of data all the time—but, in its raw form, this data doesn’t really mean anything. This is where data analytics comes in. Basically, Data analytics is the process of analysing raw data in order to draw out meaningful, actionable insights, which are then used to inform and drive smart business decisions.

Data analysis is the process of gleaning insights from data to inform better business decisions on their strategies and investments.

It is the process of exploring and analysing large datasets to make predictions and boost data-driven decision making. Data analytics allows us to collect, clean, and transform data to derive meaningful insights. It helps to answer questions, test hypotheses, or disprove theories.

Armed with the insights drawn from the data, businesses and organisations are able to develop a much deeper understanding of their customers, their industry, and their company as a whole—and, as a result, are much better equipped to make decisions and plan ahead.

What does a Data Analyst Do?

A data analyst will extract raw data, organise it, and then analyse it, transforming it from incomprehensible numbers into coherent, intelligible information. Having interpreted the data, the data analyst will then pass on their findings in the form of suggestions or recommendations about what the company’s next steps should be.

You can think of data analytics as a form of business intelligence, used to solve specific problems and challenges within an organisation. It’s all about finding patterns in a dataset which can tell you something useful and relevant about a particular area of the business—how certain customer groups behave, for example, or how employees engage with a particular tool.

Data analytics helps you to make sense of the past and to predict future trends and behaviours; rather than basing your decisions and strategies on guesswork, you’re making informed choices based on what the data is telling you.

Data Analyst Vs Data Scientist

• Data Scientist
  • Data scientists are big data wranglers. They take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics and programming to clean, massage and organize them. Then they apply all their analytic powers — industry knowledge, contextual understanding, skepticism of existing assumptions — to uncover hidden solutions to business challenges.
• Data Analyst
  • Data analysts collect, process and perform statistical analyses of data. Their skills may not be as advanced as data scientists (e.g. they may not be able to create new algorithms), but their goals are the same — to discover how data can be used to answer questions and solve problems.

Applications of Data Analytics

Data analytics is used in most sectors of businesses. Here are some primary areas where data analytics does its magic:

• Data analytics is used in the banking and e-commerce industries to detect fraudulent transactions.
• The healthcare sector uses data analytics to improve patient health by detecting diseases before they happen. It is commonly used for cancer detection.
• Data analytics finds its usage in inventory management to keep track of different items.
• Logistics companies use data analytics to ensure faster delivery of products by optimizing vehicle routes.
• Marketing professionals use analytics to reach out to the right customers and perform targeted marketing to increase ROI.
• Data analytics can be used for city planning, to build smart cities.

Types of Data Analytics

Data analytics can be broadly classified into 3 types:

1. Descriptive Analytics
   It tells you what has happened. It can be done using an exploratory data analysis. Example: Studying the total units of chairs sold and the profit that was made in the past.
2. Predictive Analytics
   It tells you what will happen. It can be achieved by building predictive models. Example: Predicting the total units of chairs that would sell and the profit we can expect in the future.
3. Prescriptive Analytics
   It tells you how to make something happen. It can be done by deriving key insights and hidden patterns from the data. Example: Finding ways to improve sales and profit of chairs.

Data Analytics Process Steps

There are primarily five steps involved in the data analytics process, which include:

1. Data Collection: The first step in data analytics is to collect or gather relevant data from multiple sources. Data can come from different databases, web servers, log files, social media, excel and CSV files, etc.
2. Data Preparation: The next step in the process is to prepare the data. It involves cleaning the data to remove unwanted and redundant values, converting it into the right format, and making it ready for analysis. It also requires data wrangling.
3. Data Exploration: After the data is ready, data exploration is done using various data visualization techniques to find unseen trends from the data.
4. Data Modelling: The next step is to build your predictive models using machine learning algorithms to make future predictions.
5. Result interpretation: The final step in any data analytics process is to derive meaningful results and check if the output is in line with your expected results.

A Data Analyst Daily Tasks and Job Responsibilities

A data analyst is a person whose job is to gather and interpret data in order to solve a specific problem. The role includes plenty of time spent with data but entails communicating findings too.

Here’s what many data analysts do on a day-to-day basis:

• Gather data: Analysts often collect data themselves. This could include conducting surveys, tracking visitor characteristics on a company website, or buying datasets from data collection specialists.
• Clean data: Raw data might contain duplicates, errors, or outliers. Cleaning the data means maintaining the quality of data in a spreadsheet or through a programming language so that your interpretations won’t be wrong or skewed.
• Model data: This entails creating and designing the structures of a database. You might choose what types of data to store and collect, establish how data categories are related to each other, and work through how the data actually appears.
• Interpret data: Interpreting data will involve finding patterns or trends in data that could answer the question at hand.
• Present: Communicating the results of your findings will be a key part of your job. You do this by putting together visualizations like charts and graphs, writing reports, and presenting information to interested parties.

What tools do data analysts use?

During the process of data analysis, analysts often use a wide variety of tools to make their work more accurate and efficient. Data Analysts often work with computers, and are proficient in statistical software and programming languages such as JavaScript, Python, and Extensible Markup Language (XML). Some of the most common tools used by Data Analysts are:

• Microsoft Excel
• Google Sheets
• SQL
• Tableau
• R or Python
• SAS
• Microsoft Power BI
• Jupyter Notebooks
• XML

Why Do Data Analysts Use Python?

There are many programming languages available, but Python is popularly used by statisticians, engineers, and scientists to perform data analytics.

Here are some of the reasons why Data Analytics using Python has become popular:

• Python is easy to learn and understand and has a simple syntax.
• The programming language is scalable and flexible.
• It has a vast collection of libraries for numerical computation and data manipulation.
• Python provides libraries for graphics and data visualisation to build plots.
• It has broad community support to help solve many kinds of queries.

Python Libraries for Data Analytics

One of the main reasons why Data Analytics using Python has become the most preferred and popular mode of data analysis is that it provides a range of libraries.

• NumPy: NumPy supports n-dimensional arrays and provides numerical computing tools. It is useful for Linear algebra and Fourier transform.
• Pandas: Pandas provides functions to handle missing data, perform mathematical operations, and manipulate the data.
• Matplotlib: Matplotlib library is commonly used for plotting data points and creating interactive visualizations of the data.
• SciPy: SciPy library is used for scientific computing. It contains modules for optimisation, linear algebra, integration, interpolation, special functions, signal and image processing.
• Scikit-Learn: Scikit-Learn library has features that allow you to build regression, classification, and clustering models.

Types of Data Analysts

Depending on your interests and skill set, you can pursue several types of Data Analyst roles. Some common types of Data Analysts include:

• Business Analyst
  Business Analysts use data to help businesses navigate decisions. They are responsible for collecting, analysing, and interpreting complex data sets to help companies make informed decisions. They work closely with stakeholders to identify business requirements and design supporting data models. They may also develop reports and dashboards to present data insights to decision-makers.
• Marketing Research Analyst
  A Marketing Analyst uses data to help companies understand their customers and develop marketing strategies. They analyse customer behaviour, demographic data, and market trends to help companies effectively target their marketing efforts. They may also build marketing performance metrics to track the success of marketing campaigns.
• Financial Analyst
  A Financial Analyst uses data to help companies make financial decisions. They may analyze financial data such as revenue, expenses, and profitability to help companies identify areas for improvement or growth. They may also develop economic models to forecast future performance and inform strategic planning.
• Healthcare Analyst
  A Healthcare Analyst uses data to help healthcare organisations improve patient outcomes and reduce costs. They may analyse healthcare data such as patient records, clinical trials, and insurance claims to identify trends and patterns. They may also develop predictive models to help healthcare providers make more informed decisions.
• Data Scientist
  A Data Scientist is responsible for designing and developing complex algorithms and models to solve data-driven problems. They work with large, complex data sets and use advanced analytical techniques to extract insights and develop predictive models. They may also work with other Data Analysts to develop data-driven solutions for businesses.

What is the Data Analyst Salary in Malaysia?

Data science fresh graduates can demand starting pay in the range of RM4,000-RM8,000 — making it the highest paid entry level job in the country today.

An experienced professional in the field can demand up to RM15,000 a month.

Many employers are building their teams from ‘scratch’, accepting candidates with entry-level industry experience. The large salary range reported by employers for a data scientist from more than RM 15,000 per month to less than RM 5,000 per month may reflect a lack of precision in defining the role of a data scientist. While compensation will differ by candidate experience and performance, it is important that this role is not undervalued. Data science competencies such as statistical modelling and machine learning require a high education investment which should be recognised.

PersolKelly Malaysia Salary Guide 2022/2023

• Financial Analyst with 3-5 years experience earn between RM4500 to RM7000 a month
• Market Research with 3-5 years experience earn between RM3000 to RM5000 a month

2023 HAYS ASIA SALARY GUIDE

Banking & Financial Services

• CORPORATE FINANCE, M&A, ECM/DCM
  • Analyst earns RM48k to RM79k a year
• ASSET MANAGEMENT
  • Research Analyst earns RM48k to RM102k a year
  • Senior Research Analyst earns RM84k to RM300k a year
  • Head of Research earns RM300k to RM480k a year
• PRIVATE EQUITY
  • Analyst – RM60k to RM108k a year
• HEDGE FUND – INVESTMENT
  • Analyst – RM48k to RM72k a year
• HEDGE FUND – EXECUTION
  • Risk Analyst – RM40k to RM72k a year
  • Risk Manager – RM72k to RM114k a year
  • Quantitative Analyst – RM114k to RM168k a year
• RESEARCH/STRATEGY (FICC & EQUITY)
  • Analyst – RM48k to RM102k a year
• CHANGE MANAGEMENT/PROJECT MANAGEMENT
  • Business Analyst – RM96k – RM168k a year
• INVESTMENT CONSULTANT
  • Analyst – RM54k – RM72k a year
• PRODUCT MANAGEMENT
  • Analyst – RM48k to RM84k a year

Insurance

• PROJECTS & TRANSFORMATION
  • Business Analyst – RM56k to RM300k a year
• STRATEGIC
  • Analyst – RM56k to RM120k a year
• INVESTMENT
  • Analyst – RM48k to RM102k a year
  • Senior Analyst – RM78k to RM300k a year

Marketing & Digital

• MARKETING – MARKET RESEARCH
  • Market Research/Consumer Insights Analyst – RM82k – RM140k a year
  • Market Research/Consumer Insights Manager – RM96k – RM160k a year
  • Market Research/Consumer Insights Director – RM240k – RM360k a year
• DIGITAL – TRANSFORMATION & ANALYTICS
  • Web Analytics Manager – RM48k – RM96k a year

Technology

• PROJECT MANAGEMENT
  • Business Analyst – RM96k to RM156k a year
• DATA SPECIALISTS
  • Data Architect – RM144k – RM300k a year
  • DBA – RM96k – RM300k a year
  • Data Modeller – RM144k – RM240k a year
  • Data Warehouse Consultant – RM120k – RM240k a year
  • Business Intelligence – RM120k – RM240k a year
• DATA ANALYTICS
  • Data Analyst – RM96k – RM180k a year
  • Senior/Lead Data Analyst – RM120k – RM216k a year
  • Head of Analytics – RM240k – RM480k a year
  • Chief Data Officer – RM300k – RM550k a year

Randstad 2023 Job Market and Salary Trends Malaysia

• Consumer Insights Manager with 4-10 years of experience can earn from RM8,000 to RM18,000 a month

What is the Job Outlook for Data Analysts?

Malaysia’s national ICT agency Multimedia Development Corporation (MDeC) has unveiled a plan, supported by seven public and private institutes of higher learning (IHLs), to increase the number of local data scientists from the current 80 to 2000 by the year 2030.

Statistics show that by the year 2020, there will be about two million job openings for data professionals and that the demand for people with this knowledge and skill will outstrip supply by a ratio of two to one. It’s a global phenomena which is already in motion and Malaysia has set its sights on developing 20,000 data professionals and 2,000 data scientists by 2030.

In addition, a report from the recent Digital Workforce of The Future by LinkedIn, which revealed that a combination of skills encompassing Big Data, data analytics and web development registered a 21% growth in demand. In Malaysia, the top five in-demand digital skills are big data, software and user testing, mobile development, Cloud computing and software engineering management.

Data Analyst Career Paths

Below is a list of some of the many different roles that you may encounter when searching for or considering data analysis.

• Business analyst: analyzes business-specific data.
• Management reporting: reports data analytics to management on business functions.
• Corporate strategy analyst: this type of role will focus on analyzing company-wide data and advising management on strategic direction. This role may also be focused on mergers and acquisitions.
• Compensation and benefits analyst: usually part of a human resources department that analyzes employee compensation and benefits data.
• Budget analyst: focuses on the analysis and reporting of a specified budget.
• Insurance underwriting analyst: analyzes individual, company, and industry data for decisions on insurance plans.
• Actuary: analyzes mortality, accident, sickness, disability, and retirement rates to create probability tables, risk forecasting, and liability planning for insurance companies.
• Sales analytics: focuses on sales data that helps to support, improve, or optimize the sales process.
• Web analytics: analyzes a dashboard of analytics around a specific page, topic focus, or website comprehensively.
• Fraud analytics: monitors and analyzes fraud data.
• Credit analytics: the credit market offers a wide need for analytics and information science in the areas of credit reporting, credit monitoring, lending risk, lending approvals, and lending analysis.
• Business product analyst: focuses on analyzing the attributes and characteristics of a product as well as responsibility for advising management on the optimal pricing of a product based on market factors.
• Social media data analyst: social media and growing tech companies rely on data to build, monitor, and advance the technology and offerings that social media platforms rely on.
• Machine learning analyst: machine learning is a developing technology that involves programming and feeding machines to make cognitive decisions. Machine learning analysts may work on a variety of aspects including data preparation, data feeds, analysis of results, and more.

How to Become a Data Analyst in Malaysia?

A data analyst gathers, cleans, and studies data sets to help solve problems. Here’s how you can start on a path to become one after completing your SPM or IGCSE/O-Levels.

The first step is to take a Pre-University programme such as Foundation. Upon completion of the 1-year Foundation programme at a Private University, you can go for a degree in Data Science, Business Analytics, Actuarial Science or Statistics depending on which area suits your interest, strength and career goals.