In this digital age, information is generated at a fast pace. Every click, like and purchase is recorded. Businesses can use this overwhelming amount of data to boost their growth, but many don’t really know how to read complex data and transform it into invaluable insights.
This is where big data analysts come into the picture. These professionals use tools and techniques to convert seemingly incomprehensible data into actionable steps that guide businesses towards success.
The big data analyst role is relatively new compared to traditional job titles. However, it has experienced high demand in recent years due to technological advancements and data explosion.
This article explains everything you need to know about big data analysts to help you determine whether it is the right career path for you.
What is a big data analyst?
A big data analyst is a professional skilled in understanding, collecting, exploring and transforming complex datasets, also called big data. They use a combination of techniques to find hidden patterns, trends, and customer behaviour insights from different data sources.
Their primary goal is to help businesses make data-driven actions to optimise their strategies and increase sales with their recommendations based on their analysis.
What roles does a big data analyst play?
Big data analysts wear many hats in an organisation. From collecting data to visualising it and creating reports, they do everything required to comprehend data and share gained information with others.
They are expected to play the following roles.
- Collecting the data: They use different techniques to identify external and internal data sources. Once they have found them, they set up data collection processes to collect high-quality data consistently.
- Cleaning the data: Before starting data analysis, it is crucial to perform data cleaning to confirm it contains only reliable information. Hence, data analysts precisely remove irrelevant and inaccurate elements from the dataset, ensuring it is ready for analysis.
- Analysing the data: It is the heart of big data analysts’ role, where they thoroughly examine the collected data to gather helpful information. They use statistical techniques and machine learning algorithms to uncover hidden trends, potential risks, buying patterns, etc.
- Visualising the data: Analysts use visualisation techniques and software to simplify complex information collected from data. It allows non-technical managers and stakeholders to digest complicated information easily. Common examples of visualisation types are heat maps, charts, graphs, and scatter plots.
- Predicting trends: In addition to identifying current trends, they also forecast future trends by using predictive modelling. It helps companies plan future developments and make decisions on time.
- Reporting the data: They prepare comprehensive reports of their findings and share them with decision-makers so they can turn insights into data-driven actions.
- Maintaining databases: Databases need ongoing maintenance so they can stay efficient for a long time. Analysts execute numerous tasks such as data archiving, backup planning, and database performance optimization to ensure data remains protected and the system runs smoothly.
- Collaborating with other departments: They work with departments like marketing and IT to meet their data needs and give them insights based on their objectives.
What are the essential tools and techniques used by big data analysts?
Research shows that around 328.77 million terabytes of data are produced, consumed, or used every day. This vast amount of data is only useful if you know how to collect, clean, and analyse it using different tools and techniques.
Big data analysts get formal training to learn different tools. They use a variety of tools, each for a different purpose. Common examples of tools that are used in this industry are:
- Rapid Miner
- APACHE Hadoop
- APACHE Spark
- APACHE Kafka
- APACHE Hive
- Power BI
- Spark Streaming
What are the skills and qualifications required for this role?
Companies demand big data analysts to have the following skills and qualifications.
Data analysis: It is the essential skill that refers to multiple processes such as data cleaning, exploration, hypothesis testing, etc.
You should know how you can extract data from different resources and use it to deduce a hypothesis. You should also know how to visualise its results in a way that even non-technical people can easily understand information.
Data mining: Data analysts research big data to identify trends and patterns. They turn the gathered information into predictions and insights, helping their company make data-driven decisions in the future.
Data interpretation: They use different analytical research methods to review raw data sources, including tables, graphs, etc. Then, draw meaningful conclusions from them to solve problems, answer questions, and take proven actions.
Cloud computing: Big data analysts must know how to work with different cloud computing technologies and platforms like Google Cloud Platform (GCP) and IBM Cloud.
Data warehousing: They collect data from different resources to form a single database. It allows companies to retrieve data whenever they want and use it to gain insights, design strategies, or make crucial decisions.
Programming: They should know how to use different programming languages such as Python, R, and SQL to handle large datasets and work more efficiently.
Data visualisation: This skill is required to help people comprehend complex datasets by creating easy-to-understand visual elements such as charts, graphs, etc.
Machine learning: They should have knowledge of various machine learning techniques. They should be able to apply them specifically for tasks like predicting future outcomes and classifying data into categories.
Probability and statistics: They should have knowledge of different probability and statistical methodologies. It will allow them to analyse big data systematically and ultimately contribute to business outcomes.
Relevant education: Organisations look for potential candidates who have a bachelor’s or master’s degree in a relevant subject. Common examples of relevant fields are computer science, data science, statistics, and engineering.
Relevant certifications: Additionally, certifications in big data technologies can increase your chances of getting hired.
Problem-solving skills: Your role will involve analysing large datasets to find solutions to problems, making problem-solving skills crucial. They will allow you to identify and resolve issues on time.
Communication skills: Furthermore, good communication skills are required so you can convey your findings and insights in an understandable manner to non-technical stakeholders.
Teamwork: Data analysts need to collaborate with data engineers and other team members to process data. Teamwork skills allow them to share knowledge with others and enhance the quality of analyses. All in all, it helps improve the overall performance of all departments collectively.
How to become a big data analyst?
- Get required education: Start with a bachelor’s or master’s degree in a relevant field such as computer science, data science and statistics.
- Learn relevant skills: Next, familiarise yourself with big data analyst skills and technologies. People with data analysis, manipulation, visualisation, programming, and warehousing skills have a high chance of getting hired. You should also know how to use the essential tools mentioned above.
- Get certifications: Earn relevant certifications from reliable organisations to enhance your credibility.
- Gain hands-on experience: Get experience by doing internships, working on real-world projects, or handling big data freelance projects. It will allow you to build a solid portfolio before you apply for your dream job.
- Network with professionals: You should connect with industry experts by attending conferences, joining communities and participating in meetups. It will allow you to learn from their experiences and get career opportunities.
Several factors influence the expected salary of a big data analyst. Some examples of these factors are experience, geographical location, the size of the company, and the industry they work in.
According to data from Glassdoor, the average annual salary for a Big Data analyst is approximately $113,805. This figure covers a base salary of $94,443 and additional pay of $19,362.
However, it’s essential to note that these figures can vary significantly based on individual circumstances and market conditions.
Difference between big data analysts and data scientists
Both big data analysts and data scientists work with data to extract information and use it to make data-driven decisions. However, they are two different roles, having distinct responsibilities in organisations.
Big data analysts mainly use large volumes of structured datasets to provide insights. They study past and current trends to solve business problems.
They are responsible for data collection, data visualisation and trend identification.
Contrarily, data scientists use more advanced techniques to work with both structured and unstructured datasets. Mostly, they make predictions about the future. They have a broader focus and possess a more extensive skill set.
They are mainly responsible for developing predictive models and building machine-learning solutions.
Big data analysts help organisations make sense of information that they gather from different datasets. They basically unlock secrets hidden in large volumes of datasets.
To achieve this goal, they perform multiple tasks, including data collection, data organisation, pattern identification, and report preparation.
The need for data interpretation is continuously growing with time, making this role one of the most in-demand and high-paying jobs in the world. If you enjoy reading data and solving problems, this career path may be the right choice for you.