Big data analyst is responsible for conducting data analysis and research (data analytics) using specific tools.
In the business world, data analysts conduct research for marketing or product development purposes, where they see the activity of the target market on the internet.
It can be from what the target market is often looking for on search engines and social media.
A data analyst is now very much needed in the business world because behaviour, tastes, and trends close to the target market change quickly.
Compared to manual research, which takes a long time because they have to create and collect questionnaires for respondents, this data analytics process saves time with more accurate data.
For those interested and passionate in academia and research, this data analysis capability makes it easy to conduct research with dense, accurate, and time-efficient data.
In addition, researchers and lecturers with this skill have an excellent opportunity to get other research projects with various parties.
The Roles of Big Data Analyst
Big data analytics has many uses. For business development, big data analytics can help management make better decisions.
In fact, this prominent data analyst also plays a role in the company’s strategic planning. One of them through the identification of market trends.
A prominent data analyst in business management is also helpful for human resource management in a company.
Among other things, assisting in recruiting suitable employees can help improve business performance.
Hard Skills for Big Data Analyst
Since the major role of a Big Data Analyst is to help in the growth of the business, there are various skills required.
1. Programming
Due to the ongoing development of big data, working with complex datasets requires proficiency in popular programming languages such as Python, R, C++, Java, SQL, and Ruby. A
s a Big Data Analyst, you will be handling complex datasets that cannot be solved through standard processes, making knowledge of programming languages essential.
Additionally, you will also need to be able to make customizations to handle unstructured data on a daily basis.
2. Data Warehousing
Having knowledge of both non-relational and relational databases is a significant advantage when pursuing a career as a Big Data Analyst.
Relational databases, such as Microsoft SQL Server, IBM DB2, Oracle Database, and MySQL, are commonly used in this field, while non-relational databases, also known as NoSQL databases like MongoDB, Cassandra, HBase, Neo4j, are also essential.
3. Computational Frameworks
To be a successful Big Data Analyst, it’s important to have a good understanding of frameworks such as Apache Spark, Apache Flink, Apache Samza, and technologies like MapReduce and Hadoop. These tools aid in the processing of big data streams.
4. Business Knowledge
For focused analysis, it’s necessary to validate, sort, relate, and evaluate data. The high demand for Big Data Analysts is largely due to the shortage of professionals with the necessary technical skills.
Therefore, it’s important to have a deep understanding of technical knowledge and business needs to excel in this field.
5. Statistical Knowledge
In addition to technology, a fundamental understanding of statistics is also necessary for a career as a Big Data Analyst.
Statistics forms the foundation of Big Data Analytics, and core concepts such as probability distribution, summary statistics, and random variables are crucial for success in this field.
Soft Skills for Big Data Analyst
Apart from hard skills, data analysts are also required to balance soft skills so that their work results are satisfactory. Well, what are you doing?
1. Good communication skills, such as negotiation between teams, presentations, and public speaking.
2. Think critically when solving various solutions to existing problems.
3. Project management.
4. Attention to detail because data analysts need an extreme focus when working with data sets in the form of numbers supported by technology.
5. Think logically by utilizing various methods.
What Does Big Data Analyst Do?
The main task of a prominent data analyst is to extract useful information from large data sets. Through big data, you can find hidden patterns, market trends, and consumer preferences for decision-making by a company or institution.
There are several steps you need to take in this process. First, you need to identify essential data. You do this by analyzing two data formats: born digital and born analogue data.
1. Born Digital Data
Born Digital Data is information contained in digital media, such as computer applications, smartphones, and others. This data type has an ever-expanding reach as the system continues collecting various information from users.
2. Born Analogue Functions
Meanwhile, born analogue functions to track and provide demographic information, such as Cookies, Web Analytics and GPS tracking.
Delivered analogue data is information in the form of images, videos, and other formats related to physical elements.
This data must be converted into a digital format using sensors, such as cameras, voice recordings, digital assistants, etc.
In the next step, you can collect and store data identified as big data. Methods that can be utilized are distributed storage and Massive Parallel Processing (MPP).
Industries Sector
1. Banking
Big data analytics is needed by banking and securities to monitor financial markets through network activity monitors and natural language processing to reduce fraud.
The Exchange and Trade Commission uses big data analytics to ensure that no illegal trades occur by monitoring the stock market.
2. Media and Communications
In the communications and media sector, big data analytics is used to broadcast real-time event reports worldwide simultaneously on multiple platforms, such as mobile, web and TV.
In addition, the music industry also uses big data to monitor the latest trends so that it can be used by “autotuning software” to produce exciting songs.
3. Sports
The sports sector is one of the most exciting sectors using big data analytics. The sector uses big data analytics to understand the viewing patterns of various events in specific regions and monitor the performance of individual players and teams with analysis. E
Examples of sporting events that use big data analytics are the cricket world cup, FIFA world cup, and Wimbledon.
4. Healthcare
The healthcare and healthcare sector uses big data analytics to collect public health data to respond more quickly to individual health problems and identify new viral strains globally.
A real example of the application of big data analytics is an analysis of the world’s spread of the COVID-19 virus.
5. Education
Big data analytics is used in the education sector to update and improve literature in various fields.
Several universities worldwide use big data analytics to monitor and track student and faculty performance and map student interests according to different subjects through attendance.
6. Manufacturing
Big data analytics is also used in manufacturing to improve productivity and supply management so companies can allocate production resources optimally and generate maximum profits.
Salaries
Choosing a career path requires considering various factors such as interests, skills, and passion. A career in Big Data analysis may be a great fit for individuals who have a passion for technology and data.
It’s also important to consider factors such as work environment and schedule when making your decision. Additionally, salary is often a consideration when comparing different options.
The future looks promising for careers in data analysis, with an average annual salary for Big Data analysts in the United States being $84,955 according to PayScale. A career in Big Data analytics can offer stability and growth in a field with endless possibilities.