With enterprises of all sizes increasingly utilising big data, the demand for big data analysts will continue to grow. Analysts of big data study massive amounts of diverse data. They elucidate previously unknown patterns, customer preferences, and market trends. One of the key distinctions between a big data analyst and a data scientist is their educational background — data scientists often require a master’s degree, whereas big data analysts typically have experience and may not have a degree in data science. Nonetheless, an increasing number of businesses blur the line between the two roles.
A big data analyst’s objective is to assist enterprises in making more informed decisions. Traditional data analysis methods are incapable of dealing with the volume of big data, which contains both structured and unstructured data. Significantly more than the capacity to browse relational databases and compute statistical results is required. What big data analysts most need need are the abilities to convert relevant data into actionable observations. This involves a collaboration of technology, creativity, intuition, and experience.
The Bureau of Labor Statistics does not yet report on big data analysts, but they expect that jobs in the sectors of information and computer research would expand by 19 percent between 2016 and 2026. This is an abnormally rapid rate of growth when compared to other occupations. Big data analysts earn between $76,000 and $130,000 per year, on average, according to Glassdoor’s salary survey. The processing of big data is a relatively new sector that has the potential to give businesses with a competitive edge, and there is currently a lack of big data analysts and data scientists.
Big data analytics systems examine structured and unstructured data in order to uncover relevant information, such as potential new revenue streams or improved marketing techniques. This technique involves the examination of internet clickstream data, social media content, web server logs, customer email text, and data collected via the Internet of Things.
Critical Skills Required of Big Data Analysts
A big data analyst must possess a diverse set of abilities in order to succeed. Effective interpersonal skills are extremely beneficial when speaking with employers and team members about big data outcomes. Additionally, big data analysts should possess the necessary technological skills, which include proficiency with cloud computing technologies such as Amazon Web Services (AWS) or Microsoft Azure. While it is fairly uncommon for a freelance big data analyst to have a favourite cloud service, they may be required to work with the company’s cloud for security reasons. Management abilities can also be advantageous while supervising personnel and collaborating with helpers.
Big data analysts are typically expected to possess the following qualifications:
Business Experience: Whether it’s astronomy or banking, analysing large data requires an expertise of the industry. This comprehension gives a screening procedure, or paradigm, for defining and framing the questions being addressed. The more experience one has in a particular field and in life in general, the better equipped one is to conduct research. A diverse range of experience enables the interpretation of data.
Statistics: Processing large amounts of data necessitates an understanding of statistics. Statistics serves as the foundation for Data Science, probability distributions, and random variables.
Java, R, Python, C++, Hive, Ruby, SQL, MATLAB, SAS, SPSS, Weka, Scala, and Julia are all supported languages. A big data analyst should at the very least be proficient with R, Python, and Java.
Frameworks for Computation: It is critical to have a firm grasp on frameworks such as Apache Spark, Apache Samza, Apache Flink, Apache Storm, and Hadoop. These technologies enable the processing of large amounts of data, the majority of which may be processed in real time as it is streamed.
Data Warehousing: It is critical to understand how data is stored and accessed. Experiential knowledge of non-relational database systems is also advantageous. Cassandra, Hbase, CouchDB, HDFS, and MongoDB are all examples of non-relational (NoSQL) databases.
Data Visualization: It can be challenging to interpret and communicate large amounts of data. This is why images (also known as visuals) facilitate the discussion of massive data. Even a cursory examination of data visualisation tools such as Tableau or Qlikview can disclose the form of data, revealing previously hidden insights.
While data visualisation is beneficial, the ability to communicate intelligently and clearly is a requirement for big data analysts. The results and the process by which they were obtained must be conveyed to those who will be paying the bill. Following data research, big data analysts may be required to make presentations to various departments inside the firm.
Reports in Writing: For clients or employers, a written report serves as a lasting record of observations and conclusions.
Responsibility
Big data analysts are tasked with the responsibility of implementing three critical real-time solutions – affordability, speed, and quality – and giving business insight to clients or employers. They may collaborate with Data Quality teams to ensure the accuracy and thoroughness of data, or they may work directly with management to organise and execute data studies. Additionally, big data analysts may assist in the planning of organisational changes aimed at maximising profits and minimising losses. “The capacity to deliver products and services at the right time, in the right place, and to the right client, instantly,” claimed Abhishek Mehta, founder and CEO of Tresata, a predictive analytics business.
On a regular basis, a big data analyst will:
Establish organisational objectives
Collaborate with management, information technology teams, and data scientists
Extraction of data from a number of sources
Data screening and cleansing to eliminate superfluous information
Trends and patterns in research
Discover and seize fresh chances
Provide management with succinct data reports and visuals.
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