So You Want to be a Big Data Analyst?

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Big Data Analyst

With the increasing use of big data by organizations in every field, the need for big data analysts will continue to grow. Big data analysts examine vast amounts of varied data. They uncover hidden patterns, customer preferences, and market trends. One of the primary differences between a big data analyst and a data scientist is the level of education – data scientists often need a master’s degree, while big data analysts come with experience and may not have a degree related to Data Science. That said, a growing number of organizations make no distinction between the two positions.

The goal of a big data analyst is to help organizations make better, more-informed decisions. Old school, traditional data analysis cannot cope with the volume of big data, which includes both unstructured and structured data. Far more is needed than the ability to navigate relational databases and calculate statistical results. What big data analysts need most are the skills to translate relevant information into useful observations. This requires technology to join hands with creativity, intuition, and experience.

The Bureau of Labor Statistics doesn’t yet report on big data analysts, but they have estimated 19 percent job growth from 2016-2026 for scientists in the fields of information and computer research. This much growth is unusually fast when compared to other occupations. Per Glassdoor’s salary report, big data analysts earn between $76,000 and $130,000 per year and generally average $81,850. The processing of big data is a relatively new field which can provide a competitive edge for businesses, and currently there is a shortage of big data analysts and data scientists.

Big data analytics applications are designed to analyze structured and unstructured data with the goal of identifying useful information, such as possible new sources of income or better marketing strategies. This process includes the analysis of internet clickstream data, social media content, web server logs, text from customer emails, and data captured by the Internet of Things.

Key Skills Needed by Big Data Analysts

A big data analyst needs a broad range of skills to achieve their goals. Effective interpersonal skills are quite useful in communicating big data results to employers and team members. Additionally, big data analysts should have the technical abilities needed for the work, which includes working with cloud services such as Amazon Web Services (AWS) or Microsoft Azure. It is not uncommon for a freelance big data analyst to have a preferred cloud service, but for security purposes, they may have to work with the company’s cloud. Management skills can also be helpful in overseeing staff and working with assistants.

The following qualifications are generally expected from big data analysts:

  • Industry Experience: Analyzing big data requires an understanding of the industry, whether it be astronomy or finance. This understanding provides a screening process, or a paradigm, which is used to define and frame the questions being asked. The more experience one has in a particular field and life in general, the more understanding one will have when doing research. A broad background of experience provides an understanding of how to interpret data.
  • Statistics: Processing big data requires a knowledge of statistics. Statistics is a fundamental building block for Data Science, probability distribution, and random variables.
  • Languages: Java, R, Python, C++, Hive, Ruby, SQL, MATLAB, SAS, SPSS, Weka, Scala, Julia. At a minimum, a big data analyst should be familiar with R, Python, and Java.
  • Computational Frameworks: Having a solid understanding of frameworks such as Apache Spark, Apache Samza, Apache Flink, Apache Storm, and Hadoop is essential. These technologies support the processing of big data, which, for the most part, can be processed as it is streamed.
  • Data Warehousing: Understanding how data is stored and how to access it is important. Experience with non-relational database systems is also quite useful. Examples of non-relational (NoSQL) databases include Cassandra, Hbase, CouchDB, HDFS, and MongoDB.
  • Data Visualization: Big data can be difficult to comprehend and discuss. This is why pictures (also known as visuals) make discussing big data easier. Exploring even just a sample of data visualizing tools like Tableau or Qlikview can show the shape of data, revealing hidden details.
  • Communication: While data visualization is a useful tool, the ability to speak intelligently and clearly is a necessity for big data analysts. Results and how they were produced must be explained to the people paying the bill. After researching the data, big data analysts may also have to make presentations to different departments within the organization.
  • Written Reports: A written report provides a permanent record of observations and conclusions for clients or employers.

Normal Tasks and Responsibilities

Big data analysts are responsible for realizing three key real-time solutions – affordability, speed, and quality – and providing business intelligence to clients or employers. They may work with Data Quality teams ensuring data integrity and thoroughness, or perhaps with management to plan and perform data analyses. Big data analysts may also participate in planning organizational changes to maximize profits and minimize losses. Abhishek Mehta, the founder and CEO of Tresata, a predictive analytics company, stated, “The ability to deliver products and services at the right time, in the right place, and to the right customer, instantly, is the future.”

A big data analyst will, on a regular basis:

  • Determine organizational goals
  • Work with management, IT teams, or data scientists
  • Data mine from a variety of sources
  • Screen and clean data to remove irrelevant information
  • Research trends and patterns
  • Find and identify new opportunities
  • Provide clear and concise data reports and visualizations for management

Gaining Credibility

Adam Gibson, the co-founder of Skymind and the originator of the open-source library, Deeplearning4j, suggests that becoming involved with the open-source community will improve a big data analyst’s resume. He takes the position that members of the open-source community are experienced in dealing with big data, and that an organization can make trustworthy choices by hiring analysts from this group. According to Gibson, “one of the smartest things you can do is engage the open-source community.”

Hadoop was an early open source product that opened the door to researching non-relational data. It has been referred to as the beginning of the Second Industrial Revolution, referencing the importance of data to modern industries. Hadoop became quite popular and promoted other open-source projects by way of association. Delivering products and services at the right time, to the right place instantly is the goal, and the technology finally exists to do that.

A second way to bump up the resume, would be taking free online classes on the study of big data and Data Science. There are also bootcamps available for total submersion. While the basic experience may already be there, a class or certification helps prove skill, and there is always more to learn. In cases of little experience, a Data Science certification program can provide training in advanced analytics, big data management, data visualization, and machine learning.

A third method for improving the resume would be listing projects previously worked on. If no projects have been worked on, the novice big data analyst should consider offering their services for free on three or four projects to build up experience and provide a work history. Ultimately, experience in this field is what counts.

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