What does "Big Data Analytics" mean?

Big Data analytics is a method that involves the process of analysing massive amounts of data, often known as Big Data. This massive amount of data originates from a wide number of sources, such as social media platforms, movies, digital photographs, sensors, and records of financial transactions. The analysis of all of this data is being done with the intention of discovering patterns and relationships that would not otherwise be visible, as well as gaining useful insights into the users who provided the data. As a result of this, businesses may be able to obtain a competitive advantage over their rivals and improve the quality of their business decisions.

Traditional corporate systems are unable to access enormous volumes of transaction data and other data sources. However, Big Data analytics makes this information available to data scientists and other users so that they may analyse it. It's possible that traditional systems fall short because they can't analyze as many data sources as newer ones.

Big Data analysis makes use of complex software programmes; yet, the unstructured data employed in Big Data analytics may not be a good fit for typical data warehouses. It's possible that standard data warehousing won't work well for Big Data because it has such demanding processing requirements. As a direct consequence of this, brand-new settings and technologies for doing data analytics, such as Hadoop, MapReduce, and NoSQL databases, have come into existence. These technologies include a framework for open-source software that is used to process enormous data sets across multiple clustered servers.

Who is a Big Data Analyst?

A person who studies, analyses and reports on large amounts of data that an organisation has saved and preserved is known as a Big Data analyst.

Big Data analysts have jobs and skill sets that are comparable to those of data analysts, but their primary focus is on Big Data analytics rather than traditional data analysis.

Big Data analysts go through vast amounts of raw and unstructured data using both human methods and automated Big Data analysis and analytics software in order to discover useful information such as business insights, intelligence, and other pertinent details.

A solid understanding of data mining and extraction methods is often required for someone to be considered a Big Data analyst. Other essential qualifications include familiarity with Big Data principles, expertise in database query languages, and the ability to use Big Data analytics tools. In most cases, a Big Data analyst will work in conjunction with data scientists, database developers and administrators, and a management team that is responsible for determining the purpose and scope of Big Data analysis.

Big Data Analyst Roles and Responsibilities 

Now that we have an idea  of who a Big Data Analyst is, let's take a look at the key responsibilities that come with the position:

• The act of gathering and organising data from a variety of sources, as well as cleaning, organising, processing, and analysing the collected data in order to derive useful insights and information.
   
• Finding additional data sources and developing improved methods for data mining, analysis, and reporting are two of the goals of this project.
   
• To construct SQL queries for the purpose of extracting data from the data warehouse.
   
• For the purposes of analysis, to establish data definitions for brand new database files or make modifications to already existing ones.
   
• In order to aid the management team in making choices, to present the findings in reports (in table, chart, or graph format).
   
• Keeping an eye on how well data mining technologies are doing and fixing any problems that crop up along the way using tools of statistical analysis in order to discover and analyse data regarding consumers for the purpose of tracking patterns of correlation and trend in massive data sets.
   
• Analysis tasks that are performed on a regular basis to support the day-to-day operations of a firm and the decision-making process.
   
• To collaborate with data scientists in the development of cutting-edge analytical tools.
   
• Work together with both the IT team and the business management team to accomplish your company's goals.
   
• Data importation and collection, as well as data cleaning, conversion, and analysis with the purpose of gaining insights and drawing conclusions.
   
• Presenting data in the forms of graphs, charts, and tables, in addition to the design and development of relational databases for the purpose of data collection
   
• Conduct research on data mining products, protocols, services, and standards in order to lend support to procurement and development initiatives. Recommendations should result from this research.
   
• Maintaining constant vigilance over the operation of the data mining system and being prepared to address any problems that crop up.
   
• By simplifying complicated issues into more manageable frameworks, you may design, implement, and keep cutting-edge analytic systems running smoothly.
   
• Conduct in-depth analyses of large data sets to spot patterns and chances for expansion.
   
• Evaluating organisational strategies, providing source-to-target mappings, and producing information model specification documents are all required for data sets.
   
• Create reports that adhere to best practices by employing data mining, analysis, and visualization.
   
• Evaluating internal systems in terms of their efficiency, faults, and inaccuracies, as well as designing and maintaining rules for the management, processing, and cleansing of data
   
• Engage in face-to-face communication with management and end-users in order to collect needs, share progress updates, and forge working relationships

When dealing with extensive data sets, it is important to keep track of patterns, trends, and correlations.

In order to help management in taking decisions, Data Analysts’ roles and responsibilities would include preparing succinct data reports as well as data visualizations.

• Maintain close working relationships with the members of the IT team as well as the data scientists in order to determine and accomplish organisational objectives.
   
• In the event that it is required, offer assistance to the data scientist in the process of developing new analytical tools and procedures.
   
• Developing new data systems and databases, as well as maintaining existing ones and fixing any coding mistakes or other data-related problems that may arise.
   
• Mining data from primary and secondary sources, followed by reorganising it into formats that can be read conveniently by both humans and machines, is referred to as data mining.
   
• Utilizing statistical tools in order to understand data sets, with an emphasis on trends and patterns that could potentially be useful for diagnostic and predictive analytics initiatives.
   
• Developing reports for the executive leadership of the organisation that make use of pertinent data in order to effectively explain trends, patterns, and projections.
   
• Working in tandem with software developers, hardware engineers, and company executives to investigate potential areas for process improvement, suggest modifications to existing infrastructure, and draught data governance policies.
   
• Creating the relevant documentation so that stakeholders can comprehend the stages of the data analysis process and, if necessary, reproduce or replicate the study is one of the tasks involved in this project.

Other key Big Data Analyst roles and responsibilities 

Finding out the organization's overall objective is the first and most crucial task for a data analyst. To do this, Big Data Analyst roles and responsibilities include working along with:

• Data Mining 

Data mining is a method that makes use of mathematical and computational algorithms to structure raw data and formulate or recognise a variety of patterns hidden within the data. This process can be thought of as "pattern recognition." It contributes to the production of fresh data as well as the identification of new insights. It is common practice for data analysts to be responsible for data mining and collection. In order to carry out research, one of the most significant responsibilities of any Data Analyst is to either get data from the database maintained by the organisation or to extract it from sources located outside of the company.

• Purification of the Data

The first phase in the entire process of data preparation is the process of analysing, detecting, and fixing jumbled, raw data. This is the first step. When doing an analysis of the data held by an organisation in order to arrive at strategic conclusions, data analysts need to begin with a comprehensive data cleansing procedure. It's as easy as that solid analysis depends on having clean data to work with. Either eliminating data from your set that has the potential to distort your analysis or transforming all of your data into a uniform format is what is meant by "cleaning."

• Using specialized computer software for Data Analysis

It goes without saying that every Data Analyst needs to be proficient in this particular function. The practice of extracting facts from data in order to provide an answer to a particular query is known as data analytics.

The method entails using analytic and logical reasoning for each component of the data that is presented for examination. Statistical tools are utilised in the processes of data analysis and interpretation.

• Finding Out the Current Trends and Patterns

Finding trends, correlations, and patterns in massive datasets takes up a significant amount of time for data analysts because of the nature of their work. The direction of trends is also quite important. Data analysts search for patterns during the short term as well as the long term.

Big  Data Analyst Roles and Responsibilities on a daily and monthly basis

• Collaborate closely with Project Managers to gain an understanding of their analytical needs and concentrate on meeting those needs. This should include locating important indicators and KPIs and providing key decision-makers with actionable insights.

• Investigate and communicate potential areas for increased productivity and efficiency, and engage in proactive data analysis with the goal of answering critical questions posed by stakeholders or arising from your own natural curiosity about the factors that drive organisational performance.

• Build and maintain high-quality, interactive visualisations by understanding and analysing data and combining a variety of reporting components derived from a wide variety of data sources.

• Define and implement the data acquisition and integration logic, making sure to use the proper combination of methods and tools from within the stated technological stack. This will ensure that the solution's scalability and performance will be at their ideal levels.

• Build and keep updated databases by gathering information from primary and secondary sources, as well as building scripts to make our data review process more adaptable and scalable across different data sets.

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Conclusion 

One primary objective that can be distilled from the data analyst roles and responsibilities is to conduct data analysis in order to provide clients with assistance in pushing their businesses ahead in accordance with their strategic goals. Information gathered for no good purpose is a complete and utter waste of effort. By providing clients with an understanding of their present position and assisting them in making educated business decisions, data analysts provide clients with value. The job description for a data analyst typically includes a dictionary of the many sorts of data and analysis that are expected. 

A data analyst has the ability to modify their job and the solutions they provide in response to any given circumstance. For instance, if manufacturing is experiencing delays and unforeseen shortages, a diagnostic analytics method may be able to help the manufacturer determine what is causing the delay and find a solution to the problem. After that comes the possibility of conducting other kinds of analysis, such as predictive or prescriptive.