The essentials of data analytics are data pipelining, exploratory data analysis, and machine learning algorithms, along with visualisation tools which are being used to analyse, interpret, and extract meaningful information from the raw data. Data analytics has versatile applications in finance, manufacturing industries, healthcare, marketing, etc.
Data analytics utilises an exploratory data analysis process to identify and recognise the trends, patterns, correlations, etc to make reasonable decisions in businesses. In this current digital era, the volume and variety of data allow us to leverage data analytics techniques and their power to solve critical problems in due course of time in the organisation.
A few key aspects like improving customer experiences, ease of operations, risk mitigation, and making strategic decisions in any organisation are essential, which can be achieved by data analytics techniques to revolutionise any business. To effectively utilise the power of data analytics within an organisation, a few key points must be considered, like embracing a data-driven culture, using the right tools, recruiting or outsourcing work, data privacy, etc.
Let us first understand different types of data analytics methods. We can categorise data analytics into three types namely, predictive, descriptive, and prescriptive analytics. All you need to know is from the expert Dr. Salur Srikant Patnaik, Assistant Professor, Vijaybhoomi, School of Science and Technology (VSST) Vijaybhoomi University.
- Descriptive Analytics: The data collected from past events that occurred and then further analysing them to depict what must have happened is called descriptive analytics. Data analytics has been useful in explaining and summarising such past data for marketing and sales figures, website visitors, or customer origin. This type of analytics is useful for identifying trends and patterns in data.
- Predictive Analytics: To make predictions about future events, we use past raw data and analyze them, which is called predictive analytics. Different efficient machine learning algorithms can be deployed to perform predictive data analytics processes to identify patterns in data and predict future outcomes. Predictive analytics is useful for forecasting sales, predicting customer behavior, and identifying potential risks.
- Prescriptive Analytics: The best example of prescriptive data analytics is a recommendation system. What actions should be taken can be decided by prescriptive data analytics by implementing some of the advanced machine learning algorithms. Prescriptive analytics is useful for optimising business operations, improving customer experience, and reducing costs.
Talking about the benefits and challenges of data analytics, data analytics has certain key benefits for businesses, like improved decision-making, cost savings, effective flow process, etc. whereas the challenges we face in this area are, quality of the data, data privacy, and the skill set of an individual.
The data analytics field provides reputed job roles along with decent packages for individuals. Nowadays, it is one of the most trending areas where a lot of organisations are fuelling their data analytics department by providing them with sufficient funding.
The main attraction of this field is it offers a variety of roles where one can utilise different technological tools, which eventually leads to enhancement in the technical skills of an individual. Career advancement and job security are the add-ons that anyone will dream of in their respective professional careers.
- Data Scientist
The job description involves collecting, analysing, and interpreting large sets of complex data, using statistical and machine learning techniques to identify patterns and trends in data and making predictions, whereas the skills required are sound background in computer science, statistics, or mathematics and proficiency in programming languages such as Python and R.
- Data Engineer
The job description involves designing and developing data pipelining and systems that support data collection, storage, and analysis, managing and maintaining large data sets whereas the skills required are strong programming skills in languages such as Python, Java, and SQL along with experience with big data technologies such as Hadoop and Spark.
- Business and Financial Analyst
The job description involves identifying and analysing business problems in terms of gaps and opportunities. One should be capable of developing solutions to improve business processes and performance and communicating with stakeholders to gather requirements, whereas the skills required are strong analytical and problem-solving skills, excellent communication and presentation skills, and experience with project management along with SQL, Excel, Tableau and Python.
- Data Visualisation Engineer
The job description involves creative thinking, designing and developing an interactive data dashboard using visualisation software and tools to present data in a way that is easy for all stakeholders to understand, whereas the skills required are strong analytical and problem-solving skills, experience with data visualisation software and tools such as Tableau and R Shin, strong communication and presentation skills along with skills in Python and R, Tableau, and Power BI.
Data analytics is one of the trending industries nowadays which offers jobs and opportunities in different roles with decent remunerations. One should have good knowledge of basic aptitude, mathematics, and statistical tools to become eligible for one of the job positions in data analytics. Finally, one should have clear ethics when it comes to dealing with data in their respective jobs since it can be used either way, hence one should always abide by legal compliance.