What is Data Analytics?

Data analytics, also known as data analysis, is a crucial component of modern business operations. It involves examining datasets to uncover useful information that can be used to make informed decisions. This process is used across industries to optimize performance, improve decision-making, and gain a competitive edge.

In this article, we will explore the different types of data analytics, methods used in data analysis, jobs related to data analytics, and the importance of data analytics in today's world. Let's begin by understanding the basics of data analytics.

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What is Data Analytics?

Data Analytics is a systematic approach that transforms raw data into valuable insights. This process encompasses a suite of technologies and tools that facilitate data collection, cleaning, transformation, and modelling, ultimately yielding actionable information. This information serves as a robust support system for decision-making. Data analysis plays a pivotal role in business growth and performance optimization. It aids in enhancing decision-making processes, bolstering risk management strategies, and enriching customer experiences. By presenting statistical summaries, data analytics provides a concise overview of quantitative data.

While data analytics finds extensive application in the finance industry, its utility is not confined to this sector alone. It is also leveraged in diverse fields such as agriculture, banking, retail, and government, among others, underscoring its universal relevance and impact. Thus, data analytics serves as a powerful tool for driving informed decisions and fostering growth across various industries.

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Process of Data Analytics

Data analysts, data scientists, and data engineers together create data pipelines which helps to set up the model and do further analysis. Data Analytics can be done in the following steps which are mentioned below:

1. Data Collection : It is the first step where raw data needs to be collected for analysis purposes. It consists of two steps in which data collection can be done. If the data are from different source systems then using data integration routines the data analysts have to combine the different data whereas sometimes the data are the subset of the data set. In this case, the data analyst would perform some steps to extract the useful subset and transfer it to the other compartment in the system.
2. Data Cleansing : After collecting the data the next step is to clean the quality of the data as the collected data consists of a lot of quality problems such as errors, duplicate entries and white spaces which need to be corrected before moving to the next step. By running data profiling and data cleansing tasks these errors can be corrected. These data are organised according to the needs of the analytical model by the analysts.
3. Data Analysis and Data Interpretation: Analytical models are created using software and other tools which interpret the data and understand it. The tools include Python, Excel, R, Scala and SQL. Lastly this model is tested again and again until the model works as it needs to be then in production mode the data set is run against the model.
4. Data Visualisation: Data visualisation is the process of creating visual representation of data using the plots, charts and graphs which helps to analyse the patterns, trends and get the valuable insights of the data. By comparing the datasets and analysing it data analysts find the useful data from the raw data.

Types of Data Analytics

There are different types of data analysis in which raw data is converted into valuable insights. Some of the types of data analysis are mentioned below:

1. Descriptive Data Analytics : Descriptive data Analytics is a type of data analysis which summarises the data set and it is used to compare the past results, differentiate between the weakness and strength, and identify the anomalies. Descriptive data analysis is used by the companies to identify the problems in the data set as it helps in identifying the patterns.
2. Real-time Data Analytics: Real time data Analytics doesn’t use data from past events. It is a type of data analysis which involves using the data when the data is immediately entered in the database. This type of analysis is used by the companies to identify the trends and track the competitors' operations.
3. Diagnostic Data Analytics: Diagnostic Data Analytics uses past data sets to analyse the cause of an anomaly. Some of the techniques used in diagnostic analysis are correlation analysis, regression analysis and analysis of variance.The results which are provided by diagnostic analysis help the companies to give accurate solutions to the problems.
4. Predictive Data Analytics: This type of Analytics is done in the current data to predict future outcomes. To build the predictive models it uses machine learning algorithms, statistical model techniques to identify the trends and patterns. Predictive data analysis is also used in sales forecasting, to estimate the risk and to predict customer behaviour.
5. Prescriptive Data Analytics: Prescriptive data Analytics is an analysis of selecting best solutions to problems. This type of data analysis is used in loan approval, pricing models, machine repair scheduling, analysing the decisions and so on. To automate decision making companies use prescriptive data analysis.

Methods of Data Analytics

There are two types of methods in data analysis which are mentioned below:

1. Qualitative Data Analytics

Qualitative data analysis doesn’t use statistics and derives data from the words, pictures and symbols. Some common qualitative methods are:

• Narrative Analytics is used for working with data acquired from diaries, interviews and so on.
• Content Analytics is used for Analytics of verbal data and behaviour.
• Grounded theory is used to explain some given event by studying.

2. Quantitative Data Analysis

Quantitative data Analytics is used to collect data and then process it into the numerical data. Some of the quantitative methods are mentioned below:

• Hypothesis testing assesses the given hypothesis of the data set.
• Sample size determination is the method of taking a small sample from a large group of people and then analysing it.
• Average or mean of a subject is dividing the sum total numbers in the list by the number of items present in that list.

Skills Required for Data Analytics

There are multiple skills which are required to be a Data analyst. Some of the main skills are mentioned below:

• Some of the common programming languages which are used are R and Python.
• For databases Structured Query Language (SQL) is a programming language used.
• Machine Learning is used in data analysis.
• In order to better analyse and interpret probability and statistics are used.
• For collecting and organising data, Data Management is used in data analysis.
• To use charts and graphs Data visualisation is used.

Data Analytics Jobs

In Data Analytics For Entry level these job roles are available:

• Junior Data Analyst
• Junior Data Scientist
• Associate Data Analyst

In Data Analytics For Experienced level these mentioned job roles are available:

• Data Analyst
• Data Architect
• Data Engineer
• Data Scientist
• Marketing Analyst
• Business Analyst

Importance and Usage of Data Analytics

Data analytics consists of many uses in the finance industry. It is also used in agriculture, banking, retail, government and so on. Some of the main importance of data analysis are mentioned below:

• Data Analytics targets the main audience of the business by identifying the trends and patterns from the data sets. Thus, it can improve the businesses to grow and optimise its performance.
• By doing data analysis it shows the areas where business needs more resources, products and money and where the right amount of interaction with the customer is not happening in the business. Thus by identifying the problems then working on those problems to grow in the business.
• Data analysis also helps in the marketing and advertising of the business to make it popular and thus more customers will know about the business.
• The valuable information which is taken out from the raw data can bring advantage to the organisation by examining present situations and predicting future outcomes.
• From data Analytics the business can get better by targeting the right audience, disposable outcomes and audience spending habits which helps the business to set prices according to the interest and budget of customers.

Conclusion

In conclusion, Data Analytics serves as a powerful catalyst for business growth and performance optimization. By transforming raw data into actionable insights, it empowers businesses to make informed decisions, bolster risk management strategies, and enhance customer experiences. The process of data analysis involves identifying trends and patterns within data sets, thereby facilitating the extraction of meaningful conclusions.

While the finance industry is a prominent user of data analytics, its applications are not confined to this sector alone. It finds extensive use in diverse fields such as agriculture, banking, retail, and government, underscoring its universal relevance and impact.

The above discussion elucidates the processes, types, methods, and significance of data analysis. It underscores the pivotal role of data analytics in today’s data-driven world, highlighting its potential to drive informed decisions and foster growth across various industries. Thus, Data Analytics stands as a cornerstone in the realm of data-driven decision making, playing an instrumental role in shaping the future of businesses across the globe.

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Article Tags :

• GBlog
• Data Analysis
• AI-ML-DS
• GBlog 2024