What is Data Analytics and why do people learn Data Analytics, Reasons and Benefits?

Introduction

Analytics is undoubtedly the way of the future, as well as the way of the present. Having been adopted in all sorts of different industries, you will now find analytics being used everywhere from aviation route planning to predictive maintenance analysis in manufacturing plants. Even industries like retail that you can't relate to with big data are using analytics to improve customer loyalty and craft unique offerings.

Data analysis is an exciting field to enter and the career prospects are amazing. You don't need knowledge of maths/computer science/coding to learn data analysis or get a job. In this post, we'll tell you everything you need to know about getting started data analysis with SPSS. Reasons and benefits of learning data analysis and which tool is best for data analysis?

Data Analytics

In short, SPSS data analysis involves sorting out large amounts of unstructured information and deriving important insights from it. Insights like these are extremely helpful for decision-making in businesses of all sizes.

Data analysis focuses on drawing practical conclusions from raw data. Numerous data analytics methods and procedures have been mechanised into mechanical procedures and algorithms that use raw data for human consumption. Data science and data analysis are not the same things. Although they belong to the same family, data science is usually more advanced, programming a lot, creating new algorithms, building predictive models, etc.

Types of data analysis

What is the main purpose of data analysis? It depends on what kind of data analysis skills you are using. Here are five categories of data analytics.

Descriptive Analysis

Descriptive Analysis "What Happened?" It is designed to answer the question. The goal of descriptive analysis is to summarise the data in a meaningful and descriptive way, not to make a prediction.

Exploratory Analysis:

Looking for discernible patterns and trends in the data, exploratory analysis delves a little further than descriptive analysis. It can also be considered in terms of the preliminary screening stage.

Diagnostic analysis

This type of analysis expands on the conclusions drawn from both descriptive and exploratory analysis in order to identify the root of the problem. Data scientists frequently employ this form of analysis more than data analysts do. To determine the likelihood of future events based on the data, machine learning algorithms and techniques are used along with data, statistics, and data.

Prescriptive analysis:

This method uses knowledge from all types of data analysis (descriptive, exploratory, diagnostic, and predictive) to decide the optimal course of action.

Why You Should Learn Data Analysis Skills

Why is it a good idea to learn data analysis and make a career in this field? Here are some reasons to learn Data Analysis:

The anticipated job growth for data professionals is:

Projected job growth for market research analysts is 25% between 2020-2030, based on data from the Bureau of Labor Statistics. This is a significant number of new posts being created.

Data analytics is in demand:

According to the market research, there is a demand for people who can use data for reporting and data analysis with spss, thus helping businesses and organisations alike. It helps in making important decisions.

Higher than the average salary in data roles:

Data analysts are well paid, According to PayScale, entry-level data analysts will receive an annual salary of between $40,000 - $73,000 (average $57,000). Senior data analysts can raise this to $83,000.

Have a Competitive Advantage:

The ability to query your data is a powerful competitive advantage, resulting in new income streams, better decision-making, and better productivity.

Universal need (all types of companies need data support):

Every business generates data. But its value is that data: Depends on your ability to process, manipulate, and ultimately translate into useful insights. Ultimately, data analysis is valuable to both organisations and individuals. You can make this a career on its own or use it as a stepping stone to other data roles.

The Benefits of Data Analytics

Data has the potential to deliver enormous value to organisations, but the analytics component is essential to unleash that power. As the importance of data analytics grows in the corporate world, it becomes important for firms to understand how to use it. Let's look at several options for achieving this.

Make informed and better decisions

When Big Data combines with Artificial Intelligence, Machine Learning and Data Mining, companies are better equipped to make accurate predictions. For example, predictive analytics can suggest what may happen in response to changes in the business, and prescriptive analytics can indicate how a company should react to these changes. Additionally, enterprises can use data analytics tools to determine the success of changes and visualise results, so decision-makers know whether to roll out changes throughout the business.

More effective marketing

What makes organisations stand out is the unique approach chosen by them for marketing their products. Using data analytics, companies can pinpoint exactly what customers are looking for. The data enables businesses to conduct an in-depth analysis of client trends, which companies can use to develop successful, focused and targeted marketing.

Improved and personalised customer experience

By sharing their data, customers expect companies to know them, create relevant interactions, and provide a seamless experience across all touchpoints. Being able to respond in real-time and make the customer feel valued is only possible through advanced analytics. The data allows interactions tailored to the individuality of the customer, thus helping to understand their approach to providing personalisation in a multi-channel service environment.

Streamline operations

Data analytics can enable enterprises to optimise processes and identify potential ways to increase revenue. It helps in identifying potential issues, thus preventing them from happening. It allows enterprises to see which activities produced the best overall results under different circumstances.

Reduce fraud

Adequate data analytics capabilities will provide the highest level of fraud protection and overall protection for your firm. The use of statistical, network, path and data methodologies for predictive fraud will ensure that real-time threat detection methods, automated warnings, and mitigation respond rapidly.

Popular Jobs Relying on Data Analysis

One of the great things about acquiring data analysis skills is that they don't lock you into a single career. If you like to learn data analysis, this makes data analysis skills useful in many roles. The top jobs that include data analysis are listed below.

Data Analytics Tools

There are many tools for data analysis like- SPSS, Excel, Python, SAS, STATA, R Programming, and NVIVO. But out of all these the best tool is SPSS.

In 1968 SPSS Inc. Developed, this tool has been around for more than 50 years. IBM later acquired SPSS (in 2009), and the tool's name was changed to IBM SPSS Statistics. But no one ever refers to software by that name - everyone calls it SPSS.

SPSS is a sophisticated tool originally designed to support the analysis and management of social science data. Today, the software sees widespread use in academia, but academics are not the only professionals using it.

Number crunchers in insurance, government, financial services, market research, healthcare, retail, risk management and telecommunications swear by SPSS. You will not find a data analysis tool that is more versatile than SPSS.

SPSS is very easy to use, and it is user-friendly. You can get SPSS help to prepare for descriptive statistics, linear regression, factor analysis, or cluster analysis.