Data, as we know, is a piece of information about individual, company, services, offers, products or anything. Whether the data is generated from a small business or a large scale enterprise, it has to be examined carefully to reach the conclusion about what part of data is useful and can be beneficial. This includes specialized systems and software. So, data analytics refers to the process involved in the refinement of data from each and every aspect to make it useful and readable.
Analyzing data is beneficial in the sense that it can help businesses to improve operational efficiency, increase revenues, respond more quickly to emerging market trends and gain a competitive edge over rivals. They all share a common goal of boosting business performance. Professionals skilled in data analytics, especially those who have completed a data analytics course, are in high demand in such companies.
Why is Data Analytics Important?
As we have seen that data analytics helps to improve business performance, we realize that it is important in many ways; let us see how.
Data Analytics is the process of inspecting, cleansing, transforming, and modelling data with the intent of finding out useful information for supporting decision-making.
The raw data is analyzed to extract hidden insights and then it is interpreted in the format according to the requirement of the business. Besides gathering the hidden insights, data analytics helps in report generation. Reports are generated from the data and are sent to the respective teams or individuals who use it for their further actions in order to improve the business.
To understand the strengths and weaknesses of the competitors, market analysis can be performed. Market analysis reports may help a business to recognize the areas where it is on the top and what areas still need improvement. Customer satisfaction is the ultimate goal every business wants to achieve. Data analysis helps determine the same and lets businesses improve through their customer’s perspectives.
Types of Data Analytics
Based on the different areas of focus, data analytics is divided into four basic types. They are:
- Descriptive analytics: It describes the data growth with respect to time as in, the rise in the number of views, or sales with respect to last month or last quarter.
- Diagnostic analytics: Why did something happen? This is determined by diagnostic analysis. A hypothesis is involved in this, as in, what is the effect of weather on sales of wine?
- Predictive analytics: Here the data is predicted on the basis of last time’s data. As in, what is the weather forecast for the next month?
- Prescriptive analysis: It prescribes what should be the next step. On the basis of predictions made in the previous step, the next course of action is prescribed in this step.
Now let us see what are the top tools in data analytics.
Top Tools in Data Analytics
There are many tools that have emerged in the market for Data Analytics. Let us have a look at some of them.
Python: Being on the top of the list because of its easy readability, maintenance, and coding, Python is an open-source, object-oriented programming language.
R Programming: it is the leading analytics tool for data modelling and statistics. R can be compiled and run on various platforms such as UNIX, Windows, and Mac OS. There are tools in R that allow you to automatically install all packages according to the requirement of the user.
QlikView: To provide results to the end-users quickly, this tool offers in-memory data processing. Data is compressed to almost 10% of its actual size with the provision of data association and data visualization.
SAS: it is a programming language and provides an environment for data manipulation and analysis. Data analysis can be done from different sources by using this tool. It is easily accessible.
Tableau Public: This tool can be connected to any data source like Excel, corporate Data Warehouse, etc. Then comes the creation of visualizations, maps, dashboards, etc with real-time updates on the web.
Microsoft Excel: Being one of the most widely used tools for data analytics, this tool is mostly used for the client’s internal data. This tool analyzes the tasks that summarize the data with the preview of pivot tables.
Besides these tools, other data analytics tools that are widely used these days are KNIME, Rapid Miner, OpenRefine, ApacheSpark and many more.
How to Become a Data Analyst?
To become a data analyst, a basic requirement is a bachelor’s degree that may be in any of the disciplines such as Finance, Economics, Mathematics, Statistics, Computer Science, and Information Management.
If you don’t have prior work experience as a data analyst, the most important task you need to do is to gain work experience. A data analyst translates numbers into English statements. So gaining work experience includes taking information about specific topics, and then analyzing, interpreting and presenting useful information in the form of reports.
Data analysis deals with understanding changing trends and technologies, which makes it essential for a data analyst to commit himself/herself to learn. MOOCs can be taken to ensure that you keep learning new things related to data analysis, which helps you stay on the top.
If you are capable of collecting data from the various resources, analyze the data and extract hidden insights from them, and generate a report regarding the same, you can become a data analyst.
Apart from these capabilities, additional skills required are Statistics, Data Cleaning, Exploratory Data Analysis, and Data Visualization. The knowledge of Machine Learning can add to the benefits.
Data analytics really matters for a company because its implementation reduces costs by identifying more efficient ways of doing business and by storing large amounts of data. It helps companies to make better decisions by analyzing customer trends and satisfaction. So, it definitely makes sense to learn data analytics and explore your career options in this thriving domain. Taking a data analytics course from a reputed institute is one of the ways to get ahead.