Data Analytics and Jobs Opportunities

Data Analytics : If you are an internet connected person then you must have heard the term data analytics. A very simple question would be in your mind that what is data analytics? Why is it needed? Where and why is it used? What is the future of data analytics? What are the job opportunities in Data Analytics etc.? If you want to know the answer to all these questions related to data analytics, then you have to be patient and read the complete article.

Nowadays, through modern data science and computer systems in all businesses, small and big, it is known in a very short time that due to which there has been ups and downs in the business. Storing and analyzing all the business related data is a big challenge. This complex task is done under a special process, which is named as 'Data Analytics'. Now it will be discussed further in detail.

What is Data Analytics?

Data analysis is a process by which useful information is extracted from raw and unstructured data, so that effective decisions can be made based on these extracted information. Data analysis process includes inspection of data, its processing, cleaning, transforming and modeling, and using all these processes, information about its work is extracted from the raw data, so that better decisions can be made on the basis of this information.

Data analytics refers to the science of analyzing or monitoring raw data to draw conclusions about the given information. Most of the processes and techniques involved in data analytics nowadays are automated in related mechanical algorithms and processes that are known to operate on a range of raw data to be used by consumers.

Techniques related to data analytics can help reveal metrics and trends that might otherwise be lost in the given information. The information provided is used to optimize processes under the pretext of increasing the overall efficiency of the system or business.

We can explain in simple way, “all the ways you can break down data, measure trends over time, and compare one sector or measure to another. It also include different ways of understand about the data to simplify trends and relationships at a glance”. Data analytics also includes asking questions about what happened, what is happening, and what will happen (predictive analysis).

The word Data Analytics means to analyze the data i.e. to test the data. This data can be of any type and from any field, such as data of medical, data of construction or data of any big organization or retail. But now the question arises, why data analytics is done at all.

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Why is data analytics done?

The reason behind doing data analytics is to collect useful information directly, so that effective forward strategy can be prepared and appropriate steps can be taken according to the collected information.

Data analytics is a major part of any business today, where all the previous data of the business is analyzed by a data scientist or data analyst, and the data is processed through a set process of analytics, after which the business useful information related to this is obtained, which is also called Processed Data.

From these useful information, a complete report of the business is obtained, for example, what steps were taken to develop the business, what was the profit or loss to the business, etc. are taken.

Method Of Data Analytics

Data Analytics Process is used to extract useful information from any raw data. It is a process that is followed only after the analysis is completed and useful information emerges. The following steps are involved in the data analytics process.

Data Requirement : This is the first and main step of data analytics, in which you have to understand your need, that is, what type of data analysis you want, and what result do you want from it. The purpose of this step is to understand your data analytics needs like what, how and why so that there is clarity.

Data Collection: After the first step, you will get clarity and the next step is data collection. This is an important stage as the result of the analytics depends on the selection of the right data sources. In the data collection, the data is first collected from the internal sources. For example, from CRM Software, ERP System, Marketing Tools etc., which contains information about customer information, finance information and sales etc.

Now come the second source i.e. External Sources, these include both Structured and Unstructured data, which are gathered from many external sources, which are associated with your brand in any form like Review Sites, Social Sites etc.

Data Cleaning : In this phase of data cleaning, the data is cleaned, that is, whatever data has been collected, it is not fully usable or can be said that it cannot be understood. So for this cleaning and sorting of this data is done by the Data Team, this is a very important step of Data Analytics.

To get the correct result in its cleaning, duplicate, inconsistent data is removed, that is, any type of error is removed from this data. Various tools are used for this process of data cleaning.

Data Analysis : Once the data is collected, processed and cleaned, it is ready for analytics. There are different techniques of data analytics available, out of which which technique you use depends on your need.

In this step, the analyst finds all the elements that are related to your target. That is, in this phase, a lot of manipulations and changes are done in the data, so that the similarity in the data variables can be found and software tools are used for this, so that in the end the information can be extracted according to your need.

Communication : After going through all the above steps, the last step is communication, that is, whatever information has come out in the analytics, how should it be presented according to the need of the user, such as in the form of Tables or Charts, so that the user can clearly see The result or information of the analytics should be visible and understood. Or it may also be that the user wants additional analytics.

Types of Data Analytics

Based on the data, data analytics is divided into the following parts : 

1. Descriptive analytics : Descriptive analysis provides information about what has happened over a given period of time. Example: Has the number of views increased? Are sales stronger than last month? And so on.

2. Diagnostic analytics : Diagnostic Analytics explains why this happened, how It happened. It depends on more diversified data and estimates. Example: Does the weather affect beer sales? Did that latest marketing campaign impact sales? etc.

3. Predictive analytics : Predictive analytics gives the go-ahead for what will happen in the near future. Example: What happened to the last winter or summer sale in the winter/summer? How many weather experts predict extreme heat this year?

4. Prescriptive Analytics: Prescriptive Analytics comes in the field of suggesting the course of action. Example: If the probability of a hot summer, measured as the average of these five-season models, exceeds 58%, we should shift to Evening Brewing and rent additional tanks to increase output.

Models of Data Analytics 

Decide on Objectives – Set goals for data science teams to determine whether the business is progressing toward its goals; Quickly identify metrics or performance indicators.

Identify business levers - identify goals, metrics and levers in data analysis projects to expand the scope and focus of data analysis; This means the business must be prepared to improve its key metrics and make changes to reach its goals.

Data Collection - Collect as much data as possible from different sources to build better models and get more actionable insights.

Data Cleaning – improving data quality to deliver correct results and avoid drawing incorrect conclusions; Automate the process but involve staff to oversee the cleaning of the data and ensure accuracy.

Develop a Data Science Team - Add infrastructure engineers, software developers and ETL experts to your science team, along with individuals with advanced degrees in statistics focusing on data modeling and prediction. Then, it will be better to provide the team the large-scale data analytics  platforms needed to automate data collection and analysis.

Optimize and Repeat – Optimize your data analysis model so that you can repeat the process to generate accurate forecasts, reach goals, and continuously monitor and report.

Future of data analytics

One thing is certain – data analytics will gain manifold momentum in the future and will be the mainstay of countless new business technologies. Data is being used the most in many areas including banking, e-commerce, finance, insurance, big data, data center, artificial agency, cloud computing, IT. As all business models are becoming based on data technology.

That is why data analysts are using this data according to their ability to grow the business or reach a meaningful platform and based on this, they are creating new business plans, how to protect companies from challenges and their challenges. . How to get ahead of the opponent etc.

The biggest potential is in the technology sector like Big Data, Machine Learning, Cloud, Internet of Things, Tech Manufacturing etc. Gradually the demand for Data Analyst is increasing in many cities of many countries.

Jobs opportunities in data analytics

There is a huge shortage of qualified data analysts and data scientists in every country today, and it is expected that this may persist for many years to come.

Prepare yourself for this opportunity, start planning now by identifying new job opportunities and planning the right training for them to make the most of your potential in large business companies by following the steps below . Select the correct course as per your qualification.

According to information and technology job specialist, till present there is a demand of 27 thousand in service sector, 24 thousand in startup, 28 thousand in IT sector, 97 thousand in foreign companies and 1.45 million in others.

If the professional has done the right course then they can get the right place for employment. These days massive opportunities are being created in social networking sites, finance, online shopping websites, market research firms, consultancy services sector and data analytics firms, NGOs, small and big startups, cloud computing, artificial intelligence, internet of things sector. As the online market is expanding, so too is the demand for professionals.

How to start in the Data Analytics field?

All courses related to data analytics encompass technology and research in itself. Presently you will find online courses on many platforms like Skillshare, Masterclass, Udemy etc. Research method & design, Tools use in data analysis data research, Data collection process and data research etc. are the major course areas which are getting highest priority.

Most of the courses in data analytics field are of PG Diploma or Master level. Duration range of these courses are from 6 months to 2 years. In such a situation, those students, who have a degree in science or engineering , they can take admission in related master and PG diploma courses. In this, students of Mathematics, Statistics, Computer Science or Accounting are given more priority.

There are different sectors in data analytics which I am going to discuss further:

Data coordinator : Every business collects data, be it sales figures, market research, logistics, medical records or the cost of transportation. The job of a data coordinator is to organize data to help data analysts and companies improve.

Data coordinators help to collect and manage data so that it can be used for business functions, productivity and analysis. Coordinators ensure that data is properly collected and observed, particularly data that includes personal information.Data coordinators by the use of complex software systems,  primarily work with electronic data and to maintain and organize the data.

These professionals have to get the required information (data). For this they may have to spend hours on computer.

Data Architect : The role of data architecture is more in doing business data modification, their job is related to protecting data, designing them or creating data policies. They are also responsible for work like logical data modeling or physical data modeling, data warehousing. Further responsibilities of a data architect include developing solutions for storing and retrieving company information.

Installing and configuring information systems to create functionality. Analyzing the structural requirements for new software and applications etc.

Data Visualizer : They play a vital role in detecting data trends and formulating data management strategy. It works by creating a storage system that is separate from the hardware in a device, and is required for cloud computing etc.

In data visualization, making large and complex data more accessible, understandable and usable and locating rich data sources is part of the working style of a data visualizer.

Data Manager : They enter the required data into the computer as well as prepare an evaluation report based on it. These use techniques for storing quality data to ensure the adequacy, accuracy and validity of the data. Create efficient and secure processes for data management and analysis, taking into account all technical aspects.

In addition to monitoring information and data systems and protecting digital databases, assist in reporting and extracting data whenever necessary, whether or not data managers are safe from breaches and loss.

Business Analyst : The job of a systems analyst is to determine how well the software, hardware, and overall IT systems of the business are suitable for the business needs of their clients. His work is related to the maintenance of equipment used in data analysis.

They write the requirements for new systems and can also help implement them and monitor their effectiveness. Typical job responsibilities include: Testing existing systems, having equal knowledge of both hardware and software.

Business Intelligence Developers : Business intelligence developers protect the company from loss by controlling the plan. The field of BI is tailored to business decisions and achieve success through technology, business processes, data and analysis.

Today, business intelligence follows policy methods, and has become a technical priority for many organizations. Along with collecting data, it is very important for any company to solve the problems that arise. Business intelligence developers protect the company from loss by controlling the plan.

Benefits of Data Analytics

Data analysis is a proven way for organizations and enterprises to obtain the information they need to make better decisions, serve their customers, and increase productivity and revenue.

The benefits of data analysis are enormous, and some of the most rewarding benefits are finding the right information for your business, getting more value out of IT departments, creating more effective marketing campaigns, gaining a better understanding of customers, and much more. 

But, today there is so much data available that data analysis is a challenge. Namely, handling and presenting all the data are two of the most challenging aspects of data analysis.

Traditional architecture and infrastructure are not capable of handling the huge amounts of data being generated today, and it takes longer than expected for decision makers to derive actionable insights from the data.

Fortunately, data management solutions and customer experience management solutions deliver more intelligently on insights across enterprises to listen to customer interactions, learn from behavior and contextual information, create more effective actionable insights, optimize goals, and improve business practices. has the ability to. provide capability.

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Last Word

Friends, we believe, now you must have come to know what is data analysis. Now we know that in data analytics, there is a process of making conclusions and decision making while discovering the important information of the business. How did you all like this article, tell me by commenting, if you like the information, then share it with your friends too.