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What is Data Analytics? – Definition, Importance and More

In the modern digital era, due to the growing reliance on big data effective leadership in any industry depends on the capacity to make data-driven decisions and to drive growth that is guided by analysis. If you are thinking about becoming a data analyst, you probably have a lot of questions about what data analytics is about, how the analysis works, the importance and application of data analysis, or the prospects for anyone looking to enter this highly lucrative field. This article will provide you with the information you need to know about data analytics (DA).

What is Data Analytics – Definition, Importance and More

Definition

The methods, technologies, and procedures used by an organization to use data to increase productivity and boost financial benefit are referred to as data analytics or data analysis and they are a subset of data science. 

In other terms, Data Analytics is the science of filtering unstructured data to find answers or make significant discoveries that can be used in many industries. To collect, arrange, and evaluate insights that help companies make wise decisions, it uses multidimensional techniques. 

Data analytics are used in practically every business today to influence strategic thinking. Companies that use data analytics get a competitive edge because they can alter things more quickly, resulting in higher revenue, cheaper expenses, and more innovation.

The uses of data analytics are broad and varied, ranging from significant healthcare analytics to small-scale communities that conduct climate change.

From configuring the proper analytical system to the final representation of the studied data, the analytics industry lives on a network of collaborations.

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Significance of Data Analytics

We now live in a data-driven society, and this trend will continue tomorrow and into the future. You need the correct data to make the decision that will help your firm flourish. 

It is essential to the company because it enables management to develop strategies that are supported by facts, better target marketing campaigns at the right customers, and boost overall productivity.

Let’s look at the importance of data analytics in depth, as it affects various areas of a brand and business.

Finding Opportunities

Organizations may better utilize their data by using data analytics to find new possibilities. This leads to more profitable operations, effective business decisions and happier clients.

Getting Information

Gaining insights and studying the corporate value chain are made possible by data analytics.

The usage of analytics may help analysts learn more about their respective industries. Researchers, analysts, and engineers for business organizations use data analytics technologies to efficiently access the data.

Customer Interaction in a Unique Way

Only data analytics will allow you to respond fast and provide your consumers with feelings of worth. Big data provides the chance for interactions to be based on the customer’s personality.

Client Relationships

Employing educated insights from data analytics, organizations will be able to maximize the customer experience and create long-lasting partnerships by understanding the demands of their customers.

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Get over Competition

When demand changes or new technology is produced, effective data collection and analytics help businesses remain competitive. 

Additionally, it enables them to predict what the market wants and deliver the goods before it is required.

Being preventive and forecasting needs

Customers want businesses to know them, have meaningful interactions with them, and deliver an effortless experience across all contact points in exchange for sharing their data with them.

Organizations will be able to improve the customer experience and create lasting partnerships by knowing the demands of their consumers.

Providing Relevant Products

When demand changes or new technology is produced, effective data collection and analytics help businesses remain competitive. Additionally, it enables them to foresee market needs and deliver the product before it is required.

What Are the Steps Followed During Data Analysis?

The process needed to make sense of data that is currently accessible form of data analysis process.

It should be obvious how to identify the crucial steps in a data analysis process.

To make sure that the data is properly evaluated and produces useful and actionable information, each stage is equally crucial. Let’s examine the important steps that comprise a data analysis process.

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Step I: Explain Why Data Analysis is Necessary.

Before getting into the specifics of data analysis, a corporation must first establish the reason why it needs a solid procedure in the first place.

Identifying the need for data analysis is the first step in a data analysis process. This requirement often results from an issue or query related to business, such as:

  • How can production costs be decreased without compromising quality?
  • What are some methods we can use with the resources we have now to boost sales opportunities?
  • How well-liked is our brand among consumers?

Along with determining a goal, think about the metrics you should monitor along the way. When it comes to gathering data, make sure to identify the sources as well.

Creating a plan can help your data team be more prepared for the difficult and lengthy work that lies ahead.

Step II: Prepare or Collect Information

It is time to start collecting the data required for analysis after the goal is decided. The type of sources used to acquire the data will influence how in-depth the analysis is, therefore this phase is crucial.  The Analyst must gather information from several sources per the task assigned.

Data that is inside the organization you work for is known as internal data and data that is outside of your company is known as external data.

Internal sources, sometimes referred to as primary sources, are where data collection begins. Typically, this is structured data that has been obtained from applications like CRM, ERP, marketing automation, and others. These sources provide data about customers, budgets, sales gaps, and other topics.

Next are outside sources, sometimes referred to as, secondary sources. This data, which may be acquired from a variety of sources, is both organized and unstructured such as world health data, census bureaus, global finance data, Google public data, Google trends, review websites, etc.

First-party data is information that an individual collects using their own resources.

Second-party data is the term for the information that is gathered and sold.

Third-party data is the term for the information that is gathered from sources outside of an organization. Interviews, surveys, questionnaires, and feedback are popular sources through which data is gathered.

A spreadsheet or SQL database can be used to store the collected data.

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Step III: Cleaning Up Unnecessary Data

Your data team will be responsible for cleaning and going through the data once it has been gathered from all the required sources. Because not all data is good data, data cleansing is crucial during the data analysis process.

To provide reliable results, data analysts must find and eliminate duplicate data, anomalous data, and other irregularities that might distort the study. 

Data integrity is mostly responsible for clean data. The duplicate data is eliminated and cleaned since there may be duplicates or the data may not be in the proper format. Both SQL and Excel offer a variety of tools for cleaning up data.

Thanks to developments in data science and machine learning platforms, all of those more advanced automation can help a data analyst save significant time while cleaning data.

Step IV: Perform the Analysis

Analyzing and altering the data is one of the final processes in the data analysis process. There are several ways to accomplish this. The cleaned data is used to conduct analysis and identify trends. To get better results, it also performs calculations and merges data.

Data mining, which is referred to as “knowledge discovery within databases” is one method. Data mining methods such as clustering analysis, discovering detection, association rule mining, and others may reveal previously undetectable underlying patterns in the data.

The analyzed data now has to be made into a visual. Additionally, there is software for data visualization and business intelligence, both of which are designed with decision-making and business users in mind. These options provide reports, dashboards, scoreboards, and graphics that are simple to interpret.

Step V: Review the Outcomes

The analysis of the data analysis’s finding is the last phase. Because it’s how a firm will actually benefit from the first four processes, this step is crucial.

If they accepted your suggestions, they will continue forward with your solutions. 

Even if the outcomes of your data analysis are not entirely convincing, you will need to look farther to uncover additional potential answers. It is necessary to reorganize each phase. To check for any gaps, we must go through each step again.

It is necessary to evaluate the acquired data to check for bias and to suggest potential solution.

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Types of Data Analytics Techniques

Every firm makes extensive use of data. Analytics cannot be done without data, even if not all data is usable for analytics. The term, “data analytics” is used in a variety of ways by organizations and suppliers, which may serve to illustrate the vast range of technologies required across data, all of its use cases, and the analysis of that data.

According to the definition, analytics uses four techniques: 

Predictive Analysis

  • The most prevalent subset of data analytics may be Predictive Analytics.
  • Business uses predictive analysis to identify trends, causations, and correlations.
  • Although the category may be further divided into predictive modeling and statistical modeling, it’s crucial to understand that the two are interrelated.
  • Predictive Analytics may be used, for instance, in a Facebook t-shirt advertising campaign to ascertain how closely the conversion rate correlates with a target audience’s geographic location, economic level, and interests. 
  • The statistics for two or more target audiences may then be analyzed using predictive modeling and each demographic’s potential income values can be presented.

Prescriptive Analysis

  • AI and big data are used in prescriptive analytics to help forecast outcomes and choose the best course of action. The two subcategories of this analytics area are optimization and random testing.
  • Prescriptive analytics can assist in providing answers to queries like, “What if we try this?” and “What is the best action?” using developments in ML.
  • You may test the right factors and even recommend brand-new ones that have a better possibility of producing a successful result.

Diagnostic Analysis

  • Diagnostic data analytics refers to the analysis of data to identify occurrences, causes, or the reasons why something occurred.
  • Diagnostics data analytics explain why something happened. It is divided into two additional categories, discover and alerts and query and dig downs, much like the previous categories. 
  • To extract more information from a report, query and drill down are used.
  • Discover and alerts can find a possible problem before the issue occurs, such as a notice about the reduction in work hours that could affect the number of sales.  It can also discover the best-qualified applicant for a new position at your company.

Descriptive Analysis

  • The foundation of reporting is descriptive analytics without which business intelligence (BI) tools and dashboards are impossible. 
  • Ad hoc reporting and canned reports are the two additional categories into which descriptive analytics may be divided. 
  • A canned report has already been created and contains details on a certain subject. A monthly report from your advertising team or agency that provides performance information on your most recent advertising initiatives is an illustration of this.
  • On the other hand, when a specific business question has to be addressed ad hoc reports are planned. These reports help me learn more particular details about a query. 
  • A custom hoc analysis might concentrate on your company’s social media presence, demographics, and other interaction data, as well as the sorts of individuals who have liked your pages in your industry. Due to its super accuracy, a full image of your social media audience may be provided and you won’t need to review this kind of report again unless your audience significantly changes.

The analytics puzzle is made up of all four levels: predict, prescribe, diagnose, and describe. You can only fully win with a data analytics approach when all four of them work together. The data analytics strategy of the organization is incomplete if the four don’t operate well together or if one component is entirely missing.

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Sectors That Need DA

Data analytics has been used in many sectors; the following is a list of a few of the highly competitive industries that use big data analytics.

Banking and Finance

All the big boys of the banking and financial markets, including hedge funds, retail trade, and huge banks, have used big data technologies to monitor trade analytics used in predictive analytics, pre-trade decision support analytics high-frequency trading, etc. Enterprise risk management, KYC, risk analysis, and anti-money laundering are just a few popular fields that significantly rely on data and analytics solutions.

Healthcare

Big data analytics are being used in healthcare to revolutionize how illnesses are identified and treated, how individuals live their lives, and the potential reduction in mortality. 

Right now, the emphasis is on developing an extensive knowledge of a patient early in their life so that early warning signs of serious disease may be identified and treated more quickly.

The study team would have been able to develop algorithms that can identify diseases 24 hours in advance of their onset of physical symptoms with the use of this vast data.

Education

Due to course materials and instructional strategies, the education sector generates a significant quantity of data science. Based on these findings, it is possible to achieve more effective teaching strategies, recognize students who could be learning inefficiently, and alter the way education is delivered.

Data is being used by educational institutions for a variety of reasons, from developing school transportation services to improving classroom well-being.

Insurance

One of the major sectors investing extensively in predictive analysis is insurance. Data is being used by them to boost profits and sales. The sector is collecting information from many sources. To analyze and manage their connections with clients about claims, underwriting, future consumer requirements, etc., use data insights to enhance client services, which help them maximize income.

Manufacturing

Big data is essential in the manufacturing world of today. The advancement in automation and robots is changing how the sector looks. Even in production context that are more commonplace, big data may have an influence. 

By integrating supply chain management sensors into their gear, firms can access the productivity and overall condition of their assets. Other items, like yoga mats and aircraft engines, are increasingly being fitted with sensors, allowing manufacturers to get important data on how their products are being used and how well they are working.

Media and Entertainment

High-quality media is now expected by consumers in a variety of formats whenever and wherever they want it on various devices. It is being used to understand real-time media content use trends and to take advantage of social media and mobile content.

Companies may produce more effective content for various target audiences, analyze the efficacy of the material, and suggest on-demand content by using data analytics tools.

Government 

Analytics have been immensely beneficial to the government and its organizations. The government is using big data to enhance services and satisfaction in all areas such as financial market analysis, health-related research, environmental protection, fraud detection, or energy development.

Job Prospects of DA

Data analytics services are in high demand around the world.

For prospective data analysts as well as those already in this area who are interested in switching industry focus, there are now many interesting and well-paying job options available.

A survey states that 2,50,000 new job vacancies in the field of data analytics will arise in 2023, which is over 60% more than the need in 2019-2020.

Organizations are using data analysts to keep one step ahead of the competition and demand for these professionals will only grow.

Seeking a steady, in-demand job with promising prospects is only reasonable because of the widespread job cuts and quick growth and implementation of AI in the workplace. 

From e-commerce and manufacturing to healthcare and finance, all the mentioned industries above collect and analyze data. Big data is used more often for corporate development, which increases the need for experts to gain valuable insight from it.

All of this shows that investing in your future by mastering data analytics is a wise decision.

Future of DA

Every choice and every action now matters since the world has changed to a completely different standard. The need for data analysis is expanding along with the use of big data in industry.

In the past ten years, this has led to a sharp increase in the need for data analytics and an exposition of knowledge that can be used to offer insightful information about any industry. To improve decision-making, data collection and analysis can be combined.

Data analytics appear to have a brighter future. The area of innovation can continue to support the expansion efforts of businesses around the world as more sophisticated tools and technologies emerge and deliver new capabilities in the scope of data collection, the degree to which it can be automated, the capability of the algorithms and the speed at which the data can be processed.

List Of Professional Courses from IIM SKILLS

FAQs

Q. How can I start my data analytics career?

There are five basic stages you must follow if you wish to work as a data analyst:

  • Learn the fundamentals of data analysis
  • Obtain a credential that reflects these abilities
  • Work on various projects to hone your data analysis abilities
  • Build a portfolio of your work
  • Apply for a data analyst job at an entry-level

Q. Is it difficult to find a job as a data analyst?

Although it may need some technical skills that may be more difficult for some people than others, being a data analyst is not very difficult. Data analysis is a profession that needs constant education due to the industry’s ongoing improvements.

Q. What skills are needed for a data analyst?

Data analysis requires a combination of technical and soft skills to succeed. Technical skills include proficiency in data analysis such as statistics, machine learning, programming language, and many more. Communication, decision-making, problem-solving and critical thinking are some examples of soft skills.

Conclusion

Today, nearly all industries are attempting to use data analytics’ potential, this number will only grow as data analytics technology gets more dependable and affordable. 

To take advantage of data analytics prospects, you must be familiar with industry-specific problems and data characteristics of each industry to match market demands with unique skills and solutions.

 

Author:
Swati Varli is an intern at IIM SKILLS as a Content Writer. Always being the happy kid whenever it comes to nature, she holds a Master’s degree in wildlife. She is a weeb and passionate about arts and crafts, reading, journaling, dancing and solo travel. For her interest she is always ready to explore new things against her better judgment. At the same time also being the person who will meow back to stray cats just to get their attention.

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