Why Data Analytics is Gaining HYPE in the 21st Century (2023)

Data Science and Data Analytics are two buzz words of the year. Today, data is more than oil to the industries. Data is collected into raw form and processed according to the requirement of a company and then take this data for the decision making purpose. All this process, helps the business to grow in the market. But, who will do this work? Who will process the data? etc. Everything is done by a Data Analytics and a Data Scientist.

Data or information is in raw format. With increasing data size, it has become a need for inspecting, cleaning, transforming, and modeling data with the goal of finding useful information, making conclusions, and supporting decision making. This process is known as data analysis.

Data mining is a particular data analysis technique where modeling and knowledge discovery for predictive rather than purely descriptive purposes is focused. Business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. In statistical applications, some people divide business analytics into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA).

EDA focuses on discovering new features in the data and CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics does forecasting or classification by focusing on statistical or structural models while in text analytics, statistical, linguistic and structural techniques are applied to extract and classify information from textual sources, a species of unstructured data. All are varieties of data analysis.

So, the Data wave has changed the ways in which industries function. With Big Data has emerged the requirement to implement advanced analytics to it. Now experts can make more accurate and profitable decisions.

Looks like confused between Data Science and Data Analytics?

The analysis is an interactive process of a person tackling a problem, finding the data required to get an answer, analyzing that data, and interpreting the results in order to provide a recommendation for action.

A reporting environment or business intelligence (BI) environment involves calling and execution of reports. So, outputs are then printed in the desired form. Reporting refers to the process of organizing and summarizing data in an easily readable format to communicate important information. Reports help organizations in monitoring different areas of performance and improving customer satisfaction. In other words, you can consider reporting as the process of converting raw data into useful information, while analysis transforms information into insights.

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1. Business Understanding

The very first step consists of business understanding. Whenever any requirement occurs, firstly we need to determine the business objective, assess the situation, determine data mining goals and then produce the project plan as per the requirement. Business objectives are defined in this phase.

2. Data Exploration

The second step consists of Data understanding. For the further process, we need to gather initial data, describe and explore the data and verify data quality to ensure it contains the data we require. Data collected from the various sources is described in terms of its application and the need for the project in this phase. This is also known as data exploration. This is necessary to verify the quality of data collected.

3. Data Preparation

Next, come Data preparation. From the data collected in the last step, we need to select data as per the need, clean it, construct it to get useful information and then integrate it all. Finally, we need to format the data to get the appropriate data. Data is selected, cleaned, and integrated into the format finalized for the analysis in this phase.

4. Data Modeling

Once data is gathered, we need to do data modeling. For this, we need to select a modeling technique, generate test design, build a model and assess the model built. The data model is build to analyze relationships between various selected objects in the data, test cases are built for assessing the model and model is tested and implemented on the data in this phase.

5. Data Evaluation

Next is data evaluation, where we evaluate the results from the last step, review the scope of error, and determine the next steps to perform. We evaluate the results of the test cases and review the scope of errors in this phase.

6. Deployment

The final step in the analytic process is deployment. Here we need to plan the deployment and monitoring and maintenance, we need to produce a final report and review the project. In this phase, we deploy the results of the analysis. This is also known as reviewing the project.

We call the above process as business analytics process.

There are 4 types of techniques used for Data Analysis are-

1. Descriptive Analysis

With the help of descriptive analysis, we analyze and describe the features of a data. Descriptive Analysis deals with the summarization of information. Descriptive Analysis, when coupled with visual analysis provides us with a comprehensive structure of data.

In the descriptive analysis, we deal with the past data to draw conclusions and present our data in the form of dashboards. In businesses, Descriptive Analysis is used for determining the Key Performance Indicator or KPI to evaluate the performance of the business.

2. Predictive Analysis

With the help of predictive analysis, we determine the future outcome. Based on the analysis of the historical data, we are able to forecast the future. It makes use of descriptive analysis to generate predictions about the future. With the help of technological advancements and machine learning, we are able to obtain predictive insights about the future.

Predictive Analytics is a complex field that requires a large amount of data, skilled implementation of predictive models and its tuning to obtain accurate predictions. This requires a skilled workforce that is well versed in machine learning to develop effective models.

3. Diagnostic Analysis

At times, businesses are required to think critically about the nature of data and understand the descriptive analysis in depth. In order to find issues in the data, we need to find anomalous patterns that might contribute towards the poor performance of our model.

With diagnostic analysis, you are able to diagnose various problems that are exhibited through your data. Businesses use this technique to reduce their losses and optimize their performances. Some of the examples where businesses use diagnostic analysis are –

• Businesses implement diagnostic analysis to reduce latency in logistics and optimize their production process.
• Using diagnostic analysis in sales to update marketing strategies that would otherwise lead to a fall in revenue.

4. Prescriptive Analysis

Prescriptive Analysis combines the insights from all of the above analytical techniques. It is referred to as the final frontier of data analytics. Through the details provided by the descriptive and predictive analytics, prescriptive analytics allows the companies to make decisions based on them. It makes heavy usage of artificial intelligence in order to facilitate companies into making careful business decisions.

Major industrial players like Facebook, Netflix, Amazon, and Google are using prescriptive analytics to make key business decisions. Furthermore, financial institutions are gradually leveraging the power of this technique to increase their revenue.

We have already seen characteristics of Big Data like volume, velocity, and variety. Let us now see characteristics of Data Analytics which make it different from traditional kind of analysis.

Data analysis has the following characteristics:

Programmatic

There might need to write a program for data analysis by using code to manipulate it or do any kind of exploration because of the scale of the data.

Data-driven

It means progress in an activity compel by data and program statements describe the data that match and the processing require rather than taking steps of defining a sequence. Many analysts use a hypothesis-driven approach to data analysis, Data can use the massive amount of data to drive the analysis.

Attributes usage

For proper and accurate analysis of data, it can use a lot of attributes. In the past, analysts dealt with hundreds of attributes or characteristics of the data source, with Big Data there are now thousands of attributes and millions of observations.

Iterative

As whole data is broken into samples and samples are then analyzed, data analytics can be iterative in nature. More compute power enables iteration of the models until Data analysts are satisfied. This has led to the development of new applications designed for addressing analysis requirements and time frames.

Following are some of the most popular applications of data analysis –

Fraud Detection & Risk Analytics

In Banking, Data Analytics is heavily utilized for analyzing anomalous transaction and customer details. Banks also use data analytics to analyze loan defaulters and credit scores for their customers in order to minimize losses and prevent frauds.

Optimizing Transport Routes

Companies like Uber and Ola are heavily dependent on data analytics to optimize routes and fare for their customers. They use an analytical platform that analyzes the best route and calculates percentage rise and drop in taxi fares based on several parameters.

Providing Better Healthcare

With the help of data analytics, hospitals and healthcare centers are able to predict early onset of chronic diseases. They are able to predict diseases that might occur in the future and help the patients to take early action that would help them to reduce medical expenditure.

Managing Energy Expenditure

Public-sector energy companies are using data analytics to monitor the usage of energy by households and industries. Based on the usage patterns, they are optimizing energy supply in order to reduce costs and cut down on energy consumption.

Improving Search Results

Companies like Google are using data analytics to provide search results to users based on their preferences and search history. Furthermore, companies like Airbnb use search analytics to provide the best accommodation to its customers. Amazon also makes use of search analytics to provide recommendations to customers.

Optimization of Logistics

Various companies are relying on Big Data Analytics to analyze supply chains and reduce latency in logistics. Companies like Amazon are using consumer analytics to analyze their requirements and send them products without any latency.

In order to have a great analysis, it is necessary to ask the right question, gather the right data to address it, and design the right analysis to answer the question. Then only analysis we can call as correct and successful. So, let’s discuss this in detail.

The framing of a problem means ensuring that must ask important questions and layout critical assumptions. For example, is the goal of a new initiative to drive more revenue or more profit? The choice leads to a huge difference in the analysis and actions that follow. Is all the data required available, or is it necessary to collect some more data? Without framing the problem, the rest of the work is useless.

For a great analysis, we frame the problem correctly. So, this includes assessing the data correctly, developing a solid analysis plan, and taking into account the various technical and practical considerations in play.

We can analyze any business problem for 2 issues:

Statistical Significance

How the problem is statistically important for decision making. Statistical significance testing takes some assumptions and determines the probability of happening of results if the assumptions are correct.

Business Importance

It means how the problem is related to business and its importance. Always put the results in a business context as part of the final validation process.

Data Analytics tutorial is incomplete without discussing the skills. In today’s world, there is an increasing demand for analytical professionals. It is taking time for academic programs to adapt and scale to develop more talent.

All the data collected and the models created are of no use if the organization lacks skilled Data analysts. A Data analyst requires both skill and knowledge for getting good data analytics jobs.

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To be a successful analyst, a professional requires expertise on the various data analytical tools like R & SAS. He should be able to use these business analytics tools properly and gather the required details. He should also be able to take decisions which are both statistically significant and important to the business.

Even if you know how to use a data analysis tool of any type, you also need to have the right skills, experience and perspective to use it. An analytics tool may save a user some programming but he or she still needs to understand the analytics that occurs. Then only we can call a person as a successful Data analyst.

Business people with no analytical expertise may want to leverage analytics, but they do not need to do the actual heavy lifting. The job of the analytics team is to enable business people to drive analytics through the organization. Let business people spend their time selling the power of analytics upstream and changing the business processes they manage to make use of analytics. If analytics teams do what they do best and business teams do what they do best, it will be a winning combination.

Now let us discuss the required technical and business skills.

Technical skills for Data analytics –

1. Packages and Statistical methods
2. BI Platform and Data Warehousing
3. Database design
4. Data Visualization and munging
5. Reporting methods
6. Knowledge of Hadoop and MapReduce
7. Data Mining

Business Skills Data analytics –

1. Effective communication skills
2. Creative thinking
3. Industry knowledge
4. Analytic problem solving

Data Analytics will get a new boost in the market. According to research, the global predictive analytics market is expected to grow $14.95 billion by 2023. So, what are you waiting for? Now, its time to uplift your career with Big data Analytics. Now, its time to boost your career with Big data Analytics.