Data Analytics and Data Analysis (Data Analytics vs Data Analysis) are terms that we have all heard before. But are these terms synonymous? Not exactly, although individuals occasionally confuse the two terms.

Data Analytics or Data Analysis (mostly referred within many of the companies and professionals) is the process of examining, improving, converting, and training historical data in order to gather knowledge, offer hypotheses and make judgments.

To obtain better insight and create better plans, in data analytics data experts use data, machine learning techniques, statistical analysis, computer-based patterns and also basic techniques of analysing data.

While data analysis focuses majorly on analyzing the data and extracting useful insights from the data using general techniques of analyzing data like data clustering, segmentation etc. For data analysis, data analysts use common tools like MS Excel, Business Intelligence software like Tableau, Power BI etc. and other reporting softwares.

Data analysis we have already discussed in our other articles. Today, we will learn about data analytics in details, its types, methods, applications and more.

What is Data Analytics?

Organizations’ usage of data and their methods for data analysis and interpretation get increasingly complex as they gather more data. Analytics is a wide phrase that can mean many various things depending on where you are on the data analytics maturity curve, but data without analytics isn’t very useful. Data analytics, as stated above, is therefore nothing more than the use of data, machine learning techniques, statistical analysis, and some computer-based patterns to develop better strategies and acquire more insight.

Data analytics is a broad word that covers many different types of data analysis. Any form of data can be subjected to data analytics techniques to gain knowledge that can be applied to make things better. Gaming companies, for instance, employ data analytics to create award schedules for players that keep the majority of players active in the game. Other business models employ data analytics in a similar way, depending on their requirements.

Data Analytics vs Other Data Processing Techniques

In other words, we can say that Data Analytics is a global terminology or super-set of all other data analysis, data science, data munging, data mining, data wrangling and business intelligence (BI) methods, processes, techniques, technology and tools.

Data Analytics is like an umbrella term which consists of or encompasses all other data processing related methods, processes, techniques, technologies, and tools.

What is the need for Data Analytics?

Considering how fiercely competitive the market is now and how everyone in the company wants to see their companies expand quickly, Data analytics’ job is to extract and organize data so that businesses may identify and assess links, patterns, and trends, gain insights from the data, and then draw conclusions from the data to help them make decisions.

Data analytics is crucial because it aids organizations in improving their performance as a result. By finding more cost-effective ways to do business and retaining a lot of data, firms can help cut expenses by incorporating it into their business strategy. Additionally, a corporation can use data analytics to improve business decisions and track consumer preferences and trends to develop fresh, improved goods and services.

Data Analytics: Steps Involved

The need for data analytics is now abundantly obvious from the aforementioned, but are you aware of the actions involved? Data analytics involves a number of distinct steps, including:

1. Identifying the data needs or how the data is grouped is the first stage. Data might be divided based on gender, income, age, or other factors. Data values might be categorical or numerical.
2. The process of gathering data is the second phase in data analytics. Multiple tools, including computers, the internet, cameras, environmental sources, and human employees, can be used to accomplish this.
3. Data must first be arranged so that it may be studied after it has been gathered. A spreadsheet or other piece of software that can handle statistical data may be used for this.
4. After then, the data is cleaned up for analysis. This indicates that it has been cleaned up and double-checked to make sure there is no duplicate, errors, or missing information. Before the data is sent to a data analyst for analysis, this stage aids in the correction of any inaccuracies.

Types of Data Analytics

There are five types of data analytics: 

1. Predictive (forecasting) Analytics
2. Descriptive (business intelligence and data mining) Analytics
3. Prescriptive (optimization and simulation) Analytics
4. Diagnostic Analytics
5. Cognitive Analytics

1. Predictive Analytics

Through predictive analytics, the data are transformed into useful knowledge. Predictive analytics uses data to estimate the chance of a condition arising or the likely course of an occurrence.

In order to anticipate future events, predictive analytics uses a number of statistical approaches from modeling, machine learning, data mining, and game theory. These techniques analyze both current and past data. The following methods are employed in predictive analytics:

• Linear Regression
• Time series analysis and forecasting
• Data Mining

There are three basic cornerstones of predictive analytics:

• Predictive modeling
• Decision Analysis and optimization
• Transaction profiling

2. Descriptive Analytics

In order to understand how to approach future events, descriptive analytics examines data and analyses prior events. By analyzing historical data, it examines prior performance and analyses performance to determine what caused past success or failure. This kind of analysis is used in almost all management reporting, including that for sales, marketing, operations, and finance.

In order to categorize consumers or prospects into groups, the descriptive model quantifies relationships in data. Descriptive analytics uncovers a variety of interactions between the client and the product, in contrast to predictive models that concentrate on forecasting the behavior of a specific customer.

Core competencies include things such as:

• Data modeling fundamentals and the adoption of basic star schema best practices,
• Communicating data with the right visualizations, and
• Basic dashboard design skills.

Common examples of Descriptive analytics are company reports that provide historic reviews like: 

• Data Queries
• Reports
• Descriptive Statistics
• Data dashboard

3. Prescriptive Analytics

In order to produce a prediction, prescriptive analytics automatically combine large data, mathematical science, business rules, and machine learning. They then propose a choice alternative to capitalize on the prediction.

Prescriptive analytics goes beyond forecasting outcomes by additionally recommending actions that will benefit from the forecasts and outlining the implications of each decision option for the decision maker. In addition to predicting what will happen and when prescriptive analytics also considers why it will happen. Additionally, prescriptive analytics can recommend options on how to seize a future opportunity or lessen a future risk, and it can also explain the implications of each option.

Prescriptive analytics, for instance, can help strategic planning in the healthcare industry by leveraging operational and consumption data mixed with data from outside elements like the economy and population demographics.

4. Diagnostic Analytics

Similar to descriptive analytics, diagnostic analytics analyses historical data to address a query. Diagnostic analytics, however, answers the crucial query of why an occurrence or abnormality occurred in your data rather than concentrating on “the what.” The most neglected and skipped step in the analytics maturity paradigm is diagnostic analytics. Anecdotally, I observe that most clients skip over the “why did it happen” step in favor of moving directly from “what occurred” to “what will happen.” Companies can use this kind of analytics to find the answers to issues like:

• Why did our company sales decrease in the previous quarter?
• Why are we seeing an increase in customer churn?
• Why are a specific basket of products vastly outperforming the prior year’s sales figures?

Common techniques used for Diagnostic Analytics are: 

• Data discovery
• Data mining
• Correlations

5. Cognitive Analytics

Cognitive analytics combines several cognitive technologies, such as semantics, artificial intelligence algorithms, deep learning, and machine learning, to do some jobs with intelligence akin to that of a human.

Today’s examples of cognitive analytics include IBM’s Watson, Apple’s Siri, and Microsoft’s Cortana. Cognitive analytics is being used by businesses to access unstructured data sources like photos, emails, text documents, and social media posts.

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What are some Data Analytics Tools? 

Data analytics has rapidly advanced in terms of technological capabilities, in addition to a wide range of mathematical and statistical methods for crunching numbers. Data analysts can now gather data, store information, process data, and publish conclusions with the use of a wide variety of software tools.

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Data Analytics: Techniques Involved

To process data and extract information, data analysts might employ a variety of analytical approaches and procedures. The following is a list of some of the most used techniques.

• Regression analysis involves examining the connections between the dependent variables to see how a change in one can impact a change in the other.
• Factor analysis involves reducing a large data set to a smaller data set. By using this technique, it is hoped to uncover tendencies that might have been harder to spot otherwise.
• Cohort analysis is the division of a data set into sets of related data, frequently divided into a consumer demographic. This enables data analysts and other data analytics users to go deeper into the statistics pertaining to a certain subset of data.
• Monte Carlo simulations simulate the likelihood of various scenarios. These simulations, which frequently include many values and variables and frequently have better predicting abilities than other data analytics techniques are frequently utilized for risk reduction and loss prevention.
• Time series analysis collects data through time and establishes a link between the importance of a data point and its occurrence. This method of data analysis is frequently employed to identify cyclical patterns or to forecast financial outcomes.

Many industries, including the airline and hospitality sector, whose turnaround times are often rapid, have embraced data analytics. Today, you may learn a lot more about a variety of topics much more quickly because of the expanding number of data and the readily available advanced analytics solutions. We anticipate that everyone looking for knowledge about data analytics will find this article to be helpful.