Introduction to Data analytics

Data analytics is the science of extracting trends, patterns, and relevant information from raw data to draw conclusions. It has multiple approaches, multiple dimensions, and diverse techniques. It helps in various scientific decision making and effective business operations. It is used for analyzing data, gaining profits, making better use of resources and improving managerial operations.

Data analytics is the process of examining and analyzing raw data sets to:

• Draw conclusions
• Derive more information
• Improve businesses, products, and services

In addition to making business decisions, it is used by data scientists and researchers to verify scientific models and theories.

The given visual shows the data analytics process flow which we will study in the future articles.

Types of Data analytics

According to the visual shown above we can observe that as we are going further in X-axis the Complexity of various types of analytics is increasing and as we are going above in Y-axis the value of the analytics is also increasing. Also the range of information is increasing as we go further in various types of analytics, starting from Descriptive analytics to that of Prescriptive analytics.

There are four main types of analytics based on the workflow and requirements of data analytics:

• Descriptive analytics
• Diagnostic analytics
• Predictive analytics
• Prescriptive analytics

Descriptive analytics

Descriptive analytics help us to answer the question like what has happened. In descriptive analytics existing data is analyzed to understand what is happened in the past or is happening currently. This analytics is a simplest form of analytics as it deals with data aggregation and mining techniques. The insights gathered by this analytics can be useful for planning various strategies in targeting marketing.

Some of the points of descriptive analytics

Data aggregation is the process of gathering and expressing information in a summarized form. Tools used for data aggregation include MS Excel, MATLAB, SPSS and STATA. Company report is an example of descriptive analytics.

Diagnostic analytics

Diagnostic analytics helps to answer the question about why this things happened. In diagnostic analytics it focuses more on the current events rather than past and to determine which are the factors which are influencing the current trends. In order to explore the data into much deep different techniques like data mining, drill down, data discovery, etc. are used.

Some of the key points of diagnostic analytics are:

They can be used to discover a causal relationship between two or more data sets. Diagnostic analytics is helpful for those concerned with day-to-day operations. For example, it helps identify why a sales representative has sold fewer items than usual.

Predictive analytics

Predictive analytics help answer the question about what will happen in the future. This analytics use the past historical data to another and analyze it and give the insights which can recur in the near future. It uses various statistical models and ML techniques. They can achieve higher level of accuracy. One of the most common example is regression analysis.

Predictive analytics is used in:

• Predicting future outcomes in terms of probablity of an event to occur.
• Analyzing sentiments where all opinions posted on social media are collected to predict a person’s sentiments.
• Identifying target audience for the promotional campaign.
• Forecasting weather, plan-failure prediction, and various recommendation systems.

A predictive model is built on the preliminary descriptive analytics stage.

Prescriptive analytics

The Prescriptive analytics helps answer questions about what is to be done. By the insights gained form predictive analytics company can make different data driven decisions. Through this company can take various decisions through facts and insights gained. Prescriptive analytics mostly depends on the patterns you get in previous analytics.

Predictive analytics is at the budding stage of implementation and companies have not used its full potentials. Advancements in predictive analytics is paving the way for its development.

The above mentioned types of analytics provide the insights that various organizations and businesses need to make effective and data driven decisions. If the analytics techniques are used properly they provide accurate insights according to company’s need and opportunities.

Benefits of Data analytics:

Benefits in Decision making:

• Companies use business analytics to enable faster and facts-based decision making.
• Data-driven organizations make better strategic decisions.
• Companies enjoy high operational efficiency, improved customer satisfaction, robust profit and revenue level.

Data analytics helps you define your target audience based on

• Customer age group
• Customer preferences
• Location-based purchases
• Popular brands or products people seek

Benefits in Cost Reduction:

• Data analytics helps understand shopper behaviors by monitoring their browsing interest.
• Seller identifies shopping patterns and customer demand.
• Customer data helps companies minimize failed campaigns and reduce cost associated with them.
• Data analytics helps in reducing marketing and logistic costs.
• Marketers use technologies to evaluate customer behaviors and make strategic decisions.
• Predictive analytics is used for better performance, higher ROI, and faster success.
• Marketing campaigns use measured activities to plan campaigns.

Let us consider a case study of Amazon to know how it uses various techniques of analytics:

Amazon used data analytics to improve efficiency and reduce cost. Analytics help customer to predict what to buy and anticipate shopping.

Such predictions help increase sales and reduce shipping, inventory, and supply chain costs.

Amazon has more than 200 fulfillment centers worldwide. Supply chain and logistic optimization helps companies reduce costs and improve performance.

Amazon used data analytics for choosing the warehouse closest to the customer and reduces shipping costs by 10-40 percent.

It uses data analytics to attract customers and increase profits by an average of 25 percent annually.

Prices are based on customer activity on a website, competitor’s pricing, and product availability.

Product prices typically change every 10 minutes as data is updated and analyzed.

Amazon typically offers discounts on the best-selling items and earns larger profits on less popular items.

Examples:

Descriptive analytics:

• Spent $20M in different sales training the previous year.

Diagnostic analytics:

• Amazon revenue increased in the West Coast during the past one year
• Increased spending on sales training

Predictive analytics:

• Purchase factor: price, time, weather and festive seasons.
• Predicted 10-12 percent increase in revenue.

Prescriptive analytics:

• Sales training fetched good ROI.
• Implemented a suitable optimization plan to maximize profit.

Core advantages of Data analytics

• Data analytics helps in identifying potential opportunities to streamline operations.
• It identifies potential problems and gives time to take actions.
• It allows companies to identify operations that yield the best results.
• It identifies and improves error-prone operational areas.
• Organizations implement data analytics in product or service development.
• Data analytics helps in understanding current state of business.
• It provides valuable insights to predict future outcomes.
• It helps businesses align new process or products with market needs.
• Data analytics tools are capable of handling heterogenous data and providing insights.

In the next blog we will understand about different types of data and data analytics process. Still than stay tuned.

Happy Learning !! 🔥🔥