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4 Types of Data Analytics to Improve Decision-Making

Data is a valuable resource that organizations can leverage at an unprecedented scale. It can help influence decision-making, shape strategy formulation, and enhance business performance when used effectively.

This is where Data Analytics steps in. Data analytics transforms raw data into meaningful insights that can help in decision-making. It can be classified into four types based on the complexity and purpose of the analysis – descriptive, diagnostic, predictive, and prescriptive. Each data analytics type has advantages and limitations and can be used for different scenarios and objectives.

In this blog, I will briefly define what data analytics is for business and then cover the four types of data analytics in detail.

What Is Data Analytics In Business?

It refers to analyzing data to gain insights and make informed decisions. It involves collecting, processing, and interpreting data to identify patterns, trends, and relationships to help businesses improve their operations, products, and services.

Business Analytics is a subset of data analytics that uses data to drive business decisions. It involves using statistical and quantitative analysis to identify patterns and trends in data and then using those insights to make infrared decisions. Business analytics can be used to optimize business processes, improve customer experience, and identify new growth opportunities. Data analysis can be done using various tools, frameworks, and software that can help explore data from different perspectives and create visualizations. Some examples of these tools are Microsoft Excel, Power BI, Google Charts, Data Wrapper, Infogram, Tableau, and Zoho Analytics. It also involves using algorithms and machine learning to collect, sort, and analyze data at a larger scale and faster speed.

Types Of Data Analytics

Data Analytics can be divided into four types – Descriptive, Predictive, Prescriptive, and Diagnostic. Each type can be used for different aspects of a business. Let’s compare these different types of analytics and see how they can be applied to business needs.

Descriptive Analytics refers to using historical data to describe past events and why. It can help understand data trends, patterns, and relationships and provide decision-making insights. Descriptive analytics can use various techniques, such as data collection, preparation, exploratory data analysis, visualization,n, and statistical analysis.

Examples of Descriptive Analytics include Sales reports, Customer satisfaction surveys, Inventory reports, etc.

Descriptive Analytics helps answer questions such as:

● How much revenue did the business generate last quarter?
● What are the most popular products or services offered by a business?
● How satisfied are the customers with our business’s products or services?

Diagnostic Analytics: It helps businesses understand why something has happened in the past. This type of analytics helps identify the causes and effects of trends, patterns, and relationships in data. Diagnostic analytics can help in decision-making by providing insights that can help explain, justify, and improve decisions.

By using Diagnostic Analytics, businesses can gain a deeper understanding of the data and the factors that influence outcomes, and the findings and recommendations can be communicated using data visualization tools and reports.

Diagnostic Analytics can help answer questions such as:

● Why did the sales drop last month?
● What are the main drivers of customer loyalty and retention?
● What are the root causes of operational inefficiency?
● How does seasonality affect our demand and supply?

Predictive Analytics uses historical and current data to forecast future outcomes and trends. It can help decision-making by providing businesses with valuable insights and data-driven guidance. Businesses can use predictive analytics to make more informed and strategic decisions rather than rely on guesswork or intuition.

Predictive Analytics relies on advanced algorithms, machine learning, g, and artificial intelligence to analyze data and make informed predictions. It helps answer questions such as:

● How will the sales change in the next quarter?
● What are the best products or services to offer to each customer segment?
● How likely is a customer to leave or renew their subscription?
● What are the main factors that affect profitability?

Prescriptive Analytics: It helps decision-making by providing data-driven recommendations for the best action. It uses historical and current data, advanced algorithms, machine learning, and artificial intelligence to analyze scenarios and suggest optimal decisions. Prescriptive analytics can help businesses improve their performance, efficiency, profitability, customer satisfaction, and innovation. It can also help businesses identify and mitigate risks, fraud, and threats.

It helps answer questions such as:

● What should we do next?
● How can we achieve our goals?
● What are the best strategies to adopt?
● How can we optimize our resources?

Final Words

In Conclusion, data analytics is a powerful tool that empowers organizations to extract valuable insights from vast datasets, driving informed decision-making. Each type plays a crucial role in uncovering patterns and trends, from descriptive and diagnostic analytics to predictive and prescriptive analytics. Utilizing services for managed data analytics effectively can help combine the four types of data analytics to paint a comprehensive picture and create a guide for informed decision-making. Descriptive analytics helps grasp the current state of a business, while diagnostic analytics uncovers the reasons behind it.

Predictive Analytics forecasts the direction in which the situation is heading, while Prescriptive Analytics aids in evaluating all facets of present and potential future scenarios to develop practical strategies. Embracing diverse analytics approaches enables businesses to harness the full potential of data for strategic growth and innovation.

Written By

A professional and security-oriented programmer having more than 6 years of experience in designing, implementing, testing and supporting mobile apps developed. Being techno geek, I love to read & share about the latest updates in technology including but not limited to IoTs, AI, application development, etc. In my free time, I like to play football, watch movies and explore new places. I have been learning mobile app development since 2012. With having a good understanding of programming languages, I develop native as well as web apps for both iOS & Android using latest tools & technologies. I am also having experience in both front-end & back-end development.

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