Data analytics are primarily concerned with answering questions and making decisions. And, just as there are several sorts of questions, there are various forms of data analytics tailored to your objectives, which helps in making informed decisions that are based on facts rather than intuition.

Data analytics is also a way of measuring the past performance of a company or an individual to predict future outcomes. Data analytics is a process that collects, organizes, and analyses data from different sources, including online sources. It also helps in identifying patterns and trends which are then used for decision-making purposes by companies and individuals alike.

It is essential for any business professional who makes decisions to have a solid understanding of data analytics. Data is more accessible than ever. If you do not look at your available data before formulating strategies or making decisions, you may miss opportunities or red flags it communicates.

Source : https://www.gartner.com/en/information-technology/insights/data-analytics

Decision-Making Based On Data Analytics

A well-organized, accurate, and easily interpreted set of data is essential for making data-driven decisions. The first step is to develop a standard procedure for integrating data from different sources both within and outside the organization. Once this first phase has been automated, it is time to monitor and analyse the results.

Understanding the analytics progression and starting in the right place will help to guarantee success with advanced analytics and lead to AI utilization.

The data analysis is done through interactive dashboards, which make data analysis visual and intuitive so that the information can be understood easily and quickly. This system also extracts data in real time, allowing for more accurate analysis. Using data to guide business strategy decisions is known as “data-driven decision making”.

There are four main types of data analysis to manage a business more smartly.

1. Descriptive analytics

The cornerstone for all other types of analytics is descriptive analytics, which is the most basic type. It enables you to quickly summarize what occurred or is happening by drawing trends from the raw data. Consider the situation where you are studying statistics for your business and discover that sales of one of your goods, smart TV, are increasing at a seasonal rate. Here, descriptive analytics can inform you, “Sales of smart TV increase each year in early December, early November, and October.”

Charts, graphs, and maps may clearly and understandably display data patterns, as well as dips and spikes, making data visualization a viable choice for expressing descriptive analysis.

2. Diagnostic analysis

Descriptive analytics reveals what occurred, but diagnostic analysis explains why it occurred by looking for a cause-and-effect connection. In addition to the revelations from the descriptive analysis, it offers a wider perspective.

You may easily spot trends and outliers in your data using diagnostic analytics to determine how various aspects are related to one another. A distributed result, likelihood, or probability may be included in this sort of analytics. It suggests methods including sensitivity analysis, principal component analysis, and regression analysis.

3. Predictive analytics

Predictive analytics is used to make predictions about future trends or events and answers the question, “What might happen in the future?” Your company can make informed predictions by analysing historical data in conjunction with industry trends.

As an example, you can predict the same trend next year if you know that TV console sales have spiked every year in October, November, and early December. Considering the overall upward trend in the Television industry, this prediction seems reasonable. Developing strategies based on likely scenarios can help your organization formulate predictions for the future.

4. Prescriptive Analytics

As a result, prescriptive analytics provides a clear answer to the question, “What should we do next?”

An actionable takeaway can be derived from predictive analytics by considering all factors in a scenario. This type of analytics can be especially useful when making data-driven decisions.

To complete the TV example, given the predicted seasonal trend due to winter freebies, what should your team decide? You decide to run an A/B test with two ads: one aimed at end-users of the product (children), and one aimed at customers (their parents). The data from this test can shed light on how to take more advantage of the seasonal peak and its suspected cause. Or you decide to ramp up your marketing efforts in September with holiday-themed messaging to try and extend the peak into another month.

All four types of data analysis should be used together to create a complete picture of story data and make informed decisions. While different forms of analytics may provide varying amounts of value to a business, they all have their place.