4 Types of Data Analytics for an Improved Business Decision Making

Data Analytics

Much has been written about analytic techniques and which kind of analysis can bring the most value to your business. So, what are the four types of data analytics that you can use?

Too many companies, however, still rely on the analysis of only one data source. To make more informed decisions, it’s crucial to start with some basic analytics that present simple metrics on that data.

Consider Data Blending for deeper business insights, combining two or more datasets often brings invaluable information that would not be found in the data was not combined – and this information provides a new perspective that leads to better business decisions.

The growth of a business can be described as a growth of its research and development efforts, which leads to the introduction of new products.

Say, for example, the introduction of new products results in a change of market share and, therefore, its revenue and profitability.  

These changes can either be positive or negative. Identifying these changes early can help business leaders drive their business in the right direction. Analytics can help in understanding these changes, the underlying reasons and help businesses make informed decisions. Analytics will help mitigate risks and handle setbacks.

All enterprises today have access to huge amounts of data that can generate valuable insights. Data analytics can help enterprises use this data from pitching a product to a prospective customer to streamlining operations and mitigating risks.

We can classify data into two major categories, Qualitative data, and Quantitative data. Qualitative data refers to any data that is not numbers. Qualitative data can be in the form of images, documents, surveys, etc. Quantitative data is all about numerical analysis. Quantitative data can be statistics, numbers, percentages, etc.

We can use the below 4 Types of Data Analytics methodologies to analyze the different types of data:

1. Descriptive Data Analytics

Descriptive Analytics is the first step in analytics. It helps answer the question of what happened by providing some basic descriptive information like position, size, shape, color, etc. It helps to better understand the data.

Using descriptive analytics, the data can be presented in the form of charts, tables, and graphs. Sales volume can be presented as though it were a line chart indicating growth of sales over the last 4 quarters. Similarly, the operating expenses could be visualized as a pie chart or a bar chart to reveal the distribution of expenses during different periods. These charts and graphs are simple to understand and allow further analysis.

2. Diagnostics Data Analysis

The diagnostic analytics approach requires figuring out the ‘why’. It is about breaking down the available information and identifying the causes behind specific problems, events, and behaviors.

For this reason, diagnostic analytics should be focused on specific problems, meaning that other issues should not be included in the analytical process. This approach allows for better visualizing the data. With the help of diagnostic data analytics, marketing professionals can identify marketing channels that are beneficial to their company or understand audience behavior. Such insights give the companies an edge over the competition.

Individual visualization of each problem is possible through diagnostic data analytics. As an example, if a business wants to know what causes customers to abandon their shopping carts, then they can just analyze that particular issue without interfering with other problems or events.

For instance, when a company is looking for ways to increase the conversion rate, the diagnostic analytics will assist in understanding which tasks and steps make it difficult for shoppers to convert. With this information in hand, they can then focus on fixing or eliminating the obstacles.

It is equally important that a company collects diagnostic data from a variety of sources because it gives a holistic view of a specific problem.

3. Predictive Analytics

Predictive analytics is one of the most well-known and often used types of data analytics. It helps businesses in learning about the future.

It takes the help of the snapshot of the descriptive and diagnostic analytics to identify clusters and exceptions and to predict future trends. It is the most valuable tool for forecasting.

Predictive analytics belongs to advanced analytics types, which brings in many advantages, like sophisticated analysis based on machine or deep learning, and proactive approach that predictions enable.

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A predictive model is built during the process of predictive modeling. It is a complex tool that can be used in many industries, including healthcare, marketing, finance, legal, industrial production, etc.

The predictive model focuses on anticipating the future behavior of an object or phenomenon by analyzing past data.

The data used for predictive modeling can be numeric or categorical. Data mining algorithms use it to create models that represent certain real-life patterns.

4. Prescriptive Analytics

Prescriptive analytics is about using predictions to deliver value. It helps in providing the key to the future by prescribing the best course of action among the available alternatives.

Prescriptive analytics is also known as “what-if” analysis. The data model describes the steps required to achieve desired business goals.

Prescriptive analysis is the process of determining the steps to take in order to achieve a particular result or outcome. It helps decision-makers determine actions that are most likely to succeed based on the current business environment. 

The analysis is usually spearheaded by data scientists who create algorithms that predict how human behavior will react to future events.

Prescriptive analytics has been in use since the 1980s in the pharmaceutical industry, where it’s used to optimize sales forecasts and sales calls. 

It has developed over time and today it’s applied in other industries including marketing, IT, insurance, risk management, and finance. While predictive analytics can tell you what will happen, prescriptive analysis can tell you what should be done to get the desired result.

Conclusion

Today, data science is offering incredible value across most industries. All the four types of data analytics mentioned above continue to contribute to transformation in their own ways!

Get in touch with us to explore how DiLytics can help your company leverage the advantage of Data Analytics in an efficient way.