Business Analysis

2021

4 Types of

Business Analytics to know

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For various processes of market research, a large volume of data is stored at different levels.  Depending on the level of the workflow and the data processing criteria, there are four primary forms of analysis – descriptive, diagnostic, predictive and prescriptive. All four forms together react to all the organisation wants to know from what’s going on in the company to what solutions to follow.

Various aspects of analytics are typically conducted in stages, and no one type of analytics is claimed to be better than the other. They are interrelated, and each of them gives a particular perspective. Given that data is important to so many different industries – from engineering to electricity grids – most businesses focus on one or more of these types of analysis.

 

1. Descriptive Analytics

This can be referred to as the easiest method of analytics. The scale of big data is beyond human understanding, and the first step thus requires the crunching of data into manageable pieces. The aim of this method of study is basically to summarise the results and understand what is going on.

Among other commonly used words, what people call predictive analytics or market intelligence is simply the use of descriptive statistics (arithmetic operations, mean, median, max, percentage, etc.) on actual data. It is said that 80% of market analytics primarily contains explanations focused on aggregations of past results. It is an essential step to make raw data easy for consumers, shareholders and managers to understand. This makes it easier to recognise and resolve areas of strengths and deficiencies that can aid in the approach.

The two key methods involved are data aggregation and data mining, which claim that this approach is used primarily to explain the underlying behaviour and not to make any predictions. By extracting historical data, corporations may evaluate customer preferences and commitments towards their enterprises that may assist with targeted ads, quality enhancement, etc. The tools used in this phase are MS Excel, MATLAB, SPSS, STATA, etc.

2. Diagnostic Analytics

Diagnostic analytics is used to assess whether something has happened in the past. It is distinguished by techniques such as drill-down, data discovery, data mining and correlation. Diagnostic Analytics explores evidence more thoroughly to understand the underlying causes of incidents. It is helpful to assess what causes and activities have led to the result.

Diagnostic analytics can help you explain whether revenues have declined or improved over a given year or so in a time series of sales results. However, this method of research has a limited capacity to offer actionable observations. It just offers an interpretation of causal interactions and sequences when thinking backwards.

Any of the approaches that use diagnostic analytics include attribute importance, analysis of core elements, sensitivity analysis, and collaborative analysis. Classification and regression training algorithms also fall in this category of analysis.

3. Predictive Analytics

As described above, predictive analysis is used to forecast possible outcomes. However, it is important to remember that it cannot forecast when an incident will occur in the future; it only estimates the probabilities of the occurrence of the event. The predictive model is based on the preliminary descriptive analytics level in order to extract the probability of results.

The essence of predictive analytics is to formulate models such that current data can be understood as extrapolating future events or actually forecasting future data. One of the popular applications of predictive analytics can be seen in sentiment analysis, where all opinions expressed on social media are gathered and processed (existing text data) to determine a person’s feelings about a specific topic.

Predictive research also requires the creation and evaluation of models that make reliable forecasts. The predictive analysis relies on machine learning algorithms such as random forest, SVM, etc., and statistics for data learning and analysing. Companies typically require qualified data scientists and machine learning specialists to develop these models.

Forecasting future data is dependent on current data since it cannot be accessed otherwise. If the formula is correctly aligned, it can be used to support complex revenue and marketing predictions. It’s a bit ahead of the regular BI in delivering reliable forecasts.

4. Prescriptive Analytics

Predictive analytics is the base of this research, but it goes beyond the three described above to propose future solutions. It may mean all desirable outcomes according to a given course of action and may also recommend alternative courses of action in order to achieve a specific outcome. It uses a powerful feedback system that continuously improves and changes the relationship.

Computations include optimization of some of the functions that are connected to the desired outcome. For eg, when calling a cab online, the application uses GPS to link you to the correct driver from a variety of drivers found nearby. This optimises the gap for quicker arrival time. Recommended engines also use prescriptive analytics.

The other approach involves modelling, where all main output fields are merged to design the right solutions. It guarantees that the main performance metrics are included in the solution. The optimization model will continue to work on the effect of the predictions already developed. Because of the power to recommend favourable options.

Conclusion

Four analytical techniques can make it look as if they need to be carried out sequentially. In most cases, though, businesses will jump straight to prescriptive analytics. As for other organisations, they are aware of or are now applying descriptive analytics, so where one has defined a key field that needs to be improved and focused on, they must use prescriptive analytics.

According to analysis, prescriptive analytics is still in the budding stage, and not many companies have thoroughly used its strength. However, advances in predictive analytics will definitely pave the way for its growth. Hope this report has given you a greater view of the continuum of analytics.

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