June 30, 2021
Data Analytics as a Business Booster. 4 Types of Data Analytics
We want to talk about Big Data today explaining some basic principles. In this post, we’ll take a detailed look at types of data analysis that can be implemented and can be very beneficial.
Descriptive Analytics
It describes what has already happened.
  • For example, a retailer can learn the number of clients, the average bill, the most popular goods, etc.
  • A medical company can evaluate the most common illnesses and susceptibility to disease.
With the help of descriptive analysis, any company is able to group its customers by social factors, behaviour and other features, as well as monitoring peak activities according to seasonal or local factors.
DESCRIPTIVE ANALYTICS leads the march these days as it manipulates figures from multiple data sources to provide valuable information about the past. They can predict important trends and signal the necessity of preventative or stimulative actions.
Diagnostic analytics
This type aims at finding the reason behind why something happened. We use it to identify the patterns and consequences of our policies and actions. It provides a deeper understanding of any given problem.
  • In IT, for this purpose, we use business intelligence (BI), to be more specific — it’s machine learning technics again. These are computer-based methods of analysing and reporting valuable information. For example, using ML in healthcare, medics can diagnose a person’s susceptibility to cancer-based on medical screenings.
Predictive analytics
Predictive analytics address what might happen.
To be able to predict trends and see into the future, this type of analytics uses the results of the previous two — i.e. it bases its results on true facts of the past.
  • With the help of the predictive analysis, an entrepreneur can optimise the raw material storage and the warehouse stock. Computer systems predict stock exchanges, market fluctuations and currency exchange rates. This is specifically useful in finance, production, logistics and banking.
The accuracy of data and the stability of the situation have a significant influence on the result. It requires careful processing and constant optimisation.
Combining the approaches gives the best, most relevant results.
Prescriptive analytics
Prescriptive analysis is based on mathematical modelling. Its mission is to show the consequences of certain actions based on possible changes to data and conditions.
When is it time to use prescriptive analysis?
  • Prescriptive analytics is a branch with a high degree of responsibility: it utilises top-notch tools and technologies, such as machine learning and data mining.
  • It helps in decision-making by constructing a potential future and estimating the probability and extent of any given factor’s influence. (Remember “Back to the Future” — they were realising the prescriptive analysis in a series of events).
  • It’s complicated and expensive. If you are working with vital factors, it lets you save much more than you spend. But for some cases, it’s worth to use mathematical modelling and statistical analysis to get value for money solution.
Predictive vs prescriptive analytics — what’s the difference?
Predictive analytics helps collect the figures needed for informed decision-making, while prescriptive analysis constructs different solutions for you to choose from.
Which approach to analysis is the best?
To provide true-to-life results, the combination of all four methods is the best option. The wide variety of information sources and compilation of different mathematical approaches to interpretation increase the accuracy and value of the results.
Data analytics in a business context helps better recognise the dependencies, intricacies and cardinal processes to provide key points for improvement based on probability rather than pure intuition.
Summing up
Big data analysis creates advantages for the business owners by showing the not evident connections of courses and consequences.
It significantly improves the forecasting and planning, reduction of the pick loads and operative costs, and optimisation of the business flow.