4 Types of Data Analytics Which Can Help Your Business

In the modern digital world, data is the core of every business, and only by understanding this data and extracting valuable insights from it, businesses can grow and satisfy the customers with relevant services and offers.

Data analytics is a set of processes of analyzing raw (unprocessed) data and extracting valuable insights from it. In order to obtain the most accurate results, it is recommended to follow the data quality management standards so your data is always consistent, error-free, and accurate.

Since the process of data analysis can be extremely complex, there are four main data analytics types, each with a specific role. Below we will describe all four types, their purposes, and the needed tools. But first, let’s see the exact benefits that data analytics brings to a company.

4 Types of Data Analytics Which Can Help Your Business

The benefits of data analytics

Some business owners might say: why would I need to implement costly data analytics tools if my business is doing just fine? 

While it sounds reasonable at first glance, this assumption is not very correct. Just think about the speed at which customers’ decisions and behavior changes. Add to this the fact that the world is becoming more and more digital and you will understand why it is so important to quickly adapt to the changing needs and demands of the market.

The implementation of data analytics can bring the following benefits to your business:

  • Accurate business decisions based on the actual customers’ needs – not on the assumptions and guesswork,
  • Timely preventative measures instead of fixing occurred mistakes,
  • Mitigation of financial risks and/or fraud,
  • Delivery of relevant products and services (and revenue increase as an outcome),
  • Ability to pinpoint the problems and knowing how to fix them.

To sum up, data analytics helps business owners stay aware of the customers’ preferences and behavior, better understand their business and its internal and external processes, foresee potential risks and find the most suitable ways to avoid them.

But to receive the maximal value from data analysis, it is important to use the right data analytics type. Below we will overview each type in detail.

Descriptive analytics

Question answered: what happened?

Descriptive analytics is the simplest form of data analytics. It answers the “what happened?” question and presents just the facts without any assumptions about them. 

This type of analytics is often met in the reports and graphs, showing the most profitable marketing channels, the peak tourism seasons, or the most popular product among the customers. It is important to remember that descriptive analytics does not explain why a certain event happened – it simply states the event and nothing more than that.

Despite being the simplest type, descriptive analytics is used in lots of daily business operations and serves as a base for more advanced analytics types.

Descriptive analytics techniques:

  • Data mining
  • Data aggregation

Descriptive analytics tools:

  • MS Excel
  • SPSS

Diagnostic analytics

Question answered: why did it happen?

The next and more advanced form of data analytics is diagnostic analytics. This analytics type provides the reason for an event and explains why it actually happened. It is important to note though that the reasons provided are only the assumptions based on the history data. 

Diagnostic analytics can help understand certain outliers, identify patterns and relationships, and receive advanced insights into an issue. For example, diagnostic analytics can help understand what caused a sales peak or why the revenue was low in the last month.

Diagnostic analytics techniques:

  • Principle components analysis
  • Sensitivity analysis
  • Training algorithms
  • Conjoint analysis

Predictive analytics

Question answered: what might happen?

Unlike descriptive and diagnostic analytics, predictive analytics is a much more advanced data analytics type that not only describes an event but also provides a possible future outcome. Please note that predictive analytics does not give a definite judgment or a 100% accurate forecast. Instead, it uses historical data to make an assumption of what might happen if certain conditions are met. 

Some of predictive analytics use cases include:

  • Customer churn and lifetime value
  • Predictive maintenance
  • Risk modeling
  • Fraud mitigation
  • Customer segmentation

By using predictive analytics, companies can better understand what kind of results they might expect in the future and what kind of actions might bring these results. Due to its complexity, predictive analytics demands the use of advanced tools such as machine learning and therefore is not applied by a big number of companies due to high costs.

Predictive analytics techniques (included in machine learning algorithms):

  • Confidence intervals
  • T statistics
  • P values
  • K-S statistics 
  • Random forests

Prescriptive analytics

Question answered: how to make something happen?

Based on the outcomes drawn by predictive analytics, prescriptive analytics provides recommended actions to take. This is the most complex type of data analytics that constantly keeps “learning” through the received feedback and incoming data. 

A prescriptive analytics model simulates a situation that will most possible happen and provides information about this situation and the conditions that should be met. In this way, business owners receive a data-supported recommendation and can better identify opportunities that might bring value to their business.

Prescriptive analytics is the most complex and most expensive analytics type to implement. Predictive analytics techniques include:

  • Artificial intelligence
  • Machine learning
  • Neural network algorithms

How to choose the right data analytics type for your business

As said above, each data analytics type differs in purpose and complexity. In order to choose the most suitable option and avoid the loss of finances and resources, you should answer the following questions:

  • What kind of complexity do you expect from your data insights? 
  • Why do you need to analyze the data? What goal are you setting for your company?
  • What is the current data analytics state of your company?
  • How are you planning to use the obtained insights and what is your future strategy?

If you just want to better understand the customers or analyze your business decisions, descriptive and diagnostic analytics should be enough. However, if you are a big (or even a medium-size) business that wishes to make major and impactful decisions that will play a critical role in your future growth, you need to implement predictive or even prescriptive analytics to help you align future strategy.

As well, remember that you can combine different data analytics types in accordance with your business needs – but make sure you assign an experienced professional to help you with that.

Final word

Data analytics is a valuable asset that helps companies make accurate decisions, mitigate risks, and better understand their customers and internal processes. From our own experience, we would say that data analytics is a must for any modern company but the analytics type that you choose will depend heavily on the available budget and resources and the set business goals.

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