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Understanding different types of data analytics

What is Analytics?

Analytics is a broad term that encompasses the processes, technologies ,frameworks and algorithms to extract meaningful insights from data. Raw data in itself does not have a meaning until it is contextualized and processed into useful information.

Analytics is this process of extracting and creating information from raw data by filtering, processing, categorizing, condensing and contextualizing the data. This information obtained is then organized and structured to infer knowledge about the system and/or its users, its environment, and its operations and progress towards its objectives, thus making the systems smarter and more efficient.

The choice of the technologies, algorithms, and frameworks for analytics is driven by the analytics goals of the application. For example, the goals of the analytics task may be:

  1. to predict something (for example whether a transaction is a fraud or not, whether it will rain on
    a particular day, or whether a tumor is benign or malignant),
  2.  to find patterns in the data (for example, finding the top 10 coldest days in the year, finding which pages are visited the most on a particular website, or finding the most searched celebrity in a particular year),
  3. finding relationships in the data (for example, finding similar news articles, finding similar patients in an electronic health record system, finding related products on an eCommerce website, finding similar images, or finding correlation between news items and stock prices).

Types of data analytics

There are 4 different types of analytics. Here, we start with the simplest one and go further to the more sophisticated types. As it happens, the more complex an analysis is, the more value it brings.

types of data analytics

Descriptive Analytics

Descriptive analytics comprises analyzing past data to present it in a summarized form which can be easily interpreted. Descriptive analytics aims to answer – What has happened?

A major portion of analytics done today is descriptive analytics through use of statistics functions such as counts, maximum, minimum, mean, top-N, percentage, for instance. These statistics help in describing patterns in the data and present the data in a summarized form.

For example, computing the total number of likes for a particular post, computing the average monthly rainfall or finding the average number of visitors per month on a website. Descriptive analytics is useful to summarize the data.

Diagnostic Analytics

Diagnostic analytics comprises analysis of past data to diagnose the reasons as to why certain events happened. Diagnostic analytics aims to answer – Why did it happen?

Let us consider an example of a system that collects and analyzes sensor data from machines for monitoring their health and predicting failures.

While descriptive analytics can be useful for summarizing the data by computing various statistics (such as mean, minimum, maximum, variance, or top-N), diagnostic analytics can provide more insights into why certain a fault has occurred based on the patterns in the sensor data for previous faults.

Predictive Analytics

Predictive analytics comprises predicting the occurrence of an event or the likely outcome of an event or forecasting the future values using prediction models. Predictive analytics aims to answer – What is likely to happen?

For example, predictive analytics can be used for predicting when a fault will occur in a machine, predicting whether a tumor is benign or malignant, predicting the occurrence of natural emergency (events such as forest fires or river floods) or forecasting the pollution levels.

Predictive Analytics is done using predictive models which are trained by existing data. These models learn patterns and trends from the existing data and predict the occurrence of an event or the likely outcome of an event (classification models) or forecast numbers (regression models).

The accuracy of prediction models depends on the quality and volume of the existing data available for training the models, such that all the patterns and trends in the existing data can be learned accurately. Before a model is used for prediction, it must be validated with existing data.

The typical approach adopted while developing prediction models is to divide the existing data into training and test data sets (for example 75% of the data is used for training and 25% data is used for testing the prediction model).

Prescriptive Analytics

While predictive analytics uses prediction models to predict the likely outcome of an event, prescriptive analytics uses multiple prediction models to predict various outcomes and the best course of action for each outcome. Prescriptive analytics aims to answer – What can we do to make it happen?

Prescriptive Analytics can predict the possible outcomes based on the current choice of actions. We can consider prescriptive analytics as a type of analytics that uses different prediction models for different inputs.

Prescriptive analytics prescribes actions or the best option to follow from the available options. For example, prescriptive analytics can be used to prescribe the best medicine for treatment of a patient based on the outcomes of various medicines for similar patients. Another example of prescriptive analytics would be to suggest the best mobile data plan for a customer based on the customer’s browsing patterns.

What types of data analytics does your business need?

To define the right mix of data analytics types for your organization, we recommend answering the following questions:

  • What’s the current state of data analytics in my company?
  • How deep do I need to dive into the data?
  • Are the answers to my problems obvious?
  • How far are my current data insights from the insights I need?

The answers to these questions will help you settle on a data analytics strategy. Ideally, the strategy should allow incrementally implementing the analytics types, from the simplest to more advanced. The next step would be to design the data analytics solution with the optimal technology stack, and a detailed roadmap to implement and launch it successfully.

You may try to complete all these tasks with the efforts of an in-house team. In this case, you’ll need to find and train highly qualified data analytics specialists, which will most probably turn lengthy and pricey. To maximize the ROI from implementing data analytics in your organization, we advise you to turn to an experienced data analytics provider with a background in your industry.

A seasoned vendor will share the best practices and take care of everything, from the analysis of your current data analytics state and selection of the right mix of data analytics to bringing the technical solution to life. If the described approach resonates with you, our data analytics services are at your disposal. Just comment below and we shall respond.

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