Types of Analytics

The big data revolution has given birth to different kinds, types and stages of data analysis. Boardrooms across companies are buzzing around with data analytics – offering enterprise wide solutions for business success. However, what do these really mean to businesses? The key to companies successfully using big data, is by gaining the right information which delivers knowledge, that gives businesses the power to gain a competitive edge. The main goal of big data analytics is to help organizations make smarter decisions for better business outcomes.

Thomas Jefferson said – “Not all analytics are created equal.”

Big data analytics cannot be considered as a one-size-fits-all blanket strategy. In fact, what needs to be done is to identify the kind of analytics that can be leveraged to benefit the business – at an optimum. The four dominant types of analytics –Descriptive, Diagnostic, Predictive and Prescriptive analytics, are interrelated solutions helping companies make the most out of the big data that they have. Each of these analytic types offers a different insight. 

Fig 1 – Types of Analytics

What is Descriptive Analytics?

Descriptive analytics is the interpretation of historical data to better understand changes that have occurred in a business. Descriptive analytics describes the use of a range of historic data to draw comparisons.

Descriptive analytics takes raw data and parses that data to draw conclusions that are useful and understandable by managers, investors, and other stakeholders. A report showing sales of $1 million may sound impressive, but it lacks context. If that figure represents a 20% month-over-month decline, it is a concern. If it is a 40% year-over-year increase, then it suggests something is going right with the sales strategy. However, the larger context including targeted growth is required to obtain an informed view of the company’s sales performance.

Fig 2 – Source : https://www.freepik.com

Descriptive analytics uses a full range of data to give an accurate picture of what has happened in a business and how that differs from other comparable periods. These performance metrics can be used to flag areas of strength and weakness to inform management strategies.

The best example to explain descriptive analytics are the results, that a business gets from the web server through Google Analytics tools. The outcomes help understand what happened in the past and validate if a promotional campaign was successful or not based on basic parameters like page views.

What is Diagnostic Analytics?

Diagnostic analytics is a form of advanced analytics that examines data or content to answer the question, “Why did it happen?” It is characterized by techniques such as drill-down, data discovery, data mining and correlations.

Analysts identify the data sources that will help them interpret the results. Drilling down involves focusing on a certain facet of the data or widget. Data mining is an automated process to get information from a massive set of raw data. And finding consistent correlations in your data can help you pinpoint the parameters of the investigation.

It is the analysts job to identify the data sources that will be used. Often, this requires them to look for patterns outside the company’s internal datasets. It may necessitate pulling in data from external sources to identify correlations and determine causality.

Diagnostic analytics helps you get value out of your data by asking the right questions and making deep dives for the answers

If an HR wants to analyze its employees’ performance, based on quarterly performance levels, absenteeism, and overtime hours per week. He could set up data models and harness a high-level, customizable, real-time analysis of employees’ time and performance.

What is Predictive Analytics?

Predictive analytics is all about forecasting. Whether it is the likelihood of an event happening in future, forecasting a quantifiable amount or estimating a point in time at which something might happen – these are all done through predictive models.

Predictive models typically utilize a variety of variable data to make the prediction. The variability of the component data will have a relationship with what it is likely to predict (e.g. the older a person, the more susceptible they are to a heart-attack – we would say that age has a linear correlation with heart-attack risk). These data are then compiled together into a score or prediction.

In a world of great uncertainty, being able to predict allows one to make better decisions. Predictive models are some of the most important utilized across several fields.

Predictive analytics can be used in healthcare to determine the patients who are at the risk of developing certain conditions such as diabetes, asthma and other lifelong diseases. The clinical decision support systems incorporate predictive analytics to support medical decision making at the point of care. 

What is Prescriptive Analytics?

Prescriptive analytics is a type of data analytics—the use of technology to help businesses make better decisions through the analysis of raw data. Specifically, prescriptive analytics factors information about possible situations or scenarios, available resources, past performance, and current performance, and suggests a course of action or strategy. It can be used to make decisions on any time horizon, from immediate to long term.

Prescriptive analytics relies on artificial intelligence techniques, such as machine learning—the ability of a computer program, without additional human input, to understand and advance from the data it acquires, adapting all the while. Machine learning makes it possible to process a tremendous amount of data available today. As new or additional data becomes available, computer programs adjust automatically to make use of it, in a process that is much faster and more comprehensive than human capabilities could manage.

Fig 4 – Source: https://www.freepik.com

If an airline wants to maximize its company’s profits. Prescriptive analytics can help do this by automatically adjusting ticket prices and availability based on numerous factors, including customer demand, weather, and gasoline prices. When the algorithm identifies that this year’s pre-Christmas ticket sales from Los Angeles to New York are lagging last year’s, for example, it can automatically lower prices, while making sure not to drop them too low in light of this year’s higher oil prices.

Conclusion

While different forms of analytics may provide varying amounts of value to a business, they all have their place. As increasing number of organizations realize that big data is a competitive advantage and they should ensure that they choose the right kind of data analytics solutions to increase ROI, reduce operational costs and enhance service quality.

References

  1. https://www.kdnuggets.com/2017/07/4-types-data-analytics.html
  2. https://www.dezyre.com/article/types-of-analytics-descriptive-predictive-prescriptive-analytics/209

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