For upon |Back in the 17th century, John Dryden wrote, “He who would search for pearls must dive below.” Despite the author did not have advanced data analytics in mind, the quote perfectly describes its essence. Let’s find out how deep one should go into data in search of a much-needed and fact-based insight.
Types of data analytics
There are 4 types of analytics. Here, we start with the simplest one and go down to more sophisticated. As it happens, the more complex an analysis is, the more value it brings.
Descriptive analytics
Descriptive analytics answers the question of what happened. For instance, a healthcare provider will learn how many patients were hospitalized last month; a retailer – the average weekly sales volume; a manufacturer – a rate of the products returned for a past month, etc. Let us also bring an example from our practice: a manufacturer was able to decide on focus product categories based on the analysis of revenue, monthly revenue per product group, income by product group, total quality of metal parts produced per month.
Descriptive analytics juggles raw data from multiple data sources to give valuable insights into the past. However, these findings simply signal that something is wrong or right, without explaining why. For this reason, highly data-driven companies do not content themselves with descriptive analytics only, and prefer combining it with other types of data analytics.
Diagnostic analytics
At this stage, historical data can be measured against other data to answer the question of why something happened. Thanks to diagnostic analytics, there is a possibility to drill down, to find out dependencies and to identify patterns. Companies go for diagnostic analytics, as it gives a deep insight into a particular problem. At the same time, a company should have detailed information at their disposal, otherwise data collection may turn out to be individual for every issue and time-consuming.
Let’s take another look at the examples from different industries: a healthcare provider compares patients’ response to a promotional campaign in different regions; a retailer drills the sales down to subcategories. Another flashback to our BI projects: in the healthcare industry, customer segmentation coupled with several filters applied (like diagnoses and prescribed medications) allowed measuring the risk of hospitalization.
Predictive analytics
Predictive analytics tells what is likely to happen. It uses the findings of descriptive and diagnostic analytics to detect tendencies, clusters and exceptions, and to predict future trends, which makes it a valuable tool for forecasting. Despite numerous advantages that predictive analytics brings, it is essential to understand that forecasting is just an estimate, the accuracy of which highly depends on data quality and stability of the situation, so it requires a careful treatment and continuous optimization.
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Thanks to predictive analytics and the proactive approach it enables, a telecom company, for instance, can identify the subscribers who are most likely to reduce their spend, and trigger targeted marketing activities to remediate; a management team can weigh the risks of investing in their company’s expansion based on cash flow analysis and forecasting. One of our case studies describes how advanced data analytics allowed a leading FMCG company to predict what they could expect after changing brand positioning.
Prescriptive analytics
The purpose of prescriptive analytics is to literally prescribe what action to take to eliminate a future problem or take full advantage of a promising trend. An example of prescriptive analytics from our project portfolio: a multinational company was able to identify opportunities for repeat purchases based on customer analytics and sales history.
This state-of-the-art type of data analytics requires not only historical data, but also external information due to the nature of statistical algorithms. Besides, prescriptive analytics uses sophisticated tools and technologies, like machine learning, business rules and algorithms, which makes it sophisticated to implement and manage. That is why, before deciding to adopt prescriptive analytics, a company should compare required efforts vs. an expected added value.
What types of data analytics do companies choose?
To identify if there is a prevailing type of data analytics, let’s turn to recent surveys on the topic.
For the Global Data and Analytics Survey: Big Decisions, PwC asked more than 2,000 executives to choose a category that describes their company’s decision-making process best. Further, C-suite was questioned with what type of analytics they rely on most. The results were the following: descriptive analytics dominates (58%) in the “Rarely data-driven decision-making” category; diagnostic analytics tops the list (34%) in the “Somewhat data-driven” category, while it is closely followed by descriptive (29%) and prescriptive (28%) analytics; predictive analytics (36%) leads in the “Highly data-driven” category.
This survey proves that at different stages of a company’s development, there appears a need for one or the other type of analytics. In fact, the companies that strive for informed decision-making, find descriptive analytics insufficient, and add up diagnostics analytics or even go as far as predictive one.
The same survey reveals another trend. Executives want decision-making to be faster and more sophisticated. This means that more businesses will strive to gradually enlarge the share of predictive analytics. Another survey of business intelligence trends for 2017 carried by BARC proves this hypothesis: 2,800 executives confirmed the growing importance of predictive analytics and data mining.
To sum up
With various types of analytics, companies are free to choose how deep they need to dive in data analysis to satisfy their business needs best. While descriptive and diagnostic analytics offers a reactive approach, predictive and prescriptive analytics makes users proactive. Meanwhile, current trends show that more and more companies come to the situation when they need advanced data analysis, and choose to adopt it.
The article was originally published here.
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