Types of Data analytics
There are 4 types of analytics: • Descriptive analytics • Diagnostic analytics • Predictive analytics • Prescriptive analytics 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. 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 in-depth insights into a particular problem. For example, in the healthcare industry, analysis of customer segmentation coupled with several filters applied such as 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.
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. 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. 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? 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 in the “Rarely data-driven decision-making” category; diagnostic analytics tops the list in the “Somewhat data-driven” category, while it is closely followed by descriptive and prescriptive analytics; predictive analytics 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. In summary, 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.
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. 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. 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? 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 in the “Rarely data-driven decision-making” category; diagnostic analytics tops the list in the “Somewhat data-driven” category, while it is closely followed by descriptive and prescriptive analytics; predictive analytics 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. In summary, 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.