With the advent of the remote / hybrid work era, many organizations are grappling with a decrease in operational visibility, creating demand for in-depth performance metrics. Metrics are dependent on three distinct types of data analytics: descriptive, predictive, and prescriptive. Understanding how to apply these analytics to raw data is key to optimizing workflows and resource allocation as well as maximizing employee and business efficiencies.
These three categories of analytics cannot exist as standalone categories or as substitutes for one another. Instead, they must all be given equal weightings as the fundamental steps in effective data analytics.
Imagine a doctor trying to treat your symptoms without making a diagnosis first. They wouldn’t know what they are treating and your symptoms would likely remain the same or potentially worsen. However, once a correct diagnosis has been made, the doctor is equipped to give you a well-defined course of antibiotics and set you on the path to recovery.
On a more complex level, descriptive analytics achieves the same goal – it helps you understand and identify the problem before formulating a solution. Using the right tools helps you identify past patterns in data which in turn pinpoints strengths, gaps, and performance issues.
Descriptive data can be snapshot or historical. It is performed by laying out predefined data markers and then collating the achievement of each one.
Are you wondering what kind of data you get from a descriptive analysis? It gives you comprehensive, accurate, and live data with a diverse range; everything from reports on KPI’s and ROI’s to competitor benchmarks.
An organization needs to do more than just evaluate the past – it needs to learn from it. Predictive analysis takes collated descriptive data, understands the core influencers of those results and applies the information to make educated predictions about the future.
What are some of the benefits of predictive analytics? For starters, predictive analysis can generate insights on an organization’s core strengths, value proposition and competitive advantages. Next, predictive analytics can lead to resource optimization by matching expected trends to historical performance. For instance, after observing past trends, a financial institution may expect a roaring economy post-pandemic. To conduct predictive analytics this institution could overlay past performance during a prior economic boom with insight gathered from the new remote work models. Finally, predictive analytics can help identify target audiences and optimize your approach by highlighting profitable clients, deals, and optimal pricing as well as decreasing churn rate and improving retention rate based on past statistics.
Prescriptive analytics is the final and most difficult analytical step to accomplish. This stage involves using data to recommend what should be done for pre-defined optimal future outcomes. In order to conduct effective prescriptive analytics, it is necessary to stress test predictive analytics for changes in the underlying variables, both at the macro level as well as at the organizational level. AI and machine learning are increasingly being employed to hone prescriptive analytics.
To summarize, a reliable data set and robust analytical protocols can be of significant benefit to an organization. A Single Source of Truth platform like TRIYO can collate and analyze granular keystroke level data for your organization. TRIYO’s secure platform integrates seamlessly into existing workplace toolkits like Office365, G Suite, and more. Our API-economy first approach means that 80% of users do not need to visit the TRIYO platform, therefore requiring minimal behaviour change.