Advancing Global Payroll: Unleashing the Power of Analytics and AI
Descriptive and diagnostic analytics have a role to play in payroll processing. The challenge that many organisations face is that they are unable to consolidate and standardise their data in a single location so that the analytics process can begin.
Too many different data flows from disparate systems, multiple service providers and local vendors is the issue here. You cannot analyse data that you cannot see, consolidate and store. If you first fix the data, then gather it all in 1 location, you will then be able to leverage AI-powered descriptive and diagnostic tools to produce a range of reports that can reveal what is happening and why across your multi-country payroll processes.
The well-known Gartner Analytics Ascendancy model is a useful way to categorize and understand a company’s analytics capability.
This model breaks reporting analytics into categories of capabilities. These go from basic reporting up to sophisticated AI based modelling. The initial categories are simpler to implement and provide some information, the later are more complex and expensive to implement but provide deep insights of more significant value.
The analytics phases are:
Descriptive analytics is about understanding what has happened. This is usually based on reports which give information about a process or system. For example, if a payroll is late or on time. Often these reports are used to populate dashboards which present the information in real time.
Diagnostic analytics adds the ability to say why these things are happening. For example, why a certain payroll is late. Both Descriptive and Diagnostic analytics are based on early data warehouse capabilities; simple relational databases (RDBMs), and online analytical processing (OLAP) techniques.
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A company can gain very significant value from implementing descriptive and diagnostic analytics, but usually lack the ability to do this on the payroll process. Their data is fragmented across many systems, and they don’t have a global data model. The Payslip Platform solves both these issues by brining all payroll data into a single repository using a global data model and provides a suite of descriptive and diagnostic reports out of the box.
The final two phases of the Gartner Analytics Ascendancy are predictive and prescriptive analytics. Predictive Analytics attempts to uncover “what will happen, why, and when?” and Prescriptive to say what “the company should do” if these things happen. These two phases generally require AI based technologies to implement.
AI can uncover and derive rules buried in very large sets of data and use these rules to predict a new outcome. This allows far more sophisticated scenarios to be modelled and understood then can be created by a business analyst using standard database tools.
The goal of predictive and prescriptive analytics is to help organisations achieve full clarity on their payroll data processes. This will empower them to make informed decisions around payroll process improvement, helping them deliver and manage multi country payroll in the most efficient way possible.
Author: Gus Legge , Chief Technology Officer, Payslip
This article originally appeared on payslip.com