Data analytics is transforming decision-making across the business landscape. By 2025, the market is projected to expand to approximately $34–$36 billion and surpass $100 billion by 2033.
The sheer volume of data can be mind-boggling in global payroll processing, but it is crucial to analyze and leverage it. After all, analyzing payroll data can help organizations understand costs, optimize existing processes, identify trends, and make data-driven decisions.
Organizations can identify inefficiencies, detect fraud, and ensure compliance with labor laws and regulations by analyzing payroll data. It enables organizations to understand their workforce better and improve their financial performance.
But what are the different types of payroll data?
Types of Global Payroll Data
Quantitative Data: In payroll, this refers to data that can be quantified and measured, such as an employee's demographic data, work hours, leaves, compensation and benefits, payroll taxes, and data variance from one pay cycle to another.
Quantitative data help organizations understand aspects of their performance, workforce planning, budgeting, etc.
Qualitative Data: In payroll, this refers to any descriptive data that cannot be counted or measured, such as employee surveys, employee queries, or employee feedback.
Qualitative data provides insights and valuable input into how the organization is performing and employees' perspectives on their satisfaction with the organization, etc.
With the exponential growth of data in today's organizations, traditional data processing methods have become inadequate.
Big data analytics enables the processing of extensive amounts of structured and unstructured data, yielding valuable insights that guide decision-making across domains, including payroll.
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Data Analysis and Utilization
Data analysis is a complex process that includes data identification, collection, raw data cleaning, and analysis and visualization to identify patterns and trends that drive clear, concise decision-making.
- Decision Making: This is the first step for data analytics. The organization must identify the area or decision required to be made. Subsequently, the following stages of data identification will be initiated.
- Data identification: It is essential to identify the data that correlate with the objective and end goal, which can be used in subsequent processes. This is like identifying the various inputs, such as time, absence, payroll taxation, compliance, compensation, and benefits, required to process payroll to understand the market trends.
- Data collection: Once the required data is identified, the next step is to collect it. The data can be either available within the organization or obtained from outside the organization. In the data collection step, identifying the data source is critical and will be further used in the process.
Neeyamo's eHub is a cloud-based platform that optimizes data storage and streamlines internal workflows for organizations. Neeyamo Compliance stores data per country's compliance requirements from external sources, which can be used for analysis.
- Data cleaning: Raw data often includes unnecessary and inconclusive information, such as irrelevant data, duplicates, inconsistent formatting, and disorganized arrangement. To make the data more usable, it is essential to clean and organize it. This step is a crucial part of the data analysis process and typically takes up most of the analyst's time.
- Data modeling: creating conceptual representations of data objects, known as data models. These models can store both new and updated data, enabling the dataset's continuous expansion and improving model accuracy.
- Data analysis: The examination of collected and refined data to identify patterns and fluctuations to improve performance. There are four types of data analysis: diagnostic, descriptive, predictive, and prescriptive. The analysis method best suited to a company will depend on where it is in its development process.
- Data visualization: About 90% of the information processed by the brain is visual, which is why presenting data through graphs and charts can significantly improve understanding and make it easier for the viewer to comprehend.
Neeyamo offers Global Reports that deliver contextual insights, helping you minimize bottlenecks and make intelligent decisions with actionable, real-time insights.
Also Read | Common payroll challenges that businesses must overcome
Data analytics provides answers, but data utilization enables the creation of strategies and drives action.
To become data-driven, companies must use the analyzed data effectively to enhance operational efficiency and boost productivity.
Gathering and verifying data is a crucial aspect of payroll processing and requires an effective system with integrated tools to simplify these tasks.
Automated solutions that collect time and absence data can store and manage it, allowing companies to streamline workflows and improve payroll processing.
Neeyamo offers tools like Time, Absence, eHub, and Global Reports as part of its Global Payroll Tech Stack. To gain more insights, write to us at irene.jones@neeyamo.com
