Big Data Drives the Need for Global Payroll Automation

The term “big data,” virtually unheard of 15 years ago, is now ubiquitous. In fact, in typical tech fashion, big data is now considered by some as passé – superseded in significance (among certain IT types, at least) by newer concepts such as smart data, long data, deep data, and even ‘the paradox of information.’

Few areas of the enterprise, however, are as technologically savvy as the IT department. Big data initiatives remain nascent in many important parts of even the most sophisticated global companies, despite the profound effect they can have on the way businesses are led, products are sold, and people are managed.

Why have certain business areas been left behind in the ‘big data revolution’? Because big data starts with high-quality data, which must stem from standardized processes. Big data initiatives demand holistic enterprise management to ensure that:

  • inter-dependencies between functions are considered;
  • incoherence and inconsistencies in organizational structures are addressed;
  • processes are standardized, centralized, and automated; and
  • there are no unnecessary data siloes or ‘hiding places’ of important information.

For too long, payroll has been a business area where the above objectives have rarely been prioritized. In many multinational organizations, payroll is the ignored “child” of its estranged parents, HR and Finance – belonging to neither, and often left out of department-specific improvement initiatives. 

Traditionally, payroll has been managed through as many different and inconsistent approaches as there are countries across the globe. But with the emergence of better technological systems to serve the global payroll space, payroll is growing up – finally earning the automation and centralization required for payroll to seize its big data opportunity (and earn greater strategic importance to the enterprise, as a result).

What is Big Data?

Leading IT analyst Gartner describes big data as “high-volume, high-velocity, and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.”

In simpler terms, big data is about pulling together vast amounts of data generated from many different sources, ideally in real time, in order to cross-reference and analyze the data for business intelligence purposes. With massive amounts of information comes the ability for trends and correlations and trends to be identified at a greater scale than enterprises could ever assess before the rise of more sophisticated technologies.

Big data efforts aren’t just about reporting or making the more common applications of business intelligence for company-wide analysis. Rather, it’s a more forensic use of data designed to derive critical insights that can provide competitive advantage, predict opportunities and challenges, and highlight persistent problems or issues.

Big data is a natural fit for big business given the large datasets, especially for application in business informatics, across the corporate landscape. Today, big data initiatives have become integral to the process improvement and business transformation efforts of multinational companies large and small – though some struggle with big data more than others, due to their reliance on less-than-optimal technology tools and services.

To capitalize on the value of big data, enterprises must utilize modern, automated systems to full extent of their data-collection and analysis capabilities. Given the broad scope of big data programs arise, the key challenges of deploying big data initiatives often relate to the scalability – or lack thereof – of the software solutions involved. By their nature, big data projects bridge functions, departments, and accountabilities… which means they also often highlight deficiencies in the operations of functions that fail to contribute the aggregated data sets effectively.

Big Data & Payroll

Given that payroll is often a source of data deficiencies, big data programs are increasingly placing global payroll operations under a spotlight. Though it tends to receive less attention from the C-suite than departments like Marketing, Sales, and IT, payroll represents a very significant proportion of most multinational companies’ total expenses. The data that can be collected and aggregated from payroll also contains elements vital to the predictive analytics and forensic analysis of workforce demography and geographic dispersal.

Yet despite its importance, global payroll data too often goes uncollected or inconsistently aggregated at the enterprise level. Why? Because payroll is all typically managed at a smaller level – region by region, country by country, legislature by legislature. Seldom is payroll managed globally or centralized, enabling only disparate, inadequate data to be collected at the enterprise level. 

Payroll is also either frequently outsourced to local payroll providers (in various geographies) or to companies that manage local payroll providers under a corporate umbrella – even as almost all other aspects of HR and Financial management have been automated at a global level. Large software companies such as IBM, Oracle, and SAP – as well as a wealth of cloud-based software providers such as Workday, NetSuite, Concur, and Xactly – have been able to define best-practice at a global level across all aspects of HCM and ERP.

Yet, none have demonstrated the same appetite to transform data in global payroll. One major reason for this is the complexity of payroll, the calculation algorithms that vary from legislature to legislature (and there are many more legislatures than countries), and the continual state of flux as taxation methods and rates are adjusted for fiscal advantage by treasury departments of governments worldwide.

For payroll, a one-size approach cannot fit all – which has long made providing global payroll services, and standardized processes, a challenge. Until recently, no company had attempted to deliver global payroll services through a single application. Now that CloudPay has, it is possible for payroll data to be leveraged for stronger analysis, change management, and big data initiatives.

Ultimately, in order for big data (and the analytics it requires) to succeed in payroll, two things are necessary: 

  1. A technology platform across which a truly centralized service can be provided and standardized data can be cultivated and tracked; and
  2. An enterprise-wide, top-down recognition of the strategic importance of payroll to company-wide performance.

If you’re ready for payroll automation to help your organization “think bigger” with data, then consider how a unified, integrated global payroll solution can help your business achieve long-term benefits.