Strategically Integrating HR, Operational, and Financial Data for a Competitive Edge
In the dynamic world of modern business, organizations must depend on data-driven insights to make well-informed choices and maintain a competitive edge. However, traditional business systems have struggled to consolidate the necessary data sets to the extent needed for gaining a competitive advantage, until now.
An increasing number of companies are adopting targeted metrics that merge pertinent data sets, such as those originating from operations, finance, and HR. This focused approach helps address data silos and promotes informed decision-making. We will explore the process of transforming data into actionable, context-driven alerts that contribute to long-term value generation while maintaining regulatory compliance.
The Challenge
Progressing towards this objective is challenging due to three primary reasons. Firstly, it entails fostering a cultural shift, where a more comprehensive set of relevant cross-domain metrics will be managed to steer the business.
Secondly, someone must determine the depth of change, understand & spearhead the transformation, and identify the metrics that need measuring and how the resulting data will be utilized for future decision-making. This process also involves garnering support from senior management, especially since data can traverse functional areas where office politics may present resistance to change.
Lastly, it is crucial to acknowledge that accomplishing end goals necessitates the establishment of auditable and repeatable end-to-end processes that extract data from multiple sources and transforms them into required outcomes. Fundamentally, this approach removes transactional obstacles that are currently the main source of inefficiencies in business operations.
As such, whether implementing extensive change across various functional areas or focusing on attaining “quick wins,” the primary goal is to reduce transactional friction, progressively improve business efficiency and foster value creation over time.
With the above in mind some additional inputs:
1. Data Quality with Traceability
Data quality is an essential component of the journey from data to decisions. Unreliable, incomplete, or inconsistent data can result in less-than-ideal decisions, negatively affecting an organization’s performance. Managing data flows has historically been a daunting task, but as data management takes centre stage and data literacy grows from a modest starting point, enhancing data quality will remain challenging, yet at the same time be more feasible than ever before.
Commentary:
Recent regulatory developments are placing greater emphasis on more stringent data management, prompting a more expansive way of thinking. These changes include regulations and best practices related to cybersecurity, privacy, and cross-border data transfers.
Cutting-edge process technologies enable the creation of various intra- and inter-departmental workflows, whether qualitative or quantitative, that can be executed at an ultra-fine level with robust data governance. Additionally, data can be enriched with accompanying tags or analysis code information during processing to assist with subsequent transaction traceability.
Examples of traceability include identifying the specific corporate entity that provided particular transactional data while concentrating on consolidated reporting levels, or enriching multi-tiered allocation postings with details on how individual accounting entries have been calculated.
2. Targeted X-Application Reporting @anywhere @anytime within a Process
Conventional reporting methods and tools might struggle to adequately manage the increasing volume, granularity, and complexity (including various data types) associated with the transformation of data originating from Operations, HR, Finance, and other areas.
Commentary:
While numerous analytical tools are available, reporting and visualizations are often situated at the end of a process cycle. Contemporary process technologies enable reporting at any point and location within a process. This becomes especially potent when considering that today’s processes can be defined end-to-end on an ultra-granular basis, allowing the system to present only pertinent data for analytical purposes.
Examples: Processes can be employed to conduct powerful simulations both retrospectively (using last year’s data) and prospectively (for budgets and forecasts) to ensure they operate as intended. Your business units in various countries can utilize the same process design, offering additional productivity insights into changes that can be targeted elsewhere in the organization to boost efficiency.
3. Contextual and Actionable Insights X-Application
In the data-to-decisions journey, systems should work for you, not the other way around. Converting raw data into contextual and actionable insights helps prevent data overload to emphasize the most pertinent information to the right person without the need to sift through extensive reporting output.
Commentary:
The ability to define end-to-end processes that operate at an ultra-granular level is just one aspect of this approach, but there’s more to it. By nature, these processes are tailored for specific purposes, resulting in the absence of excessive unused code. Consequently, these highly maintainable processes execute more rapidly at any given moment due to their compact size and, in some solutions, the utilization of state-of-the-art compression technologies.
Examples: In the financial consolidation process, users can 1) identify and rank the top 10 variances etc for aggregated or individual entity data sets (think here metrics x-applications), and 2) generate or modify workflows or contextual actionable reports accompanied by supporting information, with the level of simplicity or complexity determined by the user. This approach ensures that reporting is scalable as your business grows. Additionally, within operations, swift handling of positive or negative inventory considerations is possible through actionable contextual workflows and alerts directed to the right product managers.
4. Eliminate Monthly Reporting Limitations and Extend Horizons
Data silos arise when information is stored separately and not freely shared, hindering easy access and utilization by others. Retaining silos, as mentioned, is inefficient and can significantly affect the bottom line.
Commentary
However, navigating digital enablement can quickly lead to a lack of clarity regarding approach and overall scope. Focus is crucial, but as previously mentioned, not everyone possesses domain expertise in every area. Thus, priorities are essential, but where should you start to look at a macro level?
Considering how systems have evolved from standalone applications to the current combination of Apps, Process Technologies, and Applications, the key for many will be harnessing data from multiple applications within a single process and transforming it into actionable contextual workflows and reports. At present, a substantial amount of human interaction is required to address process friction at every stage associated with operational decisions, management activities, process controls, and decision support.
Examining spreadsheet usage can be a useful start and can help identify inefficient resource areas for improvement. They can be eliminated entirely or retained in a manner that maintains their operational flexibility but with tight backend control. Some may argue that this approach might not be ambitious enough, but it’s essential to start somewhere.
Example: Combined financial and HR data assists with resource planning and forecasting. Unplanned staff turnover, hiring delays, and training periods for new employees typically have a direct negative impact on revenue generation and, consequently, the bottom line. In many cases, clear visibility into these challenges lags behind operational reality and is only recognized when the numbers are affected, beginning with one missed month and cascading as the impact multiplies until the situation is resolved—especially since new hires take time.
Additionally, it’s vital to ensure that existing staff are well taken care of at all times to prevent further churn. Scenarios that may prompt employees to resign often involve those who produce excellent results but are unable to take time off due to other operational constraints, such as limited resources in a particular area of expertise.
5. Promote Data-Driven Mindsets for Optimal Performance Management
To fully capitalize on their corporate data, organizations must cultivate a data-driven culture and explore their options through collaborative processes. This involves encouraging employees to incorporate data metrics into their decision-making and leveraging it as a valuable resource for driving success. It is also crucial to execute all processes in compliance with relevant rules, particularly when using detailed HR data sets ie not aggregated.
Commentary
A more focused comprehensive approach to reporting reduces i) data duplication and ii) inefficiencies associated with duplicated reporting processes, allowing more time to be dedicated to strategic decision-making.
It is important to note that data silos exist both within and across departments, and addressing them requires varying levels of change management. Regardless, before initiating any project, ensure that i) system integrations have been thoroughly considered, specifically on who will handle the technical complexity, and ii) project teams do include representation from all impacted departmental/functional areas, all combined with access to senior management support to overcome any execution barriers.
Examples: Integrating HR metrics is particularly relevant in this context. Overall employee engagement becomes more transparent. This encompasses employee satisfaction, retention, and productivity metrics, as well as the ROI surrounding human capital investments.
Combined Financials and HR data can identify skill gaps and areas for professional development, and empowers you to invest in targeted training programs to enhance employee capabilities and organizational success.
At a more holistic level the combination of financial and HR data helps identify areas where workforce adjustments, automations or reallocations can improve productivity, reduce costs (and redundancies), or drive revenue growth. Equally it enables you to ensure that competitive compensation and benefits packages are actually in place, which can help attract and retain top talent while maintaining fiscal responsibility.
Lastly, it helps to guide recruitment initiatives to target candidates with the skills and experience needed to drive value creation in the organization.
This journey not only empowers organizations to make well-informed decisions but also fosters innovation and growth in a competitive business landscape. However, as mentioned earlier, there are considerations to address regarding how an organization propels identified change forward.
6. Safeguard Privacy
State-of-the-art technologies can facilitate the integration of operational, financial and HR data by enabling transparent, repeatable, and auditable transformations. However, a vital aspect of broader data utilization is ensuring privacy and cybersecurity are established from the outset.
Commentary
HR continues to advance boundaries with various technologies providing enhanced data management controls. This, coupled with regulatory changes that impose significant penalties and, in some cases, criminal liability, have extended focus beyond Apps, Process Technologies, and Applications, to critical aspects of data handling. For example, what data will be stored, why, and for how long, as well as considering data security during storage and transit. Consequently, this provides the operational framework for not only driving deeper value creation, but ensuring that it is executed under a tighter regulatory framework with reduced risk.
Examples: Corporates can request independent auditor verifications from vendors to ensure that data centres and applications meet documented standards. An emerging theme involves annual or more frequent data management audits, whether voluntary or mandatory. These audits will become an essential safety net for corporates in maintaining desired operational standards if not already implemented.
7. Compliance with Local Data Management Regulations
As organizations collect, store, and analyse Operational, HR and financial data, they must adhere to relevant data management rules and regulations established by the countries in which they operate.
Commentary
Cross-border data transfers were mentioned earlier. Many organizations use the GDPR as their baseline for achieving a minimum global level of proficiency. As necessary, this standard is further adjusted to accommodate more stringent rules, such as those in China, thereby ensuring regulatory compliance.
It is important to note that regulations can vary in both scope and depth. For instance, the definition of personal information in China includes the concept of “sensitive personal information,” which means that corporates must be cautious not to transfer this data type without obtaining an employee’s consent.
By integrating compliance considerations into your data-to-decisions journey, you can ensure that your organization’s data practices are not only effective but also legally compliant. Advanced technologies enable granular control over the location of data storage and computation at every stage of a process.
Example: Some scenarios involving personal information demand extra caution. For example, when using APIs to process information (such as employment information, AI, banking, etc.), care must be taken to ensure data storage and computation comply with relevant regulations, like the geo-location of the data centre / cross border data transfer rules etc. This also applies when using multiple payroll and HR systems worldwide, especially when personal information is transferred to the global system of choice for generating global metrics.
8. Achieving Sustainable Value Generation
Companies must reach a point where they can leverage their current situation to their advantage. Many organizations already create reports featuring some elements of operations, finance and HR data, but often the information is not timely and focuses more on past events rather than projecting numbers forward. Typically, the scope of these reports is limited; however, the goal is to use technology for more comprehensive reporting, incorporating both qualitative and quantitative aspects from operations, financial, and HR departments to drive deeper value and reduced risk.
Commentary
This cultural shift involves fostering a data-driven culture, where high-quality data is combined with timely reporting to propel the business forwards. Implementing structured change management techniques is crucial for successful project execution. Defining the operational scope and assessing potential impacts on other departments or functional areas is particularly relevant, as complexity plus internal political considerations increase when change management initiatives span across different areas of the organization.
Conclusion:
In the end, transforming Operational, HR and Financial data into valuable insights is not just about achieving a specific goal. It’s about embracing the journey, cultivating a data-driven culture, and harnessing the power of data to make well-informed decisions that propel success in a competitive business environment. By investing in the appropriate tools, processes, and personnel, organizations can unleash the full potential of their data and attain a sustainable competitive edge while maintaining operational compliance. Data-informed decisions are a powerful contribution to an organization’s long-term value generation and strategic expansion.
FlexSystem Limited 