Typical implications of insufficient control of time data may include:
Inability to properly track workload and measure utilization of employees and suppliers.
Incorrect charges to customers on time and materials (T&M) projects resulting in troubled relationships with suppliers and customers, as well as loss of revenue.
Delays in time records processing which delay the cost and revenue recognition in financial reporting.
Discrepancies between actual payroll cost and costs accrued via time tracking, resulting in demanding reconciliations in balance sheets and big write-offs, all of which undermine the reliability of accounting data and result in major audit findings.
Even though the time data are not usually the most complex structures they are still tracked in multiple data sources, which are very often not integrated. Typically, time data are part of enterprise resource planning (ERP), payroll, and time tracking systems, sometimes even separate project planning and scheduling systems.
T&M projects are the standard remuneration model in many industries and in some they are even the prevailing source of revenue. Together, controllers and accountants are tackling the potential low data quality in time records. Bad data quality not only impacts the invoicing to customers but also complicates the control over the progression of T&M projects. Typical causes of bad data quality can be:
Missing integration/controls over time tracking in various primary sources of time data i.e., separate project and other time tracking. The result can be double or even missing charges.
Unclear or undefined rules for charging which results in incorrect charges e.g., work week against the work fund or public holidays and weekends. As a result, there can, for example, be charges for work actually spent (hence company cost) which will not be accepted by customer (which equates to no company revenue and, in worst case scenarios, a net loss).
Time spent is not aligned to contractual commitments with clients. This can result in spending hours over the committed hours, which will not be accepted by the client.
To mitigate the negative impacts outlined above, the deployment of data governance software might be the right choice of action. The Accurity platform, from Simplity, provides the right tools for these scenarios by integrating the business model framework with actual time data quality control operational measurement and evaluation.
Using Accurity Business Glossary you can easily define all business terms that refer to your time management and create business rules you would then apply to your time data processing. And then connect this framework to the actual data model to understand the data lineage for your time data. To tackle such problems as outlined above, your rules could include:
Checking the over/undercharges per day or per month.
Checking the completeness of time charged by combining all data sources.
Controlling the time spent on holiday on weekends or comparing the hours spent to commitments.
For each rule you can set tolerance thresholds for warnings and showstoppers for applying such controls. Application of these rules can then be easily pictured in business intelligence visualization tools i.e., Power BI. Another advantage is that your rules are independent to the data sources and will be automatically deployed on any new systems you may use. Another notable feature of Accurity – the Browser Dictionary plugin (for Chrome / Edge)– allows you to easily align the BI tool reports with your data model in your business glossary.
Some controls e.g., for time data completeness, are more complex. For such a deployment, Accurity Quality is ideal – with this tool you can directly access your data sources and run the data controls on a monitored basis using scheduled runs. You can also define aggregate controls that will ensure that you have a comprehensive overview of the overall quality of your time data.
Many companies often underestimate the impact of low quality in their time data, varying from errors in reporting their profit and loss statements (P&Ls), to their revenue generation. This article shows that by identifying quality rules in an appropriate data governance tool, like Accurity, will efficiently help to mitigate them.
If you are facing similar challenges in your organization and would like to see how Accurity can help you tackle them, feel free to schedule a free personalized 1-on-1 demo to discuss your data challenges with us.