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Data management strategy – Top 5 changes in 2021

A data management strategy is key in your company’s ability to make informed decisions around your people, processes and future.

Many businesses struggle with the idea that time and resources should be dedicated to creating and implementing a data management strategy. However, organizational data is key in your company’s ability to make informed decisions around your people, processes and future business. Not dedicating planning time to your data management strategy opens your business up to risk around the process accuracy, user experience and resiliency in the face of disruption and change.

In this article, we review how data management strategy has evolved in recent years and what your business needs to consider- to ensure your data-driven decision-making tools can keep up with those of your competitors.

1. Increased reliance on reporting and analytics

Until recently, data was perceived as a by-product of organizational activities that held little value outside of recordkeeping. In today’s data-driven world, the rise of reporting and analytics as means to better understand business operations and workforces has propelled data to the top of the priority list to clean up, maintain and optimize. Without processes in place to guarantee the accuracy of company data, investing in advanced reporting and analytics technology amounts to wasted budget.

2. Proactive versus reactive strategy

When presented with the opportunity to create a data management strategy, business executives of successful companies may fail to see what problem the investment will solve. Today though, few successful businesses operate in terms of creating retroactive solutions to current problems and rather focus on creating proactive approaches to prevent problems in their future. If your data management strategy does not follow this same approach, you risk exposing a foundational piece of your business to risk.

3. Data governance is now global

HR and financial cloud technology now enables your global workforce to access organizational data from anywhere in the world. If your global leadership is unable to access accurate, holistic data due to mismanagement or poor design, you may not be realizing the full ROI of your global system and limit your ability to quickly pivot in times of disruption and change. Creating a data management strategy that accounts for what regional locations of your business will need access to what data mitigates the risk of compliance errors and geographical data silos.

4. Technology users want self-sufficiency

Gone are the days of walking down the hall to ask HR a question. As more organizations continue with a fully virtual or hybrid workforce, the need for self-sufficiency with workplace technology is increasing. If your data management strategy isn’t accounting for anomalies in self-service data and proactively identifying potential issues with the business processes your workforce uses the most, you may be hindering your employees’ ability to keep their people data up-to-date and accurate in your system.

5. The rise of AI and RPA

Artificial intelligence and robotic process automation serve to review, report and analyze organizational data so your team has the time to act on these findings. While seen as a future technology even just a few years ago, now 66% of finance leaders report expecting to spend significantly more time with RPA and other workflow automation technologies.* By eliminating tedious and time-consuming tasks with these technologies, teams are able to focus more on executing strategic goals instead of tactical ones. Without a data management strategy that looks at what data could be automated for better analytics and anomaly detection, deploying AI and RPA may not meet your business stakeholder’s expectations for process improvement and be perceived as a sunk cost.

*Gartner for Finance: Top Priorities for Finance Leaders in 2021

Alight's Data Quality Management services

The benefits of a robust data management strategy are clear — greater confidence in data-driven decisions, reduced risk of transactional errors and increased employee and business stakeholder satisfaction.

Alight’s global HCM Data Quality Management (DQM) services allow your team to take a proactive approach to your data management strategy through transactional support, ongoing tenant monitoring and strategic oversight of your Workday HCM data. Our tiered approach allows your team the flexibility to resolve the data issues Alight's proactive monitoring uncovers on your own, or the ability to utilize Alight's experts to fix so you can dedicate your time to more strategic business initiatives. 

Want to learn more about Alight's Data Quality Management services? Download our product sheet below for more information. 

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