Data Drives Digital World Class: Finance Must Drive Data Quality To Thrive as a World-Class Organization

For decades, The Hackett Group^® has analyzed the efficiency and effectiveness of finance functions, how their performance relates to world-class organizations, and the adoption of proven best practices. Data is a key driver of Digital World Class^® performance for finance organizations, and advanced analytics and data visualization tools are among the finance technologies with both the highest adoption levels and highest growth rates for 2022, according to The Hackett Group’s new Finance Key Issues research.

When used effectively, data& analytics can be an ongoing competitive advantage for finance organizations driving digital world class performance. Beyond just raw data, however, it’s crucial to consider both the quality and accuracy of that data.

While it is well-established that data drives digital, many business and finance organizations are not effectively leveraging their data as a source of sustainable competitive advantage. Finance places a premium on accuracy of information, which it absolutely should – whether it’s for closing the books, preparing financial disclosures, business decision support or tax filings, among other finance responsibilities.

In a digital economy and world, which is where we are headed, accuracy must also be done with speed and scale with agility, adapting to rapidly evolving business and operating conditions. Traditional ways of working simply allows the finance organization to adequately support and report for the business it serves, using legacy approaches to work. In “The Living Company,” Arie De Geus pointed out that “In the future, the ability to learn faster than competitors may be the only sustainable competitive advantage.” The digital implications here are that the finance function must be the source of accuracy, speed, scale, and agility to best support and influence the business as a world-class business partner.

The Hackett Group’s own 2022 Key Finance Issues Study shows significant investments are being made in improving analytical, modeling and reporting capabilities (86%); reducing operational cost through greater process automation (66%); implementing new finance technologies, including robotic process automation, cognitive computing, chatbots and blockchain (64%); expanding use of self-service tools, including business intelligence and online budgeting (52%); and improving and expanding master data management governance (48%). But, there is no explicit call out for data quality – most of which is transactionally based. So, while all these technologies are important parts of a successful digital transformation, they will not provide the greatest value unless they are benefiting from high-quality business data.

Improving data quality requires time and commitment, but you and your organization can begin a transformation in five steps and achieve digital world class performance:

1. Drive Improved Accuracy: Measure current data quality levels as a “first-time throughput yield” measure for data. Be sure to include how much time, energy and rework is done to make corrections so that your data is fully usable.
2. Benchmark: Set data quality goals as part of key performance indicators across the entire company, especially where data is being created, not just reported. Take action to ensure the data collected is complete, consistent, accurate, valid and timely.
3. Gather Support From Leadership: Underscore the importance of data quality with your company’s senior leadership, emphasizing the pitfalls of poor data quality and the negative effects its having on the organization (see Step 1), as well as a clear plan for how various parts of the organization should collaborate to prioritize quality data as a digital differentiator for success. They will need to help communicate the importance of improving data quality to the organization to show that it has senior leadership support.
4. Leverage Technology: Engage relevant technologies to identify quality issues at their source as pre-work to finding and fixing the root causes of poor data quality across the entire organization. Artificial intelligence and machine learning can be utilized to ensure an accurate, repeatable, and scalable process.
5. Monitor Data Quality Performance Over Time: Continue to monitor data quality levels over time. Report the positive effects improved data quality has had on the organization, and frequently look for additional areas to increase efficiencies and adopt best practices.

Time is a precious resource that we cannot create more of, therefore, time spent on reworking data-quality issues cannot be reclaimed and utilized by finance or the business to create additional value and thrive as a world-class organization.

More information on the value of data and analytics – and finance’s other top priorities for 2022 – is available in The Hackett Group’s 2022 Finance Key Issues research, which is available on a complimentary basis, with registration, at http://go.poweredbyhackett.com/22finkey2201sm.