Data management – Understanding the issues Finance faces and how to address the root causes

How can CFOs build a robust finance data architecture to meet the growing demands placed on them? Finance sits at the junction where upstream data is transformed into downstream financial information. This positioning allows Finance to add real business value, but also brings unique challenges.

Challenges occur all along the Finance data chain. Source system deficiencies and data quality issues; proving traceability back to source transactions; converting business events into accounting entries that satisfy standards and regulations, often for multiple jurisdictions simultaneously; complexities in distilling actionable insight from data; managing change and new data demands for reporting and analysis.

The rapid growth in data capabilities, AI applications and other recent technological advances provide CFOs with new tools to solve data issues. Ultimately, a more robust data architecture will help Finance to meet the numerous and ever-changing business information needs.

Continuing our Finance Technology & Data blog series, we explore the typical data issues Finance faces and how they hamper efforts to meet business demands. We then assess their root causes and review the fundamental building blocks needed to address them.

What are the typical problems seen by Finance? Finance receives data from upstream transactional systems, and from other functions (such as Risk, Actuarial and Operations). Finance is then tasked with transforming this into meaningful financial information to drive strategic and tactical management decisions and meet a wide range of external reporting requirements. These upstream data owners do not always appreciate Finance’s need for complete, timely, accurate and controlled data, and Finance often spends time and effort on cleansing to make it fit-for-purpose.

Regular challenges shared by client CFOs include:

-?“Technical debt / system issues in upstream systems are not a business priority to fix, so Finance has to post a recurring manual fix in the ledger.”

-?“Data groupings in a source system do not match those required for financial reporting, hence Finance has to maintain a cumbersome mapping table.”

-?“An overnight feed is consistently late, setting back Finance’s daily process by hours.”

-?“A late source system entry is not captured before the feed cutoff, causing a sub-ledger break which is time-consuming for Finance to reconcile and adjust.”

-?“Finance must maintain numerous EUCs to cleanse poor source data, increasing effort required to maintain control and demonstrate lineage, and substantially increasing operational risk.”

-?“The business reorganizes, so Finance has to change all the reporting hierarchies and data histories to reflect both the new structure and comparatives to the old.”

Data is at the root of all these challenges, poor data drives challenges around control, efficiency, and cost. Finance often has little influence over upstream data creation and must work around quality issues, which increases the cost and complexity of running the Finance function, both for BAU and for change.

There is an important people dimension too - Finance faces key resource dependency as often there are only a small number of staff who understand the complexity and can operate the manual processes needed to remediate the data. CFOs must incur additional cost to retain key people who know the history of the organization and architecture and can cover deficiencies in legacy systems.

The extensive manual effort needed also has morale and staff attrition impacts. Finance staff are increasingly unwilling to suffer inferior systems and processes at work when they can use smarter technologies on their own cell phones!

What Finance really needs: To deliver quality outputs efficiently, Finance needs complete, accurate and timely data inputs, supplied on flexible architectures that can meet new data demands simply and manage and orchestrate the data journey from source to report. Data feeds must be reliable, with clear traceability to “golden sources”. Data handling processes and tools must be flexible to adapt to change whilst preserving relevant historical data.

Consistent data quality requires data elements to be well defined in line with Finance’s needs, with data owners clearly responsible for meeting pre-agreed standards for completeness, accuracy, and timeliness. Consistent, reliable golden sources should be identified for each data element and Finance’s access to these sources defined and agreed with owners. Finance requires toolsets to quickly identify, notify and monitor data quality issues, and require a clear route to communicate data problems back to source system owners. When fixes are needed, these should be made in accordance with agreed service level agreements.

Flexible data architectures are needed to ensure resilient and properly governed data sources. Required data feeds supporting full coverage of Finance’s data needs should be catalogued, each with identified feed owners who acknowledge and support Finance’s usage. Formal SLAs should provide contingency planning for feed failures and a clear escalation path for breaches.

The ability to respond to change includes “Horizon Scanning”, where potential new internal data demands and external regulatory demands are proactively identified, with the data supply chain impacts anticipated. Finance should be consulted on any impacts to their upstream data supply, such as amendments or additions to relevant data definitions, or proposed architectural changes, to ensure its needs continue to be met.

How can CFOs build a more robust, flexible data operating model? A comprehensive approach to data management goes beyond implementing a robust technical architecture. It also encompasses the people, processes and governance required to deliver the outcomes Finance needs. Finance needs to reset and take a fundamental data-driven approach to improve data completeness, quality, and simplification of data sourcing.

Indeed, CFOs should aim to instill a clear data culture, rooted in a deep awareness of the importance of data quality across the Finance function. People should be incentivised to promote and maintain robust data standards, with clear governance in place to ensure smooth touchpoints with other parts of the organization. CFOs need everyone involved in the data journey, including the business data owners, to support data quality, so Finance does not have to continually “sweep up the mess” through reconciliation, downstream manipulation, and adjustment.

CFOs should set a clear target state architecture and capabilities, including an ability to flex rapidly to accommodate new demands and adopt future tooling. New technology is developing at pace and Finance needs the ability to take advantage of these capabilities as they become available.

In summary, building a robust finance data architecture requires a blended approach encompassing tooling, governance, and culture. Addressing the root cause of data management issues and ensuring data quality is front and centre of everyone’s mind will give the biggest transformative benefit to the Finance function. To continue to add real business value and meet the growing demands placed on Finance it is not something CFOs can afford to get wrong.

?

The views reflected in this article are the views of the author and do not necessarily reflect the views of the global EY or its member firms.