Financial Data: Disparate Systems and Disjointed Data Sources

By Eric Noren  |  Posted 05-06-2011 Print Email

The real difficulty, however, arises when companies attempt to coordinate external and internal reporting. Disparate systems and disjointed data sources -- along with the lack of a consistent view from senior management as to what really drives enterprise growth and profitability -- lead to redundant and costly efforts to reconcile internal and external reporting. There are a number of reasons why these problems continue to plague companies, despite extensive investment in enterprise reporting and business intelligence capabilities. These include:

  1. Companies using the wrong metrics. Research indicates that 70 percent of companies use metrics that lack statistical validity, and only 23 percent of companies with balanced scorecards can prove a link between the scorecards and growth in shareholder value.[1] The lack of precision in metrics not only creates confusion but makes it hard to execute business strategy.
  2. Companies having too much data to manage. An estimated $40 billion per year is spent on data warehousing applications, with more than 60 percent of that amount spent on cleansing data.[2] Despite this huge investment, data users find themselves overwhelmed by the amount of data they need to manage. More than 40 percent of employees in a recent survey said they believe that the high volume of data delays important decisions and affects the organization's overall ability to make decisions.[3]
  3. Companies finding that results are hard to quantify. No more than 12 percent of companies can tie quality measures to positive stock returns.[4]
  4. Companies ignoring and under-managing important assets. Research indicates that a large part of a company's valuation is attributable to intangibles. A study among 300 buy-side investors -- including institutional investors, portfolio managers and research staff -- showed that some 50 percent of the allocation decisions made were based on non-financial performance. [5] Companies still, however, fail to act on the need to properly analyze and manage their financial business information.

Of course, enumerating problems is one thing, but developing effective solutions for such problems is quite another. The argument can be made that improving data quality is not an IT process at all; rather, it is an integrated enterprise process led by finance. In any case, the finance function defines the data standards and governance structures to ensure that the right data is in the right accounts and dimensions of the business. From this starting point, corporations can undertake a number of actions to arrive at a single, integrated view of essential data.

  1. The format of the data architecture needs to be designed so that it is easily understood by business stakeholder and decision makers. Many companies have complex data architecture described in technical terms that is not easily understood by the business. The key here is to focus on the right data structure to deliver the right information to business stakeholders, helping them make the right decisions on business performance. The data architecture should be aligned to the company's model for accountability but should retain the flexibility to efficiently address changes in the company's business or go-to-market strategies.
  2. Consistent and complete preventive controls can help ensure the right data is captured in the right account at the time of recording the transaction in the systems of record. This avoids heavy reliance on defective controls and reduces non-value added time spent reconciling or reclassifying data between accounts or dimensions.
  3. Companies with multiple systems of records that spend large amounts of time on data aggregation and reconciliation should consider an enterprise data warehouse, a business intelligence approach designed to establish a common source of information.
  4. Standardization of the accounting and finance process to enable a consistent approach to recording transactions and allocations to efficiently analyze and consolidate data across the enterprise.

In our experience, organizations that integrate an enterprise data warehouse with an effective approach to business intelligence and standardized data integration architecture improve their ability to manage and analyze data. A single data warehouse can serve the needs of internal and external stakeholders without compromising data quality or accuracy of reporting. Aligning such an architecture to both external financial reporting requirements and to internal business management requirements can allow companies to obtain two views of the truth -- the external and the internal perspectives -- from a single data source.  This, in turn, can provide significant benefits in terms of reducing costs, improving operating efficiency and enhancing the quality of data for analytics to support key business decisions.

About the Author

Eric Noren is an executive in the Finance & Performance Management service line at Accenture, a global management consulting, technology services and outsourcing company.

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