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The shift toward smart data management

The role of an asset manager, regardless of position, should be to analyze financial information and originate successful investment strategies. Today, however, many investment management professionals spend too much time managing data, and accordingly, not enough time on business-critical activities. With respect to data management, index and benchmark data require a lot of extra management work because of their composition. While data validation and remediation is a task typically delegated to data management, sometimes, senior professionals in other areas such as performance and risk get involved.

When investment management professionals manage data in-house, they receive it from the data supplier in separate feeds, which can lead to data being managed in silos. The same data sets may be manipulated differently for specific use, across teams and departments. At times, the feed itself may contain errors, which if factored into calculations and reporting, could potentially result in misguided decision-making. Some asset managers cope with the challenges associated with in-house data management even if it’s not exactly part of their job

Attitudes toward data governance are changing
While in North America data governance can at times still be defined by best practices, in Europe, it is beginning to be overseen on a regulatory level. The EU is taking an interest in the way that  data is handled and stored, and that trend is likely to catch on with U.S. regulators soon. Business Systems & Technology pointed out that during the financial crisis of 2008, institutions saw the direct link between operational risk and quality of analytical data. Today, many financial services companies are beginning to rethink their approach to data management. Primarily, these organizations are finding that data management involves a lot more than finding the relevant data provider. They must consider the functional uses of that information as well as how much time and effort executives will spend checking it for accuracy.

Business Systems & Technology advised financial services companies to first reflect upon the top-level issues. How does the data affect risk, performance, compliance and customer satisfaction? Secondly, organizations should evaluate the computational aspect. How does the data factor into financial models and critical investment calculations? It is likely that the same data sets are manipulated in alternate ways, based on the needs of end users. Also, one department may need a customized index for benchmarking and another group may require a similar index for forecasting investment scenarios.

Taking into consideration the reality on the ground, most financial institutions should realize that a managed data service is more scalable and beneficial than direct feeds from several data providers. Using a managed data service will significantly reduce the amount of time spent validating and remediating data, which is often required for different uses. The provider can deliver all the data as it is needed, presented in the format required, ready to feed a whole host of internal or third party solutions. This can alleviate data governance or compliance issues, while lowering the overall cost of data and raising efficiency at the same time.

Smart data management raises efficiency and lowers cost.

The most profitable companies spend less on finance and operations
CFO Magazine recently reported on a profitability assessment of 543 companies, based on the APQC’s Metric of Month: Total Cost of the Finance Function as a Percentage of Revenue. The APQC uncovered that of the organizations included in the study, the most profitable were those that spent approximately one third less on the finance function. Using the assessment as a foundation, the news source advised companies to focus on raising efficiency and lowering the cost of their finance functions. Similarly, many financial services companies could benefit from reducing their data management costs. The cost of data is second only to salaries. As such, standardizing business processes and eliminating duplicate work and time spent data scrubbing can reduce overheads by a considerable amount. The costs of outsourcing data management is in most cases cheaper than doing the work in-house – and that does not even factor in the hours of productivity that would be saved.

Ultimately, better data management is a good idea, because sooner or later, data governance in the U.S. will be a regulatory issue as well as an operational one. Raising efficiency, lowering overheads, meeting compliance standards and freeing up executives for more value-added activities are just side effects of wise data management.

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