Using ZEMA to Validate Data Quality
Data is often the lifeline of a corporation. It comprises the most integral component of any entity due to its ability to directly influence crucial business decisions. Therefore, it is extremely important to consider the quality of the data being utilized to establish critical processes. Having the ability to distinguish between the successes and failures of data events, as well as pertinent errors within data, makes it a lot more convenient to determine exactly which variables should be taken into account.
Companies often face the burden of dealing with incomplete asset databases, outdated documents, unsynchronized information, and poor data quality, due to a lack of systematic auditing practices. This, in turn, heavily increases the risk of operational errors, and the possibility of receiving fines from regulators due to miscalculations. Commodity, energy, and finance companies collect a diverse range of data in order to obtain the best collective snapshot of their markets. Complete, accurate, and timely data can greatly assist with investment and risk management incentives.
Data can be deemed inaccurate if it contains missing values in a column or table, or if it arrives later than required. Two other quality standard factors to consider are whether or not the data values fall within standard boundaries, and also if the data is up to date based on previously recorded entries.
According to FIMA’s 2014 Data & Corporate Actions Trend Report, data quality is the most important factor that the managers of financial institutions take into account when considering data vendors and suppliers. 1 Approximately 55% of the 200 reference data managers surveyed indicated that accuracy of content is the most important aspect they consider when choosing sources and suppliers. Completeness of coverage and timeliness of the supply came in as the second and third most important factors.
Unfortunately, these very crucial requirements are not always straightforward to accomplish. The 2013 Data and Analytics Survey by State Street revealed that the accuracy of data (37%) and timeliness of data (34%) are the two most significant data and analytics challenges faced by financial services executives in the investment industry. 2
ZEMA Ensures Superior Data Quality
ZEMA enables users to check the following key aspects of their data:
Users may also configure a set of rules which can then be applied to preferred data reports, either in real time or on a certain schedule. Filtering options make it easy to locate the status of particular data. Pre-determined groups of data are easily accessible, so that end-of-day processes or numerous other business inquiries can be efficiently fulfilled.
Data Validation also shows whether or not certain sets of data have passed, failed, or are awaiting evaluation. It enables email alerts to be set up so that users can be notified if data does not successfully pass quality checks. The results of the validation process can also be viewed in Microsoft Excel.
ZE is continuously overseeing the market and data feeds supplied to our clients. The data monitoring team works 24 hours a day seven days a week, year-round, to ensure that any errors and issues are addressed, and that clients are obtaining accurate, complete, and timely data.
1 FIMA. 2014 Data & Corporate Actions Trend Report. http://www.insightforenterprise.com/images/FIMA2014%20Data%20and%20Corporate%20Actions%20Trend%20Report.pdf
2 State Street. “Becoming a Data Leader” Leader or Laggard? How Data Drives Competitive Advantage in the Investment Community. 2013. http://www.statestreetglobalexchange.com/downloads/DataFullReport.pdf
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