Financial Data Model Standardization

Transcription

1 2081 Business Center Dr. Irvine, CA Financial Data Model Standardization A Zendeux Business Data Solutions White Paper Majd Izadian Managing Partner

2 Contents Client: 2 Introduction 2 Problem Statement 2 Zendeux s Solution 3 Implementation 4 Outcome Summary 4 Client A leading company in financial data aggregation with over 30 million users and 300 financial institutions (including Bank of America, Wells Fargo, Citi Bank, Chase, HSBC, and many others) is also the leading provider of financial data insight to Fortune 100 financial companies. Introduction Financial data aggregation is now a multi-billion dollar industry where many new companies have merged causing a robust competition in the marketplace. This has caused the competitive race for the best data coverage and quality for both transaction data users and corporate aggregation data consumers. The client has been trying to identify how they measured against the competition from data quality and completeness perspective. They also needed to benchmark themselves against their competitors to create a competitive edge. The client provides an end user tool for consumers to create an aggregation of all of their financial accounts. This would allow them to use a centralized login and profile to view all of their financial data instead of having to log into many different banks or sites. The client then would use sophisticated technology to provide this aggregation to the users. They also aggregated this data for high level business intelligence and big data insight that was of great value to large financial institutions for purchase. Problem Statement Client was seeking to improve its competitive position vs. several key industry competitors on some key Financial Data elements and believed that the lack of an industry standard for these data elements and the quality of data it received from source data providers had caused lack of satisfactions with customers in some areas.

3 Client initially wanted to understand how they could validate the quality of their competitor s data quality against their in a benchmarking exercise. This industry lacked data standards by which they could compare themselves with. This lack of data quality transparency in the market, would allow any competitor or new comer to claim their data was of higher quality than the client s. organization to identify intent and objectives that have concerned stakeholders and leadership to want to benchmark their data quality against their competitors. This impacted the competitiveness of the client in the market and at times losing opportunities to these competitors and impacting their profitability. Zendeux s Solution Measuring data quality of an organization without having access to their data was obviously a significant challenge. There were many ideas that surfaced such as surveys, interest groups, and research. All of these approaches were proven to be expensive and unreliable. Zendeux took a different approach to the issue by digging deeper into the true business intent of the client instead of their initial request. Zendeux uses its proprietary data management framework, ZDMF, that is based on discovery of business intent and objective. Zendeux Data Management Framework uses intent driven analysis of different aspects of data management discipline. This would allow both architects and clients to work together to align analysis with true business objectives rather than perceived wants. Zendeux worked closely in partnership with the client s IT and business Zendeux then took and out of the box approach to define another more standard and reliable approach to solving the client s problem. The team s approach was to first create an industry standard by which all parties could measure their data quality against rather than just the competition. This was more feasible since the data providers that the client and its competitors were using were virtually the same. Therefore, the proposed approach now was to first define a common data model and data quality requirements that would satisfy the majority of the financial institutions and then benchmark the client s data against that standard. This would allow the client to be able to first understand how they performed against a true standard. This standard was the reference that both client s users and corporate customers used as their expected benchmark. Using this approach, once the client profiled their data against the new standard, they could realize their risks and opportunities ahead of their users and customers allowing them to resolve it before it data was published or sold externally.

4 The client s project team accepted this approach and understood its benefit while recognizing that it would actually provide them with more leverage than their own initial solution idea. their own data quality risk and opportunities. Implementation Zendeux team worked closely in partnership with the client s IT team to get access to a full list of financial institutions and their data integration profiles. By analyzing a large number of sample size from each data feed type, bank type, and other characteristics, Zendeux captured a complete list of data attributes used by these data providers. These data points then were analyzed and normalized to a single unique list of data attributes that were used to form a data model for each financial account type. These new data models could now satisfy virtually all of the financial institutions and their data models. Each financial data provider model could then be mapped to this new standard data model. Consequently the team could now benchmark the client s own data model for these financial account types to this new standard. This proved to be a crucial outcome of the project. Zendeux then performed data quality measurement on the client s own data in compare to the providers original data. This data quality exercise provided the client with a great deal of insight into Outcome Summary Zendeux presented the results of the project to a number of client s business and IT executives by highlighting the approach and the strategic benefit of this benchmarking method. The team highlighted the fact that the client sales and marketing now had the ammunition to challenge the competitors claims about their higher data quality. Furthermore, this benchmarking method could now be used for all financial data types in a reliable and repeatable process. The client sales and marketing executives were able to quickly resonate with Zendeux s methodology and outcome and were excited to use the results. IT executives were able to use the project outcome to plan for risk mitigation based on the findings of the study and asked Zendeux for proposal of the repeat of this analysis on other financial data types.

5 About Zendeux Zendeux is a data management consulting company that offers solutions based on data quality, knowledge of information relationships, and the balance of managing business stakeholder risks and opportunities. Zendeux's approach is based on its proprietary Zendeux Data Management Framework (ZDMF), which assesses and optimizes data processes to help organizations achieve a single vision of operational excellence through self-awareness and governance. ZDMF produces a Data Management action plan, operational efficiencies, and/or platforms of technical excellence. For More Information Please Contact: Zendeux Business Data Solutions 2081 Business Center Dr. Irvine, CA info@zendeux.com