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What's the Difference Between Data Integrity and Data Quality? S&P Capital IQ's Rui Carvalho Explains

Rui Carvalho, Managing Director of Enterprise Solutions for S&P Capital IQ, discussed the importance of data quality and data consistency for today’s businesses.

Can you start by telling us a bit about your background and role at S&P Capital IQ?

Sure, my name is Rui Carvalho and I am Managing Director of Enterprise Solutions for S&P Capital IQ, a McGraw Hill Financial brand. Enterprise Solutions is the data feed, on-demand products and content licensing business for S&P Capital IQ. Based in New York, I lead a global team of 150 product management, platform management, operations and fixed-income evaluated-pricing professionals.

Before my career at S&P Capital IQ, I was the Vice President and Global Head of Fundamentals and Corporate Actions Content for Reuters. I was based in Singapore, where I led strategic data operations executions for the Asia Pacific and Emerging regions, including the Reuters Japan, Middle East and India growth strategies for the research & asset management segment.

I hold a bachelor’s degree from York University in Toronto and also attended the Reuters Executive Training Program at the Ross School of Business at the University of Michigan.

In your opinion, is there a difference between data quality and data integrity?

Yes, I believe there is a difference between data quality and data integrity, but they are also connected. Data integrity refers to the validity of data, but it can also be defined as the accuracy and consistency of stored data. Data quality pertains to the completeness, accuracy, timeliness and consistent state of information managed in an organization’s data warehouse. Data quality would appear to be the foundation and/or building blocks that make up data integrity. The construction of quality data from internal and external sources produces an end product, which is data integrity. The derivative of data quality is data integrity; valid information producing trustworthy knowledge that ultimately drives organizational business intelligence.

Why is data integrity and data quality more important than ever to organizations?

Data integrity and data quality are more important today for a few reasons. The first reason would be the lack of data quality management. This results in inconsistent and inaccurate data, which leads to poor investment and management decisions. I believe many organizations have come to realize the impact of the lack of data quality management more so in recent times. Poor decisions, in turn, can lead to inefficiencies, errors, additional costs or loss of business, which no one can afford today.

A second factor in the growing importance of data integrity and data quality is directly related to the increase in global regulations such as Dodd-Frank, FATCA, Solvency II and the ongoing efforts to establish an LEI. Even topical news affects data integrity and data quality.

For example, the recent news pertaining to government issued sanctions now calls for customers to rely on their enterprise solution and data management providers to provide their exposure to entities, subsidiaries and securities. Having such a system and process in place ensures that data is successfully managed, providing clients’ with high-quality data coupled with data integrity.

How have organizations successfully organized data to ensure quality?

Many organizations will establish a sound enterprise data management system, much like we have done at S&P Capital IQ. In order to properly ensure good data quality, the foundation begins with a data warehouse built on rules and standards that are properly governed. An enterprise data management system allows for data to be integrated from different sources, both internal and external, creating a central repository of data. After data is collected in the warehouse, it can then been integrated and organized. Rules, testing and governance measures will all be used to ensure high data quality.

At S&P Capital IQ, data is managed by content subject matter experts on our global operations team. We have dedicated teams responsible for managing various types of data received and disseminated out to our clients, including fundamental, estimates and referential data. The data is analyzed daily to ensure data completeness and accuracy.

There are a lot of data gaps at organizations, how have you seen companies strategically address the data gaps?

Many organizations are faced with the potential to have data gaps. One measure we use here at S&P Capital IQ is to perform a routine gap analysis. A typical data gap analysis will seek to improve an organization’s efficiency. A data gap analysis helps an organization identify missing data that could result in possible inefficiencies. At S&P Capital IQ, we assist our customers in managing their gap analysis, which is an integral part of the foundation to building strong client/company relationships.

“I believe many organizations have come to realize the impact of the lack of data quality management more so in recent times. Poor decisions, in turn, can lead to inefficiencies, errors, additional costs or loss of business, which no one can afford today.”

How are organizations able to consume data consistently across the firm?

One way to ensure data consistency across a firm would be through a solid Enterprise Data Management (EDM) system. In order to consume data consistently, an EDM will have a few key components such as a global data management team, data management goals, a governance model, issues management and resolution and data monitoring and controls. Once you have these components in place, an organization can now ensure consistent consumption and delivery of data.

Additionally, you need to establish consistent behavior within an organization by incorporating rules and policies around data consumption, if you do this you will accomplish consistency.

What are some of the innovative ways you have seen data visualized and interpreted? What are some of the challenges translating to real-time?

According to Vitaly Friedman, author of Data Visualization and Infographics, “The main goal of data visualization is to communicate information clearly and effectively through graphical means.” I totally agree with this statement; visualization allows vendors to show the breadth and depth of data coverage, making large data sets easily understandable.

Traditionally, visualizing data was through tables, graphs and pie charts. Some of the new innovative ways I have seen data exhibited include displaying news where users can zoom in and out with interactive screens. An example of this would be Newsmap. Newsmap is an application that visually reflects the ever changing landscape of the headline news aggregator. Another good example of innovative visualized of data is Dygraphs. Dygraphs is a fast, flexible open source JavaScript charting library that allows users to explore and interpret dense data sets. It’s highly customizable, works in all major browsers and you can even minimize to view on mobile/tablet devices.

Keeping in-line with the call for innovative data visualization, at S&P Capital IQ, we are using some of these shared methods when enhancing specific products such as our S&P Capital IQ Desktop Platform.

Regarding some of the challenges translating to real-time today are around the translation of unstructured data, meaning it consists of words rather than numbers. Visualization, as I just discussed, would be another challenge, what is the best way to translate visually for making good sound decisions with real time data. One last challenge would be how fast an organization captures and responds to business events and I will mention again how having a good, sound EDM in place would allow for this. We here at S&P Capital IQ have a dedicated Real-Time Data and Product team now positioned to address and further meet these needs today.

BIO:

Rui Carvalho is the Managing Director of Enterprise Solutions for S&P Capital IQ, a McGraw Hill Financial brand. Enterprise Solutions is the data feed, on-demand products and content licensing business for S&P Capital IQ. Based in New York, Rui leads a global team of 150 product management, platform management, operations and fixed-income evaluated-pricing professionals.

Before his career at S&P Capital IQ, Rui was the Vice President and Global Head of Fundamentals and Corporate Actions content, for Reuters. Rui was based in Singapore, where he led strategic data operations executions for the Asia Pacific and Emerging regions, including the Reuters Japan, Middle East and India growth strategies for the research & asset management segment.

Rui holds a bachelor’s degree from York University in Toronto and also attended the Reuters Executive Training Program at the Ross School of Business at the University of Michigan.

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