CCAR and DFAST processes are time and resource intensive. It requires an incredible amount of planning, coordination, discipline, controls and execution across the bank. This effort is further burdened with intense pressure to comply, as well as the risk for not complying. Banks, who have been through CCAR and DFAST cycles, continue to find it downright challenging, while new entrants – Intermediate Holding Companies (IHCs) (i.e. US Subsidiaries of Foreign Banking Organizations (FBOs)) – are confronted with unplanned obstacles of implementing solutions to address organizational structure, capital and regulatory reporting requirements. Continue reading Planning for CCAR and DFAST in 2016 and Beyond
We’ve just released a new white paper on regulatory compliance! Continue reading White Paper: Transforming Regulatory Burden into Business Opportunity
I’m tremendously excited to announce that this is our 100th post on the Knowledgent blog! When we started this blog back in February of this year, we wanted the blog to be a channel for sharing our real-world experience and technical expertise. Along the way, over the past several months, we’ve also shared with you stories about our Knowledgent teammates and the work we do through Knowledgent Cares, our employee volunteer program.
We’ve covered a wide variety of topics in our first 100 posts, so to celebrate this milestone, here are 10 of our favorite posts since the beginning: Continue reading Celebrating our 100th Post
Want to know what it takes to work in data and analytics? Every other Friday, our own Informationists will share their thoughts, experiences, and advice on what they do and what they did to get there. Expect to see a wide range of answers from individuals in the same lines of work; our Informationists come from all walks of life, which only shows that there’s more than one way to get on the right career track.
Our first post featured Dip Kharod, Big Data Architect. Continuing the series is Reed Bradford, Data Quality Architect. Reed earned his MBA in Computer & Information Sciences from Temple University and his BS in Information Management from Brigham Young University (BYU). He has worked in information management and analytics for 28 years across the financial services, life sciences, hospitality, manufacturing, and retail industries.
Data Quality Management (DQM) is a major concern in most data-driven organizations. But many organizations are challenged with improving and remediating data quality. From the beginning, they struggle with questions that impede the progress of their data quality efforts.
For example: What is DQM? How do I get started? Do I even need it? What metrics should I use? What data quality rules should I define? Continue reading White Paper: How to Build a Successful DQM Program
Whether mandated by regulatory considerations, driven by executive dashboards, or meant to enable personalized targeting of marketing messages to consumers, the rapidly increasing reliance on analytics has made Data Quality a higher priority than ever before. In turn, this new status has reshaped the very meaning of Data Quality. There was a time when Data Quality really meant one thing: a simple, binary assessment of the accuracy of data. That was the beginning and end of the Data Quality discussion. Today, however, the questions have grown more complex.
From “Is my data correct?” to “What does my data actually mean?,” the questions surrounding Data Quality are undergoing a rapid transformation. This change has been driven by four major factors:
Data Quality Management, or DQM, is an important component of data management. However, many Data Owners and Data Stewards stumble when it comes to implementing DQM successfully. Should you profile data or define data quality first? What can you use to report data quality metrics? What should you do if data quality issues arise when you’re trying to make improvements based on your initial assessment?
“How can clients trust us with their money when we can’t even get their name right?” With statements like this, the business stakeholders at our client, a major financial services organization, expressed their frustration with the lack of consistency, accuracy, and completeness of their data.