Company Blog


Over 90% of Hierarchy Data Problems Fall into these Categories

Posted by Tony Brownlee on Sep 8, 2016 11:30:00 AM

You're at a party, striking up a conversation with your friends and colleagues, and what do you talk about?  Sports. Politics. Business. Hierarchy data?  While hierarchy data may not always be the first topic discussed, I've been to a few events with chief data officers where it does come up.  If it comes up at your next cocktail party, I want you to be ready to contribute to the conversation. And if I’m in attendance, I’ll join you in the conversation.

Joking aside, for data professionals, hierarchy data is growing in importance.  Sometimes referred to as relationship data, family tree data, legal or corporate hierarchy, this data topics is about the relationships between legal entities that indicate ownership, control, or influence of one entity over another.  

My passion for hierarchy data started in the 2003 time-frame solving global hierarchy data problems related to issuers of securities across 140 countries for public accounting firms.  As 2008 rolled around and issues in the financial markets hit, many banking and capital markets institutions and insurance companies started to realize the importance of hierarchy data for risk purposes.  Then, as regulations emerged, relationship data became a must have for regulatory reporting, risk aggregation, capital adequacy, and many other use cases.  Now, we're seeing many global companies look at the importance of hierarchies for understanding supplier business relationships, analyzing revenue and pricing strategies, and assessing cross-border client relationships.  

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Topics: Legal Entity Data, Data Quality, Data Governance, Big Data, Hierarchy data

5 Essential Steps to Measuring Entity Data Quality

Posted by Tony Brownlee on Aug 15, 2016 6:30:00 AM

Use these five steps and reduce your costs associated with data quality.

Take Advantage of New Technology

By using new technology, firms can scan their data and identify problem areas to gain a quick overview of the state of their entity data. New technology can upload data records and perform hundreds of quality scans, covering data completeness, consistency, duplication and more. You can even uncover data by attribute, and assess dozens of aspects that define quality.

Today, technology can read information from hundreds of sources just like a human and identify names, addresses, relationships, and other information…just like a human. Think about what could happen if your technology can’t readily match the right name with the right address.

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Topics: Legal Entity Data, Data Quality, Data Management, entity data

$500,000 Problems Hiding in Budget Season

Posted by Tony Brownlee on Jul 26, 2016 6:00:00 AM

Budget season is a unique, truth-telling process of sorts. It's that time of year where executives begin to put together plans for the future, align with bigger picture vision, establish goals, and also look back on what's been accomplished and how things have been going. While executives reflect, they must first ask, did we accomplish everything we set out to achieve this year?  Many times the answer to that question is "no, but we're close."  

In my opinion, executives are consistently plagued with "no, but we're close" problems, and these problems eat into next year's budget.  In simplistic terms, I call these $500,000 problems (or even larger in many organizations).  Why $500,000? 

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Topics: Vendor Relationship, Managed Services

How Analogies Bridge the Communication Gap Between IT and the Business

Posted by Matt Good on Jul 15, 2016 6:00:00 AM

Welcome back to the "From Code to Crayons" series. In Part 2, we'll take a look at how you can use analogies to connect software development and technical work to business value. When used effectively, analogies can help Software/IT Executives and Practitioners tie their technical excellence to business value for business leaders. (If you missed Part 1 of the series, you can find it here). 

Analogies, when used well, can help everyone hone in on what's important and keep your discussions at a strategic level by providing business value. IT executives "need not understand every aspect of the problem at hand. Rather, they pay attention to select features of it and use them to apply the patterns of the past to the problems of the present," according to an article from

Personally, the best analogy I have ever leveraged, and have also witnessed other Executives/Practitioners leverage, is the great "homeowner" analogy. If you have ever remodeled or built a new home, the analogy resonates even further. Consider these primary analogy points in connecting the homeowner/construction analogy to software development projects in particular:

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Topics: Leadership

Reference Data Implications of SCCL

Posted by Tony Brownlee on Jun 6, 2016 7:30:00 AM

Since 2008 many of the executives or Chief Data Officers that lead enterprise data programs throughout the financial services industry have been constantly adapting those programs to an unending list of new regulations. The latest that many are now studying is Single-Counterparty Credit Limits (SCCL) for Large BankingOrganizations. On the surface, it seems pretty simple and logical; let's ensure that the exposure to any single counterparty is not too great. As with all regulations though, the devil is in the details. Rather than summarize the regulation for you, I'll highlight a few key sections that emphasize the need for reliable reference data, or a master data strategy. In particular, let's take a look at the key terms related to a legal entity (one of my favorite topics).

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Topics: Legal Entity Data, Single counterparty credit limits, SCCL, Reference Data

The Value of Data Governance

Posted by Jeff Gorball on Jun 2, 2016 7:30:00 AM

I am often asked “what is the value of data governance?” and I am reminded of a statement I once read that said “the value of the data is directly proportional to the quality of its provenance, and the completeness and accuracy of its description”. Unfortunately, I did not make note of the source of this quote but I’d like to explore this concept today. (If this is your quote, please contact me so I can appropriately attribute it.)

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Topics: Data Governance

Public Interest Entities: How do I source this 'public' data?

Posted by Alex Olson on Jun 1, 2016 7:30:00 AM

In April 2014, the European Union adopted legislation to enhance the regulatory framework for statutory audits. These laws are just now coming into effect. A major component of the regulatory framework was the identification of certain legal entities that are in the public interest. Broadly speaking, four criteria exist to determine if an entity is in the public interest.

  1. All entities that are both governed by the law of a member state and listed on a regulated market. A regulated market is defined in MIFID II, and there currently are over 100 markets across the 28 member states.
  2. All credit institutions in the EU, irrespective of their listing status on a regulated market. A credit institution is a deposit taking organization and engages in loaning money (taking on credit risk).
  3. All insurance undertakings in the EU, regardless of whether they are listed or not and regardless of whether they are life, non-life, insurance or reinsurance undertakings.
  4. Entities designated by Member States as public-interest entities, for instance undertakings that are of significant public relevance because of the nature of their business, their size, or number of employees.
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Topics: Public Interest Entities

Data Quality: Are you a brick inspector?

Posted by Alex Olson on May 31, 2016 7:30:00 AM

In May 1980, a Barnard College student died in New York City due to a piece of terra cotta falling from a building. This terrible situation caused Local Law 10 to be passed in New York City, which was further strengthened by Local Law 11 in 1998 after a similar tragedy. These laws require the façade of each building to be inspected to ensure that the bricks are firmly connected to the structure.

Why are we talking about bricks? As I was walking to one of my client meetings in New York, I observed this process occurring. Scaffolding was erected, individuals were looking at individual bricks, and small fixes were being made. The people on the sidewalks were affected due to the scaffolding in place. I am not a building engineer, but I asked myself, "Is there a better way?" My mind then proceeded to my client meeting and data quality. Were the clients like the brick inspector?  Were they rebuilding the scaffolding, looking at the façade instead of the root causes? Plodding through records manually instead of using tools to find the problem?

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Topics: Data Quality

How Agile Unlocks Expertise

Posted by Tony Brownlee on May 25, 2016 9:30:00 AM

"An expert is someone who knows some of the worst mistakes that can be made in his subject, and how to avoid them."

- Werner Heisenberg

Over the last couple of years we have been transitioning all of our software development teams (as well as our data, marketing and even HR teams) over to agile methodologies for planning, managing, and performing work. We are seeing great improvements in productivity, improved quality, and even team member satisfaction, and while agile has a lot to do with this, I think there's another factor at play here. I think agile is unlocking the way deep expertise can positively impact our projects. 

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Topics: Agile

Data Management: What is blocking your success?

Posted by Alex Olson on May 24, 2016 9:00:00 AM

A six year old knows Indiana and India aren't the same. 

Data management is one of the few disciplines where the state (population exceeding 6 million and known for the movie Hoosiers) and country (population exceeding 1 billion and more than 22 official languages) could be confused as being similar.

Let me explain. A client of mine noticed a hair salon transaction from India on a credit card statement. Unfortunately, the business manually entered India instead of Indiana - where he actually spent the money - providing a moment of frantic thought as he wondered if his information was stolen. Fortunately, he was able to laugh off the error because he works in the data sciences field and understood the challenges of data management.

But this leads to a potentially larger question – what causes data projects to fail?

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Topics: Data Management