In our time spent with executives throughout the financial services industry, we've uncovered four categories of activity that consume leaders' time and budget for data quality. In particular, with such a focus on legal entity data, we are seeing a significant focus on improving the spend and data governance efforts around these categories of data quality management. Firms that identify data quality issues early and move quickly to fix problems typically achieve higher success with more advanced data management requests, and fewer questions from regulators. Take a look at the four categories and download the whitepaper for more information on entity data quality.
In a prior post I defined microservices and the advantages they provide over more traditional monolithic application architectures.
At Kingland we see the cloud as a key enabler of the microservice architecture. Many microservice features enable organizations to benefit from an environment that automatically scales, communicates with other services, and replace a faulty service without impacting conjoined services. Three of the most important features of the cloud, as it relates to running microservices, are rapid provisioning, service discovery, and detailed monitoring.
We've seen continued focus on cognitive, or artificial intelligence (AI) technologies from some of the larger tech companies in the world. This wave of technology is truly revolutionary, but it can be a bit confusing. With new definitions popping up like a frenetic game of buzzword bingo, I prefer this simple explanation used by New York Times reporter Quentin Hardy:
"Cloaked inside terms like deep learning and machine intelligence, AI is essentially a series of advanced statistics-based exercises that review the past to indicate the likely future."
As I've said before, I think everyone should be taking on a cognitive project. As you're working on your plans, here are six tips for understanding this space and narrowing your priorities.
Topics: Cognitive Computing
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.
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.
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?
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 HBR.org.
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:
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).
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.)
Topics: Data Governance
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.
- 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.
- 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).
- 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.
- 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.
Topics: Public Interest Entities