Think Data First, Platform Second – Why Data Fuels MDM

Fuel

 

As the volume of data coming into organizations – from both internal and external sources – continues to grow and makes its way across departmental systems in many different formats, there is a critical need to create a single, holistic view of the key data entities in common use across the enterprise. Master Data Management (MDM) aims to accomplish this goal. Not surprisingly, MDM has become a significant priority for global enterprises, with the market expected to triple from $9.4B to $26.8B by 2020 according to analysts.

But while everyone is investing serious cash into the tools to manage the data, few are putting any thought into the data itself. This is akin to purchasing a luxury sports car and fueling it with water. Sure it looks great, but it won’t get you very far.

 

The underlying concept of MDM is surprisingly simple: get everyone “on the same page” looking at the same data and ensure it is accurate. Yet, master data and its management continue to be a universal challenge across many industries.  Organizations of all shapes and sizes share similar problems related to master data and can all reap benefits from solving them. That means concentrating on the quality of the data before going shopping for the sexiest MDM platform. In essence, you must master data before you can manage it. Ensuring the quality, structure, and integrability is your responsibility; your MDM platform won’t do that for you. It’s like purchasing a top-of-the-line oven and expecting it to produce a delectable meal. You are responsible for what goes into it.

Master Data Defined

Master Data is the foundational information on customers, vendors and prospect that must be shared across all internal systems, applications, and processes in order for your commercial data, transactional reporting, and business activity to be optimized and accurate. Because individual businesses and departments have a need to plan, execute, monitor and analyze these common entities, multiple versions of the same data can reside in separate departmental systems. This results in disparate data, which is difficult to integrate across functions and quite costly to manage in terms of resources and IT development. Cross-channel initiatives, buying and planning, merger and acquisition activity, and content management all create new data silos. Major strategic endeavors, part of any business intelligence strategy, can be hampered or derailed if fundamental master data is not in place. In reality, master data is the only way to connect multiple systems and processes both internally and externally.

Master data is the most important data you have.  It’s about the products you make and services you provide, the customers you sell to and the the vendors you buy from. It is the basis of your business and commercial relationship. A primary focus area should be your ability to define your foundational master data elements, (entities, hierarchies and types) and then the data that is needed (both to be mastered and to be accessible) to meet your business objective. If you focus on this before worrying about the solution, you’ll be on the right course for driving success with MDM. Always remember, think data first and platform second.

Breaking Down the Business Relationships in Breaking Bad

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How Saul Goodman Can Teach Businesses About the Value of Understanding Relationships

This week, TV viewers witnessed the return of Jimmy McGill, a scrappy and indefatigable attorney struggling for respect and reward. Spoiler alert: If you watched Breaking Bad, you know the upstart Albuquerque lawyer goes on to become Saul Goodman, the lawyer and adviser for eventual meth kingpin Walter White, or as he’s known on the street, Heisenberg.

Now in its second season, AMC’s hit show Better Call Saul shows the transformation of the naïve McGill into what would become one of the city’s most notorious criminal defense attorneys. But it doesn’t happen by chance. His ability to understand and manipulate relationships plays a huge role, something many businesses can learn a thing or two about. But before I proceed, if you have not not watched Breaking Bad, I implore you do so immediately. Go on, watch it now, and then come back and read this article, otherwise you’re going to be a bit lost.

In Breaking Bad we learn that Saul Goodman is a key player in Walter White’s evolution from everyday chemistry teacher to criminal mastermind, constantly getting him out of several sticky situations over the course of his drug business operations. Goodman is effective in helping Walt stay one step ahead of the police and competing drug czars because of his extensive connections within the criminal underworld, as well as serving as a go-between connecting drug distributors, evidence removers, impersonators, and other criminals-for-hire.

What makes Goodman so successful is his network of relationships. He knows all the players and how they are connected to others and uses that knowledge to his advantage. Ultimately, it’s what probably keeps him and his clients alive for so long. Other entities in the Breaking Bad world are not so lucky. Shotgun blasts and burning faces aside, I’m talking about the businesses that were ultimately crippled by the chain of events that were set off by Walter White’s maniacal obsession for power.

The Breaking Bad series finale shows us the fate of all the major characters, but what about everyone else that has some underlying connection to what went down?

We learned that Walt’s meth empire was funded by a multifaceted conglomerate headquartered in Germany called Madrigal Electromotive. According to Wikia, Madrigal is highly diversified in industrial equipment, manufacturing, global shipping, construction and fast food; the most notorious being the American fried chicken chain, Los Pollos Hermanos.

Founded by Gustavo Fring, the Los Pollos Hermanos restaurant chain had fourteen locations throughout the southwest and was a subsidiary of Madrigal. As we learned during the course of the show, the restaurant provided money-laundering and logistics for illegal activities. It’s safe to assume that following the death of its founder and his reported connection to engineering a billion-dollar drug empire, business suffered. Every enterprise that was directly doing business with the fried chicken chain likely cut ties with them as soon as the news broke. From the factory providing the meat to the manufacturer supplying the utensils, these businesses were aware that Los Pollos Hermanos would suffer and were able to plan in advance for a revenue downfall.

But what about the other suppliers that did not realize they were working with entities that had connections to Los Pollos Hermanos’ parent company? Madrigal is spread across 14 divisions, including a massive investment in fast food. The fast-food division, formerly run by Herr Peter Schuler, encompasses a stable of 7 fast-food eateries, including Whiskerstay’s, Haau Chuen Wok, Burger Matic, and Polmieri Pizza. Following the breaking news of the drug ring, the resulting investigation likely sent shockwaves throughout the entire Madrigal enterprise and subsequently hurt all of its businesses in some shape or form. But let’s look at the supplier of dough for Polmieri Pizza for example. Do you think they knew the pizza shop they do business with was a subsidiary of Madrigal and would be a casualty of the meth trade? Very unlikely.

Because Polmieri Pizza is a subsidiary of Madrigal, they will be at least indirectly effected. While its parent company will be in damage control – a change of management, a freeze on funds, etc. – the innocuous pizza shop will be impacted, even if it is only short term. During this time, the dough supplier has no clue to the grievous relationship the pizza shop has to Madrigal and that it should expect some change in how they work with the pizza eatery. If they had known there was any connection, they may have been able to plan ahead and cut down on production and accounted for less orders. Instead, they are caught by surprise and left overstocked and under water.

This could have been avoided if the dough manufacturer leveraged its relationship data. Every company has relationship data; they just need to know where to look for it, or who to partner with to obtain the right information.

Relationship data is information about two or more entities that are brought together along with their business activities to inform an implied business impact or outcome. Through a combination of interpreting the right signal data and implementing advanced analytics uncovered in this data, unmet needs arise, hidden dangers surface and new opportunities can be identified.

Of course, this is just an example of the importance of being able to recognize business relationships based on a fictional show. But not being able to do so could prove to be a grave reality for businesses of all shapes and form. If the companies with business connections to Madrigal’s vast enterprise had had a sense of relationship data, what would they have seen?

If you can take anything away from the Saul Goodman’s of the world, it is this: know how all your relationships are connected and you will know how to solve problems, manage revenue – and stay out of trouble.

Prioritizing Capital Markets Data Management: Should we be concerned?

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Original content found on www.linkedin.com/prioritizing capital markets data management

I read the Enterprise Data Management Council’s (EDMC) 2015 Data Management Industry Benchmark Report with great interest and am not sure if I should be encouraged or worried. I am encouraged because the study was well done, and the report was chock full of great insight into the progress of important data management initiatives in our financial institutions. However, I am also concerned that the inability of industry leaders to effectively communicate the importance of data management initiatives to all constituents will inhibit the ability of our financial institutions to execute on their strategic priorities.

A Historically Low Priority IT Activity

I was involved in data management in the 1980s and 1990s as a technology executive for investment banks, and I believe that data management—at the time a function of the technology department—was viewed as a low priority among industry management. A lot has changed since then to raise the importance of data management, most obviously the damage of the 2008 credit crisis and the stifling regulation that has resulted from it.

Now that I’m at Dun & Bradstreet, the leading provider of commercial data, I surely see progress.

The EDMC report indicates that data management has “gained a strong and sustainable foothold” in the industry and that “data is … essential in order to facilitate process automation, support financial engineering and enhance analytical capabilities.

Capital markets institutions have made undeniable improvements—such as building faster and better models for decision making, deploying highly intelligent trading algorithms and reducing trade breaks and fails—that have elevated their business. But adoption of reference data for enhanced insights has not made a prominent impact in this growth, in large part because it has not gained prominence in these institutions.

Data management historically has resided in organizational technology silos, which greatly inhibits the collaboration that is required to maximize the benefit from analysis of the complex concepts of reference data. Ownership of reference data has not been fully integrated into operational processes. More importantly, it has not been sufficiently evangelized and its value not articulated as part of an overall strategy.

Time to Spread the Data Management Gospel

The report calls it spreading “the data management gospel.” Indeed, the successful integration of data management into a corporate or enterprise function will surely improve acceptance and adoption. As the report states, “Stakeholder buy-in increases significantly and resource satisfaction is highest in those circumstances.

Two things will get us to data management adoption:

One is for management to spread the word. Resources need to hear—and believe—that data management is a priority. In the past, it’s been given lip service and has then predictably faded in the shadow of the latest trading technology or low-latency market data solution, or has given way under the weight of unending regulatory mandates. As a result, because so many have heard it repeatedly, it is natural for them to greet statements about the importance of data management with a skewed eye.

Indeed, the EDMC report confirms such, saying that while the industry has a sufficient level of resources ready, the industry has a low level of satisfaction with support for data management initiatives, and refers to the industry’s tendency to ‘haircut’ data management program resources for other operational activities.

The industry’s experience leads to its struggle today to get sufficient resources to meet objectives. Of course, when financial institutions now need to become smarter in their knowledge of the market, this lack of commitment and resulting resource shortfall is seen as a primary cause. Organizations such as the EDM Council itself have already benefitted from the progress of this communication, generating consistent dialogue on the most important initiatives while offering a platform for executives to share their ideas for the best solutions.

So that’s where the second thing comes in — secondary drivers. Financial institutions are rapidly recognizing the value of data management for the processing part of the business. The EDMC report states that operational efficiency is cited by 68% of respondents as being a significant benefit while business value/analytics is noted by 46%. With reducing operations and processing costs being such an important part of capital markets’ strategy (supported by such initiatives as reducing the settlement cycle and investigation of distributed ledger solutions), the ability to improve efficiency will raise data management to the level it needs to attract resources.

Say It Like You Mean It

However, as the leaders of financial institutions adopt these tenets, their challenge lies in communication to others in this business. No longer can capital markets afford another “false start” and more lip service to the importance of data management.

In its introduction, the EDMC report accurately states:

 “There is no getting around the inherent difficulties associated with either altering organizational behavior or managing wholescale transformation of the data content infrastructure. And while the challenges are real, the global financial industry has clearly taken a giant step closer to achieving a data management control environment.”

It is indeed a daunting task and one that has been central to the jobs of data executives for decades.

Further, I agree completely with the report’s statement that, “we would expect to see the importance of communication clearly articulated as part of data management strategy and various approaches being created to ‘spread the data management gospel.”

This means that organizations such as SIFMA, the FISD and the EDMC itself, as well as the individual institutions and data providers like Dun & Bradstreet, should firm up our dialogue for communication with everyone in the industry. This will pave the way for sufficient resource dedication to address the data management problem.

We’ve been hearing for years about the importance of data management and have witnessed its steady, if still slow, progress to becoming a prominent business initiative. Now it’s time for executives to make the biggest push yet to attract the resources required to execute on this strategy.

Read the full report here: 2015 Data Management Industry Benchmark Report

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Learn more about Dun & Bradstreet’s perspectives on the kinds of data organizations in capital markets need to help make better decisions.

4 Wishes Data-Inspired Leaders Want This Holiday

4 Wishes Data-Inspired Leaders Want This Holiday | D&B

4 Wishes Data-Inspired Leaders Want This Holiday | D&B

 

What Every Data-Inspired Leader Wants This Holiday

With the holidays in full swing, everyone is busy making their lists and checking them twice. But while electronics and toys routinely top the wish lists for most, the data-inspired leaders of the world have some unique desires that can’t easily be purchased from your favorite store.

Whether you’ve been naughty (online hookup site for married couples was breached by hacking outfit, The Impact Team, and the personal details of 37M users were made public, leaving many men sleeping on the couch) or nice (Data Science for Social Good, a program at the University of Chicago that connects data scientists with governments, is working to predict when officers are at risk of misconduct, with the goal of preventing incidents before they happen), chief data officers, data scientists and all data stewards want better and safer ways to do their jobs.

Instead of playing Santa and asking them to sit on my lap and tell me what they want for the holidays, I figured I’d simply share some of the top things we’ve heard on data leaders’ wish lists this year.

1. A Better Way to Find Truth in Data

Mark Twain famously said, “There are three kinds of lies: lies, damned lies, and statistics.” One of the biggest problems we’re faced with every day is trying to make sense of the data we have. In a perfect world the answer to all of our questions would lie smack dab in the data itself, but that’s not the case. The premise that data can get us closer to that single version of the truth is harder to achieve than first thought. But it hasn’t stopped us from trying to form conclusions from the data that is presented. Sometimes we rush to conclusions in the face of mounting pressure from others who demand answers.

What we really need is a source of truth to compare it to, otherwise it is very hard to know what the truth actually is. Unfortunately, that is often an impossible goal – finding truth in a world of ambiguity is not as simple as looking up a word in the dictionary. If you think about Malaysia Airlines Flight 370, which tragically disappeared in 2014, there were several conflicting reports claiming to show where the downed airline would be found. Those reports were based on various data sets which essentially led to multiple versions of proposed “truth.” Until they finally found pieces of the wreckage, searchers were looking in multiple disconnected spots because that was what the “data” said. But without anything to compare it to, there was no way to know what was true or not. This is just one example how data can be used to get an answer we wall want. This same thing happens in business everyday, so the takeaway here is that we need to stop rushing to form conclusions and try to first understand the character, quality and shortcomings of data and what can be done with it. Good data scientists are data skeptics and want better ways to measure the truthfulness of data. They want a “veracity-meter” if you will, a better method to help overcome the uncertainty and doubt often found in data.

2. A Method for Applying Structure to Unstructured Data

Unstructured data – information that is not organized in a pre-defined manner, is growing significantly, outpacing structured data. Experts generally agree that 80-85% of data is unstructured. As the amount of unstructured data continues to grow, so does complexity and cost of attempting to discover, curate and make sense out of this data. However, there are benefits when it is managed right.

This explosion of data is providing organizations with insights they were previously not privy to, nor that they can fully understand. When faced with looking at data signals from numerous sources, the first inclination is to break out the parts that are understood. This is often referred to as entity extraction. Understanding those entities is a first step to drawing meaning, but the unstructured data can sometimes inform new insights that were not previously seen through the structured data, so additional skills are needed.

For example, social media yields untapped opportunities to derive new insights. Social media channels that offer user ratings and narrative offer a treasure trove of intelligence, if you can figure out how to make sense of it all. At Dun & Bradstreet, we are building capabilities that give us some insight into the hidden meaning in unstructured text. Customer reviews provide new details on the satisfactory of a business that may not previously be seen in structured data. By understanding how to correlate negative and positive comments as well as ratings, we hope to inform future decisions about total risk and total opportunity.

With unstructured data steadily becoming part of the equation, data leaders need to find a better way to organize the unorganized without relying on the traditional methods we have used in the past, because they won’t work on all of the data. A better process or system that could manage much or all of our unstructured data is certainly at the top of the data wish list.

3. A Global Way to Share Insights

Many countries around the world are considering legislation to ensure certain types of data stay within their borders. They do this out of security concerns, which are certainly understandable. They’re worried about cyber-terrorism and spying and simply want to maintain their sovereignty. Not surprisingly, it’s getting harder and harder to know what you may permissibly do in the global arena. We must be careful not to create “silos” of information that undermine the advancement of our ability to use information while carefully controlling the behaviors that are undesirable.

There’s a method in the scientific community that when you make a discovery, you publish your results in a peer-reviewed journal for the world to see. It’s a way to share knowledge to benefit the greater good. Of course not all knowledge is shared that way. Some of it is proprietary. Data falls into that area of knowledge that is commonly not shared. But data can be very valuable to others and should be shared appropriately.

That concept of publishing data is still confusing and often debated. Open data is one example, but there are many more nuanced approaches. Sharing data globally requires a tremendous amount of advise-and-consent to do this in a permissible way. The countries of the world have to mature in allowing the permissible use of data across borders in ways that do not undermine our concerns around malfeasance, but also don’t undermine the human race’s ability to move forward in using this tremendous asset that it’s creating.

4. Breeding a Generation of Analytical Thinkers

If we are going to create a better world through the power of data, we have to ensure our successors can pick up where we leave off and do things we never thought possible. As data continues to grow at an incredible rate, we’ll be faced with complex problems we can’t even conceive right now, and we’ll need the best and brightest to tackle these new challenges. For that to happen, we must first teach the next generation of data leaders how to be analytically savvy with data, especially new types of data that have never been seen before. Research firm McKinsey has predicted that by 2018, the U.S. alone may face a 50% to 60% gap between supply and demand of deep analytic talent.

Today we teach our future leaders the basics of understanding statistics. For example, we teach them regression, which is based on longitudinal data sets. Those are certainly valuable skills, but it’s not teaching them how to be analytically savvy with new types of data. Being able to look at data and tell a story takes years of training; training that is just not happening at the scale we need.

High on the wish list for all data stewards – and really organizations across the globe, whether they realize it or not – is for our educational institutions to teach students to be analytical thinkers, which means becoming proficient with methods of discovering, comparing, contrasting, evaluating and synthesizing information. This type of thinking helps budding data users see information in many different dimensions, from multiple angles. These skills are instrumental in breeding the next generation of data stewards.

Does this reflect your own data wish list? I hope many of these will come true for us in 2016 and beyond. Until then, wishing you the very best for the holiday season…

3 Reasons to Be Thankful for Data

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What better time to think about all that we’re thankful for than Thanksgiving? For businesses, there has never been a better time to be grateful for data and all it can do to help deliver new opportunities. Like a moist, juicy roast turkey, data is the one thing everyone seems to be eating up. Let’s look at three reasons why data is so delectable. We promise you won’t feel sleepy after consuming this list.

 

Bountiful Amounts of Data

The Pilgrims arrived in the New World during a particularly harsh winter, making it very difficult for them to find food and shelter. Of course we know they persevered despite the shortage of resources after they learned to harvest crops that produced an abundance of food for them to not only survive, but thrive.  Fast-forward to today, and we gorge ourselves on turkey and various complex carbohydrates in celebration of the early settlers’ efforts.

Like the sustenance which was once so hard to come by, data too has gone from scarce to abundant, and there’s plenty of it to feast on. Just as the Pilgrims struggled to live off the land, data scientists once had to work especially hard to cultivate meaningful bits of data. When data was scarce, they had to extract, track and survey to get at it. The objective was always to find out how and where to get more of it. Today, data is everywhere and the new goal is understanding how to make sense of it.

Technology has been one of the biggest catalysts of the data explosion. With the digitization of business, the amount of information we are collecting is growing so large that there are debates on just how big it is – it probably has grown even more since you read that sentence . Both structured and unstructured in nature, this wealth of information has made it possible to produce insights and achieve outcomes that were previously inconceivable. Businesses can better identify trends and risk, organizations can tackle health and wellness issues, and governments can solve economic and social challenges.

While we should certainly be grateful for the abundance of data, we must be careful how we use the information. It is important not to overindulge or horde it. Instead we must recognize the type of data that will sustain us and avoid the empty calories that may lead us astray. Just like the Pilgrims planted the right seeds that would bring them sustenance, we must choose the kernels of data that will drive meaningful value and insights.

 

Data Freedom

The Pilgrims came to America from England in search of religious freedom.  They yearned for an open society characterized by a flexible stricture, freedom of belief and dissemination of information. We are witnessing a similar evolution in the way data is accessed and shared. The concept of data sharing is officially defined as making a certain piece of data free to use, reuse and redistribute –subject only, at most, to the requirement to attribute and/or share-alike. In other words, we’re at a point in history where some information can be freely used to solve key problems or make progress towards specific goals.

There are many examples of data that can be openly shared. However, it’s not just about the numbers, but the insights that come along with it that pose the most benefits when freely distributed. This concept offers benefits across both the private and public sector. Businesses can gain a new level of transparency into new opportunities for services/goods, make better decisions based on more accurate information, and reduce costs for data conversion. But perhaps the biggest advantage of an open data ecosystem is for individual citizens. That’s because the sharing of information between governments can help everything from increase economic activity, address national disasters in a swifter manner, and even reduce health issues.

There are several types of data that can be shared among governmental functions. There is the sharing of data among governmental agencies within a single country. Second is the sharing of data across borders between International governments. And lastly, there is the sharing of data between businesses and government; this refers to voluntary sharing of data, beyond the legal reporting obligations of governments.

So what exactly should governments be sharing? Observations are crucial – such as the National Weather Service issuing a Hurricane watch. It’s about sharing conclusions that can help different agencies better prepare for that projected weather event. Ultimately, this is the value of open data: multiple organizations, with mutual interests, sharing their independent observations in a manner that lets each organization draw more accurate, informed and insightful conclusions.

Unfortunately, in many cases the decisions made by government officials are not always based on all of the information that might be pertinent to the situation. The same goes for businesses, which are somewhat reluctant to let go of their first-party data. With the freedom and ease to share information at our hands, we have the opportunity to achieve maximum economic and social benefits.

At the end of the day, data is nothing without analytics to help make sense of it.  We should always be cognizant about ways in which specific pieces shared data are used to address specific questions and help establish new ideas. The Pilgrims likely did not use all of their food sources to cook a single meal.  We shouldn’t use all the data available to solve a problem just because it’s in front of our face.

 

A Brave New World of Data

As hard as I tried, I could not come up with a very clever parallel between the Pilgrims and the Internet of Things (IoT). But, this new buzz word represents such a major data opportunity to be thankful for, and, well, the Pilgrims had, umm, things, so here we go.

The IoT refers to the growing network of interconnected objects – devices and machines which are not connected but aware of, and discoverable to, other devices and machines around them. It’s mobile, virtual and instantaneous. Data can be gathered and shared from anything like a car to a refrigerator, which means we’ll be witnessing a huge increase in the amount of data being generated – huge streams of data that will enable new types of insight. We now have an opportunity to organize this information in compelling ways to reach new conclusions.

The IoT has the opportunity to fundamentally change industries. From the automotive industry, where new data signals from cars may help improve safety conditions, to the supply chain, where the real-time passing of information can avoid disruptions in manufacturing. Organizations will quickly realize transformational change of their business models given the rate at which digital technologies are connected and constantly evolving.

As beneficial as the IoT will be, it will not flourish without the right data management and analytics capabilities. Organizations will need to invest time and resources in order to mine valuable insights from the data generated by the interactions that occur between machines.

 

In Summary

These are just three examples of how data is changing the face of business and frankly, society. There are certainly countless other reasons to be thankful for data, depending on what your business goals are and what you want to achieve. As I’ve noted, within each of these instances, while there is much to be thankful for, it is vital we be cautious and smart when taking advantage of new data opportunities. Just like the Pilgrims, we are using this new frontier to create a new world of endless possibilities.

The Fact on FATCA: How Third-Party Data Reduces Time, Cuts Costs and Improves Customer Experience

fatca_microscopeBy its very name, “big data” sounds like something that always makes business more complex. But here’s one instance, from the world of tax compliance, where the opposite is the case.

The Foreign Account Tax Compliance Act, or FATCA, requires foreign financial institutions (FFIs) to report to the IRS information about financial accounts held by U.S. taxpayers, or by foreign entities in which U.S. taxpayers hold a substantial ownership interest. The June 2016 deadline for FFIs to review existing accounts is fast approaching at the same time as many of the institutions are also planning their Common Reporting Standards (CRS) account review approach.

The time is now for them to determine the best way to use data to improve efficiency and process. Why? The main reason: customer service. A large portion of the FATCA and CRS effort requires financial institutions to reach out to preexisting account holders for new tax forms. Obtaining this client tax documentation presents numerous challenges to a financial institution and often leads to a process of multiple iteration that frustrates clients.

FATCA and CRS allow an alternative to the standard approach of soliciting and validating client tax documentation, which can also assist in automating changes in circumstances monitoring and, on top of that, further improve an FFI’s data quality. Third-party data can be leveraged to classify certain types of entity statuses for FATCA and CRS, which can ease the effort by eliminating the need to collect and validate IRS Tax Forms W-8/W-9 or entity self-certifications. Classifying accounts without the need to obtain tax documentation can save organizations the cost of soliciting and validating tax documentation and improve the customer experience.

Dun & Bradstreet is working with global tax leader KPMG to help financial firms make some of these results a reality, and here’s what you need to know.

Regulations Overview – What You’re Allowed to Do

FATCA states that a withholding agent may rely on documentation collected by a third-party data provider with respect to an entity. If it meets certain conditions, that third-party data can be used to classify offshore entity clients for certain entity types, such as Active Nonfinancial foreign entities (NFFEs) and International organizations.

FATCA regulations also state that preexisting accounts, which generally include accounts opened at a financial institution prior to July 1, 2014, and potentially entity accounts opened between July 1, 2014 and December 31, 2014, must be properly reviewed, documented and classified by June 30, 2016.

The CRS due diligence procedures also provide an exception to the requirement to obtain a self-certification where the financial institution can reasonably determine, based on information in its possession or that is publicly available, that the Account Holder is not a Reportable Person. By utilizing data, an FFI can determine that a client is not reportable and therefore does not have to solicit a CRS self-certification.

Data that can be utilized for a non-reportable entity includes:

  • Information published by an authorized government body of a jurisdiction. For example, the list of Foreign Financial Institutions published by the US tax administration;
  • Information in a publicly accessible register maintained or authorized by an authorized government body of a jurisdiction;
  • Information disclosed on an established securities market;
  • Information previously recorded in the files of the financial institution;
  • A publicly accessible classification based on a standardized industry coding system. This will include any coding system employed by the financial institution which is based on such a standardized industry coding system.

Where the financial institution relies on such information, it must retain a notation of the type of information reviewed and the date on which the review was carried out. For CRS 2017 adopters, the review of preexisting entity accounts is to be completed by December 31, 2017. 2018 adopters have until end of 2018 to review these accounts.

The Tools to Help

An FFI has can use technology tools that utilize third-party data to classify preexisting accounts for FATCA and CRS. These tools match and compare an FFI’s client account information to a third-party data source and, once compared, can catalog account records for certain FATCA and CRS classifications. This solution reduces the need to solicit and validate tax documentation, saving one costly step in the process.

Once linked, the accounts can be monitored for change in circumstance against a third-party data source, improving accuracy and providing confidence in the classifications. This procedure, along with technology, seamlessly provides the chance for an FFI to compare its client account information to a third-party source that can assist the quality assurance and improvement of the account data.

The Payoff

Two large global financial institutions recently received strong results by providing Dun & Bradstreet with a random sampling of their preexisting entity account data to determine what accounts could be classified for FATCA by utilizing D&B data.

First, the financial institutions’ data was uploaded into a tool that matched their data to ours. Based on the sampling of more 7,000 records, almost 40% of the accounts were classified for FATCA via the D&B data. For those accounts, FATCA status was clearly established, eliminating the need for the financial institution to reach out to its clients and request tax forms. Adding to the benefits, during the proof of concept, the FFIs were able to improve their customer account data by comparing it to the Dun & Bradstreet data files. For example, a number of records lacked data elements, like address or country, and the D&B data filled in many of the gaps.

By doing what is permissible under CRS and FATCA, an FFI can make a cumbersome documenting process simpler for customers, more efficient for the institute itself – and, on top of it all, improve overall data quality.

Note: This information does not constitute tax or legal advice and is provided for informational purposes only. Please consult your tax advisor for more information.

Data: Trick or Treat?

Halloween-Wallpaper-Background

 

Come October 31, hordes of miniature ghosts, ghouls and goblins will come knocking down your door for some sugar-filled treats. Of course it’s not what it seems. You know that behind the colorful masks and spooky costumes stand harmless children celebrating Halloween.

The question is, can you say the same about your data when it appears in front of you? Data, too, often is not what it appears at first sight.

Unlike enthusiastic trick-or-treaters roaming the streets in search of candy, data does not intentionally wear a disguise to trick you, yet so many of us are fooled by it on a daily basis. It’s no surprise that data can often blind us to the truth – there’s just so much of it out there. With the amount of data growing exponentially each and every day, it’s often hard to find time to make sense of it all. Instead, we take what it says on the surface without considering whether or not we are asking the right questions of the data.

Take unemployment for example. After one of the worst recessions in modern history, we’re now hearing much celebration in the media about how unemployment is down to almost record lows, 5.1% as of me writing this article. That certainly seems worthy of praise; after all, the data doesn’t lie. While the numbers are technically right, it does not factor in a ton of variables that may actually change the overtly upbeat conversation about the current economic climate.

Please don’t think of me as a ‘Debbie Downer’ trying to rain down on the recovery parade, but this is a classic example of the data not telling the complete story. The Department of Labor measures unemployment by the number of people receiving unemployment benefits. But that, of course, can be misleading since unemployment benefits expire, leaving the jobless without a way to be measured. It also does not take into consideration those who may be working part-time jobs but are actively seeking full-time work to support their families and pay back student loans – the same loans they took out in hopes of landing full-time jobs.

These are the types of factors we need to look at in order to pose the right questions before we take the data for what it’s worth. Of course it’s easy for politicians to look at the numbers without digging deeper because it makes them look good.

“It’s human nature,” says Anthony Scriffignano, Dun & Bradstreet’s Chief Data Scientist. “We have a tendency to try and organize the world around us. It’s how we probably have survived. The world around us is so chaotic so we try to find patterns in it; we try to find things that we think are there and want to believe.”

Unfortunately, Scriffignano believes, we tend to use data to make decisions based on ego or make rush judgments in response to answers we believe others want to hear instead of digging deeper to discover what really lies underneath the data. “There’s a name for it,” he says. “It’s called confirmation bias.”

Getting back to the unemployment example, it’s really not ethical to make a broad-based assumption on the data without looking at other evidence, not when that assertion clearly contradicts what others themselves are experiencing. Unfortunately there are countless examples of this happening all the time, and in much more precarious instances across business and government.

As Gartner’s senior vice president and global head of research said at this year’s Gartner ITxpo, “Data is inherently dumb. It does not actually do anything unless you know how to use it.” That doesn’t stop most of us from seeking out even more data when we don’t see the answers we want. But reaching an honest conclusion goes beyond data volume. It takes an inquisitive approach and relentless curiosity to turn data into dependable insights.

Don’t be tricked by seeing data only on face value. Look deeper.

The UN’s Lessons in Data Inspiration


unglobalpulseFive Smart Elements of a Global “Data Revolution” 

The United Nations recently held an event called “Data Playground” at the Microsoft Technology Center. The goal, according to the UN, was to “celebrate momentum around a ‘Data Revolution’ for sustainable development.”

(The tweets were particularly interesting and you can find them here: #dataplayground)

As goals go, that’s a pretty lofty one. Over the course of the evening, attendees heard presentations about crowdsourcing of data, interactive data visualizations, big data analysis and how to creatively tell stories based on data. In other words, they were having the same kind of conversations that are happening throughout executive suites around the world.

Of course, in this case, the goals are more social than business. UN representatives talked about how opening data to the planet can be democratizing. And how the act of simply asking people questions in zones affected by strife can be as much a part of providing some dignity as activating the actual results and insight from the information gathered.

To this end, there were maps of tweets after the massive Nepal earthquake. A team from Microsoft presented a climate visualization project. But a big part of the evening was spent explaining how the UN itself was already using Big Data to support 17 new sustainable development goals.

These goals were adopted by the UN’s 193 member states. While governments and community organizations are key, the UN has also said that “the new Global Goals cannot be achieved over the next fifteen years without data innovation” and “effective data collection, curation and utilization can enable a people-centered approach to sustainable development.”

The connection of this kind of data inspiration to what businesses must manage was not lost on us. And as we looked closely at the number of Global Projects the UN already has in place to further its development and data goals (you can see the full list here), we found five to be particularly notable:

  1. Crowdsourcing food prices in Indonesia: Food prices, and sudden spikes or drops in prices, can impact developing communities in big ways and lead to economic and security issues. In many rural areas, where food is sold in stalls or local markets, governments have no way to monitor prices. So the UN enlisted a group of citizen reporters, armed them with mobile phones, and built an app that lets them record and track prices of food.
  2. Monitoring biodiversity in Zimbabwe: The reduction in biodiversity, where plants and trees become more homogenous, can be problematic as it leaves them more susceptible to environmental issues or disease that wipe them out. So the UN has built a data visualization map to make it easier to track changes in populations of some animals and vegetations that are being threatened by fire or poachers in the hopes it will help policy leaders make better decisions.
  3. Citizen feedback for governments in Indonesia: The national government wants to decentralize and hand more power and decision-making to local governments. But these smaller bodies have fewer resources and less tech savvy. So the UN created a project to let citizens offer rapid feedback that could be rapidly analyzed to help local leaders make policy decisions. The feedback was also available on a public digital online dashboard to foster more transparency.
  4. Using social media to support forest fire management: Also in Indonesia, the UN created a system to monitor tweets about forest and peat fires. The UN found that in many cases, local residents were tweeting early about things like haze and visibility, possible indicators that fires had broken out. The hope is that earlier detection could help local firefighters react more quickly.
  5. Mobile phone data to track immigration: In Senegal, a group from the UN began monitoring anonymous mobile phone data to observe large changes in mobility. That is, when a large group of people suddenly decide to move elsewhere in a very short time frame. The UN hopes this can also be an early-detection system for potential humanitarian issues, like conflict or food scarcity. Again, the sooner a problem is spotted, the faster relief agencies or policy makers can respond.

 As several speakers noted, many of these projects are relatively new. But the hope is that they can be scaled across more regions over time to have a bigger impact.

While the issues are different, the way the UN is striving to experiment, report back results in a transparent way, and discuss failures and successes in an honest way, is a good model for any executive leader tackling the challenges presented by Big Data.

I know what you do, but why do you do it?

Ted Talk logoBy Tariq Sharif, VP of Product & Partner Marketing at D&B

There’s a popular TED talk by leadership expert Simon Sinek that uses the concept of a golden circle. It explains that companies and people that start with the “why” and then move to the “how” and “what” are much more successful. When customers know why you do what you do, you build stronger and more meaningful client relationships. Think Apple or Nike. We don’t buy just because they sell computers and shoes, but because of what they stand for.

D&B’s new CEO Bob Carrigan and new CMO Rishi Dave are leading D&B’s charge to be much clearer about our “why”. And this translates to our partners as well. At a recent offsite of the D&B Partner Solutions team, we talked about our mission being more than a data provider to our partners. We recognize that customers go to our partners to make significant business decisions – and that is where our “why” comes in. We are here to provide data, yes, but more importantly to help customers draw the insight and foresight from that data to make better decisions.

The discussions we are having with our partners must always be centered on that “why”. Our mission is to work together with partners to come up with the best solution that provides customers clarity, confidence and convenience in a fast-changing business environment.

That’s a lot of c’s, I know. And here’s one more – collaboration. By collaborating with our partners, we present a better solution to customers.

 

Oracle’s Take on the Data Revolution


Storm for Oracle postI’ve been reading a lot of 2014 predictions posts lately, and one that  struck me recently is the latest from Mark Hurd, president at Oracle.

He touches on three of the most critical factors impacting every aspect of business today:

  • The explosive growth of data (Hurd estimates data is growing by 40% each year)
  • The influx of a younger, tech-dependent workforce
  • The new devices that are changing how and where work

Some people may call these trends, rather than factors, but the more important consideration is when these trends converge to create a tipping point, where data strategy becomes a top corporate priority.

The volatility and volume of data has always been a persistent problem – and is only getting worse. Companies are constantly trying to understand what data matters most, how to find and incorporate it into the systems already in use and most importantly how to keep it current, updated and usable. Marketing, sales, operations and the supply chain all face the same challenge.

While this may be considered old news, the most successful businesses are finally treating data for what it truly is: the backbone of any company.  That’s why Mark Hurd is asking CEOs to become champions for data:

“The necessary changes go right to the heart of the economics of IT strategy and traditions and will require sweeping new approaches; and, this new type of approach will require fresh and decisive thinking about viewing data as a highly valuable raw material that can be shaped into products and services that customers want and need.”

Hurd challenges the CEO to address these factors head-on, but it trickles down to every level and department of a business, too.  “Revolution” is a big word, after all – and change is never easy.  What new roles, process and priorities will be created to support this new world order remains to be seen.

But Oracle, SAP and Microsoft are leading the charge, helping businesses to rethink their systems and processes so they can excel in this new normal – one step at a time.

Photo credit katgrigg