It’s Time For a Change of Season

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Maybe it’s the spring bloom underway here in the Northeastern part of the United States that has set my hopeful tone for renewal and growth. Seeing the yellow daffodils and forsythia that highlight my home’s front path always brightens my outlook. As they say, “Hope springs eternal.”

While I don’t think it was planned according to the seasons, it seems wonderfully coincidental that the new dnb.com is launching April 4. The new site will be a completely modernized experience, with a sophisticated responsive design, a heavy focus on content and an elegant overall user experience.

Along with this launch, the partner blog you’re reading will become part of a marquee editorial section called “Perspectives.” I love that Dun & Bradstreet is committed to sharing helpful content and our best answers to important trends and topics on the minds of our partners and customers – and that it will be getting priority billing on the new site.

Everything you’ve come to expect from this blog will be front and center in “Perspectives.” We will continue to share insights about the topics you care about – and hopefully that exchange of ideas, news and opinions will help as you leverage data and analytics to make important business decisions about critical business relationships.

Our partner relationships are very important to us, and the new site will showcase these relationships better than ever. The number and caliber of partners we work with continues to expand and includes market leaders in CRM, Digital Marketing, Master Data Management, Supplier Risk and Management, Credit and Risk, Compliance and Capital Markets, just to name a few. That’s a testament to our mutual customers’ needs for trusted data integrated in the tools they use every data to grow their businesses. It also shows the value our partners see in working with us.

We feel the same way, and it’s the reason Dun & Bradstreet has invested in a world-class team solely focused on our partner relationships, developing and bringing to market the solutions our mutual customers need. Exploring these solutions, and their place in the larger data world has always been the focus of this blog. Now it will be a primary content stream in “Perspectives,” alongside a variety of other related topics.

We’re dedicated to continuing an ongoing dialogue with you, in person and online. We’re excited to offer “Perspectives” on the new dnb.com as part of that information exchange. We’re hopeful you’ll find it valuable, interesting and engaging. And we so appreciate you reading this blog these last few years. We’ve enjoyed it and hope you have too.

So starting April 4, head to dnb.com “Perspectives” for great new content and stories – and in the meantime keep a spring in your proverbial step!

Hope springs eternal in the human breast;

Man never Is, but always To be blest:

The soul, uneasy and confin’d from home,

Rests and expatiates in a life to come.

 

-Alexander Pope,

An Essay on Man, Epistle I, 1733

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?

Dollar Bank photo-1443110189928-4448af4a2bc5

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.

Artificial Intelligence and Countering a Fear of the Future

19479560798_d4474e3e8f_oWhether it’s Hollywood movies or speeches by Tesla’s Elon Musk, there is a growing cultural debate around the power of artificial intelligence. “AI” is the ultimate future of Big Data, a world where machines think for themselves based on a fountain of information.

But much of the current debate is fear-based: What happens when the machines become too smart and turn on us? It is the Terminator future that alternately fascinates and terrifies people.

Beyond the sci-fi thrills, this creates a practical problem in the present. Any step toward gathering and processing more information can easily spook people who worry that it’s leading to that inevitable machine takeover.

To counteract that fear, there is a movement to research and consider the ethical implications of AI and data. The latest entry into the field was announced recently, with the creation of the Leverhulme Centre for the Future of Intelligence at Cambridge University.

Backed by a $15 million grant, the new center will pull together technologists along with people from the humanities: philosophers and sociologists. The goal: “Examine the technical, practical and philosophical questions artificial intelligence raises for humanity in the coming century.”

According to the announcement, there is a belief that the rate of advances in machine learning means we could achieve human-level intelligence in machines in the foreseeable future.

“While it is hard to predict when this will happen, some researchers suggest that human-level AI will be created within this century,” the release says. “Freed of biological constraints, such machines might become much more intelligent than humans. What would this mean for us?”

Of course, it was just this concern that prompted Musk to famously warn of the dangers of AI last year in a talk at MIT:

“I think we should be very careful about artificial intelligence,” he said. “If I had to guess at what our biggest existential threat is, it’s probably that. I’m increasingly inclined to think that there should be some regulatory oversight, maybe at the national and international level, just to make sure that we don’t do something very foolish.”

Musk was worried enough about AI to lead a $11 million donation back in July to the Future of Life Institute in Cambridge, MA. The goal of the new program is to keep “AI robust and beneficial.”

“Building advanced AI is like launching a rocket,” said Skype and FLI founder Jaan Tallinn, in a statement at the time of the donation. “The first challenge is to maximize acceleration, but once it starts picking up speed, you also need to focus on steering.”

The possibility for AI to become a not-too-distant reality is intriguing. Perhaps the right framework is to think of it through the lens of human relationships. Relationships are the core to business and society and always have been – and today data and technology enable them on a depth and scale never before possible. How can AI be developed in ways that enhance relationships – not detract from them?

Predictions are funny things. They involve a combination of data, analysis and a healthy dose of crystal ball guesswork. And sitting behind the deductive process is human collaboration and brainwork. It’s fair to say that the progress we make with the power of data and AI intelligence will continue to require the human element.

Indeed, demonstrating that there is still a human behind the wheel, that someone has thought through the consequences, could go a long way to ensuring folks that the potential benefits of AI and Big Data will far outweigh the risks.

Quality Data Drives Quality Business Decisions

Bizmology_BiesadaThis post originally appeared on Hoover’s Bizmology.com on April 16, 2014.

Garbage In, Garbage Out (or GIGO) is a phrase coined by computer scientists that means something produced from materials of low quality will also be of low quality. Conversely, quality input will result in quality output. Indeed, quality — specifically data quality — is a subject that occupies the mind of Paul Ballew, D&B’s Chief Economist, and Chief Data and Analytics Officer. Ballew and his team are charged with arming D&B customers with the quality data and predictive analytics they need to make smart business decisions.

Ballew emphasizes data quality and why it’s so important to businesses in this interview, especially in The Age of Big Data. “Data quality is a multidimensional issue for today’s organizations. It equates to having and leveraging insights that can move your business forward,” says Ballew, adding, “The pace is defined by the quality of the insight.”

Good decisions rest on a firm foundation. And quality data is that foundation. The four dimensions of “quality data” are: completeness, accuracy, depth of insight, and data latency. “Decision makers today need that level of completeness, accuracy, depth of insight, and timeliness to make good decisions,” asserts Ballew.

If that sounds like a tall order, it is for most organizations. The business world is littered with the missed opportunities and casualties that result from poor data quality and lack of data management. (Case in point: Apparently, J.C. Penney’s ex-CEO Ron Johnson either failed to gather or consult data that revealed how wedded customers were to periodic sales events before changing the store’s pricing strategy and alienating shoppers in droves.) Successful companies rationalize data to make fact-based decisions and increasingly are using new sources of data, such as social media, sentiment data, and transactional data, all of which are valuable sources of insight that can be leveraged for competitive advantage. “The bar has really gone up,” Ballew says. “Twenty years ago, having any data was sufficient. Now we’re looking for high-quality data across all these dimensions.”

Adding to the complexity is the fact that data degenerates over time: Phone numbers change or are disconnected, CEOs are hired and fired, and bankruptcies happen. As data decays, businesses can find themselves struggling with data governance and stewardship. Proactive companies look to tools, including CRM systems, supply chain management, and business intelligence partners, to get the most from their data. Ballew notes that while these are all necessary tools, “What’s perhaps more important is having a good data governance structure.”

Creating a first-rate data governance structure is itself a multidimensional process. First, businesses must identify what they’re trying to accomplish. Then they need to bring all their data and analytic assets to bear in an environment that’s responsible for those activities. “Finally,” says Ballew, “it’s important to go beyond your data and analytics team. You should have your users be involved in the process, and that includes folks from marketing, finance, and IT.”

Creating and managing an integrated data program is a team effort, and a complex one at that. But the result can be summed up very simply: Base your decisions on quality data and they will be good decisions.