Think Data First, Platform Second – Why Data Fuels MDM

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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.

Data Dialogue: CareerBuilder Consolidates CRMs, Identifies Hierarchies and Provides Actionable Insight for Sales Enablement

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A Q&A with CareerBuilder’s Director of Sales Productivity  

CareerBuilder, the global leader in human capital solutions, has evolved over recent years to better meet marketplace demands. “We’ve moved from transactional advertising sales to a software solution, which is much more complex with a longer sales process and a longer sales cycle,” says Maggie Palumbo, Director of Sales Productivity for CareerBuilder.

With that, it is critical that the sales teams Palumbo works with must be armed not only with information on which accounts to target, but also with intelligence they can use to approach – and engage – the potential customer.

More, as they continue to expand globally, their need for consistent data and CRM consolidation has become a priority. Establishing one system whereby employees all see the same information better positions the company for continued global expansion through more informed decision-making.

We talked with Palumbo about how she has been leading this effort.

What are you focused on now as you look to your priorities moving forward?

In the past, we did not have much in the way of governance. We had loose rules and accounts ended up not being fully optimized. We’re focused now on better segmentation to figure out where we can get the best return.

We use tools from Dun & Bradstreet to help us accomplish this goal. Specifically, we rely on geographical information, SIC codes for industry, employee total and other predictive elements. Then we bring it all together to get a score that tells us which accounts to target and where it belongs within our business.

How do you make the information actionable?

We are unique in that we take the full D&B marketable file, companies with more than 11 employees, and pass it along to a sales representative. Some reps, those with accounts with fewer than 500 employees, have thousands of accounts. What they need to know is where to focus within that account base. Scoring and intelligence allows us to highlight the gems within the pile that may have otherwise been neglected. The reps at the higher end of the business have fewer accounts and require more intelligence on each account to enable more informed sales calls.

Because we’ve moved from a transactional advertising sales model to one based on software solutions, our sales process is now more complex. The reps need intelligent information that they can rely on as they adjust their sales efforts to this new approach.

How do you make sure you’re actually uncovering new and unique opportunities?

We’ve gone more in-depth with our scoring and now pull in more elements. We also work with third party partners who do much of the manual digging. With that, we’re confident that we’re uncovering opportunities others may not necessarily see.

How do you marry together the data with the technology?

When the economy went south, our business could have been in big trouble. We are CareerBuilder. For a business whose focus is on hiring, and you’re in an economy when far fewer companies are hiring, where do you go with that?

Our CEO is forward-thinking and already started to expand our business to include human capital data solutions. With that, it became clear that we needed to have standardized data, which we do via our data warehouse. Once the data is normalized and set up properly, it can be pushed into the systems. Pulling the information from different sources together into one record is the challenge. We use Integration manager for that; the D-U-N-S Number serves as a framework for how we segment our data and we rely heavily on that insight.

How does Dun & Bradstreet data help within your sales force?

Data we get from Dun & Bradstreet provides us with excellent insight. While the reps may not care about the D-U-N-S Number, per se, they do care about the hierarchical information it may reveal—particularly with the growth of mergers and acquisitions over the past few years.

What other aspects of your work with Dun and Bradstreet have evolved?

We are a global company, but we are segmented. As we move everyone across the globe onto one CRM platform, we are creating more transparency. That is our goal. In order for people within the organization to effectively partner with each other, they must see the same information, including hierarchical structure. D&B has helped us bring that information together and identify those global hierarchies.

Tell me more about how linkage has helped you.

We used to be all over the place, with multiple CRMs across the organization globally. Some were even homegrown. We also wanted to get a better view on hierarchies. We lacked insight of what was going on with a company across multiple companies and therefore couldn’t leverage that information. We had to bring it all together through linkage. We’ve made great progress in terms of the global hierarchy and global organizational structure, and we couldn’t have done it without D&B.

Parting Words

As CareerBuilder continues to grow globally and evolve as a company to meet customer needs and demands of the marketplace, aligning the sales process with actionable intelligence is critical to its forward-moving trajectory. “It’s all about fully optimizing the data that’s available to us so that we can focus our efforts on where we can get the most return for the company,” said Palumbo.

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…

Data: Trick or Treat?

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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.

System Integrators Fuel Customization

Brock BuildersHome-builder D.R. Horton is the largest residential construction company in the United States. Like many companies, it has optimized its business model using simple principles: Build a simple product, make it customizable and make it easy to support.

With this strategy, D.R. Horton has realized 84 quarters of consecutive growth. There are a variety of reasons the company is thriving, including strong leadership. Let’s take a closer look at their three principles.

Simple Product: D.R. Horton sells a simple product, a home. Their core competency is building homes. Usually it offers three or four models in each suburban community it serves. Each home is built from the same materials and designed by the same architect. Each caters to a spectrum of buyers, including those willing to pay a premium for high-end finishes, upgraded appliances and designer paint colors. And that is where it ends.

Customizable Aftermarket: There is a cycle of goodness around each new housing development. Retail stores like Home Depot move in, ready to serve the needs of new homeowners. Local landscapers and service providers thrive with new business, outfitting new homes with different fixtures or adding a deck, a spa or a cabana. Spending by new homeowners stimulates the local economy, the town grows, and the housing market flourishes as new buyers settle in and existing home buyers return for more.

Maintenance Services and Annuities: Often new homes are part of a loosely organized home owner’s association. Homeowners pay a monthly fee that covers ongoing maintenance of community assets like sidewalks, lighting and water, trees and grass. The business of collecting fees and distributing proceeds back to D.R. Horton, the city, and contractors is outsourced. The fees generate revenue like a silent tax on buyers, who are beholden to their basic desire for a nice park, clean sidewalks, ample street lighting and cut grass.

Applying the Lesson Plan

The case study of D.R. Horton is interesting because there’s a strong parallel between how it approaches the housing market and how Dun & Bradstreet addresses the market for commercial data and analytics.

Product: D&B sells a product such as D&B360, DNBi, D&B Direct 2.0 and others. Features and functions cater to use cases and price points, with customers willing to pay a premium for confidence in the veracity and richness of our commercial business insight. The architecture for each product is standardized, the materials largely the same.

Customizable Aftermarket: When buyers want customization, we leverage our partners to fulfill those specific needs. Our system integrator (SI) partners – part of the Dun & Bradstreet Global AllianceNetwork program – have the specialized skills needed to stitch together the fabric of software and applications, Dun & Bradstreet commercial data, and the many workflows and business processes that fuel an enterprise. System integrators develop a myriad of features our customers want.

Maintenance Services and Support Annuity: At its core, Dun & & Bradstreet maintains and supports products like D&B Direct 2.0, DNBi and D&B360. As the variety of customizations glom onto our products, we increasingly turn to SIs to maintain the growing labyrinth of custom applications and services hanging off our products. SIs collect a recurring fee to maintain these enhancements, and customers are happy because someone is servicing their ongoing need for customization and support.

The business potential here is the same: a virtuous cycle that connects buyers to the products and services they want and need. The markets are different – homes vs. data – but the approach is similar. Figure out how much of the buyer’s need your business can address, and then refer, recommend and partner to make sure your product is going to market with the support and services needed to make it a ripping success. Because that’s how markets – and economies – grow.

Image credit: Brock Builders

 

Big Data, Reservoirs and an End to the Culture War

Seattle Municipal Archives“Culture eats strategy for breakfast,” management guru Peter Drucker used to say. The point being this: The hardest thing to change about any organization is its culture.

For instance, when it comes to managing business data, analytics and reporting, IT professionals and business managers are locked in a culture clash that makes executing on strategies difficult – if not impossible. And that’s a big problem for any organization that wants to use Big Data to get to data-driven decision-making.

Businesses have always had a thirst for information. At first, it was important to the operation of the business to have the latest and greatest intel on what competitors were doing. Today, business intelligence serves as the foundation for many — if not all — business strategies. The better the insight, the greater the chance of success.

What It Means for IT

Historically, IT departments have favored the enterprise data warehouse (EDW) as a means to organize and share data. The “single canonical form” is a traditional approach to centralizing and managing data assets, but it can be cumbersome and inflexible. More and more, business managers are pushing for decentralized tools that are highly accessible and easy to share, like Microsoft Excel spreadsheets and point products, and that offer better data quality.

IT has limited ability to influence its business counterparts, and it has the cost of data storage and management to worry about. Add to that the fact that there is more data today, moving more quickly, and has greater promise for value – if you can unlock it. What IT people need is a framework that’s flexible enough to support both structured and unstructured in a cost-effective way, while still adhering to established governance rules.

Culture Matters

One possible solution is still fairly theoretical: Create a reservoir of business data. It’s a metaphor worth looking into. A business data reservoir could use Big Data and virtualization technologies to store and move data in a cost-effective way. It could also enable business managers to build on their departmental solutions, while still operating in a framework sanctioned by IT.

We like this idea because it lets IT managers and business users meet in the middle, providing a more accommodating architecture that makes it easy to:

  • Add new data to a structured environment, using a flexible data model that can pull from departmental data marts.
  • Connect to external data, including web content, news feeds and spreadsheets.
  • Enable multiple views of the same data, using federation and virtualization with systems of record.
  • Capture and embed intelligence using data definitions and analytics, to make it easier to interact with data and derive insights.
  • Get to analytics more quickly, with better results. Data is cleaned and available from a system of record, and the same data is available to all reporting tools. A data reservoir could also enable ad hoc reporting, for greater agility.

At its heart, a business data reservoir is not simply a technology move. It’s about changing the culture of IT to better match the business culture. And it promises to help businesses of all types operate more efficiently at a global scale.

Image credit: Seattle Municipal Archives

New Partners, New Business

Marina del CastellWith the advent of Data as a Service (DaaS), customers are now looking to connect their enterprise applications – master data management (MDM) hubs, marketing automation, HCM, CRM, ERP, BI and analytics platforms – to high-quality commercial data, insight and professional contacts. That means D&B is expanding its various sales channels, selling through different types of partners and courting distributors who can place the right content with the right kind of client. It’s worth taking a moment to look at who our new partners are, how we work with them, and how our new alliances are helping end customers.

Building on All Fronts

As companies strive toward a 360-degree view of their prospects and customers, our verified company and contact data become central to any CRM or marketing automation platform. Our best-in-class reference data becomes foundational to MDM practice.

Fortunately for D&B, enterprises are hungry for verified data from a reliable source. They want to inject D&B data into their business workflows. For example, giving sales reps accurate contact data in the company CRM system and helping marketing pros calibrate spend with the right audience target.

D&B is focused on recruiting the major ISVs to help us integrate D&B DaaS into their workflow applications and services – among them CRM and ERP platforms. D&B’s Global Alliance and Partners Solutions is partnering with companies such as Microsoft, Oracle, SAP, Salesforce.com and SugarCRM so their apps can consume and present D&B data and insight right within their UIs, so it’s easy for end users to use it in the context of their business.

Our Global AllianceNetwork reseller channel continues to evolve. Here we partner with the reseller already relevant in the partner ecosystem. The value-added resellers (VAR) deliver the packaged goods: ISV software licenses and solutions, D&B content and code. Support and managed services are table-stakes. VARs are increasingly instrumental in recommending and evangelizing DaaS with their customers. We have ongoing business relationships burgeoning in our AllianceNetwork: Symphony, CodeRight, Avanade, and 30+ more.

Integration Is King

Rapidly evolving technology platforms, business processes and policies dictate agility in a complex enterprise context. This is where system integrators (SIs) help. To quote Gartner, “the enterprise is under increasing pressure to drive integration work born of LOB SaaS, APIs and other business initiatives and projects.” D&B partners with SIs to bridge process, technology and usability gaps – and to reduce waste and increase customer value for D&B products and services.

Nourished, the SI can become an incubator for further collaboration, innovation and sales. SIs can have great traction with an enterprise client. Working with them more closely, we hope to create even greater value for our shared customers — and tap into more streams of derivative revenue. So ideal partnership framework is around collaboration and implementation rather than a straight-up product resell. This framework can accelerate time-to-implementation for our customers and deliver better value, sooner. Some examples of our fantastic SIs include CTS, Enapsys, SolidQ and A Hundred Answers Inc. – and we’re expanding our alliances to include businesses in non-U.S. countries.

Have no doubt the need for SIs is growing. My next post is going to talk about a fourth category of partner, a highly skilled data expert who both works with our clients and evangelizes our goods to users and user groups around the world. We call this a community of D&B Most Valuable Professionals (MVPs). These data gurus have the skills to decrease time-to-market by driving D&B brand awareness, preference and presence — and to sharpen the cutting edge.

Image credit: Marina del Castell