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.

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.

New Research: Data-Driven Marketing Trends

charts magnifying glass blog pictureSay the right things to the right people at the right time.

Meet those three requirements and your marketing will work. But this recipe for success depends on your ability to know your market and your buyers – and how to reach them.

That’s easier said than done. Why? Because the data you rely on for marketing insights can be volatile.

Consider what happens every 30 minutes*:

  • 120 business addresses change
  • 75 phone numbers change
  • 20 CEOs leave their jobs
  • 30 new business are formed

There’s one clear answer to the challenge of constant change: access to constantly updated information about your customers and prospects. This is the foundation for data-driven marketing efforts.

Key Findings from Data Driven-Marketing Trends Research

As providers of a continuous stream of commercial data and insights on more than 250 million company records worldwide, we strive to keep a constant pulse on how marketers use data to achieve their objectives. Our partner Informatica is also committed to helping marketers make data ready to confidently connect with customers.

So together, we engaged Ascend2 to conduct a study. The goal was clear: Learn how CMOs and marketing pros are meeting the challenges of achieving data quality for data-driven marketing.

You’ll be surprised at some of the findings from the benchmark report Data-Driven Marketing Trends: How and why data quality will optimize the effectiveness of your data-driven marketing strategy.

Here are a few highlights:

  • 58% of marketers cite personalizing the customer experience as the most important objective of a data-driven marketing strategy
  • 57% say improving data quality is the most challenging obstacle to data-driven marketing success
  • 69% use both internal and external resources to improve their data quality

These marketers validated the importance, challenges and impact of data quality on their marketing success. They also recognize they can’t do it alone – getting and keeping quality data requires third party enrichment.

There are many more useful findings in this report — sign up for your complimentary copy

* Source: Dun & Bradstreet; The Sales and Marketing Institute

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Informatica and Dun & Bradstreet have partnered to enable Informatica’s customers to use D&B’s data and insights to identify risk and opportunity across the enterprise. D&B data is integrated within Informatica workflows, delivering data on demand at the point of decision, when, where, and how it is needed. To learn more, visit www.dnb.com/informatica.

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.

Calling All Data Scientists

Data Scientist photo-1427751840561-9852520f8ce8Being a data scientist is one of the hottest jobs around.

The Harvard Business Review article Data Scientist: The Sexiest Job of the 21st Century describes the role this way: “It’s a high-ranking professional with the training and curiosity to make discoveries in the world of big data. Their sudden appearance on the business scene reflects the fact that companies are now wrestling with information that comes in varieties and volumes never encountered before.”

Dun & Bradstreet’s resident data scientist, Anthony Scriffignano (@Scriffignano1), has his own take:

“Being a data scientist is both an awesome opportunity and a tremendous responsibility. The opportunity comes from the fact that we are living in a time when the amount of new data being created far exceeds any capacity to discover and make sense out of it without new skills like those offered by data scientists. The responsibility comes from the fact that we need to learn to behave differently, to think differently, and to ask new kinds of questions in a world awash in data. The cost of getting this wrong is that our competitors and those with less than noble intentions will outpace us. Being a data scientist is one of the most exciting roles one can have today.”

As companies look to capitalize on the incredible asset data can be in this evolving Internet of Things world we live in, the demand for data scientists has become massive. The main concern is that the supply of talent is too constrained. Thus, pay for people in this field is highly competitive.

As if that’s not enough, being a data scientist has yet another big plus…

According to a new report from Glassdoor.com, a website that lets employees rate their companies and jobs, the number one job for work-life balance is data scientist. Glassdoor has an index from 1 (very dissatisfied) to 5 (very satisfied). The index measures things such as feelings about hours worked, time away from family, and general stress and pressure.

Data Scientist scored highest at an average of 4.2. This number is notable not just because it’s at the top, but because it stands in stark contrast to the overall mood among workers using Glassdoor.

According to Glassdoor: “Work-life balance has decreased in recent years, as employees have reported an average work-life balance satisfaction rating of 3.5 in 2009, 3.4 in 2012, and 3.2 thus far in 2015…Maintaining a healthy work-life balance can be tough in today’s work environment, but some jobs allow for more flexibility than others.”

Like a data scientist. No doubt, given the growing demand, people with the right talent are able to call their shots. And that includes flexibility around issues of family and work. High pay. Exciting work. Huge demand. And now, happiness, too.

It makes one wonder why everyone isn’t rushing to sign up. Probably because the role requires a unique blend of talents and personality. The Harvard Business Review article puts it like this:

“Think of him or her as a hybrid of data hacker, analyst, communicator, and trusted adviser. The combination is extremely powerful—and rare.”

As demand continues to grow, we will see more data scientist up-and-comers moving into this role, even if they don’t know it yet. Companies will need to be on the lookout for these special talents, as they will be snapped up quickly.

We know Anthony is rare – and we’re lucky to have him.

Why You Can’t Just Ignore Bad Data

data_qualityWhen we talk about Data Quality, it can often be hard for everyone to agree on one, technical definition. But at its most basic level, data quality is about information that is correct.

Of course, everyone wants data that is correct, regardless of department, market segment or role. And as long as most of the data is correct, the common plan of action is no plan – literally to ignore that bad stuff and focus on the good stuff.

But Dr. Anthony Scriffignano, Senior Vice President and Leader of Worldwide Data and Insight at Dun & Bradstreet, suggests this may be one of the most common mistakes organizations make when it comes to managing their data.

“The biggest opportunity I see lost is what people do with the data they don’t like,” he said in a recent conversation. “They just ignore it. And ignoring it can be hugely detrimental to the company.”

That might sound counter-intuitive. After all, when your sales force is under the gun to work the next lead or reach the next contact, who wants to stop and think about why a phone number didn’t work? Or why a record didn’t match? Or why information seemed old or garbled?

But according to Scriffignano, those errors can be a valuable asset to an organization willing to put them under a microscope. That bad data can provide deeper insight into your broader data set if you begin to ask the most obvious question: Why is this wrong?

Is it the way your own organization is handling the data set? Is it a conflict between different platforms or data providers? Is the data simply dated because your organization is not updating as often as the data provider updates its own information? Is it all of the above? And is it getting better or worse over time?

This ought to be a basic, core concept in any company’s Master Data Management plan. Taking the time to examine the bad data may not have the same immediate pay off as chasing the next sales lead using the good data. But using that bad data to drive improvement in your overall data usage can offer big returns in terms of efficiency and accuracy over the long term.

Image credit: Intel Free Press

 

A Lively Debate about Master Data Management


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The explosive growth of data has only accelerated the demand for availability of this information within the user work-flow solutions such as CRM, ERP and BI systems. This change is provoking a debate in the MDM community on how to integrate and move data between the MDM environments and the Satellite CRM, ERP and BI systems.

Earlier this month, I had the opportunity to participate in a great discussion about two different approaches to Master Data Management (MDM) with fellow attendees at the Gartner MDM Summit in Las Vegas. The MDM summit has changed significantly in the past 5-6 years, growing from a small, IT-focused conference with just a few hundred attendees to a large conference attracting major sponsors and thousands of key decision-makers from multiple industries. It’s a sign that MDM is becoming relevant to more and more people.

In a well-attended session on MDM market trends, I presented our DaaS strategy, describing how we integrate clean, verified data into CRM, ERP and BI “satellite” systems. The audience of 60+ attendees, including customers and partners, was highly engaged. They expressed concerns about the effectiveness and risks associated with our approach, versus a more traditional approach. It was a great conversation, and we were able to use the feedback to both validate and refine several components of our strategy. That’s interactive dialogue at its best!

Here’s the gist of the debate. The traditional MDM approach centralizes the consumption, standardization and federation of data into the satellite systems like CRM and ERP. MDM practitioners are always skeptical of data flowing directly into these systems before it gets mastered. However, our audience expressed strong receptiveness to a model with DaaS integration enabled in both the MDM environment and the satellite systems simultaneously, for easy mastering of data that might be integrated directly into a satellite system.

The polarization of the two approaches was reflected in what MDM solution providers were saying on the exhibition floor. Some providers kept to the more traditional, centralized approach. Others, like Informatica, allowed for a more federated MDM environment, so that distributed satellite systems could play a role in the consumption and mastering of some data.

We expect this debate to continue, at the Garter Summit and other venues, because it’s so important to how we will tackle MDM in the future. Because our DaaS strategy uses the D-U-N-S Number to play a major role in the MDM process, we hope to continue to be a central part of the conversation – and we can and must continue to participate and drive the narrative. Our successes with integrations such as Data.com CRM and D&B Direct for MDM applications puts us in a great position to encourage adoption of MDM solutions that fit with what our customers and partners are asking for.

Here’s my take: We should continue to enable the integration and flow of D&B data across all consumption points, including the satellite systems and the MDM environments, using D-U-N-S Numbers as the link. This approach is consistent with the strategic direction the industry, and it’s the path its leaders are already headed down.