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

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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.

Will Businesses Like Facebook’s New Reaction Buttons?

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New Data Will Reveal How Customers Really Feel

If you recently updated your Facebook status, you may see a wide array of responses that goes beyond the fabled “like” riposte the social media platform has become known for. Last month Facebook unveiled “Reactions,” which offers its users five additional ways to express their opinions on everything from pictures of your cats to your brash political musings. While it will certainly give users more choices in how they interact with friends, it will also give businesses deeper insights into customer sentiment.

Reactions are essentially an extension of the like button, with six different buttons, or “animated emojis” (ask a millennial): “Like,” “Love,” “Haha,” “Wow,” “Sad” or “Angry.” Despite the public outcry for a “dislike” button, Facebook did not include one because the company felt it could be construed as negative. But that won’t stop people from using the “angry” button when they do not like something.

While this may upset a friend, it can actually help companies react and respond to complaints. What’s more, it may even help us predict trends and threats we may have not previously seen.

 

“I think it’s definitely possible to draw true insight from this, but you’ll need to do some very careful analytics before forming any meaningful conclusions.”

-Nipa Basu, Chief Analytics Officer, Dun & Bradstreet

 

Dun & Bradstreet’s Chief Analytics Officer, Nipa Basu, believes the new “Reactions” will be an opportunity for businesses to better understand their customers, but notes it will take a deep level of understanding to make perfect sense of it.

“I think it’s definitely possible to draw true insight from this, but you’ll need to do some very careful analytics before forming any meaningful conclusions,” explains Basu. “These ‘Reactions’ are typically based on real-time human emotion. Sometimes it will be fair. Sometimes it will be unfair. But if you have a large sample of a lot of people’s emotions reflected then you can begin to ask if that says something about the customer experience or the product or service from that business, and go from there.

“Then comes the second part. Does it matter? Looking deeper at what the comments suggest and how they correlate with the different types of ‘Reactions’ being received, a good analyst will be able to draw more accurate insights. Looking at both types of responses together will help understand what customers really felt.”

This is what Basu believes will make social sentiment analysis that much more effective. Not only will it open the door for brands to assess the success or relevance of their content, as well as measure customer satisfaction, it may paint a deeper picture about total risk and opportunity across industries that could benefit others.

“When you utilize a company like ours, where we already have our pulse on the health of global businesses and industries, and combine it with these social ‘Reactions,’ we can start to understand the correlation it has between business growth or degeneration,” said Basu. “Can looking at the amount of ‘angry’ comments predict the future health of a particular business or industry? Quite possibly. This is paving the way for even richer data that can drive even smarter analytics.”

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.

The Chief Analytics Officer Takes Center Stage

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From coast to coast, the business world is lauding the emergence of the Chief Analytics Officer (CAO). That’s right, we said Chief ANALYTICS Officer. Perhaps you were thinking about that other C-level role that recently dominated the headlines – the Chief Data Officer? Nope, the CDO is so 2015. Despite it being called the hottest job of the 21st century, it seems a new contender has entered the fray, that of the CAO.

All joking aside, the role of CDO has certainly not lost any of its luster; it remains an important position within the enterprise, it’s just that the CAO has now become just as significant. While both roles will need to coexist side-by-side, they face similar challenges, many of which were common themes during two recent industry events. Just looking at the massive turnout and the passionate discussions coming out of the Chief Analytics Officer Forum in New York and the Chief Data & Analytics Officer Exchange in California, it is apparent that the CAO will play a pivotal role in utilizing the modern organization’s greatest asset – data.

IDC predicts that the market for big data technology and services will grow at a 27% clip annually through 2017 – about six times faster than the overall IT market. But that windfall is not just going towards ways to obtain more data, it’s about investing in the right strategies to help make sense of data – an increasingly common perspective. Therefore, everyone is trying to figure out how to scale their analytical approach and drive more value across the organization.

Data alone is no longer the focal point for businesses, not without analytics to accompany it. We’re seeing the term ‘data analytics’ popping up more frequently. In fact, it’s the number one investment priority for Chief Analytics Officers over the next 12-24 months according to a Chief Analytics Officer Forum survey. That means an increased investment in data analytics and predictive analytics software tools. Not surprisingly, with the increased investment planned around these initiatives, the ability for data analytics to meet expectations across the company is the number one thing keeping CAO’s up at night, according to the same study.

The lively discussions during the course of both events featured some of the industry’s smartest minds discussing common challenges and objectives. Here are some of the most prevalent topics that were discussed.

  • The Need for C-Level Support: Very similar to the challenge facing the CDO, the CAO will need to secure buy-in from the C-level to make a real impact. Many speakers at both events expressed similar frustrations with getting the C-level to provide them the budget and resources needed to do their jobs. A good approach to take shared during one session was to build a business case which clearly quantifies the business value analytics will drive against specific goals. If you can tie everything back to ROI, you will have the ears of the CEO.
  • Breaking Down Silos: Even if you have attained support from the C-level, it is critical to partner with cross-functional departments. Whether it’s sales, marketing, finance, etc., tying the business value that analytics can drive to their specific goals will help the work relationship. These teams need to feel they are being included in your work. This theme was evident in many sessions, with speakers giving examples how they partnered with their colleagues to influence their business strategy and turn insights into action. At the end of the day, analytics is only as good as the data you have, and you need to ensure you are leveraging all of it across the enterprise.
  • Becoming a Storyteller: It was widely acknowledged that 80-85 percent of business executives who claim to understand analytics actually don’t. Hence the need to be able to simplify the message is critical to your success. There was a lot of discussion around what encompasses being a better storyteller. Being able to stay on point, avoiding technical jargon, relying on words versus numbers, and clearly quantifying and measuring business value were agreed upon paths to help the analytics group clearly communicate with the C-level.
  • Building the Right Team: Of all the discussions during both events, this was one of the most prominent themes and one of the biggest challenges shared by attendees. Where do you find the right talent? Views ranged from outsourcing and offshoring strategies to partnering with universities to develop a combined curriculum for undergrads and graduate students.

Everyone agreed the right candidate should have 4 distinct skills:

  • Quant skills, i.e. math/stats qualification
  • Business acumen
  • Coding skills/visualization
  • Communication and consulting skills

Since it is very difficult to find all four skills in a single person, the consensus was the perfect analytics team should consist of 3 tiers of analytics resources that should be thought through when building a team:

  • Statisticians, modelers and PhDs
  • Computer scientists
  • Measurement and reporting

From talent to strategy, the past two analytics-focused events underline the importance of employing a CAO in the enterprise. As data and analytics continue to be the core drivers of business growth, the CAO will not only need a prominent seat at the table, they will need the freedom and resources to help turn analytics into actionable insights for the entire enterprise.

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.

4 Wishes Data-Inspired Leaders Want This Holiday

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

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

 

What Every Data-Inspired Leader Wants This Holiday

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

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

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

1. A Better Way to Find Truth in Data

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

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

2. A Method for Applying Structure to Unstructured Data

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

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

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

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

3. A Global Way to Share Insights

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

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

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

4. Breeding a Generation of Analytical Thinkers

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

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

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

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

3 Reasons to Be Thankful for Data

give thanks - Thanksgiving concept

 

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

 

Bountiful Amounts of Data

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

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

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

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

 

Data Freedom

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

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

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

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

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

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

 

A Brave New World of Data

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

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

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

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

 

In Summary

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