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.”

Skills That Matter in a World Awash in Data

animal-197161Original content found on www.linkedin.com/skills-matter-world-awash-data

There is a paradox put forth by the French philosopher Jean Buridan which is commonly referred to as Buridan’s Ass. One interpretation goes something like this: Take a donkey and stake it equidistant between two identical piles of hay. Since the donkey is incapable of rational choice and the piles of hay are indistinguishable, the donkey will die of hunger. Of course, in the real world, we all presume the donkey would somehow “pick” a pile. We accept these situations all around us: fish seem to “choose” a direction to swim, birds of the same species seem to “decide” whether or not to migrate, and data seems to “suggest” things that we wish to prove. Which of these is not like the others? The answer is the data. Data has no ability to “act” on its own. We can use it or not, and it simply doesn’t care. The choice is entirely ours. The challenge is how we decide rationally what data to use and how to use it, when we have enough data, and when we have the “right” data. Making the wrong choice has serious consequences. Making the right choice can lead to enormous advantage.

Let’s look at the facts. We know that we are living in a world awash in data. Every day, we produce more data than the previous day, and at a rate which is arguably impossible to measure or model because we have lost the ability to see the boundaries. Data is not only created in places we can easily “see” such as the Internet, or on corporate servers. It is created in devices, it is created in the cloud, it is created in streams that may or may not be captured and stored, and it is created in places intentionally engineered to be difficult or impossible to perceive without special tools or privileges. Things are now talking to other things and producing data that only those things can see or use. There is no defendable premise that we can simply scale our approach to data from ten years ago to address the dynamic nature of data today.

This deluge of data is resulting in three inconvenient truths:

  1. Organizations are struggling to make use of the data already in hand, even as the amount of “discoverable” data increases at unprecedented rates.
  2. The data which can be brought to bear on a business problem is effectively unbounded, yet the requirements of governance and regulatory compliance make it increasingly difficult to experiment with new types of data.
  3. The skills we need to understand new data never before seen are extremely nuanced, and very different than those which have led to success so far.

Data already in hand – think airplanes and peanut butter.

Recently, I was on a flight which was delayed due to a mechanical issue. In such situations, the airline faces a complex problem, trying to estimate the delay and balance regulations, passenger connections, equipment availability, and many other factors. There is also a human element as people try to fix the problem. All I really wanted to know was how long I had in terms of delay. Did I have time to leave the gate and do something else? Did I have time to find a quiet place to work? In this situation, the answer was yes. The flight was delayed 2 hours. I wandered very far from the gate (bad idea). All of a sudden, I got a text message that as of 3:50PM, my flight was delayed to 3:48PM. I didn’t have time to wonder about time travel… I sprinted back to the gate, only to find a whole lot of nothing going on. It seemed that the airline systems that talk to each other to send out messaging were not communicating correctly with the ones that ingested data from the rest of the system. Stand down from red alert… No plane yet. False alarm.

While the situation is funny in retrospect, it wasn’t at the time. How many times do we do something like this to customers or colleagues? How many times do the complex systems we have built speak to one another in ways that were not intended and reach the wrong conclusions or send the wrong signals? I am increasingly finding senior executives who struggle to make sense out of the data already on-hand within their organization. In some cases, they are simply not interested in more data because they are overwhelmed with the data on hand.

This position is a very dangerous one to take. We can’t just “pick a pile of hay.” There is no logical reason to presume that the data in hand is sufficient to make any particular decision without some sort of analysis comparing three universes: data in hand, data that could be brought to bear on the problem, and data that we know exists but which is not accessible (e.g. covert, confidential, not disclosed). Only by assessing the relative size and importance of these three distinct sets of data in some meaningful way can we rationally make a determination that we are using sufficient data to make a data-based decision.

There is a phenomenon in computer science known as the “dispositive threshold.” This is the point at which sufficient information exists to make a decision. It does not, however, determine that there is sufficient information to make a repeatable decision, or an effective decision. Imagine that I asked you if you liked peanut butter and you had never tasted it. You don’t have enough information. After confirming that you know you don’t have a peanut allergy, I give you a spoon of peanut butter. You either “like” it or you don’t. You may feel you have enough information (dispositive threshold) until you learn that there is creamy and chunky peanut butter and you have only tasted one type, so you ask for a spoon of the other type. Now you learn that some peanut butter is salted and some isn’t. At some point, you step back and realize that all of these variations are not changing the essence of what peanut butter is. You can make a reasonable decision about how you feel about peanut butter without tasting all potential variations of peanut butter. You can answer the question “do you like peanut butter” but not the question “do you like all types of peanut butter.” The moral here, without getting into lots of math or philosophy, is this:

It is possible to make decisions with data if we are mindful about what data we have available. However, we must at least have some idea of the data we are not using in the decision-making process and a clear understanding of the constraints on the types of decisions we can make and defend.

Governance and regulatory compliance – bad guys and salad bars.

Governance essentially boils down to the three time-worn pieces of advice: “say what you’re going to do, do it, say you did it.” Of course, in the case of data-based decision making, there are many nuances in terms of deciding what you are going to do. Even before we consider rules and regulations, we can look at best practice and reasonableness. We must decide what information we will allow in the enterprise, how we will ingest it, evaluate it, store it, and use it. These become the rules of the road and governance is the process of making sure we follow those rules.

So far, this advice seems pretty straightforward, but consider what happens when the governance system gets washed over by a huge amount of data that has never been seen before. Some advocates of “big data” would suggest ingesting the data and using techniques such as unsupervised learning to tell us what the data means. This is a dangerous strategy akin to trying to eat everything on the salad bar. There is a very real risk that some data should never enter the enterprise. I would suggest that we need to take a few steps first to make sure we are “doing what we said we will do.” For example, have we looked at the way in which the data was created, what it is intended to contain, and a small sample of the data in a controlled environment to make sure it lives up to the promised content. Small steps before ingesting big data can avoid big, possibly unrecoverable mistakes.

Of course, even if we follow the rules very carefully, the system changes. In the case of governance, we must also consider the changing regulatory environment. For example, the first laws concerning expectations of privacy in electronic communication were in place before the Internet changed the way we communicate with one another. Many times, laws lag quite significantly behind technology, or lawmakers are influenced by changes in policy, so we must be careful to continuously re-evaluate what we are doing from a governance perspective to comply not only with internal policy, but also with evolving regulation. Sometimes, this process can get very tricky.

Consider the situation of looking for bad behavior. Bad guys are tricky. They continue to change their behavior, even as systems and processes evolve to detect bad behavior. In science, these types of problems are called “quantum observation” effects, where the thing being observed changes by virtue of being observed. Even the definition of “bad” changes over time or from the perspective of different geographies and use cases. When we create processes for governance, we look at the data we may permissibly ingest. When we create processes for detecting (or predicting) bad behavior, the dichotomy is that we must use data in permissible ways to detect malfeasant acts that are unconstrained by those same rules. So in effect, we have to use good data in good ways to detect bad actors operating in bad ways. The key take-away here is a tricky one:

We must be overt and observant about how we discover, curate and synthesize data to discover actions and insights that often shape or redefine the rules.

The skills we need – on change and wet babies.

There is an old saying that only wet babies like change all the time. The reality is that all of the massive amounts of data facing an enterprise are forcing leaders to look very carefully at the skills they are hiring into the organization. It is not enough to find people who will help “drive change” in the organization – we have to ensure we are driving the right change because the cost of being wrong is quite significant when the pace of change is so fast. I was once in a meeting where a leader was concerned about having to provide a type of training to a large group because their skill level would increase. “They are unskilled workers. What happens if we train them, and they leave?” he shouted. The smartest consultant I ever worked with crystallized the situation with the perfect reply, “What happens if you don’t train them and they stay!” Competitors and malefactors will certainly gain ground if we spend time chasing the wrong paths of inquiry, yet we can just as easily become paralyzed with analysis and do nothing, which is itself a decision that has cost (the cost of doing nothing is often the most damaging).

The key to driving change in the data arena is to balance the needs of the organization in the near term with the enabling capabilities that will be required in the future. Some skills, like the ability to deal with unstructured data, non-regressive methods (such as recursion and heuristic evaluation), and adjudication of veracity will require time to refine. We must be careful to spend some time building out the right longer-term capabilities so that they are ready when we need them.

At the same time, we must not ignore skills that may be needed to augment our capability in the short term. Examples might include better visualization, problem formulation, empirical (repeatable) methodology, and computational linguistics. Ultimately, I recommend considering three categories to consider from the perspective of skills in the data-based organization:

Consider what you believe, how you need to behave, and how you will measure and sustain progress.

Ultimately, the skills that matter are those that will drive value to the organization and to the customers served. As leaders in a world awash in data, we must be better than Buridan’s Ass. We must look beyond the hay. We live in an age where we will learn to do amazing things with data or become outpaced by those who gain better skills and capability. The opportunity goes to those who take a conscious decision to look at data in a new way, unconstrained and full of opportunity if we learn how to use it.

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.

Is Anticipatory Analytics the Path Toward Future Truth?

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New Whitepaper Explores the Arrival of Anticipatory Analytics

Most everyone is familiar with the image of the eccentric fortune-teller gazing into her crystal ball to boldly predict the future. In the business world, teams of analytic experts are doing this everyday; they’re just using data instead of a crystal ball to get a glimpse into the future.

Thanks to advanced analytics, organizations are able to understand potential outcomes and evaluate how issues can be addressed. By generating predictive models based on all the data being captured, a new level of transparency and foresight has been created that helps shape future business strategy based on historical trends. This is called predicative analytics, and it is “the fastest growing segment of the business intelligence (BI) and analytics software market,” according to Information Management.

But for all of the promise around predictive analytics, there is some criticism.  For instance, since environments and people are always changing, relying on historical trends is said to be too simplistic and sterile to say something will or will not happen with a great degree of certainty. But a new analytic approach has emerged that may be better at grasping future outcomes.

As technology has evolved, so has our ability to process data at an incredible rate, making it possible to perform what has become known as anticipatory analytics. While still a relatively new concept, anticipatory analytics is gaining prevalence as a methodology.  It leapfrogs predictive analytics in that it enables companies to forecast future behaviors quicker than traditional predictive analytics by identifying change, acceleration and deceleration of market dynamics.

In order to make this possible, the right mixture of data, processing tools, technology and expertise plays a central role. The following developments play key roles in being able to address the future, today.

Key Enablers of Anticipatory Analytics

4 trends are making anticipatory analytics a reality.

To gain a deeper understanding of the emergence of anticipatory analytics, and how it should be utilized in your organization, check out this detailed guide that outlines the differences between anticipatory and predictive.

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.

Data: Trick or Treat?

Halloween-Wallpaper-Background

 

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

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

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

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

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

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

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

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

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

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

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

The UN’s Lessons in Data Inspiration


unglobalpulseFive Smart Elements of a Global “Data Revolution” 

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

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

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

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

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

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

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

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

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

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