Breaking Down the Business Relationships in Breaking Bad

better-call-saul

 

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

Financial data and eyeglasses

 

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.

With Data, Size Matters, but it’s Really About the Relationships

14854239231_2d7c16f4f4_kI sat in the audience at Waters USA 2015 watching my colleague Anthony Scriffignano, Dun & Bradstreet’s Chief Data Scientist, capture the undivided attention of the delegates with the “inconvenient truths” about decisions we make in today’s data-driven environment. It was intellectually powerful stuff – even for a very tuned-in audience of technology leaders and C-level executives at financial institutions who had gathered to learn more about emerging IT innovations and solutions they can adopt in their organizations for better decision making and optimization.

As Anthony spoke, I thought of how all of us – in both our personal and business lives – consume data at an alarming rate. Yet despite the consumption pace, we crave even more, causing us to run endlessly faster in order to simply keep up.

As a parent, my concern grows when witnessing my children become overly focused on the data that is available to them, linking them tightly to their devices, gobbling up whatever social or structured data is in their line of sight. Try as I might, I struggle to convince my high schooler that not every text message or Snapchat alert requires immediate attention. She believes that, whatever the problem, she can get the answer by seeking data from her phone.

“You’re better off being thoughtful and engaging your closest friends and colleagues in discussion, rather than responding to every question or message by checking your phone,” I say, to a predictable teenage eye roll and cynical smirk.

It occurs to me that this lament is similar to that of most of today’s Wall Street Chief Data Officers. Their firms have mountains of data, more computing power than they could possibly need, and billions of dollars of compensation incentives driving them to perfect their correlations and analysis. The natural reaction to solving problems is often to get more data, when in fact sometimes it muddies the waters and obscures the causes.

I think of what my colleague Anthony stressed in his presentation, that many data managers run faster and faster to get just one millisecond ahead of the competition. Financial institutions continue to accumulate greater amounts of data, much of it unstructured data and social media related, to help gain intelligence on our markets. They process is ultra-fast. They hire the smartest quants in the world.

So with all this data and all the knowhow and computing power to run all these scenarios, what do they have now?

Our markets are generally more volatile and move unpredictably, while “flash crashes,” fraud and flawed market structure drive unprecedented regulation. Our institutions have volumes more data to analyze than ever before. But has that made our markets more stable? More trustworthy? Easier to understand? Are we more confident in our markets’ fairness or performance? Few believe so.

Participants’ reactions to the markets’ problems are disproportionately addressed by creating new regulation that restricts activity and often amounts to throwing the baby out with the bath water by curbing positive activity. Regulation may touch on some of the causes, however, it is often so broad that it masks the real problem, rather than pinpoint the source. There’s just too much data at hand, and without analyzing the relationships more closely, it has become impossible to target the specific data needed to identify and solve precise source issues…

So, despite all the data and intelligence, the current approach is missing something. Much of the industry is still performing data analysis like they always have (albeit faster and more effectively) – by crunching more data.

The real answer is to resist the urge to get more data for data’s sake.

The best CDOs are shifting their strategy to one that enables them to better recognize the importance of the relationship between the data, our flawed market structure, and ineffective risk management.

While the improvements in data analysis have created new opportunities for capital markets institutions, flaws remain in the system, illustrating that this data still has gaps that are not properly acknowledged. In part, this is the result of the industry’s effort to apply the most sophisticated algorithms and analytics to the most data it can find.

However, the inconvenient truth cited by Anthony is – simply – that more data is not necessarily better data. The answer lies in thinking differently about how the market consumes data and studies relationships in that data. This represents a marked change over the approach most CDOs took just a decade ago.

Now if I can only get my teenagers to think the same way…

Learn more about Dun & Bradstreet’s perspectives on the kinds of data organizations in capital markets need to help make better decisions.

4 Wishes Data-Inspired Leaders Want This Holiday

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

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

 

What Every Data-Inspired Leader Wants This Holiday

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

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

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

1. A Better Way to Find Truth in Data

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

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

2. A Method for Applying Structure to Unstructured Data

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

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

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

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

3. A Global Way to Share Insights

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

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

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

4. Breeding a Generation of Analytical Thinkers

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

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

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

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

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.

Who Owns Company Data?

Patrick Finnegan_crayCompanies spend billions of dollars each year on initiatives to improve their ERP, CRM, supply chain and business intelligence capabilities, with varying levels of success. One reason for less-than-stellar results is the quality of the data inhabiting those systems. Too often, basic data hygiene is overlooked in the pursuit of big picture goals.

What data needs is a champion. Gartner Research predicts that by 2015, 25% of companies will appoint a Chief Data Officer (CDO). Without executive-level leadership, the responsibility for data quality ping-pongs between the IT department and business managers, with no good solution in sight.

Data is most often relegated to the IT group. However, business groups generate the data, they consume the data…why then is it tossed over the fence to IT to make sure it is accurate?

Ted Friedman, author of The World Is Flat and principal analyst at Gartner Research working on data management and integration, has a great perspective. “Poor data quality is a business issue, it’s not an IT issue,” Friedman said. “The only way companies are going to be successful in improving data quality is to begin to put the responsibility and accountability where it belongs, on the business side.”

In my experience, IT departments rely on IT tools to address data quality – and that can lead to a simplistic solution to a complex problem. It’s like the old saying goes, “If the only tool you have in your toolbox is a hammer, you see every problem as a nail.” Technology solutions are important, but they only account for a piece of a more holistic solution that safeguards data quality.

A CDO can bring business and IT folks together, and marry their shared goals. IT folks can educate managers about the tools at their disposal, and business leaders can articulate their needs and prioritize projects that can bring the greater returns to the business overall.

A few years ago, Google CEO Eric Schmidt was quoting as saying: every two days, we create as much information as we did from the beginning of time until 2003. You can argue the exact number of exabytes that end users generate in a day. But the fact remains: we are in for a lot more data. My thinking is, better get out in front of this trend, or risk getting left behind.

Image credit: Patrick Finnegan