Data Science: Buyer Beware

Ray Rivera

Any field of study followed by the word “science”, so goes the old wheeze, is not really a science, including computer science, climate science, police science, and investment science.

And then there is the saying, “when sex is used to pitch something besides sex, someone is trying to get in your back pocket rather than the front.”

If both of these are true, then Thomas Davenport and D.J. Patil’s rather hyperbolic declaration that the “data scientist is the sexiest job of the 21st century” deserves a double dose of skepticism.

Such skepticism is justified. Data science has much more in common with management fads than science, by its ordaining practitioners of obscure technical specialties with instant guru status, pitting them against the supposedly ignorant masses, and infusing the latter with itching uncertainty. An especially acute aspect of this uncertainty is captured in Louis Jordan‘s 1941 hit, “I’m Gonna Move To the Outskirts of Town”:

I don’t want no iceman
I’m gonna get me a Frigidaire …
I don’t want nobody
Who’s always hangin’ around.

Indeed, the bluesmen of prewar United States were right to be wary of a technology arrangement that caused their families and lovers to be dependent on persons coming regularly to the house to deliver necessary goods, whom the bluesmen feared would take advantage of women at home alone.

Remember the fad that forgot people? It’s back!

Data science has not just emerged out of the blue, but rather is the fresh-faced third generation offspring of the 1990’s management fad Business Process Reengineering (BPR). The reader might recall Davenport as one of the captains of BPR, which true to its rhetoric of “Don’t Automate, Obliterate” became an ignominiously destructive management fad. BPR’s effects were so pernicious that its three main proponents, including Davenport, issued public apologies, which consisted mainly of blame shifting, usually to vendors, consultants, and errant management gurus, while maintaining that BPR was a good idea that unfortunately fell into bad hands.

In contrast to other management ideas of the day, BPR was charmingly simple. Yet when implemented, BPR ended up producing the opposite, requiring enormous amounts of IT investment, bureaucratic overhead, and technical specialization in order to achieve even simple results.

All too frequently such results included downsizing by the thousands, with few survivors left to deal with even greater complexity, brought about by redesigned yet overengineered business processes. Like the gruesome medical practice of bloodletting, BPR left many businesses sicker than before, experiencing a 70 percent fail rate at the time of its height. To this date there is conflicting evidence as to whether BPR is truly cost-beneficial.

BPR’s demise left behind a lot of data and excess IT capacity, along with a sense of guilt over mismanagement of IT investments, giving birth to the field of knowledge management. During the next decade, knowledge management lived a modest life, supporting IT professionals wanting to sweep up all that data and store it, and management consultants trying to help companies turn complex processes into competitive advantage.

Data science is the spry third generation of BPR, responding to vastly increasing IT capacity, unprecedented ability of businesses to create data, widespread realization that data is a valuable resource, and the burdensome need to extract data from storage in order to realize business value.

Yet, data science belongs to a family tree of business practices that for over a century have been governed by technocrats who view organizations as machines, desiring to automate everything and eliminate people wherever possible. Data science is shaping up to be a redux of its grandfather BPR, with the same structural features (BPR was never really engineering, nor as we shall see is data science really science), and its propensity for sin and indulgence.

No science please, we’re skittish

Davenport and Patil declare that “Data scientists’ most basic, universal skill is the ability to write code.” With this pronouncement, data science fails the smell test at the very outset. For how many legitimate scientific fields is coding the most fundamental skill?

The most fundamental skill for any scientist is of course mastery of a canonical body of knowledge that includes laws, definitions, postulates, theorems, proofs, and descriptions of unsolved problems. Scientists are therefore characterized by mastery of a body of knowledge, not a collection of methods. What is this body of knowledge for data science? Davenport and Patil admit there is none.

The job of scientists is to conduct independent research, contribute to a body of knowledge, and improve professional practice, while adhering to a recognized standard of conduct.

Coding is a tool that facilitates some of these objectives, but is a substitute for none of them. Lacking a definitive course of study to assure minimum competency, or a professional society to check conduct, data scientists are classified properly as faddists rather than scientists.

The principle of parsimony leads scientists to favor the theory that explains the most with the least amount of elaboration, that is, to simplify as much as possible. Coding does not simplify, but rather translates, abstracts, and sequentializes, often giving a false sense of concreteness to concepts that are poorly understood or articulated. Consequently, data science confuses the tool and the result, and the spurious science of data is confused with authentic science (an “-ology”) that drives business behavior.

That is not to deny coding is valuable if not crucial for persons conducting scientific inquiry, especially about business topics. Like many readers, much of my academic training and business career has involved demanding quantitative work, including merging databases, extensive data cleansing, giving dimensions to flat data, creating new variables, and performing analyses using numerous unconventional statistical methods. Coding certainly facilitated each of these steps. But invariably, the most valuable tool was my knowledge of the data and underlying phenomena I was studying, not coding. Scientists failing to master the former fool no one but themselves. Faddists mastering only the latter fool everyone, including themselves.

An economy of counterfeit goods

Businesses that adopted BPR were not stupid, though their opaque bureaucracies often made them feel that way. Part of the massive appeal of BPR was its approach of simplicity: begin with a blank sheet of paper, rethink key business processes, and then reduce them to as few steps as possible.

Indeed business transformation should strive for clarity and promote effective communication. It should behave similarly to a well-functioning market, with changes driven organically as knowledge is discovered and teams form around value-creating processes. It should not be dependent, like most management fads, on top-down, artificial organization changes, presided by self-defined experts and gurus posturing themselves as the only ones capable of dealing with complex organization mechanisms.

As BPR morphed into knowledge management, the virtue of simplicity was reversed, and complexity came to indicate merit. Data science promises to deliver value by unpacking some of that complexity. Yet like the two generations of fads that preceded it, data science tries to create value through an economy of counterfeits:

  • False elites, arising as persons are summarily promoted to high status (viz., “scientist”) without duly earning it or having prerequisite experiences or knowledge: functionaries become elevated to experts, and experts are regarded as gurus,
  • False roles, arising as gatekeepers and bureaucrats emerge in order to manage numerous newly created administrative processes associated with data science activities, yet whose contributions to core value, efficiency, or effectiveness are questionable,
  • False scarcity, arising as leaders and influencers define the data scientist role so narrowly as to consist of extremely rare, almost implausible combinations of skills, thereby assuring permanent scarcity and consequent overpricing of skills.

For many businesses, the data most likely to yield valuable insight may not even be contained in databases, but rather shabbily maintained spreadsheets and text files, distributed across multiple systems, and lacking a codebook.

Such data may not even be intelligible without context that is available only in the tacit knowledge of employees or the culture of the organizations. Those who manage under such conditions ought to reflect very carefully: should they trust counterfeit solutions to produce better analytics results than authentic experts who understand the deep psychological, sociological, and economic foundations of business behavior?

Nothing should come between you and your data

Real science discovers universal principles such as the gas laws, which yield many useful technologies, including refrigeration. Yet refrigeration creates value only when it is consumerized, not when it is hoarded. A refrigerator in every house is a sign of economic progression; an iceman delivering ice every day is a sign of economic retrogression.

People needed a Frigidaire in their kitchens, not dependence on icemen to come to the house every day, which the bluesmen of almost a century ago rightly identified as trouble. They were right to purchase technology that made the household self-sufficient and improved their family’s quality of life.

Analytics technology also belongs inside the house, making users independent consumers, and not requiring dubious experts to supervise a technology monopolization that creates value for mostly themselves, through false scarcity and fabricated expertise.

Rather than seeking out gurus to mollify big data anxieties, analytics users should demand that their vendors produce tools that can be used primarily by subject matter experts, in collaboration with analytics specialists, providing transparency and an appropriate level of functionality to both, and facilitating collaboration among business users.

Analytics has the potential to transform business like no technology that came before it. But if left to the sort of data science that Davenport and Patil describe, it will pursue the same life of debauchery as its grandfather BPR, becoming yet another business fad that forgets people, and probably just as destructive.

Buyer beware.

This story originally appeared on SAP Business Trends.


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13 Scary Statistics On Employee Engagement [INFOGRAPHIC]

Jacob Shriar

There is a serious problem with the way we work.

Most employees are disengaged and not passionate about the work they do. This is costing companies a ton of money in lost productivity, absenteeism, and turnover. It’s also harmful to employees, because they’re more stressed out than ever.

The thing that bothers me the most about it, is that it’s all so easy to fix. I can’t figure out why managers aren’t more proactive about this. Besides the human element of caring for our employees, it’s costing them money, so they should care more about fixing it. Something as simple as saying thank you to your employees can have a huge effect on their engagement, not to mention it’s good for your level of happiness.

The infographic that we put together has some pretty shocking statistics in it, but there are a few common themes. Employees feel overworked, overwhelmed, and they don’t like what they do. Companies are noticing it, with 75% of them saying they can’t attract the right talent, and 83% of them feeling that their employer brand isn’t compelling. Companies that want to fix this need to be smart, and patient. This doesn’t happen overnight, but like I mentioned, it’s easy to do. Being patient might be the hardest thing for companies, and I understand how frustrating it can be not to see results right away, but it’s important that you invest in this, because the ROI of employee engagement is huge.

Here are 4 simple (and free) things you can do to get that passion back into employees. These are all based on research from Deloitte.

1.  Encourage side projects

Employees feel overworked and underappreciated, so as leaders, we need to stop overloading them to the point where they can’t handle the workload. Let them explore their own passions and interests, and work on side projects. Ideally, they wouldn’t have to be related to the company, but if you’re worried about them wasting time, you can set that boundary that it has to be related to the company. What this does, is give them autonomy, and let them improve on their skills (mastery), two of the biggest motivators for work.

Employees feel overworked and underappreciated, so as leaders, we need to stop overloading them to the point where they can’t handle the workload.

2.  Encourage workers to engage with customers

At Wistia, a video hosting company, they make everyone in the company do customer support during their onboarding, and they often rotate people into customer support. When I asked Chris, their CEO, why they do this, he mentioned to me that it’s so every single person in the company understands how their customers are using their product. What pains they’re having, what they like about it, it gets everyone on the same page. It keeps all employees in the loop, and can really motivate you to work when you’re talking directly with customers.

3.  Encourage workers to work cross-functionally

Both Apple and Google have created common areas in their offices, specifically and strategically located, so that different workers that don’t normally interact with each other can have a chance to chat.

This isn’t a coincidence. It’s meant for that collaborative learning, and building those relationships with your colleagues.

4.  Encourage networking in their industry

This is similar to number 2 on the list, but it’s important for employees to grow and learn more about what they do. It helps them build that passion for their industry. It’s important to go to networking events, and encourage your employees to participate in these things. Websites like Eventbrite or Meetup have lots of great resources, and most of the events on there are free.

13 Disturbing Facts About Employee Engagement [Infographic]

What do you do to increase employee engagement? Let me know your thoughts in the comments!

Did you like today’s post? If so you’ll love our frequent newsletter! Sign up here and receive The Switch and Shift Change Playbook, by Shawn Murphy, as our thanks to you!

This infographic was crafted with love by Officevibe, the employee survey tool that helps companies improve their corporate wellness, and have a better organizational culture.


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Supply Chain Fraud: The Threat from Within

Lindsey LaManna

Supply chain fraud – whether perpetrated by suppliers, subcontractors, employees, or some combination of those – can take many forms. Among the most common are:

  • Falsified labor
  • Inflated bills or expense accounts
  • Bribery and corruption
  • Phantom vendor accounts or invoices
  • Bid rigging
  • Grey markets (counterfeit or knockoff products)
  • Failure to meet specifications (resulting in substandard or dangerous goods)
  • Unauthorized disbursements

LSAP_Smart Supply Chains_graphics_briefook inside

Perhaps the most damaging sources of supply chain fraud are internal, especially collusion between an employee and a supplier. Such partnerships help fraudsters evade independent checks and other controls, enabling them to steal larger amounts. The median loss from fraud committed
by a single thief was US$80,000, according to the Association of Certified Fraud Examiners (ACFE).

Costs increase along with the number of perpetrators involved. Fraud involving two thieves had a median loss of US$200,000; fraud involving three people had a median loss of US$355,000; and fraud with four or more had a median loss of more than US$500,000, according to ACFE.

Build a culture to fight fraud

The most effective method to fight internal supply chain theft is to create a culture dedicated to fighting it. Here are a few ways to do it:

  • Make sure the board and C-level executives understand the critical nature of the supply chain and the risk of fraud throughout the procurement lifecycle.
  • Market the organization’s supply chain policies internally and among contractors.
  • Institute policies that prohibit conflicts of interest, and cross-check employee and supplier data to uncover potential conflicts.
  • Define the rules for accepting gifts from suppliers and insist that all gifts be documented.
  • Require two employees to sign off on any proposed changes to suppliers.
  • Watch for staff defections to suppliers, and pay close attention to any supplier that has recently poached an employee.

About Lindsey LaManna

Lindsey LaManna is Social and Reporting Manager for the Digitalist Magazine by SAP Global Marketing. Follow @LindseyLaManna on Twitter, on LinkedIn or Google+.


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Data Analysts And Scientists More Important Than Ever For The Enterprise

Daniel Newman

The business world is now firmly in the age of data. Not that data wasn’t relevant before; it was just nowhere close to the speed and volume that’s available to us today. Businesses are buckling under the deluge of petabytes, exabytes, and zettabytes. Within these bytes lie valuable information on customer behavior, key business insights, and revenue generation. However, all that data is practically useless for businesses without the ability to identify the right data. Plus, if they don’t have the talent and resources to capture the right data, organize it, dissect it, draw actionable insights from it and, finally, deliver those insights in a meaningful way, their data initiatives will fail.

Rise of the CDO

Companies of all sizes can easily find themselves drowning in data generated from websites, landing pages, social streams, emails, text messages, and many other sources. Additionally, there is data in their own repositories. With so much data at their disposal, companies are under mounting pressure to utilize it to generate insights. These insights are critical because they can (and should) drive the overall business strategy and help companies make better business decisions. To leverage the power of data analytics, businesses need more “top-management muscle” specialized in the field of data science. This specialized field has lead to the creation of roles like Chief Data Officer (CDO).

In addition, with more companies undertaking digital transformations, there’s greater impetus for the C-suite to make data-driven decisions. The CDO helps make data-driven decisions and also develops a digital business strategy around those decisions. As data grows at an unstoppable rate, becoming an inseparable part of key business functions, we will see the CDO act as a bridge between other C-suite execs.

Data skills an emerging business necessity

So far, only large enterprises with bigger data mining and management needs maintain in-house solutions. These in-house teams and technologies handle the growing sets of diverse and dispersed data. Others work with third-party service providers to develop and execute their big data strategies.

As the amount of data grows, the need to mine it for insights becomes a key business requirement. For both large and small businesses, data-centric roles will experience endless upward mobility. These roles include data anlysts and scientists. There is going to be a huge opportunity for critical thinkers to turn their analytical skills into rapidly growing roles in the field of data science. In fact, data skills are now a prized qualification for titles like IT project managers and computer systems analysts.

Forbes cited the McKinsey Global Institute’s prediction that by 2018 there could be a massive shortage of data-skilled professionals. This indicates a disruption at the demand-supply level with the needs for data skills at an all-time high. With an increasing number of companies adopting big data strategies, salaries for data jobs are going through the roof. This is turning the position into a highly coveted one.

According to Harvard Professor Gary King, “There is a big data revolution. The big data revolution is that now we can do something with the data.” The big problem is that most enterprises don’t know what to do with data. Data professionals are helping businesses figure that out. So if you’re casting about for where to apply your skills and want to take advantage of one of the best career paths in the job market today, focus on data science.

I’m compensated by University of Phoenix for this blog. As always, all thoughts and opinions are my own.

For more insight on our increasingly connected future, see The $19 Trillion Question: Are You Undervaluing The Internet Of Things?

The post Data Analysts and Scientists More Important Than Ever For the Enterprise appeared first on Millennial CEO.


About Daniel Newman

Daniel Newman serves as the Co-Founder and CEO of EC3, a quickly growing hosted IT and Communication service provider. Prior to this role Daniel has held several prominent leadership roles including serving as CEO of United Visual. Parent company to United Visual Systems, United Visual Productions, and United GlobalComm; a family of companies focused on Visual Communications and Audio Visual Technologies. Daniel is also widely published and active in the Social Media Community. He is the Author of Amazon Best Selling Business Book "The Millennial CEO." Daniel also Co-Founded the Global online Community 12 Most and was recognized by the Huffington Post as one of the 100 Business and Leadership Accounts to Follow on Twitter. Newman is an Adjunct Professor of Management at North Central College. He attained his undergraduate degree in Marketing at Northern Illinois University and an Executive MBA from North Central College in Naperville, IL. Newman currently resides in Aurora, Illinois with his wife (Lisa) and his two daughters (Hailey 9, Avery 5). A Chicago native all of his life, Newman is an avid golfer, a fitness fan, and a classically trained pianist

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Ambient Intelligence: What's Next for The Internet of Things?

Dan Wellers

Imagine that your home security system lets you know when your kids get home from school. As they’re grabbing an afternoon snack, your kitchen takes inventory and sends a shopping list to your local supermarket. There, robots prepare the goods and pack them for home delivery into an autonomous vehicle – or a drone. Meanwhile, your smart watch, connected to a system that senses and analyzes real-time health indicators, alerts you to a suggested dinner menu it just created based on your family’s nutritional needs and ingredients available in your pantry. If you signal your approval, it offers to warm the oven before you get home from work.

This scenario isn’t as futuristic as you might think. In fact, what Gartner calls “the device mesh” is the logical evolution of the Internet of Things. All around us and always on, it will be both ubiquitous and subtle — ambient intelligence.

We’ll do truly different things, instead of just doing things differently. Today’s processes and problems are only a small subset of the many, many scenarios possible when practically everything is instrumented, interconnected, and intelligent.

We’re also going to need to come up with new ways of interacting with the technology and the infrastructure that supports it. Instead of typing on a keyboard or swiping a touchscreen, we’ll be surrounded by various interfaces that capture input automatically, almost incidentally. It will be a fundamental paradigm shift in the way we think of “computing,” and possibly whether we think about computing at all.

The Internet of not-things

The foundation will be a digital infrastructure that responds to its surroundings and the people in it, whether that means ubiquitous communications, ubiquitous entertainment, or ubiquitous opportunities for commerce. This infrastructure will be so seamless that rather than interacting with discrete objects, people will simply interact with their environment through deliberate voice and gesture — or cues like respiration and body temperature that will trigger the environment to respond.

Once such an infrastructure is in place, the possibilities for innovation explode. The power of Moore’s Law is now amplified by Metcalfe’s Law, which says that a network’s value is equal to the square of the number of participants in it. All these Internet-connected “things” — the sensors, devices, actuators, drones, vehicles, products, etc.  — will be able to react automatically, seeing, analyzing, and combining to create value in as yet unimaginable ways.  The individual “things” themselves will meld into a background of ambient connectedness and responsiveness.

The path is clearly marked

Think of the trends we’ve seen emerge in recent years:

  • Sensors and actuators, including implantables and wearables, that let us capture more data and impressions from more objects in more places, and that affect the environment around them.
  • Ubiquitous computing and hyperconnectivity, which exponentially increase the flow of data between people and devices and among devices themselves.
  • Nanotechnology and nanomaterials, which let us build ever more complex devices at microscopic scale.
  • Artificial intelligence, in which algorithms become increasingly capable of making decisions based on past performance and desired results.
  • Vision as an interface to participate in and control augmented and virtual reality
  • Blockchain technology, which makes all kinds of digital transactions secure, verifiable, and potentially automatic.

As these emerging technologies become more powerful and sophisticated, they will increasingly overlap. For example, the distinctions between drones, autonomous vehicles, and robotics are already blurring. This convergence, which multiplies the strengths of each technology, makes ambient intelligence not just desirable but inevitable.

Early signposts on the way

We’re edging into the territory of ambient intelligence today. Increasingly complex sensors, systems architectures, and software can gather, store, manage, and analyze vastly more data in far less time with much greater sophistication.

Home automation is accelerating, allowing people to program lighting, air conditioning, audio and video, security systems, appliances, and other complex devices and then let them run more or less independently. Drones, robots, and autonomous vehicles can gather, generate, and navigate by data from locations human beings can’t or don’t access. Entire urban areas like Barcelona and Singapore are aiming to become “smart cities,” with initiatives already underway to automate the management of services like parking, trash collection, and traffic lights.

Our homes, vehicles, and communities may not be entirely self-maintaining yet, but it’s possible to set parameters within which significant systems operate more or less on their own. Eventually, these systems will become proficient enough at pattern matching that they’ll be able to learn from each other. That’s when we’ll hit the knee of the exponential growth curve.

Where are we heading?

Experts predict that, by 2022, 1 trillion networked sensors will be embedded in the world around us, with up to 45 trillion in 20 years. With this many sources of data for all manner of purposes, systems will be able to arrive at fast, accurate decisions about nearly everything. And they’ll be able to act on those things at the slightest prompting, or with little to no action on your part at all.

Ambient intelligence could transform cities through dynamic routing and signage for both drivers and pedestrians. It could manage mass transit for optimal efficiency based on real-time conditions. It could monitor environmental conditions and mitigate potential hotspots proactively, predict the need for government services and make sure those services are delivered efficiently, spot opportunities to streamline the supply chain and put them into effect automatically.

Nanotechnology in your clothing could send environmental data to your smart phone, or charge it from electricity generated as you walk. But why carry a phone when any glass surface, from your bathroom mirror to your kitchen window, could become an interactive interface for checking your calendar, answering email, watching videos, and anything else we do today on our phones and tablets? For that matter, why carry a phone when ambient connectivity will let us simply speak to each other across a distance without devices?

How to get there

In Tech Trends 2015, Deloitte Consulting outlines four capabilities required for ambient computing:

  1. Integrating information flow between varying types of devices from a wide range of global manufacturers with proprietary data and technologies
  2. Performing analytics and management of the physical objects and low-level events to detect signals and predict impact
  3. Orchestrating those signals and objects to fulfill complex events or end-to-end business processes
  4. Securing and monitoring the entire system of devices, connectivity, and information exchange

These technical challenges are daunting, but doable.

Of course, businesses and governments need to consider the ramifications of systems that can sense, reason, act, and interact for us. We need to solve the trust and security issues inherent in a future world where we’re constantly surrounded by connectivity and information. We need to consider what happens when tasks currently performed by humans can be automated into near invisibility. And we need to think about what it means to be human when ambient intelligence can satisfy our wants and needs before we express them, or before we even know that we have them.

There are incredible upsides to such a future, but there are also drawbacks. Let’s make sure we go there with our eyes wide open, and plan for the outcomes we want.

Download the Executive Brief: Enveloped by Ambient Intelligence

Ambient Intelligence thumb

To learn more about how exponential technology will affect business and life, see The Digitalist’s Digital Futures.


About Dan Wellers

Dan Wellers leads Digital Futures for SAP Marketing Strategy.

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