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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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Why 3D Printed Food Just Transformed Your Supply Chain

Hans Thalbauer

Numerous sectors are experimenting with 3D printing, which has the potential to disrupt many markets. One that’s already making progress is the food industry.

The U.S. Army hopes to use 3D printers to customize food for each soldier. NASA is exploring 3D printing of food in space. The technology could eventually even end hunger around the world.

What does that have to do with your supply chain? Quite a bit — because 3D printing does more than just revolutionize the production process. It also requires a complete realignment of the supply chain.

And the way 3D printing transforms the supply chain holds lessons for how organizations must reinvent themselves in the new era of the extended supply chain.

Supply chain spaghetti junction

The extended supply chain replaces the old linear chain with not just a network, but a network of networks. The need for this network of networks is being driven by four key factors: individualized products, the sharing economy, resource scarcity, and customer-centricity.

To understand these forces, imagine you operate a large restaurant chain, and you’re struggling to differentiate yourself against tough competition. You’ve decided you can stand out by delivering customized entrees. In fact, you’re going to leverage 3D printing to offer personalized pasta.

With 3D printing technology, you can make one-off pasta dishes on the fly. You can give customers a choice of ingredients (gluten-free!), flavors (salted caramel!), and shapes (Leaning Towers of Pisa!). You can offer the personalized pasta in your restaurants, in supermarkets, and on your ecommerce website.

You may think this initiative simply requires you to transform production. But that’s just the beginning. You also need to re-architect research and development, demand signals, asset management, logistics, partner management, and more.

First, you need to develop the matrix of ingredients, flavors, and shapes you’ll offer. As part of that effort, you’ll have to consider health and safety regulations.

Then, you need to shift some of your manufacturing directly into your kitchens. That will also affect packaging requirements. Logistics will change as well, because instead of full truckloads, you’ll be delivering more frequently, with more variety, and in smaller quantities.

Next, you need to perfect demand signals to anticipate which pasta variations in which quantities will come through which channels. You need to manage supply signals source more kinds of raw materials in closer to real time.

Last, the source of your signals will change. Some will continue to come from point of sale. But others, such as supplies replenishment and asset maintenance, can come direct from your 3D printers.

Four key ingredients of the extended supply chain

As with our pasta scenario, the drivers of the extended supply chain require transformation across business models and business processes. First, growing demand for individualized products calls for the same shifts in R&D, asset management, logistics, and more that 3D printed pasta requires.

Second, as with the personalized entrees, the sharing economy integrates a network of partners, from suppliers to equipment makers to outsourced manufacturing, all electronically and transparently interconnected, in real time and all the time.

Third, resource scarcity involves pressures not just on raw materials but also on full-time and contingent labor, with the necessary skills and flexibility to support new business models and processes.

And finally, for personalized pasta sellers and for your own business, it all comes down to customer-centricity. To compete in today’s business environment and to meet current and future customer expectations, all your operations must increasingly revolve around rapidly comprehending and responding to customer demand.

Want to learn more? Check out my recent video on digitalizing the extended supply chain.

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Hans Thalbauer

About Hans Thalbauer

Hans Thalbauer is the Senior Vice President, Extended Supply Chain, at SAP. He is responsible for the strategic direction and the Go-To-Market of solutions for Supply Chain, Logistics, Engineering/R&D, Manufacturing, Asset Management and Sustainability at SAP.

How to Design a Flexible, Connected Workspace 

John Hack, Sam Yen, and Elana Varon

SAP_Digital_Workplace_BRIEF_image2400x1600_2The process of designing a new product starts with a question: what problem is the product supposed to solve? To get the right answer, designers prototype more than one solution and refine their ideas based on feedback.

Similarly, the spaces where people work and the tools they use are shaped by the tasks they have to accomplish to execute the business strategy. But when the business strategy and employees’ jobs change, the traditional workspace, with fixed walls and furniture, isn’t so easy to adapt. Companies today, under pressure to innovate quickly and create digital business models, need to develop a more flexible work environment, one in which office employees have the ability to choose how they work.

SAP_Digital_Emotion_BRIEF_image175pxWithin an office building, flexibility may constitute a variety of public and private spaces, geared for collaboration or concentration, explains Amanda Schneider, a consultant and workplace trends blogger. Or, she adds, companies may opt for customizable spaces, with moveable furniture, walls, and lighting that can be adjusted to suit the person using an unassigned desk for the day.

Flexibility may also encompass the amount of physical space the company maintains. Business leaders want to be able to set up operations quickly in new markets or in places where they can attract top talent, without investing heavily in real estate, says Sande Golgart, senior vice president of corporate accounts with Regus.

Thinking about the workspace like a designer elevates decisions about the office environment to a strategic level, Golgart says. “Real estate is beginning to be an integral part of the strategy, whether that strategy is for collaborating and innovating, driving efficiencies, attracting talent, maintaining higher levels of productivity, or just giving people more amenities to create a better, cohesive workplace,” he says. “You will see companies start to distance themselves from their competition because they figured out the role that real estate needs to play within the business strategy.”

The SAP Center for Business Insight program supports the discovery and development of  new research-­based thinking to address the challenges of business and technology executives.

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Sam Yen

About Sam Yen

Sam Yen is the Chief Design Officer for SAP and the Managing Director of SAP Labs Silicon Valley. He is focused on driving a renewed commitment to design and user experience at SAP. Under his leadership, SAP further strengthens its mission of listening to customers´ needs leading to tangible results, including SAP Fiori, SAP Screen Personas and SAP´s UX design services.

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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.

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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

When Good Is Good Enough: Guiding Business Users On BI Practices

Ina Felsheim

Image_part2-300x200In Part One of this blog series, I talked about changing your IT culture to better support self-service BI and data discovery. Absolutely essential. However, your work is not done!

Self-service BI and data discovery will drive the number of users using the BI solutions to rapidly expand. Yet all of these more casual users will not be well versed in BI and visualization best practices.

When your user base rapidly expands to more casual users, you need to help educate them on what is important. For example, one IT manager told me that his casual BI users were making visualizations with very difficult-to-read charts and customizing color palettes to incredible degrees.

I had a similar experience when I was a technical writer. One of our lead writers was so concerned with readability of every sentence that he was going through the 300+ page manuals (yes, they were printed then) and manually adjusting all of the line breaks and page breaks. (!) Yes, readability was incrementally improved. But now any number of changes–technical capabilities, edits, inserting larger graphics—required re-adjusting all of those manual “optimizations.” The time it took just to do the additional optimization was incredible, much less the maintenance of these optimizations! Meanwhile, the technical writing team was falling behind on new deliverables.

The same scenario applies to your new casual BI users. This new group needs guidance to help them focus on the highest value practices:

  • Customization of color and appearance of visualizations: When is this customization necessary for a management deliverable, versus indulging an OCD tendency? I too have to stop myself from obsessing about the font, line spacing, and that a certain blue is just a bit different than another shade of blue. Yes, these options do matter. But help these casual users determine when that time is well spent.
  • Proper visualizations: When is a spinning 3D pie chart necessary to grab someone’s attention? BI professionals would firmly say “NEVER!” But these casual users do not have a lot of depth on BI best practices. Give them a few simple guidelines as to when “flash” needs to subsume understanding. Consider offering a monthly one-hour Lunch and Learn that shows them how to create impactful, polished visuals. Understanding if their visualizations are going to be viewed casually on the way to a meeting, or dissected at a laptop, also helps determine how much time to spend optimizing a visualization. No, you can’t just mandate that they all read Tufte.
  • Predictive: Provide advanced analytics capabilities like forecasting and regression directly in their casual BI tools. Using these capabilities will really help them wow their audience with substance instead of flash.
  • Feature requests: Make sure you understand the motivation and business value behind some of the casual users’ requests. These casual users are less likely to understand the implications of supporting specific requests across an enterprise, so make sure you are collaborating on use cases and priorities for substantive requests.

By working with your casual BI users on the above points, you will be able to collectively understand when the absolute exact request is critical (and supports good visualization practices), and when it is an “optimization” that may impact productivity. In many cases, “good” is good enough for the fast turnaround of data discovery.

Next week, I’ll wrap this series up with hints on getting your casual users to embrace the “we” not “me” mentality.

Read Part One of this series: Changing The IT Culture For Self-Service BI Success.

Follow me on Twitter: @InaSAP

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