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Exciting big data possibilities – as well as business intelligence and business analytics possibilities -- are all well and good, but what businesses really want is to deliver value from the massive amounts of data they have amassed over time. And most agree that the best way to demonstrate that value is to monetize it. But what exactly does that mean, and how can it be achieved?

 

These are among the key questions that Professor Barbara Wixom is attempting to address in her current research. Wixom joined MIT Sloan in June 2013 as a Principal Research Scientist for the Center for Information Systems Research (CISR). At a recent MIT IDE/CDB seminar describing her preliminary work, Show Me the Money: Delivering Business Value through Data, Wixom noted that: “In a digital economy, data, and the information it produces, is one of a company's most important assets. Increasingly, companies are monetizing their data assets and generating business value via existing core products and services or new digitized ones.”BarbaraWixom.jpg

 

For Wixom’s current study, she interviewed more than 50 business leaders involved in data monetization efforts and discovered that definitions of data monetization varied widely – ranging from selling data products and services for revenue generation, to exploiting data internally to drive tangible bottom-line results. When a company explores data monetization with the latter intent in mind, Wixom noted that data providers are good companies to use as role models. Because data monetization is at the core of their business models, data providers have learned over the years how to be really good at monetizing.

 

Provider, non-Provider Examples

Wixom studied comScore, a 14-year-old marketing research firm “with 14 petabytes of online data, collected real time from around the world.” In a research paper earlier this year, she describes how comScore achieves value creation from big data via three key assets: “A cost-efficient, scalable platform; an analytics-savvy workforce; and a deep understanding of its clients.” She concluded: “Data and analytics providers are highly experienced at working with big data. They create, build, and hone capabilities to exploit their data assets.”

 

Wixom also discussed the evolution of one her early case studies: medical supply distributor Owens & Minor. Although the company’s core business is distribution, Owens & Minor has a long history of gathering, using and ultimately monetizing its data via its “spend analytics” products and services. Since the 1990’s the distributor has collected information from its supply chain and sold it to suppliers that wanted to increase market penetration and sales – and to customers that wanted to manage cost of patient care. Over the next decade, Owens & Minor won new business and generated revenue as a result of its unique analytics capabilities. In addition to hard-dollar gains, it earned brand and reputational benefits as an early technology leader and consulting partner in the healthcare industry. Nevertheless, Owens & Minor’s analytics offerings now must co-exist and compete with offerings from software vendors, consulting firms and group purchasing organizations, Wixom said.

 

The bottom line to both the comScore and Owens & Minor stories, according to Wixom is this: Data monetization is not easy. As companies consider selling their data, they need to get into the game with eyes wide open, she adds.

MIT Sloan Professor Scott Stern’s latest research draws a clear correlation between the elements present at the founding of entrepreneurial startups and their later success. In addition, he and MIT doctoral candidate Jorge Guzman, use other widely available data-- such as incorporation information, patents, trademarks, IPOs and venture capital funding-- to measure and identify the potential for future growth.

 

The findings of the study, “Nowcasting and Placecasting Growth Entrepreneurship,” were presented at an MIT IDE seminar in March by Stern, who is Professor of Management of Technology and Chair of the Technological Innovation, Entrepreneurship and Strategic Management Group at the MIT Sloan School of Management. He and Guzman were not only able to draw conclusions, but to observe data-documented entrepreneurial trends, using algorithms and estimation models. These can “help us understand the origins and dynamics of startups,” Stern said.

 

Shifting Growth Patterns

Placecasting can be used to “evaluate the role of regional ecosystems” in the growth—and decline -- of startups, and to identify clusters of “hyperinnovation.” “Our approach allows us to track the changing locational patterns of growth entrepreneurs over time,” and in real-time, he said, as opposed to traditional, static survey methods. For example, “in Massachusetts, we are able to document the transition from Route 128 growth entrepreneurship to clustering in Kendall Square in Cambridge and Boston.” Similarly, in California, he is tracking the move of entrepreneurship from Silicon Valley to San Francisco.

guzman chart.JPG


Using what he calls nowcasting, Stern expects to develop a predictive model of growth outcomes and assign a probability of growth based on current developments and past indicators. It will also be easier to spot and evaluate why some firms will not succeed. Going forward, Stern also understands that the pace of change and the “app economy” will require new criteria and there will be new shifts to track.

 

Stern works widely with both companies and governments in understanding the drivers and consequences of innovation and entrepreneurship, and has worked extensively in understanding the role of innovation and entrepreneurship in competitiveness and regional economic performance. For more about regional clusters, watch this video and for more on the research, contact Stern at sstern@mit.edu .

Paula Klein

The Good Jobs Strategy

Posted by Paula Klein Apr 3, 2014

Can workers actually be beneficiaries of the digital economy? MIT Sloan Professor Zeynep Ton believes the answer is yes. And just as importantly, she says, businesses won’t lose out in the process.download.jpg

 

Much has been written and discussed about the economic inequalities created as a result of digital technologies. In this blog, for example, MIT Research Scientist, Andrew McAfee, cites significant economic data supporting the view that IT is responsible for tectonic changes in U.S. jobs and wages. Professor Ton’s latest research offers some win-win scenarios for employees and their employers.

 

In a recent presentation, Ton, Adjunct Associate Professor of Operations Management (pictured at left), went beyond defining the problems of job displacement, dissatisfaction and despair; she offered solutions. Her latest book, “The Good Jobs Strategy,” examines ways to bridge some of the widely acknowledged economic gaps and suggests ways that organizations “can design and manage their operations in a way that satisfies employees, customers and investors simultaneously.”

 

Tossing Out Conventional Wisdom

Ton asserts that currently, one in four workers – especially in the service sector and retail—has a “bad job” where salaries are insufficient to support families, and work is rote, irregular and unsatisfying. In such environments, “workers are set up to fail.” But businesses and society fail as well, she maintains. “The conventional wisdom is that bad jobs are necessary to keep costs low and profits high. Even advocates for higher wages believe higher wages they will come at a cost—either higher prices for customers or lower profits for companies.”

 

However, better operational strategies can break the pattern, she says. It may be counter-intuitive, Ton explained at a meeting of the MIT IDE in March, but combining investment in people with smart decisions like empowering workers—not cutting back—often proves most profitable.

 

In case after case, she found that more and better-trained and motivated staff can generate higher profit and growth and help business stay ahead of competitors.

 

Zara, Mercadona and QT Find Win-Win Formulas

“It’s a virtuous cycle,” Ton says: “Good execution and good workers yield more profits.” For example, Zara clothing and the Mercadona supermarket chain, both based in Spain, are growing despite a weak economy. Mercadona offers employees twice the minimum wage, bonuses, stable work, full-time schedules and opportunities for growth.

 

In the U.S., QT, or Quiktrip, is an example of a convenience store/gas station company providing “excellent customer service, fast, clean facilities and a high employee retention rate. People want to work there,” Ton says, and store profits are above industry averages. Trader Joe’s and Costco are other good examples.

 

How do they do it?  Ton offers a matrix four strategies that need to be used in combination to reap the greatest rewards:

1. Invest in people and combine that with operational excellence to drive sales.

2.   Standardize processes to increase efficiency and empower employees to make decisions for customers

3.  Cross-train employees to encourage agility, flexibility and knowledge.

4. Offer fewer products, but operate with slack; never under-staff.

Ton sums up as follows: “In my book, The Good Jobs Strategy, I show that it is possible to offer good jobs to workers, low prices and excellent service to customers, and great returns to shareholders-- all at the same time. What makes good jobs not only possible but very profitable—even in low-cost service businesses—is a set of counterintuitive choices that transforms the company’s investment in workers into high performance. What are these choices? Offer less, combine standardization with empowerment, cross-train, and operate with slack. It’s a combination that lowers operating costs, increases worker productivity and puts workers at the center of a company's success.”https://mitsloan.mit.edu/newsroom/images/2013-goodjobs.gif

 

 

Biography:

Ton’s work has been published in a variety of journals, including Organization Science, Production and Operations Management, and the Harvard Business Review. In addition, she has written numerous cases that explore different approaches to managing retail stores and labor. Prior to MIT Sloan, Ton spent seven years as an assistant professor in the Technology and Operations Management area at Harvard Business School, where she was awarded the HBS Faculty Teaching Award for teaching excellence.

Ton holds a DBA from Harvard Business School and a BS in Industrial and Manufacturing Engineering from Pennsylvania State University.

Two papers came out last year that examined important issues around jobs and wages. Both are in top journals. Both were written by first-rate researchers, none of whom specialize in studying the impact of technology. And both came to the same conclusion: that digital technologies were largely responsible for the phenomena they examined.

The first paper, by David Dorn and my MIT colleague David Autor,  is about how jobs and wages changed in America from 1980-2005. It was published last year in the American Economic Review and called “The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market,” which is an admirably informative title.

Equally admirable are the graphs the authors draw to illustrate their main findings. Here’s the one for jobs (the one for wages has a pretty similar shape). It gives the changes in employment share — which you can think of as changes in the the ‘market share’ of jobs — between 1980 and 2005. And it shows vividly that low-skill and high-skill jobs gained market share over that period, which those in the middle of the skill range lost.

Autor

Autor and Dorn are clear on what accounts for this shift:

The adoption of computers substitutes for… workers performing routine tasks—such as bookkeeping, clerical work, and repetitive production and monitoring activities—which are readily computerized because they follow precise, well-defined procedures. Importantly, occupations intensive in these tasks are most commonplace in the middle of the occupational skill and wage distribution.

and what doesn’t:

We evaluate numerous alternative explanations for the pronounced differences in wage and employment polarization… including deindustrialization, offshoring, … and growing low-skill immigration. None of these alternatives appears central to our findings.

The second paper concentrates on wages, and tries to determine what’s caused the red line in the graph below to decline so fast in recent years

http://www.slideshare.net/amcafee/mc-afee-econ-data

profits and labor share

This line documents the labor share of GDP in the US over the post-war period — the percentage of GDP that gets paid out in compensation (wages and benefits) to workers. As the graph above shows, US labor share has  been heading down sharply just as corporate profits have reached hew heights.

Is this because of globalization? Nope, because it’s been happening around the globe. As Loukas Karabarbounis and Brent Neiman write in “The Global Decline of the Labor Share” (out in the current issue of the Quarterly Journal of Economics):

We document, however, that the global labor share has significantly declined since the early 1980s, with the decline occurring within the large majority of countries and industries. We show that the decrease in the relative price of investment goods, often attributed to advances in information technology and the computer age, induced firms to shift away from labor and toward capital.

The AER and QJE are the two top journals in the economics field, so this research is about as solid as it gets. In light of this and plenty of other work, it really is time to stop arguing about whether technology has been one of the tectonic forces reshaping work and the workforce in recent decades. The evidence is just too clear that it is, and that we see evidence of the second machine age everywhere, including in the statistics.

This post first appeared March 12 on my Business Impact of IT blog here.

As social media becomes more pervasive in business and economic life, many researchers are trying to figure out just how much impact it is having on sales and on business models.

 

Jeffrey Hu, Associate Professor at Scheller College of Business  at Georgia Institute of Technology, and an MIT Sloan alumni, is among those studying the effects of social media. In particular, he examined just how much online broadcasting channels and crowdsourcing are influencing markets and customers compared with more traditional marketing channels. “With the emergence of social media and Web 2.0, broadcasting in the online environment has evolved into a new form of marketing due to the much broader reach enabled by information technology,” Hu said.hu_jeffrey_profile.jpg1.jpg

 

Turning Buzz into Business

During 2008 to 2009, Hu studied the patterns of the MySpace music community, the largest at the time, with 14 million users. He wanted to know if broadcasting information via social media –sending updates, bulletins and texts (this was before Twitter really had a strong presence) would result in greater economic returns. In other words, he said at a recent MIT CDB lunch seminar, “whether buzz could turn into sales.”

 

Hu and his team employed a panel vector auto-regression (PVAR) model to investigate the inter-relationship between broadcasting promotions in social media and music sales. By correlating social media activity of 631 musicians for 32 weeks and comparing the data to Amazon rankings, Hu was able to see a significant effect on sales. The study accounted for control variables such as promotional spending, new album releases and size of network, among other factors.

 

The research concludes that artist-generated content -- particularly personal messages versus automated ones-- can increase sales and ranking on Amazon. By extension, Hu believes that companies can use social media to promote products and boost sales. “Our findings also point to the importance of conducting captivating conversations with customers in the organizational use of social media,” he said.

 

The Wisdom of Crowds

The second study Hu described at the seminar looked at the wisdom of crowds and crowdsourcing compared with expert advice and content online. Many people have pointed out that while Wikipedia contains errors, for example, it also can be corrected quickly from a vast range of sources versus traditional, permanent resources such as print encyclopedias. Some advocates believe that customers turn to peer-based communities, such as Yelp, for restaurant reviews over venerable sources like Michelin guides because the websites are more current, are more accessible and have wider coverage areas.

 

In his research of the financial analysis sector, Hu found that the online community Seeking Alpha--which relies on investor input instead of journalists or professional analysts-- has been “surprisingly accurate” in predicting financial trends and making investment recommendations.

 

Of course, there are also many caveats where enterprise social media is concerned. As McKinsey notes in this recent journal article, “on-demand marketing” is putting enormous pressures on businesses to respond in four key areas:

1. Now: Consumers will want to interact anywhere at any time.

2. Can I: They will want to do truly new things as disparate kinds of information (from financial accounts to data on physical activity) are deployed more effectively in ways that create value for them.

3. For me: They will expect all data stored about them to be targeted precisely to their needs or used to personalize what they experience.

4. Simply: They will expect all interactions to be easy.

 

Maybe the next studies will focus on how well social media can help achieve these daunting consumer demands.

 

 

For related MIT research about social advertising, see this blog describing Catherine Tucker’s research.


For  more of Jeffrey Hu's research, see the following abstracts:


http://ssrn.com/abstract=2201430

 

http://ssrn.com/abstract=1807265)

 

and his Georgia Tech profile here:  http://scheller.gatech.edu/directory/faculty/hu/

 

I teach at a School of Management so you won’t be surprised to learn that I think good management can make a huge difference in the performance of companies, and ultimately the economy.  But you may be surprised that there is very little economic research on the effects of management.  Sure, there’s lots of speculation and countless management books and articles, but a recent review of the economic literature by Chad Syverson concluded: “No potential driving factor of productivity has seen a higher ratio of speculation to empirical study [than management practices].”  The biggest problem has been simply a lack of a comprehensive, reliable data set of management practices.

 

To address this gap, I recently helped formulate the U.S. Census Bureau’s survey of management and organizational practices at more than 30,000 manufacturing plants across the country--the first large-scale survey of management in America. Along with Nick Bloom, Lucia Foster, Ron Jarmin, Itay Saporta and John Van Reenen, we examined three types of practices-- performance monitoring; setting targets, and offering incentives—which we called “Structured Management.”

 

Analysis of the data reveals several striking results about the relationship between performance goals and improved business. Specifically, setting business goals and monitoring results are among the practices that actually yield better business productivity and growth, according to this comprehensive survey of U.S. management conducted in 2011. The survey was funded by the National Science Foundation and had administrative support from the National Bureau of Economic Research and the MIT Center for Digital Business. It was a joint, academic-U.S. census bureau collaboration.

 

My fellow researchers and I set out to determine whether, and what type of management practices influence bottom-line business results such as productivity, output and growth. Based on the responses, we found a tight link between Structured Management and performance outcomes such as growth, expenditures and innovation as indicated by R&D and patent intensity.

 

Figure 1: Better Performance is Associated With More Structured Management

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While the survey did not focus exclusively on digital technologies, the conclusions may partly reflect the increasing adoption of information technologies, like Enterprise Resource Planning (ERP) systems, which make data collection and processing much cheaper, easier and more effective. Structured Management scores for data use have improved the most, according to the data. Presumably this reflects the growing use of IT in modern firms.

 

The study also highlights the important rise of data-driven decisionmaking, which the MIT CDB has championed for several years. Most of the rise in structured management practices, for example, has come among businesses that have implemented data-driven performance monitoring.

 

Figure 2: Average Management Scores Increased between 2005 and 2010,Especially for Data Driven Performance Monitoring

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It was also interesting to note that adoption of structured management practices has increased between 2005 and 2010, particularly for those practices involving data collection and analysis.  This is consistent with my earlier research with Lorin Hitt and Heekyung Kim on Data-Driven Decisionmaking.

 

Among other key findings:

 

-- There is a substantial dispersion of management practices across the establishments. Eighteen percent have adopted at least 75% of these more structured management practices, while 27% adopted less than 50% of these.

 

--There is a positive correlation between structured management practices and location, firm size, establishment-level measures of worker education, and export status.

 

Going forward we will continue to analyze the data and explore causality. Additionally, we may do another survey in 2015 to establish longer-term data and perhaps will focus on the retail or health-care sector.

 

Let me know what other ideas you think we should explore.


Most discussions and examples of business innovation focus squarely on the production side; how new products are created, built and marketed to improve efficiency and move the technology dial forward.

 

What gets lost in this classic view, however, is the human capital impact resulting from disruptive innovation, according to Michael Schrage, research fellow at MIT Sloan School’s Center for Digital Business. And, he asserts, that misrepresents innovation’s real role.

 

“Innovation is not just about faster, better, cheaper products and services, but an investment in the human capital and capabilities of customers and clients,” he told faculty at a recent lunch seminar on the topic of “Misunderstanding Human Capital,” at the CDB. “The transformative effect on human capital” is largely misunderstood or absent when innovation’s value is considered, Schrage argues. “Successful innovations transform users and customers” not just production processes, he asserts.

 

At the lively, interactive lunch session, Schrage noted that when Ford first mass produced automobiles he also facilitated “the mass production of drivers” – a form of human capital that hadn’t really existed before.

 

Similarly, Google created searchers, not just search engines, disrupting the way consumers interacted and adding value to Google’s algorithms. Walmart trained consumers to look for low prices, while Tesco focused on customer loyalty. Schrage wants to call attention to what he terms the “misunderstanding of mass production’s significance on human capital,” and to study the consequences – positive and negative -- of these actions on consumers.

 

This thinking borrows from two-sided market theory and platform economics where creation of one new market can lead to the creation of a new, complementary network such as iPhone apps, Schrage says.

 

Schrage’s latest research seeks to demonstrate that “consumptionary capital” is an important aspect of innovation that has been widely overlooked. In fact, technological innovation often creates and enhances human capabilities and increases competencies that can generate new business opportunities. Eyeglasses were technical innovations that made it possible and affordable for more people to read and see; hearing aids and cochlear implants were technical innovations that made it possible for more people to hear. They expanded the abilities and capabilities of their users to do and consume more, he says.

 

Schrage introduced his viewpoint in a Harvard Business Press e-book last year, Who Do You Want Your Customers to Become?  His premise is summarized by HBP as follows:

Asking customers to do something different doesn't go far enough. Serious marketers and innovators must ask customers to become something different instead. Even more, you must invest in their capabilities and competencies to help them become better customers.

A primary insight of the book is that innovation is an investment in your clients, not just a transaction with them, and that transforming customers will transform the business.

By addressing questions such as: How will skills change as a result of Google cars? How do innovations such as recognition engines and crowdsourcing impact consumer habits, behavior and income? How are new consumer norms created? –the research may help businesses better understand their customers. From that, they can adopt more meaningful marketing and product development plans that will yield more -- and more valuable -- customers.

 

Successful business innovators like Henry Ford, Steve Jobs and a Jeff Bezos, he argues, have clear visions of who they want their customers to become.

I had a chance to present my research with Joohee Oh about the value of free goods at the recent Techonomy conference in Tuscon. We now to have the full video and transcript available to read and view. As I wrote about here and as Andy McAfee described, I’m convinced that we are in the midst of a major technological revolution that is not being fully reflected in official government statistics, most notably, the GDP and productivity numbers

 

According to official government data, there has bee zero growth in the information sector. But we have all seen an explosion of new digital products and services, from Wikipedia and Facebook, to Youtube and GPS mapping. The official data say this sector is the same size as what it was in the 1960s! Now how can that be? Obviously, there’s some major measurement problems in the way we keep our statistics, and that’s a real concern because, as the saying goes, you can’t manage what you don’t measure. So we need to come up with a better way of measuring things.  That’s what we’ve been working at the MIT Center for Digital Business.

 

Here are a few highlights from my talk:

 

      • We start with the fact that many digital goods are delivered for free. In some cases, they're funded through advertising. In many other cases, users just contribute their time; they develop content and make it available. And maybe there’s a little bit of contributions or advertising that pays for the bandwidth, but those costs are relatively minimal. We know that there’s just been an explosion of the availability of these goods because you can count the number of bits produced; you can count the number of Wikipedia articles produced, for example--and those have grown ten-fold since 2004.
      • Counting bits is a start, but what we really want to know is not just the number of bits but the actual value of information goods. This is where the bug in the GDP measurement occurs; because GDP measures only the total amount spent on goods and services, not their value. So what happens if the price is zero? Well, zero times any quantity is still zero. So you could have an enormous of explosion of bits or articles or whatever else and the statisticians still see it as a big fat zero contribution for our GDP
      • Traditional metrics are really missing what’s going on in this information economy because so much of the digital economy is a free economy. We found a number of other ways to go about measuring it. One is to look at the time that people spent, and that is something that we do. If you just look at the dollars, you’re going to get a sense that actually the economy is stagnant or even shrinking. But we calculate the demand curve for information goods based on the time spent. This is a very different approach than the traditional GDP accounting, but it’s one that we think better captures the real value that goods and services are producing in the economy.
      • After you do the math and plug in the numbers, we estimate that the annual welfare gain from all these free goods is about $300 billion. Now, that’s the average over the past ten years or so. And that works out to about $1,400 per person.

 

We also came up with a way to calibrate the value of this time which can you hear more about if you watch the full video. I will continue to write about our findings as the research unfolds.

Whether you think it’s a good idea or not, mobile and social technologies are creating new ways to follow, analyze and predict how people are “embedded in society,” and how and where they spend their time and money. The implications of these changes for individuals, as well as society, are being studied by Alex `Sandy’ Pentland, director of MIT’s Human Dynamics Laboratory and the MIT Media Lab Entrepreneurship Program.

 

His current research examines four ways that Big Data can help to understand human behavior: By modeling social influence; by examining social influence dynamics; by actually shaping behavior, and by creating more data-driven societies.  Pentland hopes these insights may help reverse “many of the frustrating phenomena that we are familiar with....fads, groupthink, and projects that just go nowhere.”

 

The MIT researchers looked at social influence networks and their relationship to learning, purchases and other behaviors by following 65 young families for one year. One finding was that social influence incentives work to change behavior more than other incentives because in a group, members have common ties and an exchange network on which to rely. Local information can pressure peers to act in certain ways and to be rewarded for those behaviors. “Incenting the social ties can be efficient,” Pentland explained at a recent MIT CDB seminar.

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In call centers, for example, productivity improves with more coffee breaks, because workers share information that leads to better performance. Similarly, “social traders who aren’t isolated and aren’t in echo chambers,” perform best, he said. The point is to “encourage diversity of ideas and engagement.”

 

The Human Dynamics Lab at the MIT Media Laboratories pioneered the idea of a society enabled by Big Data. The Lab has developed technologies such as reality mining, which uses mobile phone data to extract patterns that predict future human behavior, as well as a `nervous system’ framework for dramatically more efficient transportation, health, energy, and financial systems.

 

Pentland’s latest research could be applied to what he calls, “data-driven societies.” Since geography influences behavior and patterns of communications, which creates “collective intelligence” in local groups,” city-scientist, for instance, may be able to predict the GDP of a city by looking at social-tie patterns. In turn, this might help city planners build environments that better match the habits of the local citizens.

 

Separately, McKinsey is conducting research into social intelligence. In its new report, McKinsey discusses social intelligence as a means of guiding better business decisions.

 

The report states that by tapping into social platforms, businesses can gather and harness employee knowledge.

Today, many people who have expert knowledge and shape perceptions about markets are freely exchanging data and viewpoints through social platforms. By identifying and engaging these players, employing potent Web-focused analytics to draw strategic meaning from social-media data, and channeling this information to people within the organization who need and want it, companies can develop a “social intelligence” that is forward looking, global in scope, and capable of playing out in real time.

This isn’t to suggest that “social” will entirely displace current methods of intelligence gathering. But it should emerge as a strong complement. As it does, social-intelligence literacy will become a critical asset for C-level executives and board members seeking the best possible basis for their decisions.

And in another report Capturing Business Value with Social Technologies, McKinsey conducted an in-depth analysis of four industry sectors that represent almost 20 percent of global sales.

[The analysis] suggests that social platforms can unlock $900 billion to $1.3 trillion in value in those sectors alone. Two-thirds of this value creation opportunity lies in improving communication and collaboration within and across enterprises. Frequently, these improvements will go well beyond the areas many companies have focused on to date in their social-media efforts: connecting with consumers, deriving customer insights for marketing and product development, and providing customer service.

 

Clearly, Pentland’s work supports McKinsey’s conclusion that: “Social technologies are destined to play a much larger role, not only in individual interactions, but also in how companies (and Pentland might add, societies), are organized and managed.”

 

Sandy Pentland is a member of this community. Comment on his work here.

There are many metrics and data available that quantify the use of digital goods and services. We know, for example, how many billions songs are downloaded and how much revenue that garners. We can tell how many articles there are on Wikipedia and how many hours people spend on Facebook.

 

To date, there are primarily three ways to quantify the impact digitization is having: We can look at the contribution of the transactions to the GDP; we can look at an IT company’s stock values, or we can track consumer spending and prices as indicators of consumer surplus created.

 

 

But none of these approaches measure the real value of digital services when the services are free, according to our latest research at MIT’s Center for Digital Business. For example, a money-only model may be missing 95% of the value consumers derive online. Finding that new metric is the focus of my team’s latest research. Free goods and services on the Internet have exploded in the past decade, and the average American now spends more than 32 hours a month online.

 

Once they have an Internet connection, they don't spend money to use Wikipedia, Facebook or Youtube, so the value of these services isn't properly reflected in the GDP statistics.

This gap is a problem we have been grappling with for some time, including in a Sloan Management Review article a few years ago.

 

At the recent annual meeting we offered a possible solution to the measurement problem: Consumers pay with time, not just money, and where they choose to spend their limited time and attention online is a form of voting. Increasingly, the digital economy is the 'attention economy.' Our research is calculating a demand curve for time and estimating how much consumers implicitly value free goods based on use of their time, not on dollars, spent on the Internet.

 

One preliminary finding shows that free goods added the equivalent of $139 billion in value to the economy in 2010-- more than 1% of the GDP and equal to $647 per person. The findings may have an impact of calculations such as the GDP and other economic metrics.


chart.JPG



That the amount of business data is skyrocketing is hardly news. All we have to do is consider the huge volumes of data and archives at any major financial institution, retail business or healthcare organization. Then multiply those amounts by a several times and you’ll have an idea of the staggering amount of information amassed at web-based businesses such as Google and Amazon.

 

More important than the quantity of information generated, however, is an understanding of how it is used and how it can create value for organizations, their customers and the overall economy. At the MIT Center for Digital Business, a recent statistical study on the implications of big data offers significant proof that proper use of analytics and business intelligence tools can help businesses use their digital information to grow efficiently and show bottom-line results.

 

Specifically, my paper with Heekyung Kim, Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance?, finds that “companies that use data-driven analytics instead of intuition have 5%-6% higher productivity and profits than competitors.” Research was based on the business practices and information technology investments of 179 large publicly traded firms. Huge improvements in metrics are allowing a granular analysis of data—whether it resides on mobile devices, in email or elsewhere in data centers-- to find out more about customer behavior. What’s more, the relationship between data-driven decisionmaking and performance also appears in other measures such as asset utilization, return on equity and market value. Our results provide some of the first large-scale data on the direct connection between data-driven decision making and firm performance.

 

As we wrote in the recent Atlantic magazine article:

“Today, businesses can measure their activities and customer relationships with unprecedented precision. As a result, they are awash with data. This is particularly evident in the digital economy, where clickstream data give precisely targeted and real-time insights into consumer behavior.”

 

And while web-based digital companies – notably, Amazon and Google--are in the forefront of data analysis, now we are seeing offline companies in logistics, manufacturing, retail, casinos and finance making use of these techniques as well. Gallo Wines, UPS, Caesar’s Entertainment and Match.com are a few examples cited in the Atlantic article. Marketing and sales organization are leading the way and are far ahead of some other departmental users, but increasingly, we see business units such as HR using email to help internal staff productivity benefits. Similarly, manufacturing lines are gaining access to real-time data from CRM and ERP systems to help them track trends and demand.

 

To delve into this topic in-depth, we are offering a new two-day executive education course on March 28 at MIT Sloan in Cambridge, Mass. [see related blog for details here.]

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