The Yale MBA Behind Big Data Platform Hadapt 12-17

The Yale MBA Behind Big Data Platform Hadapt

by Taylor Ellis on December 3, 2013

Justin Borgman brings big data research out of the lab and into the marketplace

At the age of 12, Justin Borgman knew he wanted to be an entrepreneur. But when he arrived for his first year as an MBA student at Yale’s School of Management he didn’t have any startup ideas and almost went into venture capital, instead.

Luckily, an internship in Yale’s Office of Cooperative Research, which helps to commercialize academic work, helped him rediscover his entrepreneurial ambitions. Borgman read a paper by Daniel Abadi, a computer science professor who was working on a solution to bridge the divide between different data platforms so they could interact with each other. Borgman was convinced that Abadi’s research held commercial potential. He met with Abadi and his PhD student Kamil Bajda–Pawlikowsk and eventually persuaded the two to cofound Hadapt, a big data analytic platform that allows customers to examine all of their data in one place.

Borgman says this platform is especially useful to the ecommerce industry where companies are trying to keep track of every click, product review, and social media response. Hadapt enables these businesses to collect their information in one area so they can study consumer behavior. “We try to help people take advantage of the data they are already collecting, but just not making good use of,” Borgman says.

Investors understand Hadapt’s advantage in the big data phenomenon. The startup raised $17 million in Series A funding from Atlas Venture, Norwest Venture Partners, and Bessemer Venture Partners. But Borgman notes that while they’ve achieved success now, securing their first check was a big challenge.

“I think the first round of funding is the hardest for anyone because it seems so risky to investors,” he says. “There’s this weird psychological phenomenon that when you raise your first $500,000, you suddenly are less risky to investors even though nothing has changed from a business perspective. But literally from the day before you get the check to the day after you get the check, your level of perceived risk just drops dramatically. So how you get that first check was a struggle, and is probably a struggle for anyone. My lesson there is to persevere and network like crazy. Don’t be afraid to get rejected a bunch of times.”

These highs and lows of the entrepreneurial experience make it one of the most emotionally taxing choices, Borgman says. The only way to survive the roller coaster is an unfaltering dedication to your startup. “You just have to commit,” he says. “You have to have this fire in your belly of commitment, drive, and determination that you are going to make this startup successful at all costs.”

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Big Data Drawing Big Student Enrollments 12-17

Big Data Drawing Big Student Enrollments

How does Netflix recommend TV shows and movies for you? How does the Center for Disease Control predict the spread of infectious diseases? Why does my computer now only display ads for MBA programs? What are the most dangerous intersections for a cyclist in Los Angeles?

All of these questions can be answered by some (relatively) simple big data analysis. Business analytics and big data analysis courses are popping up at MBA programs at a frenzied pace. Florian Zettelmeyer, a professor at Northwestern’s Kellogg School of Management, has been pondering the implications for a decade.

When Zettelmeyer was at the University of California-Berkeley’s Haas School of Business, Harrah’s casino chain was just starting to dabble in using big data to make c-suite decisions. At the time, Harrah’s had 29 casinos across the country with 29 different managers. The problem was establishing a new incentives program to get people in the doors. They were examining data from each casino as a whole. The adoption of big data analysis allowed them to examine each individual customer and their actions once inside the doors, switching the central value from the actual casino to the patrons. Management could make better decisions for their individual casinos instead of an over-arching decision with little specificity or relevance. Scary, huh?

A CORE PHILOSOPHICAL SWITCH

Big data is old news for statisticians, engineers, and (obviously) data analysts. But the reason it is increasingly essential to an MBA education is what Zettelmeyer labels as the core philosophical idea. And a wealth of business schools have jumped on this bandwagon, offering majors and minors in the field within their traditional MBA programs or MS degrees in business analytics. The programs range from online offerings, such as Indiana University’s Kelley School MS in Business Analytics for just $36,000 to a concentration in it at such schools as the University of Chicago’s Booth School of Business or NYU’s Stern School.

“Data analytics is a job management problem and a leadership problem,” Zettelmeyer says. “An MBA program should help answer the question, what do you need to be effective as a manager in the current business environment? Right now, being a good leader means possessing at least a working knowledge of data science.”

Zettelmeyer’s main point is it is a leader’s job to strategically decide which problems should be examined through the data. Once the problem is identified, someone has to interpret the data. Interpreting for interpreting’s sake can produce some intriguing insights, but is largely unhelpful unless there is a calculated problem to be solved. Zettelmeyer teaches all of his courses in a problem-solution format. That is, what is the problem? And how is data mining and analysis going to bring a solution?

According to Zettelmeyer, managers need to combine data with intuition to make decisions. More importantly, they have to know what they are seeing. Then, they need to communicate the data in persuasive ways. And so the Kellogg curriculum was forged from the stone of Bay Area entrepreneurship and Big Tech. Kellogg professor, Brian Uzzi teaches multiple classes focusing on social implications of analytics and has helped shape Kellogg’s evolving curriculum alongside Zettelmeyer and other faculty members. Two years ago, the faculty members drafted initial curriculum, gathering a list of Kellogg alumni in the Bay Area and then connecting with the Silicon Valley tech giants. The faculty gave course descriptions and syllabi to execs from Facebook and others, gathered feedback, filled holes in what the companies felt the needs really were, and finished the curriculum.

“We prepare students to understand customer to customer dynamics,” Uzzi says. “We train them to be able to pull data from Twitter API and do a marketing analysis. By the end of one class period, our students can do something that would cost $70,000 for a consultant to do on their laptops in less than an hour. They leave our program feeling like they can get the best job imaginable and be able to have a startup on the side.”

FROM ENGINEERING SCHOOLS TO A B-SCHOOL NEAR YOU

While the actual master’s degree in analytics still remains at the engineering school at Northwestern, the University of Southern California’s Marshall School of Business is one of the business programs to house a one year master’s degree in business analytics. And it has been well-received. The program started its inaugeral class of in March. According to Yehuda Bassok, chair of the Data Sciences and Operations department at Marshall, they had about 80 applicants. This year they are expecting up to 500.

Bassok says the benefits and use of big data analysis goes beyond marketing problems and figuring out how to increase profit. It can also monumental in areas like public safety issues. One of the projects students have worked on in the fledgling program is addressing pedestrian and cyclist safety issues for the city of Los Angeles.

“All of the data for streets and intersections of where there are cyclist and pedestrian accidents are compiled by the City,” Bassok says. “Our program teaches students how to take that unstructured data and structure it. Is there a connection between the structure of an intersection and the accident? How serious were the accidents? Structuring the data might lead to a simple and low-cost solution.”

‘AN MBA IS A SWISS ARMY KNIFE’

The University of Rochester’s Simon Business School also recently announced a new MS in Business Analytics program.

“An MBA is a Swiss Army Knife,” says Simon Dean Andrew Ainslie. “There are a ton of topics covered and is appropriate for someone wanting career mobility. The one year masters program is for someone wanting a more focused and specialized education.”

Ainslie says it is part of a large cycle. Big data was very much a part of business education in the late 90s during the boom of dotcom companies. The bust caused big data to fade away for a while. Now, with companies like Google and Amazon dedicating large amounts of resources to it, big data is regaining traction. It could also be gaining popularity in business schools to fill a decreasing part-time population.

“There has been a slow, steady 20 year decline in Americans taking the GMAT,” Ainslie says. “The part-time programs have taken the hit and are dying out. The popularity of big data allows schools to offer a new and attractive program.”

SOLVING A GLOBAL HEALTH CRISIS TO DRAFTING A FANTASY TEAM

In 2009, when the nation was rife with panic over the spread of the H1N1 virus, researchers at Northwestern were inspired by the website, Where’s George, which tracks how dollar bills move around the country. They used similar computer algorithms except the two mammoth datasets of air and commuter traffic trends replaced dollar bills. The result was an eerily close prediction of how many H1N1 cases would come about and where.

Dustin Pusch heads up the program at George Washington’s School of Business. He says the purpose of their program, which can be taken in two years as a part-time student or one year as a full-time student, is to prepare students to manage a business problem from start-to-finish with only analytic decision making. The school partners with Deloitte and Catalyst Consulting to create and solve the problems.

Another way George Washington students can delve into the galaxy of big data is through the Washington Nationals. Students mainly work with the Nationals’ marketing department, but are also exposed to the moneyball side of the sport.

“The cat’s out of the bag for the sports world,” Pusch says. “You are not going to be as successful as a sports franchise unless you are doing it. They run big data sets for player evaluation. The concepts we teach are absolutely applicable to this. I just use the concepts to better draft my fantasy teams.”

ALL ABOARD THE BIG DATA BANDWAGON?

The beauty of George Washington’s program is that students get a basic skill set and then the autonomy to create a program specific to their goals and interests.

“We have a corporate advisory board made up of representatives from organizations such as Deloitte, IBM, the Nationals, the Federal Treasury, JP Morgan, and others,” Pusch says. “They all weigh in on the development of the program. The importance we are seeing is not just the technical skills. We also have to emphasize soft skills and being able to communicate the analytics.”

Regardless of interest, the world is rapidly moving towards big data and being able to analyze and communicate it effectively and efficiently.

“Business, marketing, finance, the spread of infectious diseases, everyone is starting to use big data sets to make predictions and evaluations,” Zettelmeyer says. “General Electric is using big data to make decisions on building and scheduling railroads and locomotives across the country. Analytics are everywhere and managers need to know how to synthesize and make decisions with them.”

BUSINESS ANALYTICS PROGRAMS OFFERED AT BUSINESS SCHOOLS

School	Degree	Length of Program	Cost
Chicago (Booth)	Concentration in Analytic Management	Can be earned during two-year MBA	$61,520
NYU (Stern)	Specialization in Business Analytics	Can be earned during two-year MBA	$60,744
Northwestern (Kellogg)	Program on Business Analytics	Can be earned during MBA	$61,596
Indiana (Kelley)	Boasts major and minor in analytics in MBA program, plus an online MS in business analytics	Online program can be earned while at home	$36,000
University of Southern California (Marshall)	MS in Business Analytics	Full-time, 1 year or part-time, one class per semester	$47,000
The George Washington School of Business	MS in Business Analytics	Full-time, 1 year or part-time, 2 years	$50,000
University of Texas-Austin (McCombs)	MS in Business Analytics	Three semesters	$34,000 (in-state) or $40,000 (out-of-state)
Arizona State University (W.P. Carey)	MS in Business Analytics	9-month full-time or 16-month part-time	$31,300 (in-state) or $46,700 (out-of-state)
University of Minnesota (Carlson)	MS in Business Analytics	Three semesters	$39,825 (in-state) or $54,900 (out-of-state)
Michigan State University (Broad)	MS in Business Analytics	One year, full-time	$51,980 (in-state) or $54,980 (out-of-state)
University of Rochester (Simon)	MS in Business Analytics	11 months (non-internship track) or 17 months (internship track)	$62,678
Southern Methodist University (Cox)	MS in Business Analytics	Full-time, 1 year	$48,386
Drexel University LeBow College of Business	MS in Business Analytics	1 year, full-time (45 credits)	$42,750
University of Cincinnati, Lindner College of Business	MS in Business Analytics	Full-time, 1 year	$19,280 (in-state), $24,118 (out-of-state)
University of Miami School of Business Administration	MS in Business Analytics	Full-time, 9 months	$53,7000
Melbourne Business School (Australia)	Master of Business Analytics	Full-time, 1 year	$48,000

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Big Data – revolutionising sport in unexpected ways 10-09

Big Data – revolutionising sport in unexpected ways

You know analytics is serious in sport when the Massachusetts Institute of Technology shows an interest. Not only does it run a yearly conference on the topic, its boffins are busy figuring out fresh ways to revolutionise sport using Big Data.

“Sports are watched by millions and millions of people - yet, pretty much all of the strategic decisions are made by humans in a split second. These decisions could definitely be enhanced by learning from past data, but humans can’t keep large databases in their heads. I wanted to build predictive analytics tools to help teams make these decisions,” Cynthia Rudin, associate professor of statistics at MIT, tells the Guardian.

Rudin believes Big Data analytics can help in various ways, from tweaking training plans to determining patterns about competitors. “If we know, for instance, that in certain circumstances, a particular coach on the opposition team tends to make a particular decision, then we can be ready for it,” she adds. “Another example - we can place sensors all over a car while it is in training for a race, and use the sensor data to help a driver learn how to race better.”

The ever-growing world of Big Data has already permeated the sporting arena in surprising ways. Player health and safety, for instance, is one increasingly attractive use case. “Particularly in high injury sports, you think about the health of player, how we can use data and analytics to look after their bodies,” Martin Houghton, managing partner from HP’s analytics and data management division in EMEA.

Just look to the high impact, rough game of rugby, where players are being saved from long-lasting physical damage thanks to predictive analytics technology. Pro players across the globe have been fitted with sensors to measure and monitor intensity levels, collisions and fatigue to help predict their limit and injury risk. From all this information, coaches can determine how to alter players’ training schedules. Psychological data can also be collected to explore how psyches can affect performance.

American sports continue to benefit from Big Data in myriad ways. The Oakland A’s baseball team famously used analytics to make strides in Major League Baseball, as captured in the 2011 film Moneyball, whilst NBA basketball teams are now investing heavily in the necessary technologies and expertise to gain an advantage over others.

Organisations such as STATS use cameras to measure players and the ball in x and y coordinates. This brings in speed, distance, player separation and ball possession statistics, which can be filtered into user-friendly interfaces so coaches can make better decisions.

And data analysts are being employed en masse by some of the NBA’s best-known teams. The Dallas Mavericks have been investing heavily of late. Coach Rick Carlisle recently revealed a raft of new personnel has been brought into the analytics division of the team. It’s expecting to see manifold benefits heading into the new season. “We’re going to be a better team this year,” Carlisle said this month. “We know that by the analytics.”

The ultimate aim for all these sports teams is to improve performance in the same way modern businesses in almost all industries seek to exploit data to tweak their operations for the good of the company as a whole and their customers. Just looking at the business side of sport, it’s easy to see the crossover between the two ostensibly separate worlds.

HP has been working with NASCAR to boost the motor sport’s brand. Together they built the Fan and Media Engagement Center (FMEC), which collects up all the information fans post publicly online. All this data can be captured, analysed and passed on to al the relevant people, whether the drivers or their PR reps. Now they all know what the fans are thinking, they know just how to get them revved up.

And this is what Big Data should be about for any business - it’s the customers who make or break the company. They need to be impressed via information before anyone else.

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The next generation of data integration technology for the changing cloud market 09-03

The next generation of data integration technology for the changing cloud market

Data integration becomes a much more important issue with the use of both public and private cloud-based platforms. Information that was once stored locally may now be spread across several public cloud providers, and that information needs to be shared with most, if not all, enterprise systems that exist locally or within public clouds.

These days, most cloud computing-related data integration focuses on the simple process of replicating data from a public cloud to an on-premise system, as well as cloud-to-cloud. This typically uses data integration technology as a way to synchronize enterprise data between traditional on-premise systems, to those that have relocated to the cloud. Examples include sales data that resides in a SaaS-delivered CRM, and perhaps delivery systems that reside within the enterprise data center. The data integration software works to make sure that the sales data, and other information, matches in both systems.

What’s most interesting about the path of data integration and the rapidly growing cloud computing space? The traditional approach to data integration, meaning simple data replication and mediation, will become an outdated concept over the next 10 years or so. Data integration technology will make rapid moves in new directions, in light of the needs and the value of cloud computing, which will provide the enterprise with new capabilities, as well as largely disrupt the existing data integration marketplace. Also keep in mind that some data integration technologies will reach their 20^th birthday in the next few years.

What you think you know and understand about data integration could quickly go out the window, as technology providers try to match the capabilities of newer cloud-based platforms with newer data integration strategies, approaches, and technologies. Reconsider what you understand about data integration and cloud to provide an emerging path to success for existing and new data integration technology providers.

Data integration evolves

The evolution of data integration largely began in the middle 90s with the Enterprise Application Integration movement. This architectural pattern was in response to the number of enterprise systems, such as SAP and Peoplesoft, that bgan to appear in data centers, and thus the need to synchronize that information with other systems within the enterprise.

The older patterns of data and application integration were fairly simple to understand. Information was extracted from the source system(s), it was then changed in structure and content (typically), and placed in the target system(s). This usually occurred around events such as adding a new customer in the accounting system, or updating the current status of inventory.

Players in the traditional data and application integration market, which still exists today, includes Tibco, IBM, Software AG, Pervasive/Actian, and Informatica. Most have modernized their integration offerings, including cloud-based sources and targets, as well as adding some as-a-service capabilities (cloud delivered). This is not to say that the more mature companies provide better approaches to cloud-based data integration; they have just been around longer.

Somewhat newer players include WSO2, Red Hat, Jitterbit, Boomi/Dell, Composite/Cisco, Castiron/IBM, Liaison Technologies, Scribe, and many others. These technologies came in the form of a second wave of data integration technologies that were designed to address the changing needs of enterprise data integration. This wave includes some newer approaches such as data virtualization or abstraction (such as Red Hat, WSO2, and Informatica, and Composite/Cisco.), or systems that operate exclusively on-demand (such as Boomi).

As seen in Figure 1, the focus now (2010 – 2014) is on leveraging existing integration technology, traditional and not, that largely provides some, if not all of the capabilities that include: Data replication, semantic mediation, data cleansing, and mass data migration. These capabilities provide enterprises with the ability to move data from cloud-to-cloud, cloud-to-enterprise, or intra-enterprise, as needed, to support the core business processes. They have evolved in the last several years around the trends in hybrid and multi-cloud architectures, as well as the rise of much larger data sets (big data).

Figure 1: As cloud technology matures, data integration will take on new forms, new roles, and will deliver new value.

Common features include the ability to account for the differences in the ways that the cloud or non-cloud systems store data, and the ability to change both the structure and the content on the fly, thus making the target system think it’s receiving native data. Mass data migration deals with the ETL (extract – translate – load) capabilities and includes the ability to migrate large amounts of data at specified times of the day or week, also changing the content and structure to meet the needs of the target system, such as a data warehouse in the cloud or on-premise.

The use of data cleansing technology means that the data integration solution can deal with data issues, which include removing or correcting corrupt or inaccurate data sets. This is done along with the other data integration operations, typically while information moves from system to system, such as from an existing on-premise inventory control system to a cloud-based database.

Things need to change

Referring again to Figure 1, as cloud computing becomes a larger part of enterprise platforms, the world of data integration will need to evolve as well. This includes adding or expanding some data integration capabilities, such as:

• Intelligent Data Service Discovery
• Data Virtualization
• Data Orchestration
• Data Identity

Intelligent data service discovery refers to data integration technology that can automatically find and define data services that become the primary mechanism to consume and produce data from existing cloud and non-cloud systems. This means that we can discover and re-discover which data services exist within the enterprise, and, more importantly, which data services exist in public clouds, noting where they are, what they do, and how to access them. Enterprises will leverage this catalogue as a means to understand all available data assets, and thus leverage the most meaningful data assets to support core business processes, owned or rented.

Data virtualization, which is really not new, will grow in popularity as enterprises attempt to redefine existing databases using new virtual structures, and externalize these databases as well-defined data services. A new virtual database structure can be placed over existing database(s), and thus redefine how the database is made visible and consumed by other systems. There is no need to rebuild the back-end databases to meet the new needs of cloud-based systems, which removes risk and cost.

Data orchestration refers to the ability to define how the data interacts together to form and reform solutions. Much like service orchestration, this means defining composite data points, perhaps combining sales and customers, to form new data services that can be leveraged inside or outside of the enterprise. Those who leverage the data will have a greater degree of control over what the data means for each application view, keeping the physical structure and data content intact.

Data identity refers to the ability to link data, both structure and data instances, to humans or machines. This controls who or what can consume the data, and see the contents. This makes it easier to live up to ever changing and expanding regulations, and even internal data security policies. The data containers control access to the data, and the data identity rules that are set within the data. This becomes a common mechanism that spans enterprises, and public cloud providers.

Consider the rise of shared enterprise business services that will likely emerge, given the number of business systems that will exist on public cloud-based platforms. The ease with which these services can be share will also see the rise of the ability to reuse data. This will create the ability to repurpose any data sets, including structure and content, for use in new and existing systems, without having to create a new database or data service instance. An example would be the ability to integrate an historical sales database, perhaps from another enterprise that makes it available, to understand patterns of fraud for a newly built system. You don’t need to understand anything about the reused data set; it self-defines within your new use case.

Much like data identity, credential-based identity and centralized trust is the next generation that will allow data to define access credentials within the data. This just takes data identity to the next level by providing a centralized location that can validate both the data (structure and content), as well as humans and machines that would like to view or manipulate the data. This mechanism means that we’ll understand where all data exists, and match those authorized with the authorized data, down to the database, object, and instance. Again, this assumes a universal standard.

Of course, predictions are not a perfect science. However, looking at the clear and emerging patterns of cloud-based migration and development, as well as the trajectory of data management and data integration, these are pretty easy calls.

Data integration providers will need to keep up with the evolving requirements, and I’m sure that the data integration technology space will look very different in just 5 years time. Those who prepare for the changes will survive and thrive. Those who do not prepare will find themselves yet another victim of the changes that cloud computing brought to the world.

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What executives should know about open data 01-17

What   executives   should   know   about   open   data

Novel and more accessible forms of information from government and private sources represent a new and rapidly growing piece of the big-data puzzle.

Not all data that’s valuable is internal and proprietary. 

New initiatives by governments as diverse as those of the United States, Mexico, and Singapore are opening the spigots of readily usable public data. Corporate information too is becoming more “liquid,” moving across the economy as companies begin sharing data with their business partners and, sometimes, consumers. 

Also surging is the richness of the information from data aggregators, which are assembling, rendering anonymous, and selling (to interested third parties) a wide range of data flows. Then add huge volumes of data from social-media interactions, available from providers of digital platforms such as Twitter and Facebook

These new sources of open data represent an expanding trove of largely unexploited value. One everyday illustration of open data at work is a smartphone app that uses real-time data (provided by transit authorities) to tell commuters when the next bus or train will arrive. Using open or pooled data from many sources—all the businesses in a particular sector, for example—often combined with proprietary big data, can help companies develop insights they could not have uncovered with internal data alone.

Demographic data, financial transactions, health-care benchmarks, and real-time location data are among the myriad new information sources a company can exploit to create novel products and services and to make its operations more effective and efficient. 

New research from the McKinsey Global Institute, the McKinsey Center for Government, and McKinsey’s Business Technology Office suggests that $3 trillion or more in annual value could arise from the use of open data in applications across seven domains of the global economy (exhibit). About a third of those potential benefits would involve the use of benchmarks to identify areas for improvement.

Open data can help unlock $3 trillion to $5 trillion in economic value annually across seven sectors.

Whether or not individual executives at large companies choose to work with open data of various types, the magnitude of the value at stake suggests that some of them will—and that these applications will probably affect a wide range of industries, markets, and customers. 

Layering open-data mandates into the ongoing development of data and analytics strategies by considering both the use and sharing of more liquid data should therefore become an increasingly important priority for a wide range of companies. Here are a few examples of open data’s potential:

·         Energy exploration. As new technologies have made it possible to drill in a wider range of geological formations, reservoirs have become more complex. That’s raising costs and risks—estimated ratios of prospects to explored targets can be as high as 50 to 1. The sharing of information on drilling permits and on seismic and other data across companies could reduce the number of dry holes and help optimize investments. 

·         While the widespread sharing of seismic data is unlikely, sharing among even a few companies could produce significant new value in the oil and gas industry. 

·         Governments keen on maximizing resource wealth could take the lead in structuring processes for granting permits so that grants of initial drilling licenses would require greater sharing of seismic data. Sharing data on projected costs and development timetables (through third parties) could establish benchmarks that, we estimate, would reduce per-project costs by 15 to 25 percent.

·         Consumer insights. In the consumer-products sector, sharing data among retailers and manufacturers in limited circumstances—avoiding exchanges with direct competitors, for example—could lead to marketing approaches not possible with proprietary data alone. 

·         Take Nectar, a UK-based program for loyalty cards, which can be used at Sainsbury’s for groceries, BP stations for gasoline, and Hertz for car rentals. Sharing aggregated data allows the three companies to gain a broader, more complete perspective on consumer behavior, while safeguarding their competitive positions.

·         Agriculture. San Francisco–based Climate Corporation combined more than 30 years of weather data, 60 years of data on crop yields, and multiple terabytes of information on soil types—all data from public sources. 

·         With that reservoir of historical information and real-time data flows, the company offers fee-based advice to farmers and customized crop-and weather-insurance products based on sophisticated algorithms. The company was recently acquired for about $1 billion by Monsanto.

Other possibilities abound. Premium pricing for some goods could be facilitated if companies shared detailed information about products, such as the materials they use or the conditions under which they were manufactured (for example, with renewable energy).

 On the flip side, open-data applications may also create new areas of consumer value. In a budding trend known as MyData, organizations share information they have collected about individuals with them, in useful forms. Patients could access targeted medical data from a hospital, for instance, to help them manage their health.

Powerful as open data can be, many companies have valid concerns. Consider the sharing of data to establish industry benchmarks. Even if a company uses a third party and gets assurances of anonymity, there’s always a risk that its identity might be revealed and that competitors could see how well or poorly it was doing. Shared data also could give away sources of competitive advantage or compromise intellectual property. Similarly, tapping social data could heighten privacy worries among consumers.

Still, it’s hard to imagine that the open-data wave will slow down. 

Third-party open-data aggregators will certainly proceed to sell and publish corporate data, such as customer ratings, safety records, defect complaints and recalls, and comparative price data. Open-data initiatives also continue to proliferate in the public sector. In June 2013, G8 governments adopted an Open Data Charter, which establishes the expectation that the default policy should be the open publication of government data. 

Traditional competitors and entrepreneurial attackers can take advantage of open-data sources such as social-media comments and crowdsourced ideas to come up with new products and services. Open data, in short, seems to be another of the many relentless shifts in the digital landscape to appear unexpectedly, create new opportunities and strategic complexities, and leave established players with no place to hide.

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