A Chinese man who was abducted as a five-year-old has been reunited with his family - 23 years later.

Luo Gang was snatched on his way to kindergarten in the province of Sichuan and taken almost 2,000 kilometres away to live with a new family as their son.

"Everyday before I went to bed, I forced myself to re-live the life spent in my old home," Gang told Chinese news outlet Nhaidu.com, “so I wouldn’t forget.”

Two decades on, after hearing about a charity set up to reunite kidnapped children with their parents, Luo set down on paper the map he had carefully preserved in his mind over all those years, showing the layout of his home town.

Luo Gang was reunited with his parents with the help of technology and volunteers. Photo: Nhaidu.com

He posted the crude picture to the Baby Come Home website, which aims to help the 200,000 Chinese children who are kidnapped or sold each year.

A volunteer recognised something in his story, suggesting his parents might have been a Sichuan couple who lost a son in the year Luo had so carefully remembered.

Eager for any clue, Luo turned to Google Maps, and was astonished to find that he could recognise features of the town near Guangan, particularly a pair of bridges located close together.

“That’s it! That’s my home,” shouted Luo, in tears.

A satellite view of the Yaojiaba region in Sichuan province. Photo: Google Maps

Sichuan volunteers from Baby Come Home travelled to the Yaojiaba area and asked authorities to perform a paternity test. It found a match.

The family have since made a joyful reunion, but say the pain of their son's abduction will always be with them.

"I felt heartbroken. I couldn't eat or sleep and I cried every day thinking my son was missing and didn't have enough food or clothes out there," said Luo's mother, Dai Jianfang.

Baby Come Home says it has reunited 624 families, but over 12,000 parents and children are still searching for their relatives through the site.

Big data doesn't work if decision-makers can't absorb what it means. Enter the data visualization expert to sort it all out.

Computerworld - A picture's worth a million data points. That's the mantra, anyway, in business analytics these days. 

As the big data trend intensifies and analytics become more ingrained in corporations, the need for people who can present data in easily intelligible ways is rising. Last fall, Gartner predicted that there would be 4.4 million big data jobs by 2015, many requiring new, nontraditional skills like data visualization.

But what exactly is data visualization? Who exactly is doing this visualizing, and how is it different from creating a colorful graph or an interesting infographic? (For a deeper dive into those questions, see Dataviz: A brief how-to.)

Ironically, it's hard to get a clear picture of a data visualizer. The function is not yet well defined, and it's rare to see it as a job title in and of itself, IT career watchers say. Rather, it's a skill set that more and more companies are demanding as part of other roles, notably business intelligence and analytics jobs.

"Data visualization" as a requirement in job descriptions increased 12% over the past six months, according to Todd Nevins, co-founder of icrunchdata, a jobs board that specializes in data analytics positions. In contrast, "big data" as a requirement in job descriptions is up 63%. "Data visualization is still in its infancy but becoming more prominent as companies wrap their strategies around the extraction and usage of data," Nevins sums up.

The data that is getting visualized isn't coming from IT -- at least not so far. IT has a fairly limited role in data analysis and an even lesser role in visualization, data experts say. "IT is typically responsible for much of the dashboard and business intelligence delivery today," says Gregory Lewandowski, manager of analytics at Cisco. "But we often see IT in an order-taker capacity instead of trying to understand the end game."

A graph designed by dataviz guru Stephen Few uses stacked bars in simple colors to help viewers easily make comparisons between three sets of data. An arrow and annotation make the point of the graph clear. Click here to see a "before" image of the same graph without Few's principles applied.

IT is usually focused on the technology that enables visualization but doesn't use the technology itself, explains Stephen Few, principal and founder of Perceptual Edge, a consultancy that specializes in data visualization. Few, who has a background in IT and business intelligence, founded the consultancy in 2003 after taking a workshop taught by visualization guru Edward Tufte. Tufte is renowned for developing data visualization as a discipline and wrote the definitive book on the subject in 1983, The Visual Display of Quantitative Information. Few has become well-known himself as a dataviz expert and has written several books, includingShow Me the Numbers: Designing Tables and Graphs to Enlighten.

Even in IT departments that have a resident business intelligence analyst, that person is most often building production reports upon request, says Few. "But typically they don't understand the data. They don't really know how people are using the data they're putting in that report," he says.

"There's a disconnect between the people who actually work with data to make decisions and the [IT] people who supply the data they need," Few continues. "Finding people that really understand the data and understand the technologies that organization has to distribute the data -- finding those two things in a single person -- is relatively rare." (For other necessary skills, see Qualities of a good data visualizer.)

What Few and others are talking about is the more finely honed aesthetic sense that today's data visualization requires. Boris Evelson, vice president and principal analyst at Forrester Research, says there are two levels of data visualization skills emerging. One level refers to a person's ability to use the latest technology and tools to analyze and present information. Rather than using Excel or even Cognos, for example, data analysts are using Tableau or Spotfire to create more visually pleasing and more easily comprehended charts and scatter plots. (See 22 free tools for data visualization and analysis for more suggestions.)

But that's not enough in some applications. Recently a large New York City bank told Evelson it needed someone with deeper skills to visually present a sophisticated and comprehensive portfolio analysis -- analyzing thousands of clients with various types of investments and risks. Although the bank had "all the right tools and technologists," he says, it was looking for someone with a specialized understanding of how the brain reacts to and digests visual information.

"It was not about the technology of data visualization, but the psychology of visual perception," he says. The bank wanted someone who would know which types of visualization techniques work best for different types of data, as well as the limitations of certain techniques. For example, "a significant proportion [about 7%] of the population is colorblind," he notes. "So maybe they shouldn't exclusively rely on color."

The bank did end up bringing in a professional -- but as a part-time consultant rather than a full-time staffer, a trend that analysts say will likely be repeated in many companies, even as big data heats up. A third option is to outsource such data visualization projects to boutique consultancies.

Meanwhile, companies seem to be recognizing the need for data visualization training not just for their business analysts but across their organizations.

Cisco's Lewandowski took Few's course six years ago. "It really opened my eyes to the important, but subtle, things that many people miss," he says. "There are so many different things you may not notice right away but make all the difference in the world."

Like many data visualization specialists, Lewandowski circuitously gravitated into the field. He started in sales and business development at Cisco 14 years ago. Then he moved into a role of managing channel partner relationships, where he started using BI applications. He gradually expanded his expertise in BI and data analysis, and now heads a three-person team within Cisco Global Business Operations that is responsible for delivering business intelligence throughout the company. He describes the unit as a hybrid business and IT organization, although he doesn't consider data visualization to be an IT function.

Lewandowski's team spends much of its time on data visualization, both on specific visualizations of Cisco data and on promoting best practices throughout the company. The team is hoping that "a bit more education could get rid of the flaming, spinning 3D pie charts of the world," he says. "At end of the day, everybody has a responsibility to try and communicate better."

Here Few chose a simple line graph to make the main point of the data -- market share -- easy to see, with labels directly next to the data lines rather than in a separate legend box. Integrating a table at the bottom of the graph provides precise values for those who want them without cluttering up the main image. Click here to see a "before" image of the same graph without Few's principles applied.

For data visualization professionals, however, the end goal is not necessarily to present data that answers specific questions, Lewandowski notes. "Part of it is about allowing our leaders to be able to articulate questions that they never had before because they are seeing things in a way that they've never seen them before," he explains. "If we're successful, people can see the threads in a way that allows them to ask better questions, which leads to better strategy and ultimately to a better company."

Good data visualization has proven to have real bottom-line business benefits at Cisco, says Lewandowski. He developed a graphic called "Lewandowski's pyramid," for example, which "has led to changes in global strategy." It's so strategically important, in fact, that he won't give much detail. "It's basically a segmentation, or stratification, model, where we count something, for example, number of orders or number of customers, and then segment it into different layers." 

Over time, the model enables managers to track changes (in what, he wouldn't say) and identify the factors behind those changes to be better positioned to make a course correction or take advantage of an emerging market, for example. "We are depicting this in a way they've never seen before, a way that makes it very clear as to the types of questions internal stakeholders need to be asking," Lewandowski says.

Dana Zuber, an analytics manager in the enterprise data and analytics team at Wells Fargo, says she wasn't familiar with data visualization until she joined the bank six years ago, even though she had been analyzing data in a variety of jobs over the course of her 12-year career. The bank sent her through an internal training program on data visualization as well as to some outside seminars, including one by Tufte. "Before that, I didn't understand that there was a discipline around data visualization," she says.

The bank's executives obviously feel that data visualization has become a critical skill, and not just for data analysts. The internal course is available to anyone in the company, Zuber says. "As more people have taken the course, [interest in data visualization] has just spread throughout the organization," she says. "More people are seeing the value of it and understanding how it can help their job."

That's the kind of progress that Few would like to see more of. Although big data has focused attention on data visualization, it's a skill that's been sorely needed in corporations for a long time, he says. And even as companies start to recognize its importance, many of them are focusing on the wrong things. In job descriptions, for example, they are asking for technical skills, like how to do a chart in Cognos, rather than graphic design expertise.

"The kind of skills they are looking for aren't necessarily the skills they need," he says. Without an understanding of the subtler aspects, including how the human brain perceives color and shape, "you end up getting these really flashy data visualizations -- with all these colors and things spinning and flying and so forth," he explains. "It's just eye candy, and the story you're trying to tell with the data is lost behind the effects."

Many wealth management firms are using Big Data to gain new insights into their customers and prospects, discover investment opportunities, assist with risk/compliance, and provide competitive differentiation, according to the new report, Big Data in Wealth Management: The Search for Customer Insight, from Celent, the financial research and consulting firm.

Wealth management firms process, consume, and produce massive amounts of digital data on a daily basis. Many types of wealth management firms are looking at Big Data solutions, including banks, full service and self-directed brokers, and RIAs. Celent believes banks and full service brokers are more likely to use Big Data solutions in the near term as they work to establish better consolidated or 360° views of their customers.

Celent defines Big Data on three dimensions (volume, velocity, and variety), and the process includes capturing and gathering data, analytics, and visualization. This has caught the attention of financial service firms because Big Data can help firms capture and combine diverse sets of internal and external data to improve their analytics. New Big Data analytics help firms process analyst queries and experiments faster, which improves analyst productivity and provides a competitive advantage. Improved visualization tools help in the exploration and presentation of data and analytics.

Firms can deploy Big Data solutions throughout the front, middle, and back office. Celent believes priority areas for Big Data include CRM and trading in the front office, and compliance and monitoring and risk management in the middle and back offices.

Relationship managers, advisors, and traders are using Big Data and analytics to help them discover, develop, and test investment ideas and strategies. Increasingly firms want to capture more information from looking for correlations and investment opportunities across multiple asset classes and over longer time horizons. Big Data helps firms establish larger data sets which enable users to run experiments more quickly to uncover actionable investment ideas.

Celent recommends that wealth management firms make investments in Big Data and analytics technologies and projects that can help the firm uncover proprietary and actionable insights. Discovering, developing, and testing actionable ideas and strategies helps uncover investment upside that attracts new investments. Investing in Big Data solutions to aggregate internal and external data can help firms discover and mitigate risk proactively, instead of reacting to regulator audits and examinations.

Celent's findings and recommendations include:

• Firms investing in Big Data will enhance their knowledge of customers and prospects.

• Big Data tools can help improve investment research and trading.

• Big Data analytics can help reduce operational and reputational risk.

• Firms should begin experimenting with Big Data sooner rather than later.

• Vendors should put increasing focus on analytics.

• Vendors should provide technology trials and Big Data sandboxes for prospects.

Many wealth management firms have begun to examine Big Data and analytics solutions; some are at exploratory stages or early stages of deploying solutions. Celent expects the industry to catch up as more firms discover additional insights about their customers that can help increase retention.

Click to enlarge. Data source: World Values Survey

Update: A professor who studies race and ethnic conflict responds to this map.

When two Swedish economists set out to examine whether economic freedom made people any more or less racist, they knew how they would gauge economic freedom, but they needed to find a way to measure a country’s level of racial tolerance. So they turned to something called the World Values Survey, which has been measuring global attitudes and opinions for decades.

Among the dozens of questions that World Values asks, the Swedish economists found one that, they believe, could be a pretty good indicator of tolerance for other races. The survey asked respondents in more than 80 different countries to identify kinds of people they would not want as neighbors. Some respondents, picking from a list, chose “people of a different race.” The more frequently that people in a given country say they don’t want neighbors from other races, the economists reasoned, the less racially tolerant you could call that society. (The study concluded that economic freedom had no correlation with racial tolerance, but it does appear to correlate with tolerance toward homosexuals.)

Unfortunately, the Swedish economists did not include all of the World Values Survey data in their final research paper. So I went back to the source, compiled the original data and mapped it out on the infographic above. In the bluer countries, fewer people said they would not want neighbors of a different race; in red countries, more people did.

If we treat this data as indicative of racial tolerance, then we might conclude that people in the bluer countries are the least likely to express racist attitudes, while the people in red countries are the most likely.

Compare the results to this map of the world’s most and least diverse countries.

Before we dive into the data, a couple of caveats. First, it’s entirely likely that some people lied when answering this question; it would be surprising if they hadn’t. But the operative question, unanswerable, is whether people in certain countries were more or less likely to answer the question honestly. For example, while the data suggest that Swedes are more racially tolerant than Finns, it’s possible that the two groups are equally tolerant but that Finns are just more honest. The willingness to state such a preference out loud, though, might be an indicator of racial attitudes in itself. Second, the survey is not conducted every year; some of the results are very recent and some are several years old, so we’re assuming the results are static, which might not be the case.

Here’s what the data show:

• Anglo and Latin countries most tolerant. People in the survey were most likely to embrace a racially diverse neighbor in the United Kingdom and its Anglo former colonies (the United States, Canada, Australia and New Zealand) and in Latin America. The only real exceptions were oil-rich Venezuela, where income inequality sometimes breaks along racial lines, and the Dominican Republic, perhaps because of its adjacency to troubled Haiti. Scandinavian countries also scored high.

• India and Jordan by far the least tolerant. In only two of 81 surveyed countries, more than 40 percent of respondents said they would not want a neighbor of a different race. This included 43.5 percent of Indians and 51.4 percent of Jordanian. (Note: World Values’ data for Bangladesh and Hong Kong appear to have been inverted, with in fact only 28.3 and 26.8 percent, respectively, having indicated they would not want a neighbor of a different race. Please see correction at the bottom of this post.)

• Wide, interesting variation across Europe. Immigration and national identity are big, touchy issues in much of Europe, where racial make-ups are changing. Though you might expect the richer, better-educated Western European nations to be more tolerant than those in Eastern Europe, that’s not exactly the case. France appeared to be one of the least racially tolerant countries on the continent, with 22.7 percent saying they didn’t want a neighbor of another race. Former Soviet states such as Belarus and Latvia scored as more tolerant than much of Europe. Many in the Balkans, perhaps after years of ethnicity-tinged wars, expressed lower racial tolerance.

• The Middle East not so tolerant. Immigration is also a big issue in this region, particularly in Egypt and Saudi Arabia, which often absorb economic migrants from poorer neighbors.

• Racial tolerance low in diverse Asian countries. Nations such as Indonesia and the Philippines, where many racial groups often jockey for influence and have complicated histories with one another, showed more skepticism of diversity. This was also true, to a lesser extent, in China and Kyrgyzstan. There were similar trends in parts of sub-Saharan Africa.

• South Korea, not very tolerant, is an outlier. Although the country is rich, well-educated, peaceful and ethnically homogenous – all trends that appear to coincide with racial tolerance – more than one in three South Koreans said they do not want a neighbor of a different race. This may have to do with Korea’s particular view of its own racial-national identity as unique – studied by scholars such as B.R. Myers – and with the influx of Southeast Asian neighbors and the nation’s long-held tensions with Japan.

• Pakistan, remarkably tolerant, also an outlier. Although the country has a number of factors that coincide with racial intolerance – sectarian violence, its location in the least-tolerant region of the world, low economic and human development indices – only 6.5 percent of Pakistanis objected to a neighbor of a different race. This would appear to suggest Pakistanis are more racially tolerant than even the Germans or the Dutch.

Update: I’ve heard some version of one question from an overwhelming number of readers: “I’ve met lots of Indians and Americans and found the former more racially tolerant than the latter. How can these results possibly be correct?” I’d suggest three possible explanations for this, some combination of which may or may not be true. First, both India and the U.S. are enormous countries; anecdotal interactions are not representative of the whole, particularly given that people who are wealthy enough to travel internationally may be likely to encounter some subsets of these respective populations more than others.

Second, the survey question gets to internal, personal preferences; what the respondents want. One person’s experiences hanging out with Americans or Indians, in addition to being anecdotal, only tell you about their outward behavior. Both of those ways of observing racial attitudes might suggest something about racial tolerance, but they’re different indicators that measure different things, which could help explain how one might contradict the other.

Third, the survey question is a way of judging racial tolerance but, like many social science metrics, is indirect and imperfect. I cited the hypothetical about Swedes and Finns at the top of this post, noting that perhaps some people are just more honest about their racial tolerance than others. It’s entirely possible that we’re seeing some version of this effect in the U.S.-India comparison; maybe, for example, Americans are conditioned by their education and media to keep these sorts of racial preferences private, i.e. to lie about them on surveys, in a way that Indians might not be. That difference would be interesting in itself, but alas there is no survey question for honesty.

Correction: This post originally indicated that, according to the World Values Survey, 71.7 percent of Bangladeshis and 71.8 percent of Hong Kongers had said that they would not want a neighbor of a different race. In fact, those numbers appear to be substantially lower, 28.3 percent and 26.8 percent, respectively. In both cases, World Values appears to have erroneously posted the incorrect data on its Web site. Ashirul Amin, posting at the Tufts University Fletcher School’s emerging markets blog, looked into the data for Bangladesh and discovered the mistake. My thanks to Amin, who is Bangladeshi and was able to read the original questionnaire, for pointing this out. His analysis is worth reading in full, but here’s his conclusion:

The short answer is, yes, someone did fat finger this big time. “Yes” and “No” got swapped in the second round of the survey, which means that 28.3% of Bangladeshis said they wouldn’t want neighbors of a different race – not 71.7%.

26K Facebook likers and 2.5K Tweeters, take note.

Amin adds, “Bangladeshis are a tolerant bunch — it’s ok to come visit.” The error in the Hong Kong data, first discovered by Chinese-speaking users on Reddit, was flagged by Engadget Chinese editor Richard Lai. Ng Chun Hung, a University of Hong Kong professor who was the principal investigator on World Values’ survey there, confirmed via e-mail that the data had been transposed on the survey company’s Web site. He added that he has written the World Values Survey team to alert it to this and ask it to remove the faulty data. My thanks to him, as well as to Lai and the Reddit users who dug through original Chinese-language survey forms to demonstrate the error.

IT executives continually evaluate the technology trends that will impact their business in 2013 and beyond. Some simply deploy technology to advance the goals spelled out in business plans. Others take on the role of chief innovation officer and introduce different models of using existing data to generate new revenue and gain insight into who clients are and what they want.

Buzz has certainly surrounded big data for some time, but many IT executives still and wonder how they can begin to leverage the three "V's" of big data-volume, variety and velocity, or the frequency at which data is generated and captured-and augment the value of data for their organization.

Any IT organization considering a big data initiative should consider these five major selling points, which will bring clarity as well as revenue to a company.

1. You'll Manage Data Better

Many of today's data processing platforms let data scientists analyze, collect and sift through various types of data. While it does take some technical know-how to define how the data is collected and stored, many of today's big data and business intelligence tools let users sit in the driver's seat and work with data without going through too many complicated technical steps. (See big data advantage No. 3 below.)

This added layer of abstraction has enabled numerous use cases where data in a wide variety of formats has been successfully mined for specific purposes. One example is real-time video processing. The 2012 Summer Olympic Games in London made heavy use of closed-circuit video, with 1,800 cameras monitoring Olympic Park and the athletes' village. Teams of analysts used applications to process data pertaining to those who were filmed and flag any individuals behaving suspiciously.

Another example is medical transcription. As electronic health record (EHR) use grows, healthcare organizations are increasingly using natural language processing systems to transcribe, extract and process data within a clinical context.

2. You'll Benefit From Speed, Capacity and Scalability of Cloud Storage

Organizations that want to utilize substantially large data sets should consider third-party cloud service providers, which can provide both the storage and the computing power necessary crunch data for a specific period.

Cloud storage presents two clear advantages. One, it lets companies analyze massive data sets without making a significant capital investment in hardware to host the data internally. Two, as internal IT departments recognize that big data hosting platforms require new skills and training, they find that a hosted model tends to abstract that complexity, enabling more immediate deployment of big data technology. This also lets developers build a sandbox environment that's preconfigured and ready to go without having to set up the necessary configurations from scratch.

3. Your End Users Can Visualize Data

While the business intelligence software market is relatively mature, a big data initiative is going to require next-level data visualization tools, which present BI data in easy-to-read charts, graphs and slideshows. Due to the vast quantities of data being examined, these applications must be able to offer processing engines that let end users query and manipulate information quickly-even in real time in some cases. Applications will also need adaptors that can connect to external sources for additional data sets.

Usability is another consideration. CFOs, CMOs and other non-IT executives are looking to leverage data, so they need access to charts, infographics and dashboards. Fortunately, leading BI vendors are shifting from an IT-driven to self-service analytics model that puts business users in the driver's seat. This accelerates adoption as well as return on investment and expands analytics' reach beyond report writers and more technical end users.

4. Your Company Can Find New Business Opportunities

As big data analytics tools continue to mature, more users are realizing the competitive advantage to being a data-driven enterprise. The 2012 presidential election demonstrated this. Campaign managers in both the Democratic and Republican parties saw a critical need for information on voters and their specific interests; taking this info and addressing an issue through a customized email or flyer meant the potential to gain or sway a vote.

Information regarding our preferences, likes and dislikes is critical to more than just political candidates. Social media sites have identified opportunities to generate revenue from the data they collect by selling ads based on an individual user's interests. This lets companies target specific sets of individuals that fit an ideal client or prospect profile.

Finally, big data use cases in about in retail, where the focus is on gaining insights by studying consumer behavior in online stores or physical shopping centers.

5. Your Data Analysis Methods, Capabilities Will Evolve

Data is no longer simply numbers in a database. Text, audio and video files can also provide valuable insight; the right tools can even recognize specific patterns based on predefined criteria. Much of this happens using natural language processing tools, which can prove vital to text mining, sentiment analysis, clinical language and name entity recognition efforts.

One example that highlights the use of audio analysis and big data comes from MatterSight. This call center tool can match incoming caller to the appropriate customer agent by using predictive behavioral routing and other analytics technology. MatterSight performs audio analysis to identify and score the calls based on specific criteria and then match customers with the best department to ensure the best experience. These advanced capabilities highlight some of the advancements we continue to see in unstructured data analysis and Big Data capabilities.

The Big Data Challenge: You'll Need New People

In addition to buying the right software, recruiting the right talent ranks among the most important investments an organization can make in its big data initiative. Having the right people in place will ensure that the right questions are asked-and that the right insights are extracted from the data that's available. Keep in mind that data scientists, as many refer to those working with big data, are in short supply and are being quickly snapped up by top firms.

Every CIO wants to keep his finger on the pulse of innovations that can transform his company, enhance existing business models and identify potential revenue sources. Enabling this business transformation means adopting the right tools, hiring the right people and-most of all-convincing executive leadership to embrace new models for using existing and brand-new data.

A successful big data initiative, then, can require a significant cultural transformation that's driven by the IT department. Highlight these five advantages of pursuing a big data initiative, though, and your executives are more likely to give you the resources, and the talent, you need to rise to the challenge.

Reda Chouffani is a vice president at Biz Technology Solutions, which helps medium and large companies in the Southeastern United States deploy BI and EHR software as well as IT infrastructure.

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