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Canada, Israel and Switzerland are America's top innovation partners

Canada, Israel and Switzerland are America's top innovation partners | Cultural Trendz |

Despite the perennial myth of the lone genius, the fact remains that innovation depends on cooperation. And as technology becomes ever more complicated, improving that research requires larger and larger groups working together – including groups working across national borders.

In an attempt to quantify that transnational cooperation, the US-Israel Science & Technology Foundation (USISTF) has created a “U.S.-Israel Innovation Index” to measure bilateral research and development between the U.S. and other countries.

“We picked 16 countries that were geographically diverse, had links to US, and had strong innovative tech companies,” USISTF Executive Director Ann Liebschutz told me. “The Index measures cooperation between US and Israel as well as other countries.”

The Index measures cooperation in several different areas of research and development, including government-to-government linkages, human capital, R&D spending and private industry. In measuring across these categories, the USISTF found that Switzerland is the U.S.’s biggest innovation partner, followed by Canada and then Israel.

“The Switzerland and Canada results showed that Israel was in good company,” said Liebschutz. “Switzerland isn’t a surprise because of the pharmaceutical industry.

One of the reasons for Israel’s strong showing, Liebschutz told me, is the strength of its population’s technical training.

“The technical training that every Israeli receives in the army produces a country of capable engineers,” she said.

One of the reasons that the USISTF created this index in the first place, Liebschutz added, was to demonstrate the value of research investments in an era of slashing budgets.

“In a time when budgets are lean, you hear a lot about cuts and priorities in terms of where the government should put its resources,” she said. “International cooperation, when done correctly, is a way to leverage those resources. Israel is good at creating international cooperation for funding in the R&D programs they establish. And they provide a great ROI when done correctly.”

“We felt this bilateral innovation index serves not just US-Israel relationship,” she added. “But the scientific community as a whole where it shows a good ROI on international cooperation.”

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Visual data is great, real data is better | Big Data

Visual data is great, real data is better | Big Data | Cultural Trendz |

There’s a big push at the minute by marketers and technology vendors around the concept and importance of Big Data. Run a Google Search for the term and the resulting titles of posts, articles or books speak for themselves:

    Big Data: The Next Frontier for Innovation, Competition and Productivity;
    Big Data: A Revolution That Will Transform How We Live, Work and Think;
    Big Data Transforms Business;
    Put a Fork In Big Data – It’s Done (just to balance the positive/negative results).

So, Big Data is clearly big business, and – with more than 1.7 billion search results – something that businesses are looking to understand, come to grips with and benefit from.

That’s understandable – after all, the potential of Big Data is huge. My colleague Hessie Jones, for example, recently wrote an insightful piece on how Big Data is transforming advertising, and in March 2012, no less an institution than the White House itself announced the Big Data Research and Development Initiative.

So, yes, Big Data = Big News.

The thing is, though, while access to such huge amounts of data helps us be better marketers and – by association – better businesses, there’s also the danger that we let this data inform our decisions, without stopping to think of that most important aspect of any data analysis – context.
Context Drives Educated and Informed Decisions

Think of any major decision you’ve made in life, either personally or professionally. While there will be examples of impulse buys or snap decisions made in the heat of the moment, the majority of your actions will be based on the context surrounding them.

    I wanted the sports car, but it wasn’t kid-friendly;
    Job A offered more money, but Job B offered me deeper satisfaction;
    The penthouse condo in the city offered amazing views, but the suburb neighbourhood was safer.

Three very simple examples of decisions that looked at the bigger picture of context, and took into account the long-term view versus the short-term buzz. Each option would satisfy our basic instincts, but the latter option of each choice is the one I’d go for based on its deeper context.

It’s simple economics of educated decisions, based on the data available – yet as the following examples show, context is still being missed where it’s needed the most.

Visual Data is Great, Real Data is Better

Professional social network LinkedIn is continuously looking to increase connections and the viability of its service with new additions, some useful, others less so. At least, currently.

One of the new features they’ve released is the visual ability to see who’s viewed your updates, and how far they’ve spread. Visually, it’s pretty cool, as can be seen below:

The problem is, functionality-wise, it’s very limited.

While the image on the left tells me my update had 536 views, it doesn’t allow me to dive into the data to see who actually viewed the update. The same with the image on the right – I can’t click into the big purple circle to identify the type of people viewing my content.

The potential for this visual data is obvious – I can see if I’m attracting my target audience to my content – either potential clients or new employers – and, by having access to this information, tailor my sharing even more, as well as connect with these folks in particular.

It’s not just LinkedIn that’s missing the importance of context, though. Check out the image below from a technology/data company in Toronto (click to expand):

The results are from a search around the words “social business”, and show not only the main keywords around the topic, but also who’s discussing them, via what platform, and the time they’re most likely to be discussed.

This basic data offers a simple overview of that particular search – but where’s the bigger context?

For example, you can see that “business” is the most discussed word, and then I’ve highlighted “product”, “agencies”, “customers” and “platform”. As you can see from the two yellow circles I’ve overlaid, a couple of people are in multiple results. So what’s the context behind that?

    Is it simply because they mention the words together?
    is it because they’re connected to these different communities?
    Is it because they’re seen as influential around these joint topics?
    Is it because they’re more active than the other profiles?

Again, these are simple questions, but ones that the software doesn’t answer, or at least attempts to help with. Because of this, other software and analysis is needed to see how valuable these folks might be to my business.

That’s not to advocate lazy marketing, nor to forget about the legwork that real analysis requires. But if a software tool can’t provide further context around the solution it offers, why use that platform at all?
Dig Deeper, Think Bigger

And this is where Big Data’s main weakness can be found – it’s encouraging lazy solutions that seem to offer reams of data, but in reality offer very little. By doing so, it’s impacting the true potential of Big Data when used properly.

It’s this type of limitation that’s attracting valid critique of Big Data.

In his 2013 paper entitled Big Data for Development: From Information to Knowledge Societies, Martin Hilbert raised the concern that Big Data-led decisions are “informed by the world as it was in the past, or, at best, as it currently is.”

Last year, Harvard Business Review published an article, Good Data Won’t Guarantee Good Decisions, which highlighted the bigger issues around the data available to us today.

For all the breathless promises about the return on investment in Big Data, however, companies face a challenge. Investments in analytics can be useless, even harmful, unless employees can incorporate that data into complex decision making. Meeting these challenges requires anthropological skills and behavioral understanding—traits that are often in short supply in IT departments.

Simply put, we can have all the data in the world available to us, but unless we understand the context in which it’s presented, and the actions that will drive based on our analysis, we’re as effective as driving at night with the lights off.

It’s up to us to think bigger when it comes to Big Data, and start providing the context and meaning behind it, as opposed to just the “But it looks cool, right?” mindset that seems popular today.

Challenge on.

Vilma Bonilla's insight:

"We can have all the data in the world available to us, but unless we understand the context in which it’s presented, and the actions that will drive based on our analysis, we’re as effective as driving at night with the lights off."

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The Toughest Investment Task ~ Selecting actively managed U.S. stock funds.

The Toughest Investment Task ~ Selecting actively managed U.S. stock funds. | Cultural Trendz |

Steep Odds

All right, perhaps that is an exaggeration. There surely must be a more difficult investment task than finding an active U.S. stock fund manager who is likely to outperform the stock market indexes over the next decade, after expenses.


 I don't happen to know what that is, however. Whether academic studies, industry research, or Morningstar's Fund Manager of the Year selections, all results point to the difficulty of identifying consistently successful U.S. stock managers. The academic findings have been pessimistic, industry studies have echoed that note, and, while Morningstar's International-Stock Fund Managers of the Year have mostly gone on to great things and Morningstar's Fixed-Income Fund Managers of the Year on to good things, Morningstar's Domestic-Stock Fund Managers of the Year have looked distinctly ordinary after the fact.


The news isn't improving. Three Oxford University professors have released a study on the performance of pension-fund consultants when selecting active U.S. stock fund managers. Their conclusion: "We find that consultants' recommendations of funds ... have a very significant effect on fund flows, but we find no evidence that these recommendations add value to plan sponsors."


This isn't a conclusive finding. It uses only 13 years' worth of data, and it makes assumptions in translating consultants' recommendations into buy/not buy signals. The database of consultants' recommendations is self-reported, meaning that it is subject to various biases. Also, the sample size is small. Although the study tracks the seemingly large number of 1,500 fund recommendations each year, these come from an average of only 29 consultants. (Then again, there aren't many pension-fund consultants in the first place. The authors state that the consultants in the study "had a 91% share of the consulting market"--a vague statement, but nonetheless a sign that the findings are reasonably representative of that marketplace.)


Within that context, though, the professors did a thorough job. They examined each fund's results in three ways: 1) against individual style benchmarks (for example, Russell 1000 Growth), 2) against the Fama-French three-factor model that takes under consideration market, size, and value/growth performance, and 3) against the Carhart four-factor model that adds a momentum factor to the Fama-French calculation. They conducted these calculations on both an equal-weighted basis, wherein all funds counted the same regardless of asset size, and on an asset-weighted basis (which they term "value-weighted"). They then compared the aggregate results of the recommended funds to the aggregate results of the nonrecommended funds.


The showing was dismal on the equal-weighted basis. No matter which of the three performance measures was used, the single-style benchmark, the three-factor Fama-French, or the four-factor Carhart, the recommended funds lagged the nonrecommended funds. This held true for all seven investment styles (large-cap growth, large-cap value, mid-cap growth, mid-cap value, small-cap growth, small-cap value, and core). The typical underperformance was from 50 to 100 basis points per year. Most of these results were not statistically significant--but the direction was always wrong.


On the asset-weighted basis, the consultants fared better but not well enough to celebrate. Their results were positive for five of the seven investment styles according to all three models and were statistically significant (at the 1% level) for the three- and four-factor models with mid-cap growth funds. However, aside from mid-cap growth and small-cap growth funds, the margin of victory when it existed was very small.


The asset-weighted basis seems to me the fairest way of viewing the matter. When asked to select among the larger, better-known U.S. stock fund managers, the pension-fund consultants didn't cause harm. Their selections weren't worse than throwing darts (and were better with the two growth styles). The consultants were less successful as measured by the equal-weighted method because they tended to recommend the bigger fund managers, who tended to lag their smaller, less-known competitors.


In essence, then, the pension-fund consultants erred by favoring the larger managers but neither erred nor shone when selecting among those larger managers.


Perhaps the consultants were too tied to the past in making their recommendations. Perhaps there is a straightforward way to improve selections by paying less attention to past performance and more to other factors. Unfortunately, write the professors, the consultants seem already to have taken that step. Per the professors, there wasn't a strong correlation between a fund's previous returns and its recommended status. The consultants, they write, appear to have been taking into consideration various "soft factors." Just not with much success.


Of course, hidden among the averages were better showings by some consultants (the professors didn't have access to the individual data, only to the aggregated recommendations) and worse by others. It's possible that those above-average consultants could continue to thrive. I suspect that their results were an accident of the time period, such that the winners would descend in the future and the losers ascend, but that remains to be tested. This study, as I wrote earlier, is not the final word.


It's good enough for me, though. By now, the chain of skeptical evidence has grown pretty long. Sequoia (SEQUX), various offerings from American Funds, and Berkshire Hathaway (BRK.B) (which I think of these days as being a flavor of closed-end fund) are decent bets to outgain the Wilshire 5000 for the next 10 to 20 years. After that? Tough to say. I'm glad that I'm not on the team that assigns Morningstar Analyst Ratings to U.S. stock funds. Writing columns seems much the easier task. The


Revolution Starts Here

In response to my column on Ranking Smart Betas, fundamental-investing pioneer Rob Arnott writes, "I've long thought that the 'risk premium' is better thought of as a 'fear premium.' [Investors] should be rewarded for a willingness to accept discomfort, even fear. We should garner diminished rewards for an insistence on comfort."


Agreed. However, I think "dislike premium" is a better, broader term than "fear premium." As Arnott seconded that notion, that now makes two people campaigning to change the standard investment nomenclature from "risk premium" to "dislike premium." Do join us. We'd like the company.


John Rekenthaler has been researching the fund industry since 1988. He is now a columnist for and a member of Morningstar's investment research department. John is quick to point out that while Morningstar typically agrees with the views of the Rekenthaler Report, his views are his own.

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