What big-data VCs are sick of — and what they really want

sboneham/Flickr

Sprinkling some big-data sauce on sales or marketing software won’t get a dime out of these moneybags.

Four prominent venture capitalists who have funded big-data companies dished about what they’ve gotten tired of — and what they might actually like to fund — at O’Reilly Media’s Strata big-data conference in Santa Clara, Calif., on Wednesday. The talk brought nuance to the hype-filled big-data aisle of the IT market.

“The most grotesquely overinvested category I have seen is anything that has to do with sales and marketing,” said Matt Ocko, the managing partner at Data Collective.

That includes taking marketing-automation software like Marketo or customer-relationship management software like Salesforce.com and adding some natural-language processing on top to analyze and pull useful information out of plain text from other sources. Or adding in some artificial intelligence, for that matter.

“It sounds like the freakin’ Phil Hartman routine from The Simpsons,” Ocko said. “Really, it’s become infuriating.”

Do something original

Also, Ocko said, you can’t be doing what a bunch of other people have already done. To use Ocko’s words, if you want to get funded, don’t be “the eleventh [person starting] a Wi-Fi chip company, the fourteenth [person doing] scale-out storage.” It’s probably not going to work.

That also goes for companies interested in building commercial distribution of the Hadoop ecosystem of technologies for storing, processing, and querying lots of different kinds of data. “Anyone besides Cloudera is doomed,” Ocko said, referring to a company that’s in a position to go public this year.

Jake Flomenberg, a partner at Accel Partners, said he’s got some reservations on startups that use machine-learning algorithms that can provide more accurate or useful results over time as more data becomes available.

“I think the disconnect that I far too often see is that people are focused on the technology in and of itself, more than the direct use cases,” Flomenberg said.

Plus, he said, the market size might not be large. “Not only are you selling to a data scientist, you’re selling to a data scientist with a really big, gnarly data set, [using the] Random Forest [algorithm] or a type of linear regression,” he said.

“To the rest of the world, they have no idea what to do with this tool.”

Roger Chen, an investor at O’Reilly AlphaTech Ventures, worries that some startups pushing devices with data-generating sensors “have no clear pathway toward being part of any bigger system later on.” That could mean the data stays within the realm of a wearable device’s application and never becomes available for integration with other sorts of data. So the number of interesting outcomes based on the data is limited.

Ross Fubini, a venture partner at Canaan Partners, thinks business-intelligence software for analyzing and asking questions of data are “undifferentiated too often.” And it’s not just a matter of making “prettier widgets or prettier toolsets,” Fubini said.

Mobile, insurance, and more

If anything, Fubini said, startups might be able to capture funding by building technology that enables compelling user experiences on mobile devices.

And it sounds like specific industries could benefit from some new applications that draw on big data.

Chen pointed to the insurance industry. And he called out health-specific applications like finding connections between disease outbreaks and weather patterns.

Despite Ocko’s disinterest in applying big data techniques to sales and marketing tools, Flomenberg cited software for salespeople — specificallyRelateIQ — that’s much can pull in data more easily more traditional customer relationship management (CRM) tools like Salesforce.com.

“They turn a tool that people can’t stand to live with to one they can’t live without,” Flomenberg said.

Tools that can help data scientists save lots of time, such as Trifacta, which simplifies the process of getting a big heap of data ready for analysis, can also be appealing.

In fact, Flomenberg’s firm is an investor in both Trifacta and RelateIQ.

As for Ocko himself, he likes “idiot savants” trying to solve targeted problems in “narrow categories” that could result in the “complete overturn of everything in the existing quarter.”

Moleculo, one startup Data Collective invested in, found a way to pull critical signals from genetic sequencing data “better than anything else we have ever seen.” Illumina ended up acquiring Moleculo in 2012.

But these categorical generalizations can fall away, Fubini said, “when applications have that feeling of magic.” Take Amazon’s way of telling you what you want and then getting you to buy it.

“The applications that we don’t see — the technology behind it — that’s the stuff that’s going to be interesting to me,” he said.

Source : Venturebeat.com

Big data analysis reveals the top trends and brands at CES at a glance

Kontera

Kontera shows what drove conversations about CES.

Big data analysis from Kontera reveals what everybody talked about when it comes to the 2014 International CES. Kontera, a big data analytics and marketing platform, sifts through millions of sources to find out the buzz on an event or topic. Here’s the results of their study of the last three days of CES, the big tech trade show in Las Vegas.

Below Kontera shows the most popular topics in certain key terms in a tag cloud. The results show that Sony was the most popular term related to CES (in purple). Computing and Intel were the most popular terms related to wearables (in light blue-gray), and Samsung was the most popular term related to 4K (in orange).

The data comes from the Brand Insights and Discovery module in Kontera’s Content Activation platform. The platform analyzes and trends consumer interest and content consumption across more than 400 million real-time content views each day, including 300 million web and mobile content views as well as 100 million social stream activities.

The analysis is done for hundreds of sub-topics and tens of thousands of granular concepts (like a brand names, a product attributes, specific consumer needs, and daily events).

In a separate analysis of Twitter, the social media firm Hotwire/33Digital said the top five brands were: Samsung, Sony, LG, Logitech, and PlayStation. The top five trends were 4K resolution, wearables, smart gadgets, 3D printing, and the internet of things.

The Consumer Electronics Association said that the event had more than 3,200 exhibitors across 2 million square feet, with more than 150,000 attendees, including 35,000 from outside the U.S. Final numbers will be out after an audit.

Kontera

Kontera shows the interest in topics such as CES, 4K, and wearables.

Source : Venturebeat.com

Salesforce, Others in Race to Create One-Stop Shop for Marketing Data

By acquiring ExactTarget, Salesforce just entered an escalating battle for control of the marketing-services field.  Data fragmentation poses a great problem for marketers as new technologies and data sources arise.

Over the next five years, dozens of tech titans, startups, and traditional data managers will seek to become the dominant platform to integrate data across all marketing channels.  From this battle, marketers will emerge with a unified view of their customers and strategy from a social, mobile, email, display, search and direct-mail standpoint.
While data-driven marketers will benefit massively from the competition, the spoils will go most heavily to the company that wins control of the cross-channel stack -- gaining a near-monopoly on marketing budgets.

The Prize
Three trends have been redefining the marketing landscape, creating an opportunity for major disruption in the industry and a lucrative prize for the winner.

First, there is a shift of budget from offline channels to digital.  This trend is not new, nor is it surprising -- but it is accelerating and it means a significant amount of money is in play.

The second trend is the growing significance of data in marketing.  Smart targeting, measurement and modeling techniques are redefining the capability of marketers to reach the optimal audience and invest in the highest-ROI campaigns.

The final trend is fragmentation. New marketing channels are emerging much more quickly than the industry is catching up, spawning companies focused on success within a particular channel.  Within the last five years alone, mobile and Facebook have grown from negligible channels to key parts of a marketer's strategy.  This means the typical CMO is now inundated with dozens of different tools to manage their different channels.

Herein lies the opportunity: The industry’s fragmentation is keeping data siloed and preventing companies from using their data intelligently across channels. If a company can successfully reverse this trend by creating a single unified platform for all marketing, it can take advantage of huge amounts of this untapped data that was previously siloed.

The Players
This is where Salesforce’s acquisition of ExactTarget fits. Salesforce is already managing CRM data for many companies. Between the Buddy Media and ExactTarget acquisitions, it is now entering the marketing execution space -- rather than just hosting data. The integration of CRM data and email marketing can be much tighter than the integration of CRM and social marketing, allowing Salesforce to more strategically integrate data across channels.

In moving into cross-channel marketing execution, Salesforce’s vision now directly competes with dozens of other companies.  Let’s look at the other major players:

Traditional data managers:  Companies like Acxiom, Epsilon, Experian and Merkle have traditionally managed CRM data for the largest marketers and also made much of their money executing their campaigns in various offline channels.  Most of these companies have in-house email service providers, direct-mail services and are increasingly moving into display and digital channels with innovative cross-channel products. Salesforce is now squarely competing with these companies for the future of data management.

Online data management platforms:  Online “DMPs” like BlueKai, Turn and Adobe have the same vision as the traditional data managers, but start by aggregating online data (such as display-ad performance, online behavioral data, etc.) and executing in online channels.  Over the next few years, they will be moving across the stack to aggregate data from offline channels, too.

Single-channel marketing solutions:  As marketing changes, the companies that dominate a particular channel are moving to integrate into others. The most dramatic change will be among email service providers.  Even before the ExactTarget acquisition, leading ESPs like Responsys and SilverPop have tried to pivot into cross-channel solutions.

Giant tech companies:  Like Salesforce, other tech titans such as IBM, Adobe, Google and Oracle are eager to take advantage of their relationships with marketers, their possession of customer data, and their big data expertise to enter the battle.

The Battle
While these four categories compete very little today, their five-year visions converge almost identically: Each company wants to own the cross-channel marketing stack.
While the battle has been brewing for years, Salesforce’s acquisition of ExactTarget has accelerated it significantly. Marketers will now be torn between several platforms, each one competing to create an exceptional cross-channel experience and to build the dominant platform. Expect this acquisition to be the first of many as the battle unfolds.

Source : Adage.com

Google Bought Waze For $1.1B, Giving A Social Data Boost To Its Mapping Business

After months of speculation, the fate of Waze, the social-mapping-location-data startup, is finally decided: Google is buying the company, giving the search giant a social boost to its already-strong mapping and mobile businesses. Speculation has had the sale at $1 billion to $1.3 billion, and so far there is no price on the deal, but a source tells TechCrunch that it was done for $1.1 billion.



Update: Waze has also published a blog post on the acquisition. In it, CEO Noam Bardin writes that CEO Larry Page, Google GEO VP Brian McClendon and the Google Maps teams “We are excited about the prospect of working with the Google Maps team to enhance our search capabilities and to join them in their ongoing efforts to build the best map of the world.” He also notes that “nothing practical will change,” with the company, now pushing 50 million users, “will maintain our community, brand, service and organization.”

He also raises the subject of why Waze decided to sell. Bardin says that it was motivated by the fact that an IPO appeared the route that would take the company more into being focused on returns and less on growing as a product for users. “Choosing the path of an IPO often shifts attention to bankers, lawyers and the happiness of Wall Street, and we decided we’d rather spend our time with you, the Waze community.” Of course, the burden of getting a return on the investment now become’s Google’s, but as you can see below there are a number of reasons why it would buy Waze, anyway. [original post continues below]

Update 2: Israeli tech blog GeekTime also confirms our $1.1 billion figure, “of which $1.03B will be transferred in cash directly to the company and its stockholders. An additional $100M will be awarded to employees based on performance.”
This is a doubly strategic move for Google. The purchase comes in the wake of what appeared to be failed negotiations between the Israel-based startup two big rivals of the search giant: Facebook, which was eyeing up the company but apparently faltered at the due dilligence phase; and Apple (neither company ever publicly confirmed interest in acquiring Waze).
The news comes after a particularly heated few days in which reports of Google’s interest in Waze reached new heights, after first surfacing two weeks ago. In the wildfire that is internet publishing, many even went so far as to report it as a done deal, making things even more confusing.
Waze had raised some $67 million in funding from Blue Run Ventures, Magma, Vertex, Kleiner Perkins Caulfield & Byers, and Horizon Ventures. And it looks like the majority of the payout in the sale will go to these VCs. Globes, the Israeli business newspaper that first reported the latest interest from Google, estimated that payouts to co-founders — Ehud Shabtai, Amir and Gili Shinar, Uri Levine, Arie Gillon — and its CEO Noam Bardin, will be under $200 million in total.
There are at least a couple of places where you can see Google making use of Waze data.

Social. Under CEO Larry Page, Google has been especially bullish on where it positions itself on social, which it has been hinging on Google+ as a kind of web across all of its other properties to show you, the user, what those you know are doing, and also to let your connections see what you are looking at online. Taking a page from Facebook’s book, the thinking goes that this helps with discovery and engagement.
Waze, as a crowdsourced location platform, would give Google an additional, very mobile-based angle on this concept, letting users not just share places (i.e. sites) visited on the web, but actual places visited physically. As Bardim noted at the AllThingsD conference in April, “What search is for the web, maps are for mobile.” By this, he means that most of the searches you do on mobile have to do with location, and Waze is one of the few companies out there that is bringing that kind of search together with actual map data and a social layer. (The NYT ran an interesting piece yesterday with one mapping company describing how maps on mobile specifically become a “canvas” for all other apps.)

Competition. Waze could be a two-pronged fork for Google: On one hand, it gives the search giant nice, healthy wedge into the mass of consumers who are already using the app on iOS devices. But it also, if reports are to be believed, also gives Google a way of roadblocking how companies like Facebook could use Waze’s assets. As the startup likes to point out, it’s not a mapping company, but a big data player. Facebook, making its own big push on mobile, would have been a natural home for a socially-focused company like Waze, which also happens to be one of the few home-grown mapping databases around. This will mean that Facebook will need to have to continue to use third-party data for its own location-based searches and information, or less look to acquire elsewhere.
(Now could be a good time to wonder whether Nokia might consider offloading Navteq, its loss-making but strategic mapping asset, to shore up its financial position…)

It’s interesting, in any case, that Google and Waze have now kissed and made up. It was only in April that Bardin jabbed at Google when talking about who the big players in mapping were and how Waze stacks up against them: Waze used to benchmark itself with Google, he noted at the AllThingsD conference, but after the search giant cut off access to its API, Waze started to benchmark to Navteq.
When the Facebook acquisition reports surfaced, we’d heard that one of the sticking points was that Waze wanted to keep its R&D in Israel, while Facebook was leaning to a Menlo Park relocation. Since then, others have told us that this was just smoke a mirrors and that there were other reasons the deal fell through (Mountain View’s most famous resident being one possible factor). Google, unlike Facebook, has a decent presence in the country, including a new hub for startups started in December 2012, Campus Tel Aviv.

Google today made it clear that it would keep Waze’s operations going in Israel — for now, at least. “The Waze product development team will remain in Israel and operate separately for now,” Brian McClendon, Google’s VP of Geo, noted in the blog post announcing the deal. “We’re excited about the prospect of enhancing Google Maps with some of the traffic update features provided by Waze and enhancing Waze with Google’s search capabilities.”
In any case, it makes sense that Waze might want to keep its Israel-based operations intact. Just about all of the company’s 110-or-so employees are there, with only around 10 in a very modest office in Palo Alto, just down the street from another big-data startup, Palantir. That small proportion, however, is mighty: regular workers there include CEO Noam Bardin and Di-Ann Eisnor, Waze’s VP of platform and partnerships.

The U.S. is currently Waze’s largest single market — in April, Bardin noted that 12 million of its (at the time) 44 million users are based there — and this is where the company is putting its growth efforts for now, too. In February of this year, Waze expanded its U.S. operations, and its monetization ambitions, by opening an office on Madison Avenue, the heart of the advertising world in New York City, and we’ve seen that members of the team have been visiting New York recently. There is still a lot of development to be done on the advertising front — and given Google’s pole position in online and mobile advertising, that would give Waze another obvious fit with its new owner.
Ironically, the news comes as Google continues to fight other kinds of fires on the mapping front. In the U.S. it is trying to get a ruling overturned that it violated federal wiretap laws with its StreetView services.

In Europe, Google recently offered up a settlement in a search antitrust suit, originally brought by travel and mapping companies, that claimed Google, the biggest search engine in Europe by a longshot, was giving its own mapping and travel results more preference in search results over those of its competitors, making business untenable for smaller players. In that ongoing case, the EU competition regulator Joachin Almunia said at the end of May that Google still needed to make more concessions.

Source : Techcrunch

Santé : les concurrents des pharmas mènent l'offensive sur smartphone

Interview de Vincent Mangematin, chercheur à Grenoble Ecole de Management

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En quoi des applications sur smartphone menacent-elles les big pharmas ?

Ces applications reflètent une profonde mutation du secteur santé et en sont un des leviers. Nous sortons d’une médecine fonctionnelle qui détecte des symptômes, prescrit des traitements et s’incarne dans l’autorité du médecin. Aujourd’hui, l’individu associe santé et bien-être. Il veut une approche globale et personnalisée, gère son capital santé, fait de la prévention (sport, diététique…), échange au sein de communautés et utilise de plus en plus ces applications sur smartphone.

De là à estimer que l’industrie du médicament est menacée…

Elle l’est, y compris pour des pathologies lourdes ! Très récemment, certains cantons suisses ont décidé de ne plus rembourser les médicaments anti-Alzheimer : ils préfèrent financer l’accompagnement des malades et leur qualité de vie.
Quant à la maternité, à la vie sexuelle, aux maux chroniques (stress, dos, sommeil…), aux régimes ou à la prise en charge des seniors, ce sont des sujets omniprésents sur internet et sur les smartphones ; on compare ce qui est dit avec l’avis du médecin et parfois, on se passe même de cet avis.

Qui a développé les 360 applications étudiées dans la thèse ?

En Chine, aux Etats-Unis et en France, nous avons identifié trois grandes catégories : des pure players qui vendent par exemple des régimes personnalisés, selon l’âge et la pathologie de l’utilisateur ; des communautés animées par des bénévoles et axées sur du fitness et diverses activités physiques ; des firmes comme Orange, SFR, General Electric, Sony, Hewlett-Packard, qui proposent de la télémédecine, du matériel paramédical, de l’accompagnement aux malades etc.
Certaines pharmas proposent du service autour de leurs produits, par exemple dans le domaine du diabète. Mais le médicament reste le socle de leur démarche.

Est-ce vraiment gênant ?

Oui, car elles sont à contre-courant. Le bien-être, l’approche globale du corps, la prévention, la médecine personnalisée, ce n’est pas le médicament : c’est l’approche qu’on trouve sur internet ou sur smartphone et que les mutuelles privées américaines, notamment, encouragent fortement. Elles n’ont pas trouvé mieux pour améliorer le rendement de leurs contrats.

Comment imaginez-vous le secteur santé mondial dans 5 ans ?

Les big pharmas resteront prépondérantes sur les blockbusters. Mais elles vont se retrouver cernées par ces nouveaux acteurs qui empruntent d’autres voies et d’autres outils, en particulier l’informatique connectée et la gestion des bigs datas : c’est la technologie la plus puissante pour faire du prédictif et de la médecine personnalisée. Or, elle est maîtrisée par IBM ou Google, pas par les pharmas.

La thèse a été réalisée par Yi Jiang, PhD student à Grenoble Ecole de Management

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Quelques exemples d'applications santé/bien-être NB : nous indiquons ici les références des sites qui présentent les services offerts par l'application smarphone

• Pure players : www.jemangemieux.com (régimes alimentaires adaptés à différentes pathologies)
• Web communautés autour de l'exercice physique : www.skitour.fr

The Data Made Me Do It

Would you trade your personal data for a peek into the future? Andreas Weigend did.

The former chief scientist of Amazon.com, now directing Stanford University’s Social Data Lab, told me a story about awakening at dawn to catch a flight from Shanghai. That’s when an app he’d begun using, Google Now, told him his flight was delayed.

The software scours a person’s Gmail and calendar, as well as databases like maps and flight schedules. It had spotted the glitch in his travel plans and sent the warning that he shouldn’t rush. When Weigend finally boarded, everyone else on the plane had been waiting for hours for a spare part to arrive.

For Weigend, a fast-talking consultant and lecturer on consumer behavior, such episodes demonstrate “the power of a society based on 10 times as much data.” If the last century was marked by the ability to observe the interactions of physical matter—think of technologies like x-ray and radar—this century, he says, is going to be defined by the ability to observe people through the data they share.

So-called anticipatory systems such as Google Now represent one example of what could result. We’re already seeing the transformations that big data is causing in advertising and other situations where millions of people’s activity can be measured at a time. Now data science is looking at how it can help individuals. Timely updates on a United Airways flight may be among the tamer applications. Think instead of statistical models that tell you what job to take, or alert you even before you feel ill that you may have the flu.

Driving this trend is a swelling amount of personal data available to computers. The amount of digital data being created globally is doubling every two years, and the majority of it is generated by consumers, in the form of movie downloads, VOIP calls, e-mails, cell-phone location readings, and so on, according to the consultancy IDC. Yet only about 0.5 percent of that data is ever analyzed.

“There is so much more data out there that you can afford to tailor it to the individual,” says Patrick Wolfe, a statistician who studies social networks at University College, London. “Statistically, strength comes from pooling people together, but then the icing on the cake is when you individualize the findings.”

For the data refineries of Silicon Valley, like Google, Facebook, and LinkedIn, the merger of big data and personal data has been a goal for some time. It creates tools advertisers can use, and it makes products that are particularly “sticky,” too. After all, what’s more interesting than yourself? Facebook suggests who your friends might be. Google Now gets better the more data you give it.

Exposing more personal data seems inevitable. With the huge jump in sales of smartphones packed with accelerometers, cameras, and GPS, “people have become instrumented to collect and transmit personal data,” says Weigend. And that may just be the start. Already a fringe community of technophiles, known as the quantified-self movement, have been equipping their bodies with sensors, pedometers, even implanted glucose monitors. One we will feature in this month’s MIT Technology Review Business Report is Stephen Wolfram, the creator of the search engine Wolfram Alpha. Wolfram has for years engaged in a massive self-tracking project, cataloguing e-mails, keystrokes, even his physical movements. Wolfram is interested in predictive apps, but also in the insights that large data sets can have on personal behavior, something he calls “personal analytics.” Wolfram’s idea is that just as his search engine tries to organize all facts about the world, “what you have to do in personal analytics is try to accumulate the knowledge of a person’s life.”

The holdup, says Wolfram, is that some of the most useful data isn’t being captured, at least not in a way that’s easily accessible. Part of the problem is technical, a lack of integration. But much data is warehoused by private companies like Facebook, Apple, and Fitbit, maker of a popular pedometer. Now, as the value of personal data becomes more apparent, fights are brewing. California legislators this year introduced a “Right to Know” bill that would require companies to reveal to individuals the “personal information” they store—in other words, a digital copy of every location trace and sighting of their IP address.

The bill is a part of a social movement that is demanding privacy and accountability, but also a different economic arrangement between the people who supply the data and those who apply it. People want more of the direct benefits of big data, and this month’s MIT Technology Review Business Report tracks the technology, apps, and business ideas with which industry is responding.

Source :  MIT Technology Review, Antonio Regalado on May 3, 2013

Tom Davenport: big data is too important to be left to the 'quants'

The author and visiting Harvard professor says everyone should have a role in analytics. 'Narratives' might tell a clearer story.

One of the challenges with big data — or with most data analytics efforts over the years — is that it has typically been relegated to analysts and holders of Ph.Ds in mathematics or statistics. Thus, it's been highly intimidating territory for most business decision-makers. Organizations are striving to compete on analytics, and hiding the processes and management of data analysis in a back room isn't going to cut it anymore.

(Image: TomDavenport.com)

That's the view of Tom Davenport, visiting professor at Harvard University and co-author of the seminal work Competing on Analytics: The New Science of Winning. I recently had the opportunity to chat with Davenport, and the Q&A is posted at CBS interactive's SmartPlanet site.

Davenport, who is also a senior advisor to Deloitte Analytics, spoke about the difficulties of converting to an analytics-driven culture. He is finalizing a new book, Keeping Up with the Quants: Your Guide to Understanding and Using Analytics, which makes the case for better communicating analytics to business decision-makers.  

We touched upon a number of topics, and I was curious why he felt that the existing BI and analytics tools that have been on the market — graphic tools, dashboards, balanced scorecards, and the like — weren't up to the task. Davenport sais that it's time for a new type of platform for communicating data analytics results — a more "narrative" approach.

"We've all grown up on pie charts and bar charts, but there are probably at least tens, if not hundreds of alternative approaches to visual analytics," he explained. "Narratives are a pretty good way to convey information in the past, so maybe we should be converting our data and analysis into stories. People are starting to do that more. Most analysts were unfortunately not trained in how you communicate effectively about analytics, so we've got a long way to go in terms of doing a better job of that."

I also asked Davenport about the role of cloud platforms in promoting greater availability of analytics, and he said cloud isn't quite there yet.  Here's what he had to say: "It's certainly making a difference in terms of analytics on big data because its almost infinitely expandable, much cheaper, people don't have to build up the capability to analyze huge amounts of data all the time. They can expand to suit their needs. There are cloud-based versions of most of the visual analytics offerings, but they don't tend to be [as] sophisticated yet as the on-premises ones, so I think its not really driving things yet. For basic big data processing, and computation and analysis, visual stuff, narrative stuff — not as much at this point."

There is also a tendency to move or embed analytics into applications, with end-users fairly oblivious to what types of algorithms are spitting out the answers being acted on by applications. Isn't automation making it less necessary to understand the machinations of analytics? Too much reliance on automation may be a dangerous thing, Davenport responded. "We saw the challenge in financial services; you have situations like the flash crash, where there were all these automated trading things happening, and we had no idea why. The real challenge is going to be being able to trace the logic and how the algorithms work when things go wrong, so we can intervene and override."

Source : ZDNet, Joe McKendrick, 1/4/13

Why Small Data may Be Bigger than Big Data

Forget punch cards. How smart businesses can connect with customers to create loyalty.

In start-up land, the hot buzzword is Big Data--those massive data sets that can't be handled with traditional database tools. The promise is that businesses can capture, curate, and synthesize big data to enable personalization in e-commerce, smarter business decisions, and greater efficiency.

In the world of local businesses, we're not talking about big data. Instead we're dealing with Small Data.

Make no mistake, small does not mean insignificant. Think of it this way: If we leveraged data in local the same way large e-commerce companies do, we could have a greater impact on commerce than Big Data, since more than 90 percent of retail transactions still occur offline in local businesses. But most of us don’t. The reality is that most local businesses haven’t figured out how to capture or use this data to grow profits. So how do we solve the "data divide" for local businesses?

If you are an e-commerce company, it’s a straightforward exercise to use Google Analytics to see how many customers are visiting your website daily, how many are repeat visitors, and how long they spend on your site. But if you're a local business with a physical store, it’s a lot harder. Large multi-store chains can hire people to count traffic by hand or leverage new technologies that use cameras or mobile phone signals to estimate traffic, but smaller local businesses have been left out--until recently.

Forget paper punch cards for repeat visitors. Today’s cards are digital and link to your customer database. Modern loyalty programs--enabled by technology called loyalty automation--do at least three things in the area of data: capture identity, connect identity to activity, and drive personalization. That’s exactly what big businesses have been doing for years. Here’s how it works.

• Capture Identity. In the world of paper punch cards, you could drive loyalty, but you couldn't capture identity. You didn't know which people were coming to your store each month. With modern loyalty programs, local businesses can finally connect a store visitor with a real person's identity, since at signup, their identity is registered in a database that’s integrated with their point-of-sale. Then when they come back to the store with their loyalty card, businesses know their name.   
• Connect Identity to Activity. Since customers are no longer anonymous visitors and bring their identity with them, local businesses can now connect customers to specific activity--whether that's visiting their store frequently or purchasing particular products. 
• Drive Personalization. Because they now know which customers come frequently, which ones have stopped coming, and which ones prefer particular items, businesses can customize their treatment of these customers. They can make their best customers even more loyal by treating them as such and win back the ones who have stopped coming. 

One great example is The Real Deli, a small deli store in Dana Point, California, which leverages data and tools from its modern loyalty program to figure out who visits frequently and regularly opens their emails. They call these customers “super fans,” since they can be The Real Deli’s greatest word-of-mouth advocates. They give them special offers and VIP treatment and rely on them to spread the word about their business. This simple tactic of matching store visits with email activity--something e-commerce companies do on a regular basis--had previously been impossible for local businesses.

The Real Deli may have crossed the data divide. If other local businesses can join, the potential gains in personalization, sales, and efficiency with Small Data could be just as staggering as that of Big Data. 

Source : Inc., 1/04/13, Voctor Ho

Victor Ho is the CEO and Co-Founder of FiveStars, a venture-backed startup building the first loyalty automation platform for local businesses. Previously, he was a consultant at McKinsey & Co. in New York, where he helped multiple Fortune 500 companies build and manage their loyalty and customer retention programs. Victor holds three degrees from the University of California, Berkeley, and is a Rubik’s cube enthusiast with a personal best of 45 seconds. @FiveStarsCard

Meet The Next 10 Companies To Come Out Of StartX, Stanford’s Student Startup Accelerator

Stanford’s student startup accelerator, StartX, had its eighth demo day tonight in Palo Alto, showing off the latest class of 11 companies* to go through the program. The accelerator, which just raised another $400,000, has already had about 100 startups go through, raising $100 million along the way between them. This next batch is hoping to follow that lead.

Here’s the next class of Stanford StartX companies, in the order they were presented:
Pixlee. This startup wants to use user-generated photos to help brands market themselves. The idea is not just to collect and curate photos that will resonate with consumers, but also to present them in a personalized fashion. The idea is that everyone will get a different experience, not just see all the same photos. The company had been chosen by the 49ers for a fan-engagement campaign around the Super Bowl, which had more than 50,000 photos submitted. It’s also being used by brands like Yamaha, Major League Soccer, and the NBA.

Distinc.tt. If you’re gay and new to an area, how do you find out where gay people hang out? And how do you find out whether or not someone hanging out in one of those places is also gay. That’s an overly simplistic description of a very complex problem that Distinc.tt hopes to solve. The mobile app shows nearby places based on the measured popularity among the gay community, and allows its users to check in and help identify other gay patrons in those locations. In just a few weeks, the app has garnered about 40,000 users, who log in twice a day on average. The company has also attracted Peter Thiel as an investor in its seed round.

VipeCloud. This startup provides a “video business in a box,” allowing independent content creators, B2B companies, and other niche video producers to quickly get up and running with video distribution. VipeCloud not only provides a platform for sales, but also customer relationship management, enabling clients to build relationships with their audiences and determine what’s working and what’s not, what’s selling and why.

Insynctive. This startup seeks to give companies a better way to do HR, providing the connective tissue between various payment and benefits systems. It’s all brought together by a single, usable, user interface that employees, as well as in-house and outsourced HR, can all use while simplifying the usual crazy HR application stack.

Spot On. Spot On provides time-based search which will give its users more actionable information. The idea is to match the right activity to the right person at the right time, therefore becoming a user’s personal concierge. The app works by pulling temporal data starting with the start and end time, matching items based on location, and then filtering by a user’s personalized preferences. The first market the team hopes to go after is families with kids to help provide activities that parents and kids will both love when they’re free.

NuMedii. This company plans to use big data to shorten the amount of time it takes to develop and test drugs, and, in the process, massively lower the costs associated with drug creation. By matching the molecular profiles of diseases and drug actions, NuMedii can reduce the amount of time for drug testing from 3-6 years down to 3-6 months, and its accuracy has been crazy good, with 6:6 probability of success in early tests. That’s successfully translating big data into effective drug creation at a fraction of the time and cost.

Meet Mikey. We all know email is a pain in the ass, and Meet Mikey wants to change that. The mobile email app, which works with Gmail, is aimed at providing quicker results to questions, with embedded yes and no buttons that can be sent and translated into instant answers. It also can show you who’s opened emails and attachments sent, making sure that everyone on your team is on the same page. It essentially aims to increase productivity and provide easier access to the information users need, in the palm of their hands.

readImagine. readImagine seeks to empower artists and storytellers to create apps for children, with a platform for creation, curation, and distribution of high-quality kids apps. For creators, the platform makes building an app easier than before, and provides distribution through its own network of apps. For parents, it provides a curated group of high-quality apps their kids will love.

MyProject.is. Crowdfunding will be a $6 billion business this year, and that’s set to accelerate, but creators need better tools for managing their campaigns. This company seeks to make building crowdsourced projects easier than ever, by taking the guilt associated with hitting up friends, family, and other members of one’s network to fund their passion projects. The tool works by involving participants early on and making them part of the project’s story well before it gets to the funding stage, making them more willing to give and also making them feel like a bigger part of the creative process.

Kidaptive. Kidaptive hopes to provide better tools for kids to learn and for parents to help them during early development. Starting with its Leo’s Pad app, the company seeks to create a suite of 25 interactive stories that will play out over the course of a year-long curriculum kids can take part in. Better yet, the product helps empower parents to help their kids learn through a continued feedback loop that keeps them informed about how their child is doing, and what they could do to improve.

* For various reasons, one of those companies didn’t want any publicity for its pitch, so we’re excluding it from our coverage.

Source: Techcrunch

Banks Using Big Data to Discover ‘New Silk Roads’

JPMorgan Chase & Co., the largest commercial bank in the U.S., generates a vast amount of credit card information and other transactional data about U.S. consumers. Several months ago, it began to combine that database, which includes 1.5 billion pieces of information, with publicly available economic statistics from the U.S. government. Then it used new analytic capabilities to develop proprietary insights into consumer trends, and sell those reports to the bank’s clients. The technology allows the bank to break down the consumer market into smaller and more narrowly identified groups of people, perhaps even single individuals. And those new reports can be generated in seconds, instead of weeks or months, JPMorgan Chase CIO Guy Chiarello told CIO Journal.

It’s an example of how the nation’s four large universal banks—JPMorgan Chase Bank of America Corp. Citigroup Inc. and Wells Fargo & Co. — are beginning to make use of potentially powerful analytic technology known as Big Data, which describes a broad set of hardware and software and is designed to quickly process huge amounts of data, including information like social media posts and email, which don’t fit into conventional databases.

John Moore/Getty Images

The software can help analyze internal bank records and correlate them with other sources of information–to give banks a more accurate picture of their customers, and a better ability to predict which customers are likely and credit-worthy buyers of new financial products.

“Big Data is really the theme for 2013,” says Mr. Chiarello. He says that Big Data-driven “digital marketing will become a significant thing.” Currently, as with most large banks that have had to swallow acquisitions that further muddled traditionally siloed banking operations, JPMorgan Chase has a hard time aggregating all available information about a single customer. Information about checking accounts, mortgages and wealth management for the same individual were contained in independent information management systems, preventing banks from leveraging considerable analytic capabilities that could have helped account representatives provide customers with better service. Banks can also offer lower interest rates by using Big Data to reduce credit card fraud, thus reducing their overhead, says Mr. Chiarello. “We should be able to be a better credit bureau than the credit bureaus,” he said.

This opportunity comes at a time when banks are under enormous margin pressure thanks to a slack economy, and they have little room for extra spending on technology. Whatever spending they’re doing on emerging technology is coming from savings they’ve achieved by rationalizing their technology operations. Their ambitions include the use of real-time analytics, creating better mobile offerings, and further cost reduction through the use of more sophisticated ATMs. The question is whether they have enough to spend, and can create new products to create incremental revenue and put the brakes on customer churn.

The four big universal banks, far and away the U.S.’s largest banks with over one trillion dollars in assets, each spend approximately $7 billion to $10 billion annually on technology, according to Howard Rubin, principal at bank technology advisory firm Rubin Worldwide. Mr. Rubin would not discuss individual banks because many are clients of his firm. The banks do not publicly discuss technology spending separately from operations, in part because traditionally, spending on technology has been up to individual business units. Market research firm Ovum estimates that U.S. banks will spend $41.5 billion on technology in 2013.

Budget pressure notwithstanding, the big banks are moving into Big Data. Jeff Harte, a bank analyst with Sander O’Neill, says lenders are moving beyond traditional analysis of customers’ credit-worthiness and “are analyzing the behavior of customers,” seeking answers to questions such as whether they always eat dinner out or whether they offset shopping at high-end department stores with trips to discount stores. This information can be gleaned from credit and debit card statements as well as from posts to social media sites. “It’s a step beyond analyzing credit quality and towards analyzing the customers’ behavior,” he said.

Steve Ellis, executive vice president and group head of the Wells Fargo Wholesale Services Group, says “the behavioral analysis stuff is coming” in the next five years. He warns, however, that there’s still a lot to understand for the banks to learn before they can “get to one-to-one marketing. That’s the big promise, and that’s where competitive advantage will be played out in lots of industries over the next five years. And if you don’t figure it out, you’re not going to be best in class.”

Catherine Bessant, who runs technology and operations at Bank of America, says BoA used the analytic capabilities of Big Data to understand why many of its commercial customers were defecting to smaller banks. Until recently, it offered an end-to-end cash management portal which, it learned thanks to its analytic capabilities, was too rigid for its customers, who wanted the freedom to access ancillary cash management services from other financial services firms. “We started to get beat by smaller banks that could deliver more modular solutions,” says Ms. Bessant. Bank of America used data gleaned from customer behavior on its own website as well as from call center logs and transcripts of one-on-one customer interviews to determine why it was losing those customers. It dropped the all-in-one offering and launched a more flexible online product, Cash Pro Online, in 2009, and a mobile version, Cash Pro Mobile, in 2010, even though the previous product “had been seen as a cash cow,” she says. All the development work had already been done, which meant new business “went straight to the bottom line.”

Citi, for its part, is experimenting with new ways of offering commercial customers transactional data aggregated from its global customer base, which clients can use to identify new trade patterns. “New silk roads are being created, and we think this information could show signs for which might be the next big cities in emerging markets,” says Don Callahan, who manages internal operations and technology at Citi. According to Mr. Callahan, the bank shared such information with a large Spanish clothing company which it was able to use to determine where to open a new manufacturing facility and several new stores.

The banks believe Big Data can help them grow revenue in a slack market. Thomas Sanzone, a senior vice president at consulting firm Booz Allen Hamilton who advises financial services firms, says Big Data represents “a significant opportunity for cross-selling and customized marketing because you have new technologies and techniques that give you access to pools of data that used to be unreachable. That changes the game and can create significant opportunities you didn’t have before.”
Banks executives also hope Big Data will help them use better marketing techniques to address a big problem with customer churn.  JP Morgan Chase said in an investor’ day presentation in 2011 that it expected between 50% and 60% of its customers to leave as a result of new fees on checking accounts.

But first the banks must find room in the budgets to fund these initiatives. In many instances, investments in innovation are being funded by savings accomplished through rationalizing systems and automating processes.

Citi, which announced a large reorganization in December, with approximately a quarter of the savings coming from its operations and technology unit, reduced overall spending in O&T by more than $4 billion over five years, according to Mr. Callahan.

Kevin Rhein, senior executive vice president of technology and operations at Wells Fargo & Co., says the bank has largely completed the work of integrating scores of different systems, and almost two years ago began the work of moving “to the next stage of technology and operations.” For the three previous years, “we spent a lot of money on integration, and there’s a lot of pent-up demand [for technology services] as a result of that. He said approximately 80% of the company’s technology spending is now on new services and capabilities requested by the business units. Still, he said, “I think we are under-investing” in technology. “I’d like to be investing more,” he said during an on-stage Q&A with the Wall Street Journal at a CIO event sponsored by consulting firm Gartner Inc.
Mr. Chiarello said JP Morgan is plowing savings from having consolidated technology units into new initiatives around technology. But “net spend is flat from 2007,” he said. “And that’s with more investment in innovation.”

Mr. Harte of Sander O’Neill says JP Morgan and Wells Fargo are some 12 to 18 months ahead of Citigroup and Bank of America in terms of folding in the systems of merged banks, giving them a lead in the innovation race, but that the latter are catching up quickly. “The ones who got through the crisis in better shape had a head start of around six to eight months, but the others are closing ground quickly,” he said.

Bank of America has been able to shift the bulk of technology spending from activities around consolidation to investing in more innovative technologies, such as Big Data. But Ms. Bessant says the banks also must make cultural adaptations if they hope to make a good return on these types of investment.  The leaders of those large institutions must be willing to absorb unwelcome news—such as accepting that customers are unhappy about a particular service, and why — and change as a result.

The promise of Big Data is “the manufacturing of brilliant data and making brilliant use of it, and we have a drive for abject purity in listening,” she said.

By Michael Hickins
Source: Wall Street Journal 

Data Analytics and Smart Grid: The Rising Tide for Power Utilities

While power utilities like to claim that they employ data analytics, they really 

don’t.

While power utilities like to claim that they employ data analytics, they really don’t. Utilities tend to have last-gen business intelligence (BI) reporting solutions that they call “analytics,” but that typically amount to not much more than reporting tools or descriptive analytics (primarily based on older database architectures running SQL), as opposed to the real-time and predictive software using complex event processing, to which the term “analytics” is now commonly understood to refer.

Utilities are today seeking to become more proactive in decision-making, adjusting their strategies based on reasonable predictive views into the future, thus allowing them to side-step problems and capitalize on the smart grid technologies that are now being deployed at scale. Predictive analytics, capable of managing intermittent loads, renewables, rapidly changing weather patterns and other grid conditions, represent the ultimate goal for smart grid capabilities.

Based on GTM Research’s latest report, The Soft Grid 2013-2020: Big Data & Utility Analytics for Smart Grid, the leading areas of concern for utilities within data analytics are:

• Achieving an enterprise-wide IT architecture where all relevant data can be shared with all other necessary departments, systems and applications. 
• Ensuring that the enterprise is big-data-ready vis-a-vis the data storage and data management layers of its architecture.

Once utilities begin to overcome these foundational architecture issues, they can then begin to move into the deployment of analytics. The bulk of momentum behind utility analytics deployment is coming from:

• Consumer-based analytics
• Situational awareness gained through synchrophasor/phasor measurement unit (PMU) reporting the health of the transmission grid on an ongoing basis
• Grid optimization analytics of the distribution networks (e.g., voltage management)

A recent GTM Research survey of more than 70 global utilities, which was conducted in partnership with the SAS Institute, displays how well different stakeholders understand the value that analytics provide. Not surprisingly, the survey confirms that utilities themselves report having the most momentum for analytics in the domains of customer management and grid operations.

FIGURE: In What Areas of the Business Do Analytics Seem to Have the Most Momentum?


Source: The Soft Grid 2013-2020: Big Data & Utility Analytics for Smart Grid, SAS Institute

Historically, very little, if any, analytics have been performed on the consumer side. This is due largely to the fact that this industry primarily operates in a monopolistic fashion, with only a smattering of states allowing retail competition. However, the era of smart grid has sparked a renewed interest in demand response and energy efficiency. It appears that utilities are beginning to improve both the data and the level of analysis they are willing to offer customers.

In considering utilities’ progress to date, it should be pointed out that most of the early success stories are rather narrow in scope and often are limited to a single domain. It is GTM Research’s conclusion that the true implementation of broader analytics (both customer-enabling and enterprise-wide) is not yet underway.

However, another trend that is occurring is that employees and customers are beginning to ask for access to particular datasets. At the current juncture, many utilities are not equipped to fulfill these requests, as they do not have enterprise-wide data architectures in place.

In many instances, this has resulted in a growing level of frustration, particularly as employees from other non-operational departments clamor for access to smart meter data. It is our belief that this situation will put some pressure on utility CIOs to properly design the right architectures to allow universal access.

FIGURE: How Would You Rate Your Utility’s Analytics Competencies? (5 Is the Highest, 1 Is the Lowest)

Source: The Soft Grid 2013-2020: Big Data & Utility Analytics for Smart Grid, SAS Institute

Some progressive utilities, such as OGE, SCE and SDG&E, realize that there has been a paradigm shift and are beginning to make strides. However, the results of the survey indicate that the majority of utilities give themselves low marks in areas such as the utilization of analytics for reliability, the utilization of analytics for customer satisfaction, availability of enterprise-wide analytics, data integration of smart meter and grid operations data, and the propensity for data-driven decision-making in general.

The majority of utilities will therefore be challenged over the next ten years to invest properly in big data infrastructure, software, and services in order to avoid the risk of moving too slowly and having their enterprises be overwhelmed by the rising tide of smart grid data.

Source : GreenTech Media

Big Data : c'est le chef de rayon qu'il faut former

Caractérisé avant tout par un accroissement du volume de données à analyser, le Big Data peut déboucher sur d'énormes gains de productivité, en donnant une capacité de décision aux opérationnels. Le Big Data n'influence pas la stratégie. C'est en soi une stratégie, celle de la micro-décision.

 

A l'évidence, la problématique du Big Data occupe largement les esprits. Depuis quelques mois, colloques, séminaires et autres conférences se multiplient. Les solutions techniques arrivent sur le marché (avec, en décembre 2011, la sortie de la version 1.0 de Hadoop, système de traitement de données adapté aux grosses volumétries). Mais ces solutions, développées pour gérer de vastes ensembles de données, ne répondent pas forcément aux problématiques marketing. Le volume est une chose, la capacité à l'exploiter en est une autre. Le goulet d'étranglement se situe certainement moins dans le Big Data que dans le "Big Analytics" : identifier des domaines de décision, des indicateurs clés, et proposer des modèles qui s'accordent au business. L'enjeu réside donc dans la capacité à formuler de bonnes théories (quantitatives) pour organiser le traitement (rapide) des données. 

Ce qu'apporte le Big Data ? Simplement de grands volumes des données générées par la multiplication des capteurs (smartphones, cartes de fidélité, etc.). Rien de révolutionnaire, mais un énorme gain de productivité. Ainsi, dans la distribution, l'analyse des zones de chalandise nécessite de comprendre les choix des consommateurs. Cette théorie s'incarne dans ce qu'on appelle les modèles gravitaires. Avec le Big Data, calibrer ces modèles pour construire des cartes plus précises et plus dynamiques devient envisageable.

A chaque point de vente, sa carte concurrentielle

Une conséquence très simple de l'accroissement du volume des données réside dans le concept de granularité. Le nombre ne change pas la connaissance  - la théorie des échantillons nous apprend qu'au-delà de 10 000 observations, on accroît peu la précision -, mais il permet de calculer un plus grand nombre de modèles, permettant de donner un caractère particulier ou local à la connaissance générale. A chaque point de vente sa carte concurrentielle, qui peut être consultée jour par jour et, mieux, refléter l'effet des actions marketing !

C'est cette granularité qui est essentielle. On comprend de suite où se trouve l'enjeu : des milliers de cartes produites doivent être analysées, interprétées et exploitées par des milliers de managers. Cela implique que chaque responsable de point de vente, et même de rayon, possède la "culture quantitative" qui lui permet de lire ces cartes et de comprendre les modèles. Il doit acquérir un sens des données, en comprendre les limites, en apprécier les variations. Il ne bâtira pas les modèles, mais sera amené à interpréter les résultats en fonction du contexte. Le Big Data nécessite donc le développement de nouvelles compétences, et sans doute plus encore un véritable "empowerment" des unités opérationnelles : plus de données, plus de contexte dans les modèles et les indicateurs, donc plus d'autonomie dans la décision.

La pépite du Big Data n'existe pas

La tentation est grande d'aller encore plus loin, de considérer un grain encore plus fin, en automatisant les micro-décisions. Pensons aux prix, et à cette faculté des étiquettes électroniques de les faire varier quand on le souhaite. On imagine aisément qu'à partir de modèles de prix raisonnables, on produise des milliers de tarifs tendant à l'optimal adaptés aux différents produits, points de ventes et circonstances, leur affichage étant piloté par des systèmes experts (sans doute supervisés humainement, espérons le). C'est déjà en partie le cas des grands commerces électroniques. Pour Amazon, ces modèles sont issus du filtrage collaboratifs.  

On ne doit donc pas attendre du Big Data qu'il influence les stratégies. Car il est une stratégie en soi, celle qui favorise les micro-décisions opérationnelles, sur le principe des petits ruisseaux qui font les grands fleuves. De ce point de vue, le mythe de l'orpailleur s'évanouit. Non l'avantage du Big Data n'est pas d'améliorer la qualité des études, ni de fouiller dans la masse des données jusqu'à trouver cette pépite, la corrélation qui permet de savoir mieux vendre et à moindre coût. Dans la perspective de la micro-décision, l'action l'emporte sur la connaissance. On continuera à forer les données, à les analyser, les malaxer. Mais le nouvel enjeu est de les distribuer sous forme synthétique. N'attendons pas du Big Data de grandes découverte, faisons en sorte qu'il aide les opérationnels à agir avec plus d'intelligence.

Source : Les Echos Technologies