Datadog secures $6.2m in funding from Index Ventures and RTP Ventures - The Next Web

IT monitoring platform Datadog has announced a $6.2 million Series A investment round co-led by Index Ventures and RTP Ventures.

Founded in 2010, Datadog plans to use the funding to scale its operations and live up to the expectations of its growing enterprise customer base.

New York-based Datadog has built a monitoring product for IT teams in development and operations to solve the problem of growing amount of data these teams have to go through in their everyday work. It aggregates data from different applications, cloud providers, and management tools covering the full life cycle of code from writing to deployment.

“IT teams are deluged with data from an ever larger number of tools, creating as many data silos that prevent them from understanding and resolving issues,” said Olivier Pomel, CEO of Datadog.

The investment round was co-led by Index Ventures and RTP Ventures, an affiliate of ru-Net Holdings, with participation from existing investors IA Ventures, Amplify Partners, Contour Ventures and NYCSeed, which also backed the company at seed stage in 2011.

Image credit:  Highways Agency / Flickr.

Still In Stealth, Origami Logic Gets $9.3M To Help Marketers Unfold And Make Sense Of Big Data | TechCrunch

Talk to an engineer, and the world is full of big data promise. But those who work at the front end the tech industry — business development, sales and marketing people, for example — have largely been cut out of that conversation. That appears slowly to be changing, with the rise of startups that are dedicated to figuring out how to harness big data in a way that is digestible to those who would benefit from accessing it, but have not been able to up to now. One of these, Origami Logic, today is announcing that it is picking up a Series A round of $9.3 million to develop an analytics platform — still in stealth mode — that aims to give marketers access to big data in a way that is digestible and usable by them specifically.

The round was led by Accel Partners, as part of its Big Data Fund, and also had participation from Lightspeed Venture Partners and other investors.

The company is only planning to launch its product early next year — Origami is currently putting some trial customers on the platform now, but declined to say who they were. As it is described by Opher Kahane, the CEO and co-founder, Origami sounds fascinating, and very timely. The idea, he says, is to let sales and marketing people incorporate disparate strands of marketing data — covering areas like CRM, social media, email campaigns, surveys, and more — into a single platform, which then collates and processes it for them to produce salient data. In turn, that becomes usable for further campaigns, or to measure the effectiveness of those that have already been run.

The name “Origami Logic” says it all: creating pretty shapes out of what otherwise looks like a flat expanse of not very much promise. In other words, big data is something that can and will be used by more than just engineers over time.

“Modern marketers need to make daily, critical decisions amongst a growing plethora of customer touch points including social, mobile, web, search, display and email and a radically changing customer purchase journey,” says Kahane. “Today, many marketers are faced with data silos, making it difficult to capture the entire picture.” He says he wants Origami Logic to be the “single lens through which marketers derive data driven insight across all of their marketing efforts.”

Sounds great, but it takes a leap of faith for VCs to put a significant sum of money into a project that is treading into unchartered waters and has yet to be proven with actual customers.

According to partner Jake Flomenberg (who is, along with Ping Li, joining Origami’s board), part of the attraction here for Accel and the others is the fact that Kahane and his co founders, Ofer Shaked (now the CTO) and Alon Amit (VP of product) have collectively years of experience as successful entrepreneurs. Shaked had worked at Yahoo with Amr Awadalla, who is now CTO of Cloudera (another Accel portfolio company) and helped commercialize Hadoop. Shaked left Yahoo to help create CurrentTV and this is his third startup. Kahane, meanwhile, gained experience in Israeli intelligence and had also founded and sold Kagoor Networks to Juniper. Alon Amit comes from Facebook, where he had been project managing the social network’s ad engine, mobile advertising and sponsored stories.

The other important selling point is that Flomenberg believes that what Origami is doing is an essential evolution of how big data is being used by the tech industry. He calls Origami’s proposition “Splunk for marketers,” referring to the service that offers analytics to monitor enterprise apps, and he believes that this will be something that will become even more commonplace in the world of big data. “Both myself and the others at Team Accel, we think data-driven apps are the next stage for big data. Without apps to make big data usable, it will reamin a big pile of data siloed in different places.”

Indeed, the idea, says Kahane, is to incorporate as many applications as a person would want to into its platform. “We are entirely open on the idea,” he says. “The vision is to take marketing tools that are already in use today, for example Buddy Media or Hootsuite for social media management, Exact Targeting for e-marketing; Eloqua for email; Google for ads. These would become data sources on the hub that we’re building.”

There is of course the question of how Origami Logic will fit in with all the marketing platforms — and big players — that are already established in this space. I personally think that this sounds like just the sort of technology that Salesforce either needs to build or buy. On that point, Kahane is sanguine for now. He sees companies like this as competitors only in the “longer term,” he says. For now, “It’s about stitching together… We are more like partners. Their experience has been about content management, whereas we are on the analytical side. Longer term, as these companies try to stitch these things toether, there is a cometptive force, but it will be hard for the larger guys to be focused and nimble as we can be for now.”

In addition to using the new funds to continuing development of the platform, Origami Logic will also use some of it to staff up. The company is currently hiring engineering, design and data science folks, and encourages people to email them for more details if they’re interested.


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Company: Origami Logic
Website: origamilogic.com
Funding: $9.3M

Origami Logic is developing a breakthrough product for visual, self-service analytics specifically built for marketers. We apply big data analytics, data science and data visualization technologies to deliver insights through a delightful, marketer-friendly user experience.

→ Learn more
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How ‘Gamification’ Can Make Your Customer Service Worse

http://www.wired.com/wiredenterprise/2012/11/gamification-customer-service/

Abusive customers. Low pay. Tuberculosis infections. Customer support can be a miserable job. Software makers have long promised to improve the life of customer support reps, and now they’re at it again. This time, they want to turn customer support into a game.

They call it gamification. The idea is to take familiar aspects of electronic games and apply them to customer support software and other applications used in the business world. This often involves awarding points for tasks and some sort of system for turning those points into other rewards, like a “badge” attached to your online profile or perhaps prizes or bonus pay.

Companies like Badgeville and Bunchball help businesses add gamification to existing software — such as the customer relationship management service Salesforce.com or the help-desk service Zendesk — while other outfits are adding game mechanics directly to their own applications.

Just last week, a startup called PlayVox debuted with a private social network designed for contact centers that includes a gamified training system. Other gamified tools are offered by help desk services like UserVoice and FreshDesk. UserVoice offers a gamified help desk app that includes something called “Kudos,” which lets customers award points to agents for a job well done. Agents are then rated on a leaderboard.

On some level, the practice makes sense. The modern web has shown that even outside the context of game, people respond to the sort of rewards offered by these new-age applications. Jonathan Taylor, who works for a software company called Klipfolio and spends part of his time as customer service representative, has first-hand experience with UserVoice, and he says the benefits are very real.

“Each time one of us gets a kudos, we give a little ‘woot’ and share our success with the whole office,” he says. “I think it’s so exciting because you know that you’ve not only solved a customer’s issue, but you’ve made an impression on them.”

Taylor says the company also uses the Kudo leaderboard to track the number of kudos each customer service representative gets from customers. It’s stoked something of a competition in the office, one in which our customers are the true winners,” he says.

But some believe gamification may do more harm than good. Kathy Sierra, a game designer who has given talks on the dark side of gamification, tells Wired that game designers and scholars are almost universally against gamification.

As Sierra points out, gamification replaces an intrinsic reward with an extrinsic one. In other words, it shifts a participant’s motivation from doing something because it is inherently rewarding to doing it for some other reason that isn’t as meaningful. This, she says, is ultimately less motivating.

‘One could argue that customer service is crap work and therefore anything to make it more tolerable is good, but this is no path to improving customer experience. You cannot incentivize caring. You can, of course, incentivize things like how quickly they get a customer off the phone.’

— Kathy Sierra

Sierra cites research from University of Rochester psychologists Edward L. Deci and Richard M. Ryan, which was popularized by Dan Pink’s book Drive. Deci and Ryan concluded that the most powerful motivators for employees are the mastery of the task at hand, autonomy, and something called relatedness, which might involve helping a customer with a meaningful problem. Gamification replaces these motivators with extrinsic motivators like points and badges.

The other problem is that gamified applications aren’t necessarily fun. Most of what is called gamification would be better described as pointsification, according to game designer Margaret Robertson. “What we’re currently terming gamification is in fact the process of taking the thing that is least essential to games and representing it as the core of the experience,” she wrote in a 2010 blog post

In the end gamification could be just a way for companies to keep an eye on their employees. Companies frequently track the amount of time service agents spend on calls, or the average time it takes them to resolve help-desk tickets. Some call centers go so far as to implement a points system based on attendance and tardiness, and others provide incentives for “up-selling” customers. Adding points and badges into the software they use is just another way of doing the same old thing.

The quote PlayVox uses from its banner customer, GroupOn Latin America, is telling: “PlayVox lets us detect and make a quick diagnosis of underperforming agents or those who ignore certain important procedures in serving our customers.” The emphasis is on making life easier for managers, not for employees.

Sierra says there is a place to use gamification in the workplace, and that’s when workers are developing rote skills that need to be made automatic. “These things — just like having to memorize times tables — are not intrinsically rewarding,” she says. “So there is no danger of snuffing out intrinsic motivation and replacing it with extrinsic motivation.”

“One could argue that customer service is crap work and therefore anything to make it more tolerable is good, but this is no path to improving customer experience,” she says. “You cannot incentivize caring. You can, of course, incentivize things like how quickly they get a customer off the phone.”

Couldn’t gamification be used to re-enforce positive intrinsic rewards by providing feedback that allows customer service workers to do better work, as Jonathan Taylor indicates? Sierra acknowledges that something like customer feedback in the form of “kudos” can be a good thing, assuming that there isn’t a way to game the system. The real danger, she explains, is in gamficiation systems where the “player” has no real meaningful decisions to make, where points are simply awarded or not awarded based on a fixed system. In other words, you want to award points to people just for showing up.

But she says that running a “leaderboard” is still a poor move. She explains that although feedback can be helpful for building mastery, when tracking goes beyond training and becomes part of day-to-day work takes the mind of the task at hand. “It brings in that part of the brain that — subconsciously — says this is why I do this: for the leaderboard status,” she says.

In that regard, companies like PlayVox, which focus on training, are on the right track.

Rather than gamification, Sierra says companies would be better off trying to make the work more intrinsically rewarding. She cites Zappos as an example of a company that has made customer service better for both customers and employes by empowering its employees to make more independent decisions. She says training in areas like understanding the psychology of customers, or connecting employees to a larger context would also be helpful.

That hits on the real problem of gamification, which is that it doesn’t deal with the most alienating aspects of the job — low pay, lack of job security, high stress, verbal abuse from customers, etc. While company culture will vary wildly from small tech startups to large call centers, there are things companies can do to improve their employees’ situations. A recent Stanford study found that employees of a call center in China were both happier and more productive if they were allowed to work from home. They didn’t need more points.

Math and Discipline — Why Nate Silver’s Accuracy Isn’t About “Big Data”

http://scholarlykitchen.sspnet.org/2012/11/08/math-and-discipline-why-nate-si...

 

 

There are many things to remember about this week’s presidential election in the US — vast amounts of money, a polarized electorate, an empty chair, and much more. But one lasting change may be the emergence of a truly viable meta-analysis of polling and projections courtesy of Nate Silver and his FiveThirtyEight blog.

But getting the story right is important.

A post-election article by Dan Lyons on ReadWrite yesterday sought to make the case that the accuracy of statistical analyses made by Silver for the US presidential and Senate races is a sign of how “big data” will end the era of mystical predictions:

This is about the triumph of machines and software over gut instinct. The age of voodoo is over. The era of talking about something as a “dark art” is done. In a world with big computers and big data, there are no dark arts.

Lyons goes on to conflate what Silver did with other so-called “big data” triumphs, like when Big Blue defeated Kasparov (Silver covers this in his book, and a software bug might have been more important than any database in upsetting Kasparov). Chess is an interesting game, because it is bounded — it has finite data. Processing speed was responsible as much as data for the triumph of Big Blue — the data were never bigger than the mathematical possibilities of chess. The speed of processing made that finite limit approachable in chess time.

However, Silver’s approach didn’t use big data, but a relatively small, carefully curated data set consisting of a set of polls, and a lot of discipline, as he outlines in his methodology section on the blog. The factors he manages while assembling and analyzing the data include:

  • Recency – More recent polls are weighted more heavily
  • Sample size – Polls with larger samples receive more weight
  • Pollster rating – Pollsters committed to disclosure and transparency standards receive more weight

These results are then adjusted based on a few factors:

  • Trendline adjustment – If old polls haven’t been replaced, they are adjusted to reflect the overall trendline
  • House effects – Some polls tilt right, some left, and this adjustment mitigates those effects
  • Likely voter adjustment – Polls of likely voters are given a lot of credence

There are other steps outlined, which you can explore further if you’d like, but the two most important are the least mathematical, yet they are vital to the integrity of the process — Silver believes in publishing and standing behind his numbers, because the process of preparing for publication and anticipating criticism helps to ensure better analysis.

To underscore the relatively limited size of the possible data set, Silver tracked one presidential race and at most 100 Senate races, and various national and state-level polls. That’s not big enough to qualify as “big data,” which is defined as data sets that are:

. . . so large and complex that it becomes difficult to process using on-hand database management tool.

There’s even physical evidence that Silver’s not dealing with “big data” — the FiveThirtyEight forecasts were updating on his laptop in the Green Room as Silver was interviewed on Monday night’s The Colbert Report.

Silver himself is skeptical of “big data.” In his book, “The Signal and the Noise: Why So Many Predictions Fail, but Some Don’t,” which I reviewed last month, Silver writes:

. . . our predictions may be more prone to failure in the era of Big Data. As there is an exponential increase in the amount of available information, there is likewise an exponential increase in the number of hypotheses to investigate. . . . there isn’t any more truth in the world than there was before the Internet or the printing press. Most of the data is just noise, as most of the universe is filled with empty space.

Lyons is comparing Silver’s level-headed approach with a notoriously non-level-headed approach — namely, pundits. Most pundits spout statistics but don’t understand the field of statistics or how to practice it. They are entertainers, not analysts. Therefore, they are held to a completely different standard — ratings, not accuracy. If there were accountable for accuracy, they would all be fired tomorrow, because when it comes to accuracy, they can’t beat the flip of a coin. And they definitely weren’t accurate in their assessments of Silver.

Lyons nails one aspect of the aftermath of the election:

Silver has exposed [pundits] for what they are, which is propagandists and entertainers. And that’s fine. We still need entertainers. Computers haven’t learned to do that yet.

However, the New York Times found that Silver’s FiveThirtyEight blog did prove entertaining to many people, driving up traffic as the election approached:

Small data, careful curation, astute and recursive analysis, public accountability, the nerve to place bets and stand behind your projections and data — those are the things that make for good and reliable analysis. It’s not big data. It’s the audacity of competence.

Embracing Big Data Can Add Years to a CMO’s Tenure

http://m.chiefmarketer.com/lead-generation/embracing-big-data-can-add-years-c...

Nov. 5, 2012

Ian Wolfman, CMO of brand agency MEplusYou, recently compared the average tenure of a Fortune Global 500 CMO to the lifespan of a fruit fly. The analogy certainly captures the precarious nature of the role.

And while the average tenure of a CMO has increased from 23.6 to 43 months since 2004, questions remain about the CMO’s long-term fate. Why? Why do some believe the CMO will go the way of the dinosaur, while others see the CMO evolving into the CVBO (Chief Value Building Officer)?

Whether individual CMOs ignore or embrace them, transformative trends such as social business, disintegrating lines between business functions, and the demand for broader business impact beyond marketing are forging a new breed of executives. These leaders recognize big data as the fundamental consequence of our new market landscape. That makes analytics the greatest opportunity.

Big data as a key enabler
Big data refers not just to data itself, but to the challenges, capabilities and competencies required to store and analyze huge data sets to support accurate and timely decision-making. Inherent in big data is the notion that businesses can extract value from collecting, processing, analyzing and acting on vast quantities of data.

A helpful way for CMOs to view big data is in three buckets:
•    Customer: The most familiar category may include behavioral, attitudinal and transactional metrics from sources such as marketing campaigns, points-of-sale, websites, customer surveys, social media, online communities and loyalty programs.
•    Operational: Objective metrics that measure the quality of marketing and business processes may relate to marketing operations, resource allocation, asset management, budgetary planning, etc.
•    Financial: Typically housed in the organization’s financial systems, this data include sales, revenue, profits and other objective data to measure the financial health of the organization.

CMOs can gain the respect of their C-suite counterparts by integrating customer, operational and financial data, and making sense of it to drive enduring marketing and business performance. CMOs who adopt an integrated marketing management strategy with big data can have a meaningful effect on customer experience, customer engagement, customer loyalty and marketing performance—and thrive.

Customer experience
At the core of the CMO agenda is designing and delivering differentiated customer experiences, which translate into a more loyal customer base. In the past, marketers analyzed customer feedback with minimal consideration of operational and financial data. Big data offers rich insight unachievable by examining customer feedback data alone.

For instance, CMOs can use operational data in call centers (e.g., wait times or time to resolution) to improve the customer experience across channels. Operational data can also reveal training opportunities to enable front line staff to deliver better service.

Customer engagement
Today’s multichannel, multidevice world makes successful customer engagements more difficult. To engage your customers successfully, you must know who they are, where they are, what they want and when they want it.

CMOs can exert tremendous influence on customer engagement through big data analytics. They can ask, “What needs to change to achieve positive customer engagements?” Or, better still, “What do our customers want from us?”

Customer retention and loyalty
CMOs are becoming keenly aware that customer loyalty considerations must be embedded in every touchpoint with the customer.

Big data lets marketers augment existing customer touchpoints and anticipate new ones to keep valuable customers loyal in a brand-fickle world. Further, big data analytics can help CMOs allocate resources to drive revenue through successful loyalty initiatives.

Marketing optimization/performance
Today’s highly empowered customers average five to 10 different touchpoints with a brand. As marketers shift budgets from traditional to digital marketing channels (email, social media, search engine optimization, display advertising and mobile), CMOs need to know the optimal marketing spend across multiple channels. With big data, CMOs can continuously optimize marketing programs through testing, measurement and analysis. With a test-and-learn approach, CMOs can deliver on the key determinant of longevity: return on investment.

CMOs today are better poised than ever not only to retain their roles, but to deliver broad, sustainable business impact. CMOs who capitalize on big data will reap big rewards, both personally and professionally. Bottom line: Businesses that exploit big data outperform their competition.

Wilson Raj is the global customer intelligence director of SAS.

Fred Destin

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Sent: 17/10/2012 19:18
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From: noreply+feedproxy@google.com [mailto:noreply+feedproxy@google.com] On Behalf Of Frederic Destin
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http://freddestin.com)">Fred Destin


Recorded Future – Harnessing intelligence at the scale and speed of the Web

Posted: 16 Oct 2012 12:00 PM PDT

 I am at the Recorded Future's first user conference down in Washington today, watching Christopher Ahlberg and guests takes us through the next generation in business intelligence. Team Atlas has a "Big Focus on Big Data" and this is one of those days where I'm comforted that we're on to some strong opportunities in this field.

At Atlas my predecessors (and Philippe Claude in particular) played an important role in funding the first generation of business intelligence success stories, including for example Business Objects, iLog or Spotfire (Christopher's previous success). In our latest fund, we've continued with this focus and have a number of companies focused on providing intelligence under the suitably vague moniker of Big Data (Dataxu, Adsafe Media, Recorded Future, Hopper Travel and others).

In the past business intelligence was enterprise centric and non real time. It was focused on understanding history, whether in the form of Entreprise Search (Endeca, Autonomy) or traditional Business Intelligence (BO, Cognos, Tableau, Qliktech). Where understanding trends across time and mapping the future was concerned, the only plays out there dealt with highly structured data, usually based on mathematical approaches (MatLab, SPSS, SAS).

So conceptually Recorded Future is about a simple concept : horizon scanning at the scale of the web. If knowledge management is about analyzing history and trying to derive rules for the future, Recorded Future is about connecting the dots of a fast moving present and uncertain future.

As I'm listening to Recorded Future users from the intelligence community and large corporate users, I'm struck by how far we've come since my days on the board of Xerox-PARC spinoff Inxight. We've got a perfect storm of technologies coming together that makes a company like Recorded Future possible.


Next Gen Intelligence Infrastructure

A company like Recorded Future is hard to build today (just ask Quid), but would have been a pipe dream 5 years ago. We've got a number of connected dots that help make a company like this possible.

Rich Data Substrates. Yes, thank you Google for indexing every document out there. Thank you Facebook for the Open Graph. Etc, etc. Data wants to be free. Before you were linking 60 instances of SAP to build your BI cubes, now we're throwing the web at you on top.

Real Time Data. The real game changer is the need / ability to harnessing real time data. If you're trying to predict an uprising in Syria, newspaper scanning isn't going to help you. If you're trying to map an information leakage problem, you'll need to track how a leak travels across the social graphs. Real-time is the real game changer in providing actionable intelligence.

Big Data Infrastructure & Big Data Analytics: I think of Big Data in a simple way. We used to have predictive algorithms that would crunch large data sets, now we have huge data sets and more importantly complex interactions that need to be observed instead of theoretically modeled ex ante. Intelligence is derived from the actual data instead of making assumptions about models that will accurately describe the world. Anyways, we know have both the databases and the analytics to allow us to harness data at the scale of the web.  Our newest partner Chris Lynch, ex Vertica CEO, knows a thing or two about that.

The Power of Context

Context is the great enabler of Big Data. It's what helps separate signal from noise in real time environments. Whether you're extending your current service through a new mobile app or trying to derive intelligence from a pile of data like Recorded Future, context is what's going to drive the appropriate form of user interaction and help you harness your sea of data in a relevant way.

If you're mixing internal intelligence with competitive data and market data, you're now able to derive "contextual intelligence" from these data mashups enhanced by powerful analytcs and rich visualizations. To put it more simply, the world of business intelligence used to be about loading internal data into data warehouses and running scripted analytics against this. Now, we're looking at context rich, personalized, real time analytics at the scale of the web.


Impactful use Cases

The revolution is either enabling or fueled by new use cases. Here's a few of things you can do today with a platform like Recorded Future:

Providing physical security: if you're a large manufacturer, can you predict which production facility or port might be affected by unrest ? Can you protect your C-Level execs are they travel across the world ? If you're at the CDC, can you map swine flu outbreaks in real time across the globe ? Can you identify leading indicators of a likely bird flu outbreak from a government that has a tendency to suppress data ?

Catching Baddies: can you predict where senior Al-Qaeda in Maghreb operatives are moving ? Can you identify the real media influencers that drive the al Qaeda messages ? Can you trust that new informant or is he a double agent ? Can you dynamically geo-locate information that relates to your top targets ?

Managing information leakage: what if you are a leading consumer electronics company worried about product releases leakage. When a leak occurs, can you map the ways in which the leak occurred and spread across news and social networks ? Can you track back the movement of your employees and correlate them with the movement of the blogger or journalist at the source of the leak?

Map cyberthreats: when worm variants start spreading faster than Symantec can push out the relevant defenses, can you map in real time the spread of the infections and push defenses in the right way ? Can I determine whether a threat is accelerating or slowing down ? Is there an identified threat trending across the hacker community ? Etc.

Map competition: If you're trying to get a view for what a competitor is doing and you're say, a large FMCG company, it's tough to get a complete view of your opponents' strategy. But what if you could map joint ventures, new commercial agreements on sourcing and distribution, patent activity and investment activity across the globe and across time ? What if once you get a whiff of a possible acquisition you can quickly analyze the impact on your position in each market, whether there are regulatory levers you can pull in specific markets to block a merger, and so forth ?

Use cases we cannot talk about : we got a bunch of those. Makes for interesting pipeline reviews at the board meeting: Agency 3, Product X. You get the picture. Terrorism prevention, nuclear safety, covert ops, whatever these guys do.


Nailing the elusive billion dollar opportunity

You can clearly see the challenges of building a company like Recorded Future : it has everything to do with nailing repeatable and highly productizable use cases that you can use to get the company to scale effectively. With a backend that works (i.e. Technology that's proven), we'e faced with an embarrassment of riches as to where to take the products that sit on top of the FR platform and help us scale revenues fast and profitably.

It's a playbook that's been written many times before. The beauty of startups of course is that every time is different. With an opportunity of that magnitude the founding team has been working hard on getting the best assets on side. That includes building a team of senior folks out of Spotfire, Endeca, Google, Vertica and elsewhere, as well as attracting strong capital backers early, including Google Ventures (Rich Miner), IA Capital Ventures (Roger Ehrenberger) most recently Business Objects founder and CEO Bernard Liautaud, now a VC at Balderton.

We've got the tech, the people, the capital and the reference clients; watch this space for Boston's next big enterprise success.

 

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