Month: May 2018

As companies start to gather more and more data on their users and customers, including a firehose of information from a nigh-endless flow of tests, managing and maintaining that data isn’t the only place companies are hitting a wall — and figuring out who can actually access it is becoming just as big of a problem.

That was the experience Amandeep Khurana had throughout his career and as he kept talking to more and more larger companies. So he and his co-founder decided to start Okera, which is looking to make it easier for stewards of various sets of data to ensure the right people have the right access. With data coming in from a myriad of sources — and hopefully ending up in the same database — it can be increasingly complex to track who has access to what, and the hope is that Okera can reduce that problem to flipping a few switches.

Okera is coming out of stealth mode and said it has raised a new $12 million financing round led by Bessemer Venture Partners, with existing investors Felicis Ventures and Capital One Growth Ventures participating. Bessemer’s Ethan Kurzweil and Felicis’ Wesley Chan are joining the company’s board of directors, and Okera has raised $14.6 million to date.

“I was very underwhelmed by what other vendors were offering, there was pretty much nothing happening,” co-founder Khurana said. “There were not a lot of good solutions, and no vendor was incentivized to solve the problem. What we’d hear is, [employees] were spending so much time in data management and plumbing. We saw a trend — as more and more enterprises are moving into the cloud, so they can be agile, these problems amplified. There is a lot of friction around data management, and people spent a lot of time and resources and money making one-off solutions.”

Part of the problem stems from larger companies looking to move their operations into the cloud. Those companies can run into the problem of data coming in from various discrete locations, where everyone is handling something differently, and everyone has varying levels of access to that data. For example, an analyst might be trying to dig into some customer usage data in order to tweak a product, but they only have access to half of the records they need. To fix that, they would need to hunt down the people that are in control of the rest of the information they need and get the right copies or permissions to access it. All of this includes a robust audit trail for those handling security within the company.

it is going to be an increasingly crowded space just by virtue of the problem, especially as companies collect more and more data while they look to better train various machine learning models. There are startups like Collibra also looking to improve the data governance experience for companies, and Collibra raised an additional $58 million in January this year.

But streamlining all this, in theory, reduces the overhead of just how much time it takes for those employees to hunt down the right people, and also make sure it’s easier to access everything and get to work faster. For modern systems, it’s an all-or-nothing approach, Khurana said, and the goal is to try to make it easier for the right people to get access to the right data when they need it. That isn’t necessarily limited to analysts, as employees in sales, marketing, and other various roles might also need access to certain databases in their day-to-day jobs.

from TechCrunch

As sensors become cheaper and easier to install, the whole process of maintaining equipment and assets is starting to shift from just scrambling to fix problems to getting a hold of issues before they get out of control.

That’s opened the door for startups like Fiix, which are creating workflow software that helps companies manage equipment and assets. That software enables companies to keep a close eye on equipment and resolve issues quickly before they become more complex to the point of costing companies hundreds of thousands of dollars to fix. Every percentage point of efficiency, for some operations, can translate to revenue significant enough to the point that this kind of software is an easy sell. Fiix said today it has raised $12 million in a new financing round led by BuildGroup.

“It was one of the last bastions of enterprise software that’s yet to go through the same disruption that every other major software co,” CEO Marc Castel said. “If you look at human resource software, CRM software, accounting software, they’ve all gone through the same transition. This market was one of the last ones to go through that transition.”

Fiix takes the process of managing work orders, assets and inventories and throws it all into a set of software that’s designed to be easier to use when compared to existing complex asset management software. That includes making sure all of this is available on a phone, where managers and employees can monitor what kinds of work orders are in progress, approve them, or issue them. That’s designed to remove some of the time barriers that may keep managers from starting the maintenance process.

But because there’s a lot of money to be made here, there’s going to be an increasing amount of competition. Already, there are startups like UpKeep, which came out of Y Combinator’s winter class last year. By giving managers a way to prioritize and get work orders done quickly, employees and managers can have a more real-time level of communication — which means they can spot problems earlier and earlier, and keep things running smoothly.

from TechCrunch

A new U.K. self-driving car startup founded by Amar Shah and Alex Kendall, two machine learning PhDs from University of Cambridge, is de-cloaking today. Wayve — backed by New York-based Compound, Europe’s Fly Ventures, and Brent Hoberman’s Firstminute Capital — is building what it describes as “end-to-end machine learning algorithms” to make autonomous vehicles a reality, an approach it claims is different to much of the conventional thinking on self-driving cars.

Specifically, as Wayve CEO Shah explained in a call last week, the young company believes that the key to making an autonomous vehicle that is truly just that (i.e. able to drive safely in any environment it is asked to), is a much greater emphasis on the self-learning capability of its software. In other words, self-driving cars is an AI problem first and foremost, and one that he and co-founder Kendall argue requires a very specific machine-learning development skill set.

“Wayve is building intelligent software to decide how to control a vehicle on all public roads,” he tells me. “Rather than hand-engineering our solution with heavily rule-based systems, we aim to build data-driven machine learning at every layer of our system, which would learn from experience and not simply be given if-else statements. Our learning-based system will be safer in unfamiliar situations than a rule-based system which would behave unpredictably in a situation it has not seen before”.

To explain his thinking in laymen’s terms, Shah points to the way a human who is relatively proficient in driving in one city can quickly adapt to the differences in a completely new city, without having to be given extra training or instruction beforehand. It may take around 30 minutes or even a few hours to become fully climatized to new driving conditions or environment, but humans don’t need very much new data to do so.

“Humans have a fascinating ability to perform complex tasks in the real world, because our brains allow us to learn quickly and transfer knowledge across our many experiences,” he says. “We want to give our vehicles better brains, not more hardware”.

To problem, thus far, the pair argue, is that companies like Google and Uber are throwing an engineering mindset at making vehicles autonomous, in the sense of designing rule-based systems that try to pre-empt and deal with every edge case, whilst in tandem adding more sensors and capturing more data. This might produce encouraging results in the specific, narrow setting it has been engineered for, but won’t have maximum payoff longer term.

“Right now, big tech companies have cars with many different sensors of a handful of different types. Their attitude is to have more and more sensors to do more and more difficult driving tasks,” says Shah. “If I ask you to do a difficult athletic obstacle course, something like Ninja warrior, having more eyes isn’t really going to help you much. What you need is better coordination – it’s the mind-muscle connection that’s the limiting factor. In driving, it’s really the way you use your sensory information that’s key (the AI-wheel connection in the car), not the number of cameras and radars and LIDARs”.

But if a more sophisticated machine-learning approach is the correct one, surely Google (which has several AI efforts under its parent company, including being the owner of DeepMind), would already be going down that avenue, too?

“The big teams are distracted by getting something working because they have stakeholders who have been investing for a decade into autonomous driving. They are getting impatient,” the Wayve co-founder pushes back. “How will Alphabet tell their shareholders ‘we’ve invested X billion USD into Waymo and its predecessor with a team of 1,000s, but we are now throwing that approach all down the drain and hiring more AI people to solve driving’. It’s a hard sell having spent billions and when they are close to a simple product. Same reason politicians make bad long-term decisions… their output is only short-term”.

“Wayve has a very differentiated technical approach versus most other autonomous vehicle startups,” echoes Fly Ventures’ Gabriel Matuschka. “It’s a 10x improvement over the rules-based approach taken from legacy robotics to hard-code the driving actions that the vehicle takes once it understands what it sees. Wayve uses end-to-end machine learning to drive cars autonomously, with little data, in novel environments. This means that their software enables a car to drive itself using only understanding of what it can see, just like humans do”.

To that end, the ten-person Wayve is said to be made up of experts in robotics, computer vision and artificial intelligence from both Cambridge and Oxford universities, who have previously worked at the likes of NASA, Google, Facebook, Skydio and Microsoft. Their work ranges from using deep learning for visual scene understanding to autonomous decision-making in uncertain environments. Noteworthy also is that Professor Zoubin Ghahramani, Chief Scientist of Uber, is an investor in Wayve.

“There are very few teams out there with the academic background and technical capabilities to at all have a credible shot at this. Wayve is one of them,” adds Matuschka. “Some people in the industry question if Wayve’s novel approach will work. You only stand a chance to compete against Google, Uber, et al. if you try, and are able to do something that the large players haven’t done so far or don’t believe in yet. Then you can have a head start”.

from TechCrunch

The growth of China’s Bytedance, an ambitious $30 billion tech firm, and its highly-addictive Toutiao news aggregator app has set off a search for services with similar growth potential across the world.

India, second in population only to China with rapidly-growing internet access, is an obvious place to look, and would-be pretender to the Toutiao crown has been found in the shape of NewsDog, a Chinese company that stumbled on success in India. Today, NewsDog announced a $50 million Series C round led by Chinese internet giant Tencent.

Toutiao is a phenomenon in China. The app has around 200 million daily users, and it is one of the few new tech products to emerge in a China where Tencent and Alibaba dominate the consumer app landscape. Point in case, it is so mainstream now that it has even run into issues with China’s internet censors. Toutiao is essentially a news aggregation service that lets consumers catch their daily reads and discover stories with an experience tailored to their habits and likes.

That’s very much the style of NewsDog, which claims over 50 million users. The service has branched out to cover 10 of Indians many languages, while it recently established a platform — ‘WeMedia’ — that augments its content aggregation by allowing users to submit stories, too.

This round is a major milestone for the company. In a competitive environment, it is the largest fundraising round from a news app company in India while it more obviously brings Tencent, the $500 billion tech giant, on board with its experience and support. Other investors include Chinese VCs Danhua Capital (DHVC) and Legend Capital as well as Chinese mobile app firm DotC United.

NewsDog’s competition includes Dailyhunt — which is backed by Toutiao-owner Bytedance — Inshorts, which counts Tiger Global among its investors, and NewsPoint, which is owned by media firm Times Internet.

One other competition is UC News, a service from Alibaba-owned UC Web, which, like NewsDog, is Chinese.

NewsDog was launched in 2016 by CEO Forrest Chen Yukun, a computer science graduate from Tsinghua University graduate, and Yi Ma, who holds a PhD from Princeton University and previously worked at Baidu and Goldman Sachs .

Data from App Annie shows that NewsDog is the top news app in the Google Play Store in India — Android is the country’s dominant operating system — ahead of Dailyhunt and NewsPoint in second and third, respectively. NewsDog plans to use this new funding to pull further ahead of the competition by focusing on adding more languages and deepening its content library.

The company said it is already using machine learning to help produce an experience that is customized to users — the experience that Toutiao pioneered in China — and it plans to double down on that.

“Poly culture and multiple languages make content matching an incredibly hard problem,” Chen said in a statement. “So far, we have made good initial progress but content business is like an endless journey. There is no finish line, you have to just keep running.”

NewsDog is aiming to reach 100 million users as its next milestone as India’s internet population surges. The country is tipped to reach 500 million internet users by June 2018, according to a report from the Internet and Mobile Association of India (IAMAI) and Kantar IMRB. That’s up from 481 million six months prior, but internet penetration in rural areas is at just 20 percent compared with 65 percent in urban India which indicates even more growth potential.

For Tencent, meanwhile, this investment is another upping of its pace in India.

Initially, the company was slow to put money to work in India, where Alibaba entered early to buy stakes in the likes of Paytm, but gradually Tencent has got its checkbook out. Its most notable India-based deals include WhatsApp challenger Hike, healthcare platform Practo, and music service Gaana. This year, it is reportedly focusing on finding promising early-stage startups where it can invest $5-15 million.

In NewsDog, Tencent will hope to jump on the news aggregator train that it missed in China, giving Bytedance an opportunity to become a major Chinese consumer brand.

from TechCrunch

Adobe announced today that it was acquiring Magento for $1.68 billion. The purchase gives Adobe a missing Ecommerce platform piece that works in B2B and B2C contexts and should fit nicely in the company’s Experience Cloud.

It should also help Adobe compete with Salesforce, which offers its own marketing, sales and service offerings in the cloud and bought Demandware for more than $2 billion in 2016 to provide a similar set of functionality.

Brent Leary, who owns CRM Essentials and keeps a close on the intersection between marketing and CRM, says this fills an obvious hole in Adobe’s Experience Cloud. “Now they have an offering that allows them to close the loop with consumers, who are able to finalize a digital transaction that started online with digital marketing tools Adobe already offered,” Leary explained.

Leary also sees this deal bringing Microsoft and Adobe, who have already announced partnerships in the past closer together. “But maybe even more interesting may be how this may further the relationship Adobe has with Microsoft. As they also are missing an Ecommerce piece to their customer engagement platform,” he pointed out. Leary speculates this could lead to an even deeper relationship between the two companies as they are each battling Salesforce.

Magento was founded in 2008 and purchased by eBay in 2011 in a deal reported to be just $180 million. The company went private again in 2013 with help from Primera Funds. Today the company sold for almost $1.7b. That’s a heft increase in value since that 2011 purchase.

This story is developing. More to come.

from TechCrunch