Dec
04
2020
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3 ways the pandemic is transforming tech spending

Ever since the pandemic hit the U.S. in full force last March, the B2B tech community keeps asking the same questions: Are businesses spending more on technology? What’s the money getting spent on? Is the sales cycle faster? What trends will likely carry into 2021?

Recently we decided to join forces to answer these questions. We analyzed data from the just-released Q4 2020 Outlook of the Coupa Business Spend Index (BSI), a leading indicator of economic growth, in light of hundreds of conversations we have had with business-tech buyers this year.

A former Battery Ventures portfolio company, Coupa* is a business spend-management company that has cumulatively processed more than $2 trillion in business spending. This perspective gives Coupa unique, real-time insights into tech spending trends across multiple industries.

Tech spending is continuing despite the economic recession — which helps explain why many startups are raising large rounds and even tapping public markets for capital.

Broadly speaking, tech spending is continuing despite the economic recession — which helps explain why many tech startups are raising large financing rounds and even tapping the public markets for capital. Here are our three specific takeaways on current tech spending:

Spending is shifting away from remote collaboration to SaaS and cloud computing

Tech spending ranks among the hottest boardroom topics today. Decisions that used to be confined to the CIO’s organization are now operationally and strategically critical to the CEO. Multiple reasons drive this shift, but the pandemic has forced businesses to operate and engage with customers differently, almost overnight. Boards recognize that companies must change their business models and operations if they don’t want to become obsolete. The question on everyone’s mind is no longer “what are our technology investments?” but rather, “how fast can they happen?”

Spending on WFH/remote collaboration tools has largely run its course in the first wave of adaptation forced by the pandemic. Now we’re seeing a second wave of tech spending, in which enterprises adopt technology to make operations easier and simply keep their doors open.

SaaS solutions are replacing unsustainable manual processes. Consider Rhode Island’s decision to shift from in-person citizen surveying to using SurveyMonkey. Many companies are shifting their vendor payments to digital payments, ditching paper checks entirely. Utility provider PG&E is accelerating its digital transformation roadmap from five years to two years.

The second wave of adaptation has also pushed many companies to embrace the cloud, as this chart makes clear:

Similarly, the difficulty of maintaining a traditional data center during a pandemic has pushed many companies to finally shift to cloud infrastructure under COVID. As they migrate that workload to the cloud, the pie is still expanding. Goldman Sachs and Battery Ventures data suggest $600 billion worth of disruption potential will bleed into 2021 and beyond.

In addition to SaaS and cloud adoption, companies across sectors are spending on technologies to reduce their reliance on humans. For instance, Tyson Foods is investing in and accelerating the adoption of automated technology to process poultry, pork and beef.

All companies are digital product companies now

Mention “digital product company” in the past, and we’d all think of Netflix. But now every company has to reimagine itself as offering digital products in a meaningful way.

Oct
26
2020
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DataFleets keeps private data useful and useful data private with federated learning and $4.5M seed

As you may already know, there’s a lot of data out there, and some of it could actually be pretty useful. But privacy and security considerations often put strict limitations on how it can be used or analyzed. DataFleets promises a new approach by which databases can be safely accessed and analyzed without the possibility of privacy breaches or abuse — and has raised a $4.5 million seed round to scale it up.

To work with data, you need to have access to it. If you’re a bank, that means transactions and accounts; if you’re a retailer, that means inventories and supply chains, and so on. There are lots of insights and actionable patterns buried in all that data, and it’s the job of data scientists and their ilk to draw them out.

But what if you can’t access the data? After all, there are many industries where it is not advised or even illegal to do so, such as in healthcare. You can’t exactly take a whole hospital’s medical records, give them to a data analysis firm, and say “sift through that and tell me if there’s anything good.” These, like many other data sets, are too private or sensitive to allow anyone unfettered access. The slightest mistake — let alone abuse — could have serious repercussions.

In recent years a few technologies have emerged that allow for something better, though: analyzing data without ever actually exposing it. It sounds impossible, but there are computational techniques for allowing data to be manipulated without the user ever actually having access to any of it. The most widely used one is called homomorphic encryption, which unfortunately produces an enormous, orders-of-magnitude reduction in efficiency — and big data is all about efficiency.

This is where DataFleets steps in. It hasn’t reinvented homomorphic encryption, but has sort of sidestepped it. It uses an approach called federated learning, where instead of bringing the data to the model, they bring the model to the data.

DataFleets integrates with both sides of a secure gap between a private database and people who want to access that data, acting as a trusted agent to shuttle information between them without ever disclosing a single byte of actual raw data.

Illustration showing how a model can be created without exposing data.

Image Credits: DataFleets

Here’s an example. Say a pharmaceutical company wants to develop a machine-learning model that looks at a patient’s history and predicts whether they’ll have side effects with a new drug. A medical research facility’s private database of patient data is the perfect thing to train it. But access is highly restricted.

The pharma company’s analyst creates a machine-learning training program and drops it into DataFleets, which contracts with both them and the facility. DataFleets translates the model to its own proprietary runtime and distributes it to the servers where the medical data resides; within that sandboxed environment, it grows into a strapping young ML agent, which when finished is translated back into the analyst’s preferred format or platform. The analyst never sees the actual data, but has all the benefits of it.

Screenshot of the DataFleets interface. Look, it’s the applications that are meant to be exciting. Image Credits: DataFleets

It’s simple enough, right? DataFleets acts as a sort of trusted messenger between the platforms, undertaking the analysis on behalf of others and never retaining or transferring any sensitive data.

Plenty of folks are looking into federated learning; the hard part is building out the infrastructure for a wide-ranging enterprise-level service. You need to cover a huge amount of use cases and accept an enormous variety of languages, platforms and techniques, and of course do it all totally securely.

“We pride ourselves on enterprise readiness, with policy management, identity-access management, and our pending SOC 2 certification,” said DataFleets COO and co-founder Nick Elledge. “You can build anything on top of DataFleets and plug in your own tools, which banks and hospitals will tell you was not true of prior privacy software.”

But once federated learning is set up, all of a sudden the benefits are enormous. For instance, one of the big issues today in combating COVID-19 is that hospitals, health authorities, and other organizations around the world are having difficulty, despite their willingness, in securely sharing data relating to the virus.

Everyone wants to share, but who sends whom what, where is it kept, and under whose authority and liability? With old methods, it’s a confusing mess. With homomorphic encryption it’s useful but slow. With federated learning, theoretically, it’s as easy as toggling someone’s access.

Because the data never leaves its “home,” this approach is essentially anonymous and thus highly compliant with regulations like HIPAA and GDPR, another big advantage. Elledge notes: “We’re being used by leading healthcare institutions who recognize that HIPAA doesn’t give them enough protection when they are making a data set available for third parties.”

Of course there are less noble, but no less viable, examples in other industries: Wireless carriers could make subscriber metadata available without selling out individuals; banks could sell consumer data without violating anyone in particular’s privacy; bulky datasets like video can sit where they are instead of being duplicated and maintained at great expense.

The company’s $4.5 million seed round is seemingly evidence of confidence from a variety of investors (as summarized by Elledge): AME Cloud Ventures (Jerry Yang of Yahoo) and Morado Ventures, Lightspeed Venture Partners, Peterson Ventures, Mark Cuban, LG, Marty Chavez (president of the board of overseers of Harvard), Stanford-StartX fund, and three unicorn founders (Rappi, Quora and Lucid).

With only 11 full-time employees DataFleets appears to be doing a lot with very little, and the seed round should enable rapid scaling and maturation of its flagship product. “We’ve had to turn away or postpone new customer demand to focus on our work with our lighthouse customers,” Elledge said. They’ll be hiring engineers in the U.S. and Europe to help launch the planned self-service product next year.

“We’re moving from a data ownership to a data access economy, where information can be useful without transferring ownership,” said Elledge. If his company’s bet is on target, federated learning is likely to be a big part of that going forward.

Apr
08
2020
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Talking venture, B2B and thesis-driven investment with Work-Bench’s Jon Lehr

Earlier this week, the Equity crew caught up with Work-Bench investor Jon Lehr to get his take on the current market, and how his firm goes about making investment decisions.

The conversation was a treat, so we cut a piece of it off for everyone to listen to. The full audio and a loose transcript are also available after the jump.

What did Danny and Alex learn while talking to Lehr? A few things, including what Seed II-level investments need these days to be attractive (Hint: It’s not a raw ARR threshold), and what’s going on in SaaS today (deals slowing, but not for select founders; relationships are key to doing deals today), and why being a VC is actually work.

But what stood out the most was how Lehr thinks about finding investment opportunities. While some VCs like to cultivate images of being gut-investors, cutting checks based on first meetings and the like, Lehr told TechCrunch about how he researches the market to find pain-points, and then the startups that might solve those issues.

You can listen to that bit of the chat in the clip below:

Extra Crunch subscribers, the rest of the goodies are below. (A big thanks to Danny for cleaning up the written transcript.)

The audio

Aug
17
2017
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B2B platform Releaf helps African businesses by taking the guesswork out of networking

 Releaf is a new B2B marketplace that wants to help African businesses prosper by helping them find partners they can trust. The startup, which is currently taking part in Y Combinator, has signed up about 1,000 businesses since its public launch in Nigeria on August 3. Read More

May
05
2017
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Growlabs nabs $2.2M to automate outbound sales

 Just six months into its life, Growlabs, a startup using machine intelligence to support outbound sales teams, has raised a $2.2 million seed round. Growlabs helps businesses reduce their customer acquisition costs by enabling smalls sales teams to do more with less. Read More

Apr
19
2017
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Nauta Capital closes out $170M third fund

 Early stage VC firm Nauta Capital, which has offices in London, UK, Barcelona, Spain and Boston in the US, has closed out a 2016 fund raising — capping it off at $170 million. Read More

May
01
2015
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Acceleprise Accelerator Aims $3.5M Fund At Early Stage Enterprise Apps

View of woman's legs pressing gas while shifting in a car. Acceleprise Ventures, a San Francisco-based incubator anchored by investor Sean Glass, announced a new $3.5 million fund with a pronounced enterprise app bent. It also announced 10 newly funded companies.
The company works with 8-12 pre-seed B2B companies per round to help them grow from acquiring their first customers “to building scalable and repeatable processes that can fuel… Read More

Sep
23
2014
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Traxpay Raises $15M, Teams With MasterCard To Be The PayPal Of The B2B World

8549970689_78fa9bf997_h Traxpay, a German startup that has created a platform for businesses in a supply chain to make payments to each other — think PayPal for the B2B world, or an Alipay of the Western world — is making two significant announcements that point to the company’s global growth ambitions–specifically in the U.S. and Asia. First, it has raised another $15 million, in what… Read More

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