SAP announced its Q3 earnings yesterday, with its aggregate results down across the board. And after missing earnings expectations, the company also revised its 2021 outlook down. The combined bad news spooked investors, crashing its shares by more than 20% in pre-market trading, and the stock wasn’t showing any signs of improving in early trading.

The German software giant has lost tens of billions of dollars in market cap as a result.

The overall report was gloomy, with total revenues falling 4% to €6.54 billion, cloud and software revenue down 2% and operating profit down 12%. The only bright spot was its pure-cloud category, which grew 11%, to €1.98 billion.

SAP’s revenue result was around €310 million under expectations, though its per-share profit beat both adjusted and non-adjusted expectations.

While SAP’s big revenue miss might have been enough to send investors racing for the exits, its revised forecast doubled concerns. Even though the company said that its customers are accelerating their move to the cloud during the pandemic — something that TechCrunch has been tracking for some time now — SAP also said the pandemic is slowing sales and large projects.

Constellation Research analyst Holger Mueller says this is resulting in an unexpected revenue slow-down.

“What has happened at SAP is a cloud revenue delay as customers know that SAP is only investing into cloud products, and they have to migrate to those in the future. The news is that SAP customers are not migrating to the cloud during a pandemic,” Mueller told TechCrunch.

In a sign of the times, SAP spent a portion of its earnings results talking about 2025 results, a maneuver that failed to allay investor concerns that the pandemic was dramatically impacting SAP’s business today and in the coming year.

For 2020, SAP made the following cuts to its forecasts:

• €8.0 – 8.2 billion non-IFRS cloud revenue at constant currencies (previously €8.3 – 8.7 billion)
• €23.1 – 23.6 billion non-IFRS cloud and software revenue at constant currencies (previously €23.4 – 24.0 billion)
• €27.2 – 27.8 billion non-IFRS total revenue at constant currencies (previously €27.8 – 28.5 billion)
• €8.1 – 8.5 billion non-IFRS operating profit at constant currencies (previously €8.1 – 8.7 billion)

So, €300 million to €500 million in cloud revenue is now gone, along with €300 million to €400 million in cloud and software revenue, and €600 to €700 million in total revenue. That cut profit expectations by up to €200 million.

The company, however, is trying to put a happy face on the future projections, believing that as the impact of COVID begins to diminish, existing customers will eventually shift to the cloud and that will drive significant new revenues over the longer term. The trade-off is short-term pain for the next year or two.

“Over the next two years, we expect to see muted growth of revenue accompanied by a flat to slightly lower operating profit. After 2022 momentum will pick up considerably though. Initial headwinds of the accelerated cloud transition will start to turn into tailwinds for revenue and profit. […] That translates to accelerated revenue growth and double digit operating profit growth from 2023 onwards,” SAP CFO Luka Mucic said in a call with analysts this morning.

The question now becomes can they meet these projections, and if the longer-term approach during a pandemic will placate investors. As of this morning, they weren’t looking happy about it.

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.

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.

Israeli startup SimilarWeb has made a name for itself with an AI-based platform that lets sites and apps track and understand traffic not just on their own sites, but those of its competitors. Now, it’s taking the next step in its growth. The startup has raised $120 million, funding it will use to continue expanding its platform both through acquisitions and investing in its own R&D, with a focus on providing more analytics services to larger enterprises alongside its current base of individuals and companies of all sizes that do business on the web.

But not, it seems, necessarily an IPO at the moment.

Co-led by ION Crossover Partners and Viola Growth, the round doubles the total amount that the startup has raised to date to $240 million. Offer said that it was not disclosing its valuation this time around except to say that his company is now “playing in the big pool.” It counts more than half of the Fortune 100 as customers, with Walmart, P&G, Adidas and Google, among them.

For some context, it hit an $800 million valuation in its last equity round, in 2017.

SimilarWeb’s technology competes with other analytics and market intelligence providers ranging from the likes of Nielsen and ComScore through to the Apptopias of the world in that, at its most basic level, it provides a dashboard to users that provides insights into where people are going on desktop and mobile. Where it differs, Offer said, is in how it gets to its information, and what else it’s doing in the process.

For starters, it focuses not just how many people are visiting, but also a look into what is triggering the activity — the “why”, as it were — behind the activity. Using a host of AI tech such as machine learning algorithms and deep learning — like a lot of tech out of Israel, it’s being built by people with deep expertise in this area — Offer says that SimilarWeb is crunching data from a number of different sources to extrapolate its insights.

He declined to give much detail on those sources but told me that he cheered the arrival of privacy gates and cookie lists for helping ferret out, expose and sometimes eradicate some of the more nefarious “analytics” services out there, and said that SimilarWeb has not been affected at all by that swing to more data protection, since it’s not an analytics service, strictly speaking, and doesn’t sniff data on sights in the same way. It’s also exploring widening its data pool, he added:

“We are always thinking about what new signals we could use,” he said. “Maybe they will include CDNs. But it’s like Google with its rankings in search. It’s a never ending story to try to get the highest accuracy in the world.”

The global health pandemic has driven a huge amount of activity on the web this year, with people turning to sites and apps not just for leisure — something to do while staying indoors, to offset all the usual activities that have been cancelled — but for business, whether it be consumers using e-commerce services for shopping, or workers taking everything online and to the cloud to continue operating.

That has also seen a boost of business for all the various companies that help the wheels turn on that machine, SimilarWeb included.

“Consumer behavior is changing dramatically, and all companies need better visibility,” said Offer. “It started with toilet paper and hand sanitizer, then moved to desks and office chairs, but now it’s not just e-commerce but everything. Think about big banks, whose business was 70% offline and is now 70-80% online. Companies are building and undergoing a digital transformation.”

That in turn is driving more people to understand how well their web presence is working, he said, with the basic big question being: “What is my marketshare, and how does that compare to my competition? Everything is about digital visibility, especially in times of change.”

Like many other companies, SimilarWeb did see an initial dip in business, Offer said, and to that end the company has taken on some debt as part of Israel’s Paycheck Protection Program, to help safeguard some jobs that needed to be furloughed. But he added that most of its customers prior to the pandemic kicking off are now back, along with customers from new categories that hadn’t been active much before, like automotive portals.

That change in customer composition is also opening some doors of opportunity for the company. Offer noted that in recent months, a lot of large enterprises — which might have previously used SimilarWeb’s technology indirectly, via a consultancy, for example — have been coming to the company direct.

“We’ve started a new advisory service [where] our own expert works with a big customer that might have more deep and complex questions about the behaviour we are observing. They are questions all big businesses have right now.” The service sounds like a partly-educational effort, teaching companies that are not necessarily digital-first be more proactive, and partly consulting.

New customer segments, and new priorities in the world of business, are two of the things that drove this round, say investors.

“SimilarWeb was always an incredible tool for any digital professional,” said Gili Iohan of ION Crossover Partners, in a statement. “But over the last few months it has become apparent that traffic intelligence — the unparalleled data and digital insight that SimilarWeb offers — is an absolute essential for any company that wants to win in the digital world.”

As for acquisitions, SimilarWeb has historically made these to accelerate its technical march. For example, in 2015 it acquired Quettra to move deeper into mobile analytics and it acquired Swayy to move into content discovery insights (key for e-commerce intelligence). Offer would not go into too much detail about what it has identified as a further target but given that there are quite a lot of companies building tech in this area currently, that there might be a case for some consolidation around bigger platforms to combine some of the features and functionality. Offer said that it was looking at “companies with great data and digital intelligence, with a good product. There are a lot of opportunities right now on the table.”

The company will also be doing some hiring, with the plan to be to add 200 more people globally by January (it has around 600 employees today).

“Since we joined the company three years ago, SimilarWeb has executed a strategic transformation from a general-purpose measurement platform to vertical-based solutions, which has significantly expanded its market opportunity and generated immense customer value,” said Harel Beit-On, Founder and General Partner at Viola Growth, in a statement. “With a stellar management team of accomplished executives, we believe this round positions the company to own the digital intelligence category, and capitalize on the acceleration of the digital era.”

Last year, Dropbox talked about making a shift to Shingled Magnetic Recording or SMR disks for short because of the efficiency they can give a high volume storage platform like theirs. Today, Western Digital announced that Dropbox was one of the first companies to qualify their Ultrastar® DC HC650 20TB, host-managed SMR hard disks.

Dropbox’s modern infrastructure story goes back to 2017 when it decided to shift most of its business from being hosted on AWS to building their own infrastructure. As they moved through the process of making that transition in the following years, they were looking for new storage technology ideas to help drive down the cost of running their own massive storage system.

As principal engineer James Cowling told TechCrunch last year, one of the ideas that emerged was using SMR:

What emerged was SMR, which has high storage density and a lower price point. Moving to SMR gave Dropbox the ability to do more with less, increasing efficiency and lowering overall costs — an essential step for a company trying to do this on its own. “It required expertise obviously, but it was also exciting to bring a lot of efficiencies in terms of cost and storage efficiency, while pulling down boundaries between software and hardware,” Cowling said.

As it turns out, Dropbox VP of engineering Andrew Fong says that the company has been working with Western Digital for a number of years and the new SMR technology is the latest step in that partnership.

Western Digital says that these drives deliver this cost savings through increased storage density and lower power requirements. “When considering exabyte-scale needs, and associated capital and operating cost of the data center, the long-term value in terms of lower cost-per-TB, higher density, low power and high reliability can help benefit the bottom line,” the company said in a statement.

Time will tell if these disks deliver as promised, but they certainly show a lot of potential for a high volume user like Dropbox.

Last night Datto priced its IPO at $27 per share, the top end of its range that TechCrunch covered last week. The data and security-focused software company had targeted a $24 to $27 per-share IPO price range, meaning that its final per-share value was at the top of its estimates.

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The Datto IPO won’t draw lots of attention; its business is somewhat dull, as selling software to managed service providers rarely excites. But, the public offering matters for a different reason: It gives us a fresh lens into today’s IPO market.

That lens is the perspective of slower, more profitable growth. What is that worth?

The value of quickly growing and unprofitable software and cloud companies is well known. Snowflake made a splash earlier this year on the back of huge growth and enormous losses. Investors ate its shares up, pushing its valuation to towering heights. This year we’ve even seen rapid growth and profits valued by public investors in the form of JFrog’s IPO.

But slower growth, software margins and profitability? Datto’s financial picture feels somewhat unique among the IPOs that TechCrunch has covered this year.

It’s a similar bet to the one that Egnyte is making; the enterprise software company crested $100 million ARR last year and announced that it grew by around 22% in the first half of 2020. And, it is profitable on an EBITDA basis. Therefore, the Datto IPO could provide a clue as to whether companies like Egnyte and the rest of the late-stage startup crop should be content to grow more slowly, but with the benefit of actually making money.

Lessons from Datto’s IPO pricing and revenue multiple

Here are the deal’s nuts and bolts: