Posted by Richy George on 20 September, 2018

A week ago rumors were flying that Adobe would be buying Marketo, and lo and behold it announced today that it was acquiring the marketing automation company for $4.75 billion.

It was a pretty nice return for Vista Equity partners, which purchased Marketo in May 2016 for $1.8 billion in cash. They held onto it for two years and hauled in a hefty $2.95 billion in profit.

We published a story last week, speculating that such a deal would make sense for Adobe, which just bought Magento in May for $1.6 billion. The deal gives Adobe a strong position in enterprise marketing as it competes with Salesforce, Microsoft, Oracle and SAP. Put together with Magento, it gives them marketing and ecommerce, and all it cost was over $6 billion to get there.

“The acquisition of Marketo widens Adobe’s lead in customer experience across B2C and B2B and puts Adobe Experience Cloud at the heart of all marketing,” Brad Rencher, executive vice president and general manager, Digital Experience at Adobe said in a statement.

Ray Wang, principal analyst and founder at Constellation Research sees it as a way for Adobe to compete stronger with Salesforce in this space. “If Adobe takes a stand on Marketo, it means they are serious about B2B and furthering the Microsoft-Adobe vs Salesforce-Google battle ahead,” he told TechCrunch. He’s referring to the deepening relationships between these companies.

Adobe reported its earnings last Thursday announcing $2.29 billion for the third quarter, which represented a 24 percent year over year increase and a new record for the company. While Adobe is well on its way to being a $10 billion company, the majority of its income continues to come from Creative Cloud, which includes Photoshop, InDesign and Illustrator, among other Adobe software stalwarts.

But for a long time, the company has wanted to be much more than a creative software company. It’s wanted a piece of the enterprise marketing pie. Up until now, that part of the company, which includes marketing and analytics software, has lagged well behind the Creative Cloud business. In its last report, Digital Experience revenue, which is where Adobe counts this revenue represented $614 million of total revenue. While it continues to grow, up 21 percent year over year, there is much greater potential here for more.

Adobe had less than $5 billion in cash after the Mageno acquisition, but it has seen its stock price rise dramatically in the last year rising from $149.96 last year at this time to $266.05 as of publication.

The acquisition comes as there is a lot of maneuvering going on this space and the various giant companies vie for market share. Today’s acquisition gives Adobe a huge boost and provides them with not only a missing piece, but Marketo’s base of 5000 customers and the opportunity to increase revenue in this part of their catalogue, while allowing them to compete harder inside the enterprise.

The deal is expected to close in Adobe’s 4th quarter. Marketo CEO Steve Lucas will join Adobe’s senior leadership team and report to Rencher.

It’s also worth noting that the announcement comes just days before Dreamforce, Salesforce’s massive customer conference will be taking place in San Francisco, and Microsoft will be holding its Ignite conference in Orlando. While the timing may be coincidental, it does end up stealing some of their competitors’ thunder.

Posted by Richy George on 20 September, 2018

Graph databases have always been useful to help find connections across a vast data set, and it turns out that capability is quite handy in artificial intelligence and machine learning too. Today, Neo4j, the makers of the open source and commercial graph database platform, announced the release of Neo4j 3.5, which has a number of new features aimed specifically at AI and machine learning.

Neo4j founder and CEO Emil Eifrem says he had recognized the connection between AI and machine learning and graph databases for awhile, but he says that it has taken some time for the market to catch up to the idea.

“There has been a lot momentum around AI and graphs…Graphs are very fundamental to AI. At the same time we were seeing some early use cases, but not really broad adoption, and that’s what we’re seeing right now,” he explained.

AI graph uses cases. Graphic: Neo4j

To help advance AI uses cases, today’s release includes a new full text search capability, which Eifrem says has been one of the most requested features. This is important because when you are making connections between entities, you have to be able to find all of the examples regardless of how it’s worded — for example, human versus humans versus people.

Part of that was building their own indexing engine to increase indexing speed, which becomes essential with ever more data to process. “Another really important piece of functionality is that we have improved our data ingestion very significantly. We have 5x end-to-end performance improvements when it comes to importing data. And this is really important for connected feature extraction, where obviously, you need a lot of data to be able to train the machine learning,” he said. That also means faster sorting of data too.

Other features in the new release include improvements to the company’s own Cypher database query language and better visualization of the graphs to give more visibility, which is useful for visualizing how machine learning algorithms work, which is known as AI explainability. They also announced support for the Go language and increased security.

Graph databases are growing increasingly important as we look to find connections between data. The most common use case is the knowledge graph, which is what lets us see connections in a huge data sets. Common examples include who we are connected to on a social network like Facebook, or if we bought one item, we might like similar items on an ecommerce site.

Other use cases include connected feature extraction, a common machine learning training techniques that can look at a lot of data and extract the connections, the context and the relationships for a particular piece of data, such as suspects in a criminal case and the people connected to them.

Neo4j has over 300 large enterprise customers including Adobe, Microsoft, Walmart, UBS and NASA. The company launched in 2007 and has raised $80 million. The last round was $36 million in November 2016.

Posted by Richy George on 20 September, 2018

MariaDB, the company behind the eponymous MySQL drop-in replacement database, today announced that it has acquired Clustrix, which itself is a MySQL drop-in replacement database, but with a focus on scalability. MariaDB will integrate Clustrix’s technology into its own database, which will allow it to offer its users a more scalable database service in the long run.

That by itself would be an interesting development for the popular open source database company. But there’s another angle to this story, too. In addition to the acquisition, MariaDB also today announced that cloud computing company ServiceNow is investing in MariaDB, an investment that helped it get to today’s acquisition. ServiceNow doesn’t typically make investments, though it has made a few acquisitions. It is a very large MariaDB user, though, and it’s exactly the kind of customer that will benefit from the Clustrix acquisition.

MariaDB CEO Michael Howard tells me that ServiceNow current supports about 80,000 instances of MariaDB. With this investment (which is actually an add-on to MariaDB’s 2017 Series C round), ServiceNow’s SVP of Development and Operations Pat Casey will join MariaDB’s board.

Why would MariaDB acquire a company like Clustrix, though? When I asked Howard about the motivation, he noted that he’s now seeing more companies like ServiceNow that are looking at a more scalable way to run MariaDB. Howard noted that it would take years to build a new database engine from the ground up.

“You can hire a lot of smart people individually, but not necessarily have that experience built into their profile,” he said. “So that was important and then to have a jumpstart in relation to this market opportunity — this mandate from our market. It typically takes about nine years, to get a brand new, thorough database technology off the ground. It’s not like a SaaS application where you can get a front-end going in about a year or so.

Howard also stressed that the fact that the teams at Clustrix and MariaDB share the same vocabulary, given that they both work on similar problems and aim to be compatible with MySQL, made this a good fit.

While integrating the Clustrix database technology into MariaDB won’t be trivial, Howard stressed that the database was always built to accommodate external database storage engines. MariaDB will have to make some changes to its APIs to be ready for the clustering features of Clustrix. “It’s not going to be a 1-2-3 effort,” he said. “It’s going to be a heavy-duty effort for us to do this right. But everyone on the team wants to do it because it’s good for the company and our customers.

MariaDB did not disclose the price of the acquisition. Since it was founded in 2006, though, the Y Combinator-incubated Clustrix had raised just under $72 million, though. MariaDB has raised just under $100 million so far, so it’s probably a fair guess that Clustrix didn’t necessarily sell for a large multiple of that.

Posted by Richy George on 19 September, 2018

After five months in public beta, Google today announced that its Cloud Memorystore for Redis, its fully managed in-memory data store, is now generally available.

The service, which is fully compatible with the Redis protocol, promises to offer sub-millisecond responses for applications that need to use in-memory caching. And because of its compatibility with Redis, developers should be able to easily migrate their applications to this service without making any code changes.

Cloud Memorystore offers two service tiers — a basic one for simple caching and a standard tier for users who need a highly available Redis instance. For the standard tier, Google offers a 99.9 percent availability SLA.

Since it first launched in beta, Google added a few additional capabilities to the service. You can now see your metrics in Stackdriver, for example. Google also added custom IAM roles and improved logging.

As for pricing, Google charges per GB-hour, depending on the service level and capacity you use. You can find the full pricing list here.

Posted by Richy George on 19 September, 2018

GitLab, the developer service that aims to offer a full lifecycle DevOps platform, today announced that it has raised a $100 million Series D funding round at a valuation of $1.1 billion. The round was led by Iconiq.

As GitLab CEO Sid Sijbrandij told me, this round, which brings the company’s total funding to $145.5 million, will help it enable its goal of reaching an IPO by November 2020.

According to Sijbrandij, GitLab’s original plan was to raise a new funding round at a valuation over $1 billion early next year. But since Iconiq came along with an offer that pretty much matched what the company set out to achieve in a few months anyway, the team decided to go ahead and raise the round now. Unsurprisingly, Microsoft’s acquisition of GitHub earlier this year helped to accelerate those plans, too.

“We weren’t planning on fundraising actually. I did block off some time in my calendar next year, starting from February 25th to do the next fundraise,” Sijbrandij said. “Our plan is to IPO in November of 2020 and we anticipated one more fundraise. I think in the current climate, where the macroeconomics are really good and GitHub got acquired, people are seeing that there’s one independent company, one startup left basically in this space. And we saw an opportunity to become best in class in a lot of categories.”

As Sijbrandij stressed, while most people still look at GitLab as a GitHub and Bitbucket competitor (and given the similarity in their names, who wouldn’t?), GitLab’s wants to be far more than that. It now offers products in nine categories and also sees itself as competing with the likes of VersionOne, Jira, Jenkins, Artifactory, Electric Cloud, Puppet, New Relic, and BlackDuck.

“The biggest misunderstanding we’re seeing is that GitLab is an alternative to GitHub and we’ve grown beyond that,” he said. “We are now in nine categories all the way from planning to monitoring.”

Sijbrandij notes that there’s a billion-dollar player in every space that GitLab competes it. “But we want to be better,” he said. “And that’s only possible because we are open core, so people co-create these products with us. That being said, there’s still a lot of work on our side, helping to get those contributions over the finish line, making sure performance and quality stay up, establish a consistent user interface. These are things that typically don’t come from the wider community and with this fundraise of $100 million, we will be able to make sure we can sustain that effort in all the different product categories.”

Given this focus, GitLab will invest most of the funding in its engineering efforts to build out its existing products but also to launch new ones. The company plans to launch new features like tracing and log aggregation, for example.

With this very public commitment to an IPO, GitLab is also signaling that it plans to stay independent. That’s very much Sijbrandij’s plan, at least, though he admitted that “there’s always a price” if somebody came along and wanted to acquire the company. He did note that he likes the transparency that comes with being a public company.

“We always managed to be more bullish about the company than the rest of the world,” he said. “But the rest of the world is starting to catch up. This fundraise is a statement that we now have the money to become a public company where we’re not we’re not interested in being acquired. That is what we’re setting out to do.”

Posted by Richy George on 19 September, 2018

Salespeople usually spend their days talking. They are on the phone and in meetings, but when it comes to updating Salesforce, they are back at the keyboard again typing notes and milestones, or searching for metrics about their performance. Today, Salesforce decided to change that by introducing Einstein Voice, a bit of AI magic that allows salespeople to talk to the program instead of typing.

In a world where Amazon Alexa and Siri make talking to our devices more commonplace in our non-work lives, it makes sense that companies are trying to bring that same kind of interaction to work.

In this case, you can conversationally enter information about a meeting, get daily briefings about key information on your day’s meetings (particularly nice for salespeople who spend their day in the car) and interact with Salesforce data dashboards by asking questions instead of typing queries.

All of these tools are designed to make life easier for busy salespeople. Most hate doing the administrative part of their jobs because if they are entering information, even if it will benefit them having a record in the long run, they are not doing their primary job, which is selling stuff.

For the meetings notes part, instead of typing on a smartphone, which can be a challenge anyway, you simply touch Meeting Debrief in the Einstein Voice mobile tool and start talking to enter your notes. The tool interprets what you’re saying. As with most transcription services, this is probably not perfect and will require some correcting, but should get you most of the way there.

It can also pick out key data like dates and deal amounts and let you set action items to follow up on.

Gif: Salesforce

Brent Leary, who is the founder and principal analyst at CRM Essentials says this is a natural progression for Salesforce as people get more comfortable using voice interfaces. “I think this will make voice-first devices and assistants as important pieces to the CRM puzzle from both a customer experience and an employee productivity perspective,” he told TechCrunch.

It’s worth pointing out that Tact.AI has been doing this for some time on top of Salesforce giving this type of voice interaction for Salesforce users. It’s likely ahead of Salesforce at this point, but Leary believes having Salesforce enter the voice arena will probably benefit the startup more than hurt it.

“The Salesforce tide will lift all boats, and companies like Tact will see their profile increased significantly because while Salesforce is the leader in the category, it’s share of the market is still less than 20% of the market,” he pointed out.

Einstein is Salesforce’s catch-all brand for its artificial intelligence layer. In this case it’s using natural language processing, voice recognition technology and other artificial intelligence pieces to interpret the person’s voice and transcribe what they are saying or understand their request better.

Typically, Salesforce starts with a small set of functionality and the builds on that over time. That’s very likely what they are doing here, coming out with a product announcement in time for Dreamforce, their massive customer conference next week,

Posted by Richy George on 19 September, 2018

Leena AI, a recent Y Combinator graduate focusing on HR chatbots to help employees answer questions like how much vacation time they have left, announced a $2 million seed round today from a variety of investors.

Company co-founder and CEO Adit Jain says the seed money is about scaling the company and gaining customers. They hope to have 50 enterprise customers within the next 12-18 months. They currently have 16.

We wrote about the company in June when it was part of the Y Combinator Summer 2018 class. At the time Jain explained that they began in 2015 in India as a company called Chatteron. The original idea was to help others build chatbots, but like many startups, they realized there was a need not being addressed, in this case around HR, and they started Leena AI last year to focus specifically on that.

As they delved deeper into the HR problem, they found most employees had trouble getting answers to basic questions like how much vacation time they had or how to get a new baby on their health insurance. This forced a call to a help desk when the information was available online, but not always easy to find.

Jain pointed out that most HR policies are defined in policy documents, but employees don’t always know where they are. They felt a chatbot would be a good way to solve this problem and save a lot of time searching or calling for answers that should be easily found. What’s more, they learned that the vast majority of questions are fairly common and therefore easier for a system to learn.

Employees can access the Leena chatbot in Slack, Workplace by Facebook, Outlook, Skype for Business, Microsoft Teams and Cisco Spark. They also offer Web and mobile access to their service independent of these other tools.

Photo: Leena AI

What’s more, since most companies use a common set of backend HR systems like those from Oracle, SAP and NetSuite (also owned by Oracle), they have been able to build a set of standard integrators that are available out of the box with their solution.

The customer provides Leena with a handbook or a set of policy documents and they put their machine learning to work on that. Jain says, armed with this information, they can convert these documents into a structured set of questions and answers and feed that to the chatbot. They apply Natural Language Processing (NLP) to understand the question being asked and provide the correct answer.

They see room to move beyond HR and expand into other departments such as sales or customer service that could also take advantage of bots to answer a set of common questions. For now, as a recent YC graduate, they have their first bit of significant funding and they will concentrate on building HR chatbots and see where that takes them.

Posted by Richy George on 19 September, 2018

IBM has launched a software service that scans AI systems as they work in order to detect bias and provide explanations for the automated decisions being made — a degree of transparency that may be necessary for compliance purposes not just a company’s own due diligence.

The new trust and transparency system runs on the IBM cloud and works with models built from what IBM bills as a wide variety of popular machine learning frameworks and AI-build environments — including its own Watson tech, as well as Tensorflow, SparkML, AWS SageMaker, and AzureML.

It says the service can be customized to specific organizational needs via programming to take account of the “unique decision factors of any business workflow”.

The fully automated SaaS explains decision-making and detects bias in AI models at runtime — so as decisions are being made — which means it’s capturing “potentially unfair outcomes as they occur”, as IBM puts it.

It will also automatically recommend data to add to the model to help mitigate any bias that has been detected.

Explanations of AI decisions include showing which factors weighted the decision in one direction vs another; the confidence in the recommendation; and the factors behind that confidence.

IBM also says the software keeps records of the AI model’s accuracy, performance and fairness, along with the lineage of the AI systems — meaning they can be “easily traced and recalled for customer service, regulatory or compliance reasons”.

For one example on the compliance front, the EU’s GDPR privacy framework references automated decision making, and includes a right for people to be given detailed explanations of how algorithms work in certain scenarios — meaning businesses may need to be able to audit their AIs.

The IBM AI scanner tool provides a breakdown of automated decisions via visual dashboards — an approach it bills as reducing dependency on “specialized AI skills”.

However it is also intending its own professional services staff to work with businesses to use the new software service. So it will be both selling AI, ‘a fix’ for AI’s imperfections, and experts to help smooth any wrinkles when enterprises are trying to fix their AIs… Which suggests that while AI will indeed remove some jobs, automation will be busy creating other types of work.

Nor is IBM the first professional services firm to spot a business opportunity around AI bias. A few months ago Accenture outed a fairness tool for identifying and fixing unfair AIs.

So with a major push towards automation across multiple industries there also looks to be a pretty sizeable scramble to set up and sell services to patch any problems that arise as a result of increasing use of AI.

And, indeed, to encourage more businesses to feel confident about jumping in and automating more. (On that front IBM cites research it conducted which found that while 82% of enterprises are considering AI deployments, 60% fear liability issues and 63% lack the in-house talent to confidently manage the technology.)

In additional to launching its own (paid for) AI auditing tool, IBM says its research division will be open sourcing an AI bias detection and mitigation toolkit — with the aim of encouraging “global collaboration around addressing bias in AI”.

“IBM led the industry in establishing trust and transparency principles for the development of new AI technologies. It’s time to translate principles into practice,” said David Kenny, SVP of cognitive solutions at IBM, commenting in a statement. “We are giving new transparency and control to the businesses who use AI and face the most potential risk from any flawed decision making.”

Posted by Richy George on 18 September, 2018

At an event in Tokyo, Google today announced the launch of Work Insights, a new tool that gives businesses more insights into how their employees use the company’s G Suite productivity tools and how teams collaborate using those tools.

In addition, Google is also launching its investigation tool for helping business better secure their data in G Suite into general availability.

“Work Insights is a tool built specifically to help businesses measure and understand the impact of digital transformation within their organizations, driven by G Suite,” Reena Nadkarni, a group product manager for G Suite, explains in today’s announcement. Data is aggregated at the team level (where a team needs to have 10 people or more) to help businesses understand how their employees are adapting G Suite apps.

As enterprises bet on one vendor or the other, there’s always a bit of a transition period and not everybody makes the move quite as quickly as others. Most of these tools, though, only really work when the whole company adopts them. That’s especially true for communication tools like Slack, Hangouts Chat/Meet or Microsoft Teams, but also for productivity tools like G Suite.

The other use cases here, though, is actually far more interesting. Work Insights will also give companies a view of how users on different teams interact with each other (think the marketing and sales teams). If they are working on documents together, then they are probably working well together, too (or just leaving acerbic comments on marketing presentations, but you get the general idea here).

“This insight can help executives identify opportunities to strengthen collaboration and reduce siloes,” Nadkarni writes. Since few executives ever say that they want less collaboration and more siloes, chances are we’ll see quite a few companies adopt these tools.

Posted by Richy George on 18 September, 2018

Like virtually every other major tech company, Microsoft is currently on a mission to bring machine learning to all of its applications. It’s no surprise then that it’s also bringing ‘AI’ to its highly profitable Dynamics 365 CRM products. A year ago, the company introduced its first Dynamics 365 AI solutions and today it’s expanding this portfolio with the launch of three new products: Dynamics 365 AI for Sales, Customer Service and Market Insights.

“Many people, when they talk about CRM, or ERP of old, they referred to them as systems of oppression, they captured data,” said Alysa Taylor, Microsoft corporate VP for business applications and industry. “But they didn’t provide any value back to the end user — and what that end user really needs is a system of empowerment, not oppression.”

It’s no secret that few people love their CRM systems (except for maybe a handful of Dreamforce attendees), but ‘system of oppression’ is far from the ideal choice of words here. Yet Taylor is right that early systems often kept data siloed. Unsurprisingly, Microsoft argues that Dynamics 365 does not do that, allowing it to now use all of this data to build machine learning-driven experiences for specific tasks.

Dynamics 365 AI for Sales, unsurprisingly, is meant to help sales teams get deeper insights into their prospects using sentiment analysis. That’s obviously among the most basic of machine learning applications these days, but AI for Sales also helps these salespeople understand what actions they should take next and which prospects to prioritize. It’ll also help managers coach their individual sellers on the actions they should take.

Similarly, the Customer Service app focuses on using natural language understanding to understand and predict customer service problems and leverage virtual agents to lower costs. Taylor used this part of the announcement to throw some shade at Microsoft’s competitor Salesforce. “Many, many vendors offer this, but they offer it in a way that is very cumbersome for organizations to adopt,” she said. “Again, it requires a large services engagement, Salesforce partners with IBM Watson to be able to deliver on this. We are now out of the box.”

Finally, Dynamics 365 AI for Market Insights does just what the name implies: it provides teams with data about social sentiment, but this, too, goes a bit deeper. “This allows organizations to harness the vast amounts of social sentiment, be able to analyze it, and then take action on how to use these insights to increase brand loyalty, as well as understand what newsworthy events will help provide different brand affinities across an organization,” Taylor said. So the next time you see a company try to gin up some news, maybe it did so based on recommendations from Office 365 AI for Market Insights.