Google Next - Day 1 Summary

Alan Lepofsky

Vice President & Principal Analyst
Constellation Research

Alan Lepofsky - VP and Principal Analyst, Constellation Research. With almost two decades of experience in the collaboration software industry, Lepofsky helps organizations improve the way their employees work together to get their jobs done. Lepofsky’s primary research area The Future of Work, focuses on: integrating collaboration and business processes (Purposeful Collaboration), structuring work via Social Task Management, leveraging analytics and digital assistants to work more productively, the strategic impact of mobile computing on business transformation, and measuring workforce culture based on Digital Proficiency instead of age. Twitter: @alanlepoLinkedIn: https://ca.linkedin.com/in/alanlepo     , Title: Big IdeasIntelligent Collaboration: The Intersection of…...

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Down Report - Human error takes AWS S3 down in US-EAST-1 - and it is felt - 3.8 Cloud Load Toads

Holger Mueller

Vice President and Principal Analyst
Constellation Research

Holger Mueller is VP and Principal Analyst for Constellation Research for the fundamental enablers of the cloud, IaaS, PaaS and next generation Applications, with forays up the tech stack into BigData and Analytics, HR Tech, and sometimes SaaS. Holger provides strategy and counsel to key clients, including Chief Information Officers, Chief Technology Officers, Chief Product Officers, Chief HR Officers, investment analysts, venture capitalists, sell-side firms, and technology buyers.  Coverage Areas: Future of Work Tech Optimization & Innovation  Background: Before joining Constellation Research, Mueller was VP of Products for NorthgateArinso, a KKR company. There, he led the transformation of products to the cloud and laid the foundation for new Business Process as a…...

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The Cloud / IaaS industry has grown rapidly in the last years, and providers have been solidifying their systems over the years. Outages are always unfortunate and by large the cloud has shown that it is more resilient than pretty much any on premises computing setup. Nonetheless outages happen – and we are adding a new blog post type for these events – the “Down Report” – where we plan to dissect and rate what has gone wrong, and especially focus on the lessons learnt for the provider affected, the industry, but most importantly for their customers. 

 

To make the effort a little more fun – we assign ‘Cloud Load Toads” to the overall event and each circumstance. We mean no disrespect to the ‘load toads’ that work valiantly in the worlds air forces, but liked the suggestion of our colleague Alan Lepofsky (@alanlepo), who came up with the term ‘Cloud Load Toad”.

 

On the ‘Cloud Load Toad’ scala that goes from 1 (bad but ok, can happen) to 5 (very bad, should never ever happen) we rate the severity off the event overall and the events that lead to it.

AWS S3 Down in US-EAST-1

First of all, kudos to AWS, who published the post mortem post (see here) in about 48 hour past the event, faster than usual, judging from other downtime events in the past. But then each cloud outage is different, the root cause – manual error – is easier to establish than e.g. trouble shooting a battery fire, that destroys its very evidence (think Samsung).

But let’s dissect the post mortem report:

We’d like to give you some additional information about the service disruption that occurred in the Northern Virginia (US-EAST-1) Region on the morning of February 28th. The Amazon Simple Storage Service (S3) team was debugging an issue causing the S3 billing system to progress more slowly than expected.

MyPOV – Certainly production and billing systems need to be connected, and in many scenarios the production system can create issues with the load triggered for the billing system. But a production system should never be able to be stopped by an administrative system, like a billing system. Production should be kept running, billing can be worried about later. It is likely that the S3 billing system (my speculation) is using S3, too – creating a potential recursive dependency. Needless to say – these systems should be isolated. 

Rating: 3 Cloud Load Toads

 

 

At 9:37AM PST, an authorized S3 team member using an established playbook executed a command which was intended to remove a small number of servers for one of the S3 subsystems that is used by the S3 billing process.

MyPOV – Related to above, obvious that the billing system is also using S3 now. Good to drink your own champagne, but when it goes bad because of a mistake by the champagne maker – never good and not only the customers but the champagne maker gets food poisoning – not what you want to have happen. But humans can make mistakes.
 

Unfortunately, one of the inputs to the command was entered incorrectly and a larger set of servers was removed than intended. The servers that were inadvertently removed supported two other S3 subsystems. One of these subsystems, the index subsystem, manages the metadata and location information of all S3 objects in the region. This subsystem is necessary to serve all GET, LIST, PUT, and DELETE requests. The second subsystem, the placement subsystem, manages allocation of new storage and requires the index subsystem to be functioning properly to correctly operate. The placement subsystem is used during PUT requests to allocate storage for new objects. Removing a significant portion of the capacity caused each of these systems to require a full restart. While these subsystems were being restarted, S3 was unable to service requests.

MyPOV – Kudos to AWS for transparency. But any attendee to its reInvent user conference knows how much the vendor prides itself of not letting humans make mistakes, but putting key / vital processes into code. Certainly, the approach and philosophy wasn’t followed here. Would be good to chat with AWS CTO Werner Vogels about this one… I am sure that enough people in Seattle are pondering that in the future typos, manual human error should not take systems down. Of course, we still need a kill switch for the humans… 

Rating: 4 Cloud Load Toads.
 

 

Other AWS services in the US-EAST-1 Region that rely on S3 for storage, including the S3 console, Amazon Elastic Compute Cloud (EC2) new instance launches, Amazon Elastic Block Store (EBS) volumes (when data was needed from a S3 snapshot), and AWS Lambda were also impacted while the S3 APIs were unavailable.

MyPOV – AWS suggests to write critical processes to span across regions. Its own website – amazon.com and subsidiary zappos.com did not go down, and were probably coded correctly. The question is (and sorry if I have not read the fine print) – could an AWS client still use the US-EAST-1 services like EC2, EBS, AWS Lambda etc. if pointed to other S3 stores – or does an S3 failure take the whole region out? This is a deeply critical issue for any IaaS techstack in a IaaS data center. So, did customers have a chance here? A question to follow up with AWS. Not Rated.


 

S3 subsystems are designed to support the removal or failure of significant capacity with little or no customer impact. We build our systems with the assumption that things will occasionally fail, and we rely on the ability to remove and replace capacity as one of our core operational processes. While this is an operation that we have relied on to maintain our systems since the launch of S3, we have not completely restarted the index subsystem or the placement subsystem in our larger regions for many years. S3 has experienced massive growth over the last several years and the process of restarting these services and running the necessary safety checks to validate the integrity of the metadata took longer than expected. The index subsystem was the first of the two affected subsystems that needed to be restarted. By 12:26PM PST, the index subsystem had activated enough capacity to begin servicing S3 GET, LIST, and DELETE requests. By 1:18PM PST, the index subsystem was fully recovered and GET, LIST, and DELETE APIs were functioning normally. The S3 PUT API also required the placement subsystem. The placement subsystem began recovery when the index subsystem was functional and finished recovery at 1:54PM PST. At this point, S3 was operating normally. Other AWS services that were impacted by this event began recovering. Some of these services had accumulated a backlog of work during the S3 disruption and required additional time to fully recover.

MyPOV – AWS describes well that things break all the time, and they can even go down. But IaaS providers need to be certain they can come back up, and part of that coming back is also to understand how long it will take to come back up. S3 has been very popular, so the harder to take it down, test (or simulate) time for it to come back, but certainly something AWS could and should have done and known. When you run IT, and don’t know when a system that is down will come back up more or less for sure, the IT professionals are in a bad spot. 

 

 

Rating: 4 Cloud Load Toads
 

 

We are making several changes as a result of this operational event. While removal of capacity is a key operational practice, in this instance, the tool used allowed too much capacity to be removed too quickly. We have modified this tool to remove capacity more slowly and added safeguards to prevent capacity from being removed when it will take any subsystem below its minimum required capacity level. This will prevent an incorrect input from triggering a similar event in the future.

MyPOV – This section read like there was a software tool – but it malfunctioned. That of course is not good. Granted hard to simulate and test with systems of this scale – but not a good enough answer. 

Rating: 3 Cloud Load Toads

 

We are also auditing our other operational tools to ensure we have similar safety checks. We will also make changes to improve the recovery time of key S3 subsystems. We employ multiple techniques to allow our services to recover from any failure quickly. One of the most important involves breaking services into small partitions which we call cells. By factoring services into cells, engineering teams can assess and thoroughly test recovery processes of even the largest service or subsystem. As S3 has scaled, the team has done considerable work to refactor parts of the service into smaller cells to reduce blast radius and improve recovery. During this event, the recovery time of the index subsystem still took longer than we expected. The S3 team had planned further partitioning of the index subsystem later this year. We are reprioritizing that work to begin immediately.

MyPOV – Kudos to AWS for transparency, explaining that it has a solution and committing to get better going forward. School book response that all vendors with an outage should share – not all have.


 

From the beginning of this event until 11:37AM PST, we were unable to update the individual services’ status on the AWS Service Health Dashboard (SHD) because of a dependency the SHD administration console has on Amazon S3. Instead, we used the AWS Twitter feed (@AWSCloud) and SHD banner text to communicate status until we were able to update the individual services’ status on the SHD. We understand that the SHD provides important visibility to our customers during operational events and we have changed the SHD administration console to run across multiple AWS regions.

MyPOV – This is probably the worst finding, a too optimistic implementation of the key dashboard on AWS overall status. It should never have a single point of failure, but yet we see this happening over and over in outages. Vendors need to learn not to rely on their services to communicate with clients in an outage situation – as they may not be able to respond, a cardinal mistake (see e.g. for another outage issue here)... but yet vendors keep doing so. 

Rating: 5 Cloud Load Toads

 

 

Finally, we want to apologize for the impact this event caused for our customers. While we are proud of our long track record of availability with Amazon S3, we know how critical this service is to our customers, their applications and end users, and their businesses. We will do everything we can to learn from this event and use it to improve our availability even further.

MyPOV – Kudos for acknowledging and owning the issue. No blame game and scape goating (that is often seen here too, the most common scape goat being the network / network provider).
 

A pretty severe event

When doing the tally across the cloud load toads, assuming I did the math right - then I count 19 total toads, across 5 events - bringing the event to 3.8 cloud load toads. I am sure AWS will be the first to agree that this wasn't an insignificant event. But let's look at the lessons learnt. But customers could have coded their loads to avoid the down time.

 

 

Lessons for IaaS Customers

Here are the key aspects for customers to learn from the AWS S3 outage:

Have you built for resilience? Sure, it costs, but all major IaaS providers offer strategies on how to avoid single location / data center failures. Way too many prominent internet properties did not chose to do so – so if ‘born on the web’ properties miss this – its key to check regular enterprises do not miss this. Uptime has a price, make it a rational decision, now is a good time to get budget / investment approved, when warranted and needed.

Ask your IaaS vendor a few questions: Enterprises should not be shy to ask IaaS providers if they have done a few things:

• Do your run your systems by hand or with software
   
• Could the same issue that happened with AWS S3 in US-EAST-1 happen to you?
   
• How do you test your operational software?
   
• When have you taken your most popular services down last time?
   
• What is the expected up time of your most popular services?
   
• When did your produce that test of expected up time last and how has the system usage increased since then?
   
• How can we code for resilience – and what does it cost?
   
• What kind of renumeration / payment / cost relief can be expected with a downtime?
   
• What single point of failure should we be aware of?
   
• How are your operation consoles built?
   
• How do you communicate in a downtime situation with customers?
   
• How often and when do you refresh your older datacenters, servers?
   
• How often have your reviewed and improved your operational procedures in the last 12 months? Give us a few examples how you have increased resilience.

And some key internal questions, customers of IaaS vendors have to ask themselves:

• What are your customer / employee communication tools?
   
• When your IaaS vendor goes down, so may your customer and employee facing apps. How do you communicate then?
   
• Make sure to learn from AWS mistake – do not rely on the same point of failure / architecture as the production systems – as it will not be available. Simple, but always good to check and better even monitor. 

 

MyPOV

Outages are always unfortunate. The key thing is to learn from them, knowing AWS they will be ruthless to address issues (and hopefully update customers and analysts on status progress). Kudos for a fast past mortem, taking responsibility and sharing first strategies to avoid another occurrence.

On the concern side AWS needs to ask itself how it recycles and reviews architecture and servers. US-EAST is a behemoth that is nonetheless popular, but may need more rejuvenation than AWS may expect / have planned. In the cloud location monopoly race it is possible that vendors might stretch aging infrastructure beyond the breaking point. Of course, afterwards it is easy to armchair everything, but this remains an area to watch.

Overall hopefully plenty of lessons learnt all around, for AWS, other IaaS providers and customers.

2012, 2013, 2014, 2015, 2016 and 2017 (C) Holger Mueller - All Rights Reserved

Google Cloud Invests In Data Services and ML/AI, Scales Business

Doug Henschen

Former Vice President and Principal Analyst
Constellation Research

Doug Henschen is former Vice President and Principal Analyst where he focused on data-driven decision making. Henschen’s Data-to-Decisions research examines how organizations employ data analysis to reimagine their business models and gain a deeper understanding of their customers. Henschen's research acknowledges the fact that innovative applications of data analysis requires a multi-disciplinary approach starting with information and orchestration technologies, continuing through business intelligence, data-visualization, and analytics, and moving into NoSQL and big-data analysis, third-party data enrichment, and decision-management technologies. Insight-driven business models are of interest to the entire C-suite, but most particularly chief executive officers, chief digital officers,…...

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Google Cloud is adding must-have enterprise features and scaling the business to meet data platform, machine learning and AI demand. Here’s a progress report.

Google’s reputation for big data, machine learning and artificial intelligence innovation is richly deserved. The challenge for Google Cloud – which exposes that innovation to enterprises through the Google Cloud Platform (GCP) – isn’t scaling the technology so much as scaling the business.

Last week’s Google Cloud Next '17 event in San Francisco was a case in point. The event was sold out, with more than 10,000 attendees packing the Moscone Center’s West hall. Many conference sessions were overbooked and had long standby lines. You got the feeling Google could have easily filled another massive conference hall.

Google Cloud Next 17 Recap from Constellation Research on Vimeo.

So what is Google Cloud doing to meet fast-growing demand? For starters, parent company Alphabet Inc. is investing big money in the business — more than $30 billion, according to Chairman Eric Schmidt, a keynoter at the event. But Google Cloud is not only hiring on all fronts and expanding the ecosystem, it’s also doubling down on tech investments in data centers and network improvements; new big data, machine learning (ML) and artificial intelligence (AI) services; and enterprise-oriented migration, administrative, governance and security features. Here’s a closer look at the business- and data-tech-oriented announcements.

You may get started on a public cloud through clicks and credit card payments, but it takes lots of people-centric interaction and hand holding to move an entire business into the cloud as scale. That’s why Google Cloud saw the largest headcount increase of any Alphabet business in 2016, and it expects to add at least 1,000 more salespeople in the first six months of this year. What’s more, Google Cloud’s professional services team has seen 5X growth over the last year, and partnerships with systems integrators and resellers are quickly multiplying.

At Google Cloud Next, Diane Greene, senior VP of Google Cloud, announced a significant new partnership with SAP, adding to a growing list of enterprise software partners. SAP executive board member Bernd Leukert joined Greene on stage to highlight certification of SAP Hana as well as plans for joint work on enterprise-grade data governance and compliance in the cloud. The more such partnerships Google can forge with blue-chip enterprise software vendors, the better.

Of course, the big draw for many companies to Google is its expertise in ML and AI. This point was validated onstage at Google Next by C-level customer guests from Colgate-Palmolive, Disney, Home Depot, HSBC and Verizon. And bolstering the company’s case, Google Cloud Chief Scientist Fei-Fei Li announced the acquisition of Kaggle, a company that has been a magnet to more than 850,000 data scientists through its famed Kaggle competitions.

Bringing Kaggle inside the Google Cloud ecosystem “combines the world’s largest data science community with the world’s most powerful machine learning cloud,” wrote Kaggle CEO Anthony Goldbloom. It also gives Kaggle and all those data scientists access to (and, it’s hoped, familiarity and comfort using) Google Cloud infrastructure, scalable training and deployment services and the ability to store and query large data sets.

MyPOV on business-side investments: Google Cloud clearly has big momentum. But whatever it’s investing in people and customer support, the company could probably double the effort and still not match the scale of its chief rival, Amazon Web Services (AWS). Amazon does not break out AWS headcount from its far more labor-intensive retail business, so it’s not an apples-to-apples comparison, but Amazon is many times larger than Google, with nearly 350,000 employees and plans to hire more than 100,000 more full-time employees over the next 18 months.

Suffice it to say that Google Cloud needs to stay aggressive on workforce expansion. My sense is that it’s growing as fast as it can without creating the sort of internal chaos that could negatively impact customer experience. Partnerships with systems integrators and high-scale vendors like SAP and acquisitions such as the Kaggle deal are smart ways to grow the ecosystem without putting all the pressure on internal development.

Data-Platform Investments  

Google is investing on many technology fronts, but my focus is on data platforms, ML and AI, so I’m not going to get into the details of the three new data center regions, app developer news or the G Suite announcements (see Alan Lepofsky’s blog). There were also many infrastructure and security announcements, but the one most relevant to my data-to-decisions research is the new Data Loss Prevention (DLP) API. Now in beta, the DLP API promises to automatically discover, classify and redact sensitive information such as Social Security numbers, credit card numbers, phone numbers and more.

Together with security key and encryption developments, Google is addressing cloud security concerns that persist no matter how many times actual breaches show conventional corporate data centers to be far more vulnerable than clouds.

Google Cloud’s Data Loss Prevention API, now in beta, is designed to automatically discover, classify and redact sensitive data, as shown above.

As for those data platform, ML and AI announcements, here’s a rundown of the highlights:

Cloud ML Engine goes GA. This managed machine learning service based on Google’s open sourced TensorFlow ML framework is now generally available. The service features automatic hyperparameter tuning and tools for job management and resource utilization. In beta are GPU-based training and online prediction, which promise state-of-the-art performance at scale.

Cloud Video Intelligence API. Now in private beta, this service is designed to search and automatically tag large libraries of videos. The service spots entities within videos and notes the timecode of their appearance, making sense of videos and large collections of videos in automated fashion. Developers need no knowledge of machine learning or deep learning, says Google. They simply invoke the API.

Cloud Dataprep. This service, now in beta, promises to bring self-service data preparation to structured and unstructured information. The no-code, point-and-click interface provides a guided approach to joining data and automated data-transformation suggestions.

Google BigQuery Data Transfer Service. Also in beta, this managed data-import service will launch with support for moving data from DoubleClick, Google AdWords and YouTube, but Google Cloud execs say they’re keen on providing managed, automated migration options from other high-volume data sources. Stay tuned.

Cloud SQL for PostgreSQL. This managed service, now in beta, supports the popular, enterprise-standard, open-source relational database, opening the door for a world of compatible enterprise applications.   

MyPOV on the tech announcements: Google Next announcements were heavy on betas and light on general releases, although the company less than a month ago announced the GA of its Cloud Spanner service. Spanner is Google’s globally distributed relational database service. It’s unique (and distinguished from global NoSQL database services) in offering consistency for demanding financial services, advertising, retail, supply chain and other applications demanding synchronous replication.

Aside from its big data, ML and AI depth, another differentiator for Google Cloud is the open-source nature of Google Cloud Platform services. Cloud Dataproc, for example, is based on Apache Spark and Apache Hadoop, and the batch-and-stream-data processing capabilities of Cloud Dataflow were open sourced as Apache Beam. This means you could run all these technologies on premises, in other clouds or in hybrid scenarios, diminishing vendor lock-in. It’s a contrast with AWS services that are unique to that vendor, although I don’t recall any customers discussing actual hybrid deployments or moving between cloud and on-premises deployments.

The more practical and real differentiator with Google Cloud Platform – and the one that multiple customer at Google Cloud Next actually talked about — is the managed nature of its services. With BigQuery, for example, you load data and issue queries and Google automatically makes all the necessary moves to ensure adequate compute and storage capacity. With unmanaged cloud services you have to think about, plan and take care of all the provisioning. And if you get it wrong, you either overpay for capacity or suffer from poor performance. This has been Google Cloud’s ace in the hole, but it’s a card the vendor is now playing up with every customer and at every opportunity.

Related Reading:
Cloud BI and Analytics Options Aren’t Just for Cloud Data
AWS Analytic and AI Services Are No Surprise, But They Will Succeed
Strata + Hadoop World Highlights Long-Term Bets on Cloud

Google Next: Analysis of G Suite News

Alan Lepofsky

Vice President & Principal Analyst
Constellation Research

Alan Lepofsky - VP and Principal Analyst, Constellation Research. With almost two decades of experience in the collaboration software industry, Lepofsky helps organizations improve the way their employees work together to get their jobs done. Lepofsky’s primary research area The Future of Work, focuses on: integrating collaboration and business processes (Purposeful Collaboration), structuring work via Social Task Management, leveraging analytics and digital assistants to work more productively, the strategic impact of mobile computing on business transformation, and measuring workforce culture based on Digital Proficiency instead of age. Twitter: @alanlepoLinkedIn: https://ca.linkedin.com/in/alanlepo     , Title: Big IdeasIntelligent Collaboration: The Intersection of…...

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March 8-10 Google held their Google Next conference, where they shared news about Google Cloud, G Suite and more. My primary focus for the conference was G Suite (formerly known as Google Apps), Google's personal productivity and team collaboration platform. My colleagues Doug Henschen and Holger Mueller focused on the Google Cloud Platform (GCP) side, and you can see their reviews here and here. 

My Quick Take: After several years of working on architecture improvements for Drive and Hangouts, G Suite is doing several things right for their enterprise customers. However, given Google's past reputation for innovation, I'm disappointed that most of the announcements were catch-up to features/applications that other vendors are already doing in this space.

Below is a video in which I provide my full review and analysis of the G Suite news. I'm using the Acrossio video player, which allows you to jump back and forth to annotated moments of the video, as well as add your own annotations and comments. So if you're just interested in a specific section, find it in the conversation stream on the right side of the player, click and the video will start playing from there.

There were several announcements which you can read about here, including:

• Team Drives - Shared team folders
• Drive File Stream - files hosted in the cloud appear in Windows File Manager or Mac Finder as if they were local on your harddrive. (Early Adopter Program for G Suite Enterprise, Business and Education customers)
• Vault for Drive - Audit, compliance, governance.  (DLP was announced earlier in Jan)
• Acquisition of AppBridge - for migrating content to G Suite
• Hangouts Meet - video / webconferecing
• Hangouts Chat - 1:1 and persistent group chat rooms. Available to G Suite Early Adopter customers.
• General Availability of Jamboard - white-boarding device and applications
• Gmail Add-ons - A new integration platform for Gmail that works across web and mobile devices (in Developer Preview)

Is your organization using G Suite? If so, what do you think of these announcements? If not, what are you using and how do you think it compares to G Suite?

Salesforce Einstein Rolls Out; IBM Watson Awaits

Doug Henschen

Former Vice President and Principal Analyst
Constellation Research

Doug Henschen is former Vice President and Principal Analyst where he focused on data-driven decision making. Henschen’s Data-to-Decisions research examines how organizations employ data analysis to reimagine their business models and gain a deeper understanding of their customers. Henschen's research acknowledges the fact that innovative applications of data analysis requires a multi-disciplinary approach starting with information and orchestration technologies, continuing through business intelligence, data-visualization, and analytics, and moving into NoSQL and big-data analysis, third-party data enrichment, and decision-management technologies. Insight-driven business models are of interest to the entire C-suite, but most particularly chief executive officers, chief digital officers,…...

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Salesforce this week detailed progress on Einstein artificial intelligence. Less clear was how a new IBM partnership will change Salesforce AI plans. Here’s what's behind the headlines.

What’s the state of Salesforce Einstein, the portfolio of artificial intelligence (AI) capabilities announced last fall? Salesforce generated plenty of media coverage this week with two big announcements. Here’s my take on the realities of a partnership with IBM and what we have yet to understand about the packaging and pricing of Einstein.

Watson Meets Einstein?

The first announcement from Salesforce this week was the unexpected bombshell on a new partnership with IBM that headlines simplified as "Watson meets Einstein." As described by Salesforce CEO Marc Benioff in this CNBC appearance, one key benefit for Salesforce, and perhaps the first and easiest to implement, will be access to IBM cloud-accessible data from the Weather Company and other high-scale sources.

Data is a key ingredient of accurate AI, and in the wake of Microsoft’s acquisition of LinkedIN last year – a real competitive threat -- Salesforce can only benefit from significant data partnerships. Benioff cited a scenario in which weather data could be used by insurers to alert policy holders in the path of a predicted hail storm to park their cars indoors.

Benioff also noted that IBM offers cloud compute capacity and systems integration muscle. Here’s where joint IBM-Salesforce customers are most likely to benefit. IBM amped up its focus on CRM generally and Salesforce specifically last year by acquiring long-time Salesforce partner and systems integrator Bluewolf. And in yet another benefit to Salesforce, IBM committed to using the Salesforce Service Cloud internally. Given the scale of IBM’s 380,000-plus-employee workforce, that’s a significant customer win.

As for the potential combination of Watson and Einstein AI capabilities, we'll have to wait and see what the deal brings. Aside from application-integration and API-level ties expected as soon as next month (and, I suspect, already underway through Bluewolf), both companies stated that we won't see Watson-meets-Einstein synergies until the second half of this year – at the earliest.

MyPOV on the IBM Partnership: Salesforce has no lack of data science talent and capabilities, so I see IBM’s data assets and systems-integration muscle as the lure of this deal from Salesforce’s perspective. As for IBM, it’s way to grow the systems integration business while bringing Watson into the conversation with joint customers.

Einstein Generally Available

This week’s second announcement came in the form of a one-hour “Year of Einstein” videocast led by Salesforce execs including CEO Benioff and co-founder Parker Harris. Digging into the details, Chief Product Officer Alex Dayon talked about the 10 new Einstein AI features released in last month’s “Spring 2017” release. Salesforce now has more than 20 Einstein features generally available (including 16 listed in the slide below).

This slide from the Salesforce "Year of Einstein" video streamed this week lists 17 of
the 20 AI features now generally available from the CRM vendor.?

All 45 of the Einstein features planned for release in 2017 are detailed in my latest research, “Inside Salesforce Einstein Artificial Intelligence.” This 19-page report explores the foundational requirements of AI success, which include high-scale data, massive compute capacity, advanced data-science capabilities and plenty of time to work with all of the above. The report explores Salesforce capabilities on all these fronts, including the two years it spent developing the machine-learning-based predictive data pipeline that will power the vast majority of Einstein capabilities. Finally, it looks at Einstein compared to the cognitive and AI capabilities currently available or, in some cases, in development, within IBM, Microsoft, Oracle and various public cloud service providers.

In Tuesday’s video, Salesforce execs stressed that Einstein is “built into the platform” and now “available to all Salesforce customers.” But packaging and pricing varies by feature and cloud. On the most popular clouds -- Sales, Service and Marketing -- Einstein entails extra cost, and in some cases it’s available only to certain license tiers.

For example, on the Sales Cloud Einstein Opportunity Insights, Einstein Account Insights and Einstein Activity Capture are bundled together for $50 per user, per month, and they’re only available to those with Sales Cloud Enterprise or Unlimited licenses.

On the Service Cloud, Einstein Supervisor is a bundle of the pre-existing Omni-channel Supervisor and Service Wave Analytics apps with the Analytics Cloud Einstein Smart Data Discovery feature. In this case, Einstein is reserved for customers using Enterprise and Unlimited licensing levels and optional apps. Omni-channel Supervisor, for example, is included as part of Service Cloud Enterprise edition and higher. The Service Wave Analytics app is optional and starts at $75 per user per month. Analytics Cloud Einstein Smart Data Discovery, which is optional, is priced based on data volume and user counts. So Einstein Supervisor is “included” with other offerings, but they all happen to be available only to those with Enterprise-grade-plus licenses and an optional add-on application.

Marketing Cloud Einstein Journey Insights and Einstein Segmentation are available with the combination of any Marketing Cloud license and a Krux license. Pricing is based on Krux data collection volumes and storage and data processing use cases.

On some of the newer clouds that Salesforce is hoping to grow, Einstein features are included. Commerce Cloud Einstein, for example, is built into the Commerce Cloud Digital service. And in the Community Cloud, Einstein Recommendations and Einstein Trending Posts are included at no cost with any license. Other Einstein pricing is detailed in this press release.

MyPOV on the Einstein Rollout: It should be no surprise that Salesforce’s most sophisticated, in-demand and potentially labor-, time- and cost-saving capabilities are being tied to higher license levels or optional features. A key goal with Salesforce Einstein is to help people focus on what matters by getting redundant, time-consuming work out of the way or by presenting the lowest-hanging fruit, such as ripe sales leads, based on smart, predictive analysis. As I conclude in my report, the big challenge for Salesforce customers will be calculating the value of Einstein capabilities before turning those features on for lots of users.

Related Reading:
Inside Salesforce Einstein Artificial Intelligence
Oracle Preps AI Apps, Next Steps for Data Cloud
Salesforce Takes Apps-First Approach with Einstein AI

Event Report: #GoogleNext17 On Path To Enterprise Ready

R "Ray" Wang

Principal Analyst and Founder
Constellation Research

R “Ray” Wang is the CEO of Silicon Valley-based Constellation Research Inc. He co-hosts DisrupTV, a weekly enterprise tech and leadership webcast that averages 50,000 views per episode and blogs at www.raywang.org. His ground-breaking best-selling book on digital transformation, Disrupting Digital Business, was published by Harvard Business Review Press in 2015. Ray's new book about Digital Giants and the future of business, titled, Everybody Wants to Rule The World was released in July 2021. Wang is well-quoted and frequently interviewed by media outlets such as the Wall Street Journal, Fox Business, CNBC, Yahoo Finance, Cheddar, and Bloomberg. Short Bio R “Ray” Wang (pronounced WAHNG) is the Founder, Chairman, and Principal Analyst of Silicon Valley-based Constellation Research Inc. He…...

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Event Report - Google Next 2017 - Google makes progress - but is it fast enough?

Holger Mueller

Vice President and Principal Analyst
Constellation Research

Holger Mueller is VP and Principal Analyst for Constellation Research for the fundamental enablers of the cloud, IaaS, PaaS and next generation Applications, with forays up the tech stack into BigData and Analytics, HR Tech, and sometimes SaaS. Holger provides strategy and counsel to key clients, including Chief Information Officers, Chief Technology Officers, Chief Product Officers, Chief HR Officers, investment analysts, venture capitalists, sell-side firms, and technology buyers.  Coverage Areas: Future of Work Tech Optimization & Innovation  Background: Before joining Constellation Research, Mueller was VP of Products for NorthgateArinso, a KKR company. There, he led the transformation of products to the cloud and laid the foundation for new Business Process as a…...

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We had the opportunity to attend Google’s Google Next conference, happening in San Francisco from March 7th till 11th at Moscone West. San Francisco ‘dressed’ up in Google Next colors already way ahead of the event, it was clear that this would be a much bigger event than last year’s Google Cloud Platform event. And a difference it was, also aided by the combination of Google Cloud and GSuite, the number of analysts jumped from 20 or so to estimated 80+. No official attendee numbers, with a lot of Googlers lining up to attend, too, my estimate is well over 2000, when all are counted. 

 

So take a look at my musings on the event here: (if the video doesn’t show up, check here)

 

No time to watch – here is the 1-2 slide condensation (if the slide doesn’t show up, check here):
 

 

Event Report - Google Next 2017 - Good progress by Google - but is it enough? from Holger Mueller

Want to read on? 

 

Here you go: Always tough to pick the takeaways – but here are my Top 3:

 

Schmidt shares his 3 step program for moving to the cloud

Google is serious about Enterprise – It was already clear from the hiring of Greene and last year’s event. But it’s one thing to say it and then to deliver on it. Greene shared that the division is the fastest growing in headcount with over 1000 new hires, Schmidt shared today that over time Google has invested 30B+ US$ into the cloud infrastructure. As Greene shared earlier the ‘business’ things are easy – the basic ‘blocking and tackling’ – but it still needs to be executed. So Google for the first time had a partner event with the conference and showed it has attracted interest from all the large SIs. It’s a lot of progress on the partner numbers, but a totally different ball game than with the other two key players. It was also good to have more enterprise customers on stage, talking to Greene on different carefully selected use cases – Disney for an ‘All in’, HSBC for a data warehouse decommission an move to cloud, Home Depot as a website / commerce reference and eBay as an enterprise showcase for an inhouse built next gen app – a Google Home powered eBay application. 

 

Greene talks Google Cloud

Machine Learning remains the carrot – Not surprisingly, Machine Learning remains the core attraction for Google Cloud. Disney’s CTO clearly labelled it as the reason why Disney is building apps on Google Cloud. Newly minted head of AI / Machine Learning Fei Fei Li shared the visions and unveiled (next to the Cloud Speech, Vision, Translate and Natural Language APIs) the new Video Intelligence API. No surprise given YouTube is part of Alphabet, but very powerful to index and find things in video, e.g. all the beach scenes in your vacation videos. Can’t wait for it to come to Google Photos. Or find out how many of my videos where in cars, at airports or somewhere else. But the key announcement – with long term impact – is Google’s acquisition of Kaggle, the analytics, machine learning, AI community, famous for its competitions. A key acquisition that could – if well executed – further bolster the Machine Learning / AI leadership of Google: It’s not enough to have great software capabilities, you also need to educate people about them and win their hearts and minds as a community. The strategy is clear here, now Google will have to show that it can create, foster a vendor independent ecosystem around Machine Learning. 

 

Google'e Fei Fei Li announces the Kaggle acquisition

Google gets SAP HANA – The major news in regards of ‘meeting the enterprise, where the enterprise is’ (the slogan from last year) was that SAP HANA is now certified on Google Cloud. Leukert was there to unveil the news with Greene, certainly a good move, that shows SAP’s multi vendor cloud strategy. You can / will be able to run SAP products on all three major IaaS. The SAP Hana Express developer product is available, too, with SAP Cloud platform coming later. And when Colgate came on stage, it was clear that SAP to GSuite integration is also on the roadmap. More options for SAP customers is a good development, it may make it easier for SAP to sell more HANA going forward. And Google gets load for Google Cloud. The usual ‘SaaS vendor picks IaaS’ move. The only downside: SAP is working hard to get its customers to HANA – so it is not the bulk of the current load where SAP customers are from an on premises install base. That would be SAP customers running mySAP ERP etc. – mostly on Oracle. 

 

Google's Greene and SAP's Leukert on SAP HANA coming to GCP

MyPOV

A lot of good progress from Google on the Google products for the enterprise, both Cloud and GSuite. The conference has only started – so more is going to come for both products in the days to come. The Google Enterprise team is working hard, no doubt and has made progress in the last 12 months with partners, support and create more unique offerings e.g. the site reliability engineer for customers, a start up program etc. etc.

On the concern side, it is not clear if this will be enough, I asked the Greene leadership team on what they will do to catchup from ‘Bronze to Gold’ and they all came up with good and plausible answers (check out my Storify of the analyst event here). But in a year where we may see VMware running on AWS and Oracle making its cloud work first time – this may not be enough for Google to get on premises load of scale. So it all comes back to the speed of Google being able to attract enterprise load for the next few years, and the speed of the enterprise moving to cloud.

In the meantime, when enterprises have to build new software, the Google capabilities are very attractive for the next generation application use cases the 21st century demands. So in the short run this could well mean that Google can attract more next generation application use cases than attract traditional enterprise IT loads. We will be watching, stay tuned.

Want to learn more? Checkout the Storify collection below (if it doesn’t show up – check here). And if interested in the Analyst Summit from Tuesday this week - there is a Storify collection here.



 

More about Google:

• New Analysis – Google enables citizen developers and developers with Google App Maker - read here
• First Take - Google enters enterprise software space with Google Jobs API - read here
• Event Report - Google I/O 2016 - Android N soon, Google assistant sooner and VR / AR later - read here
• First Take - Google Google I/O 2016 - Day #1 Keynote - Enterprise Takeaways - read here
• Event Preview - Google's Google I/O 2016 - read here
• Event Report – Google Google Cloud Platform Next – Key Offerings for (some of) the enterprise - read here
• First Take - Google Cloud Platform - Takeaways Day #1 Keynote - read here
• News Analysis - Google launches Cloud Dataproc - read here
• Musings - Google re-organizes - will it be about Alpha or Alphabet Soup? Read here
• Event Report - Google I/O - Google wants developers to first & foremost build more Android apps - read here
• First Take - Google I/O Day #1 Keynote - it is all about Android - read here
• News Analysis - Google does it again (lower prices for Google Cloud Platform), enterprises take notice - read here
• News Analyse - Google I/O Takeaways Value Propositions for the enterprise - read here 
• Google gets serious about the cloud and it is different - read here
• A tale of two clouds - Google and HP - read here
• Why Google acquired Talaria - efficiency matters - read here

 

Find more coverage on the Constellation Research website here and checkout my magazine on Flipboard and my YouTube channel here.

2012, 2013, 2014, 2015, 2016 and 2017 (C) Holger Mueller - All Rights Reserved