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…...

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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

Digital Business Distributed Business and Technology Models Part One; Understanding the Business Operating Model

Andy Mulholland

Vice President and Principal Analyst, Constellation Research
Constellation Research

Andy Mulholland is Vice President and Principal Analyst focusing on cloud business models. Formerly the Global Chief Technology Officer for the Capgemini Group from 2001 to 2011, Mulholland successfully led the organization through a period of mass disruption. Mulholland brings this experience to Constellation’s clients seeking to understand how Digital Business models will be built and deployed in conjunction with existing IT systems. Coverage Areas Consumerization of IT & The New C-Suite: BYOD, Internet of Things, IoT, technology and business use Previous experience: Mulholland co authored four major books that chronicled the change and its impact on Enterprises starting in 2006 with the well-recognised book ‘Mashup Corporations’ with Chris Thomas of Intel. This was followed in…...

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Any definition of a Digital Business emphasizes the requirement to be marketplace responsive with Enterprise wide dynamic agility to optimize reactions to events, and opportunities. As business buyers and consumers both become adept at finding exactly what they want mass volume product and distribution becomes configurable software Products and Services. To increase revenues Enterprise have to become equally adept in the finding, and aligning the people, knowledge and operating assets to match.

The Digital Enterprise is driven by market ‘pull’, recognizing its assets in a granular model with orchestration of its capabilities and assets to meet the demands. In many ways this is a reversal of the focus on optimizing the ‘push’ model delivering a narrower range of more standardized products efficiently through defined streamlined processes. Revenue models based on Services can have wider consequences across the Enterprise in many areas, a point clarified latter.

Enterprise management is naturally concerned that this granular, orchestrated approach de-centralizes the Enterprise and risks its carefully constructed management governance. If the same controls were maintained this would be true, but the increased visibility of assets, the use of enhanced intelligence, and the use of OpEx financial management all increase management operational efficiency. The foundation of the Digital Business is built on, and maintained by, a similar change through deploying the new Technologies together to form what could be termed a ‘Business Infrastructure’.

A recognizable current change in Go to Market Business activities introduces increasing number of Cloud based Apps; Less recognized is the impact of distributed Apps requiring both localized Enterprise Edge Clouds in addition to large scale centralized Clouds. The addition of IoT provides both hyper connectivity internally, and externally, as well as the Digitization of physical objects/events supporting interactive ‘read and respond’ Smart Services. AI is just starting to make its commercial presence felt bringing near real-time optimization of these new Enterprise operations supplementing the historic reporting role of BI reporting.

Added with other changes in working practice through the adoption of Collaboration and Social Tools, Drag and Drop composition of localized processes by Line of Business managers, etc., to see the full impact in the role of Technology in enabling Enterprise Business activities. The existing Enterprise IT infrastructure was designed to support the stability of centralized Enterprise Applications. It can be cost reduced, and made more efficient, but it’s a challenge to believe it can operate in support of such a fundamental change in both Business and Technology use.

Business Technology is becoming the term used to describe the technologies of Apps, Clouds, IoT, and AI, deployed in an integrated combination to enable Digital Enterprises. A McKinsey outline of eight ways in which Business Technology trends are radically changing the capabilities of an Enterprise also serves to demonstrate how Business Technology differs from traditional IT.  It logically follows that Business Infrastructure would be the logical term for the Infrastructure that supports Business Technology. For simplicity the term Business Infrastructure is used as broad and general reference, and for similar convenience the converged technologies of Clouds, Apps, AI, Services and (io)Things are referred to by the acronym CAAST.

The technologies of CAAST together provide the transformation in capabilities that create Digital Business.  The market place, business activities, events, and assets are all represented as Digital data that an Enterprise can read and make the optimized responses in reply.

The Business advantage lies in the Enterprise becoming part of the real World able to directly relate marketplace events to internal operations. An Enterprise business model based on product selling does not have the extended relationship requirements that a Services business model requires.  To successfully maintain a service revenue stream from the installed products requires IoT connectivity, with Cloud event processing, using a new approach to Business Infrastructure.

Consider the shift from Sales to Services against the example of a manufacturer of Air Conditioning units supplying the Building Management market, a notable early adopter of IoT based solutions.  The design and manufacture of air conditioning units continues, but the revenue stream changes from selling the ownership of units to providing the market with ‘cooling’ as a Service. This business model shift introduces not only a whole range of activities necessary to keep air conditioners at work, but also changes to existing core activities.

The consequences of the shift from a Product Sales business model to a Services business are more than is perhaps immediately obvious. Selling an Air Conditioner as a product places the design and manufacturing focus on the sales price and on marketable competitive features. Continued product ownership with revenue from low cost operation changes the design and manufacturing emphasis towards increased reliability, durability and serviceability, with initial cost and additional features becoming of less importance.

The Air Conditioning unit naturally gains inbuilt IoT sensor connectivity to manage operational deployment, and provide data for predictive service maintenance, or to report a breakdown. Additionally the Digital Enterprise will need to use the full scope of the capabilities provided by CAAST to support new Enterprise wide range of Operational activities. The reorientation towards a Services model, as an example, may introduce Digital Twin models, and drive a new effort towards flexible and Lean Manufacturing. Market led Service-provisioning calls for tailoring the units produced to match the exact conditions of deployment, rather than batch production for stock or distribution.

The organizational structure of a Digital Enterprise has to accommodate an increased number of functional activities able to operate in loose-coupled relationships driven by events. (As opposed to the tight coupled fixed relationship of a process driven Back Office Enterprise IT). The diagram below illustrates;

Those familiar with the organization of complex manufacturing will recognize the concept of ‘cells’, (used to allow different manufacturing tasks to achieve individual optimization, within a synchronized environment), but now expanded across the Digital Enterprise beyond manufacturing. The term Activity Pool is used to avoid confusion with the term Cell as defined in Industrial Automation.

Some obvious examples of major activities Pools are shown, note that this can include externalized, even shared, activity Pools such as the Building Management System, BMS, in which “cooling as a Service’ is deployed. A BMS is an integrated set of Services providing a Building with the cohesive infrastructure functions ranging from Heating, Cooling, Lighting, Plumbing, Security, etc. In a Smart Building these Services may come from several companies, but must operate 24/7 integrated as an independent Pool using localized interactions and processing. The Services providers will receive periodic historic data uploads on performance of their Services items, or event alarm triggers.

A Digital Enterprise will have deployed Services in many such external Activity Pools, as well as its own internal distinct Activity Pools. In each CAAST provides the Business Infrastructure of interactive connectivity within the Activity Pool supported by necessary local, or edge, based processing, and rules to identify event data streams to be forwarded to other Activity Pools.

Localized, or Edge processing, (sometimes called Fog Computing), reduces latency in responses, can increase security, as well as offering standalone reliability. In the Building Management example emergency situations, such as a fire, or flood, could cut external links, and are sensitive to latency in responses, would benefit from localized Cloud* processing. More common benefits are a reduction in the amount and cost of network traffic flooding across network links.

* Cloud technology should be recognized for its ability to provide ‘run anywhere on demand’ processing technology rather than thought of as being solely linked to massive data center consolidation of operations.

In the diagram below the Activity Pools are shown as Localized CAAST deployments allowing individual optimization, together with an Enterprise IT large scale Cloud, (probably from an external Cloud Service Provider). The red lines indicate the Activity Pools that would interact through an event data stream indicating Service Maintenance is required for an Air Conditioner located in the Building Control Group.

A further important addition is the use of AI to optimize the huge number of real-time events and circumstances that would be happening across the entirety of a Digital Enterprise. BI continues to provide the intelligent historic data analysis on which many of the Enterprise operating Rules will be based.

The Business and Technology models of a Digital Enterprise are effectively a single common model, deployed as a true loose-coupled Business Services architecture to provide optimization operational responses to real World events and circumstances. This is a radically different environment from Transactional IT, which has, and will continue to have, the task of providing stabile compliance in recording, maintaining and using the Enterprise Data records.

CAAST provides event driven orchestration of the Enterprise responses to the real World through the management of its Assets and capabilities. IT manages the integration of optimized processes to ensure the Enterprise tracks and maintains its compliant governance and core data.

The Business Infrastructure capable of supporting the inherent dynamic flexibility of the Digital Enterprise and its business model is crucially important as is the training and knowledge of how to assess its deployment in association with the Business model.

Part Two; The Technology and Business Infrastructure identifies three leading Technology vendors, outlining their vision and the capabilities each currently provides.

Cisco Converged Infrastructure a combination of Cisco Software Defined Networking and Cisco ASAP Hybrid Cloud Data Centre.

Dell Internet of Things Infrastructure citing the benefits of the merged product portfolios of Dell and EMC; whilst

HPE Composable Infrastructure aiming to support both traditional IT as well as the new demands of Business Technology.

New Release: Latest Lists for Q1 2017 Constellation ShortList Program

Aubrey Coggins

Constellation Research

Aubrey (Levinstone) Coggins manages Constellation's marketing and communications programs and is the producer of DisrupTV, a weekly Web series on innovation and the enterprise.  She previously served as a senior manager of corporate communications at Egnyte and held account roles at Access Communications and The Hoffman Agency. Born and raised in Texas, she received a bachelor's degree in Public Relations from The University of Texas at Austin – Hook ‘em!   ...

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Back in October, we announced the new Constellation ShortList™ program. It offers guidance across our different coverage areas and helps buyers of technology identify the services and products they need to achieve digital transformation.

As a company, we advise early adopters using disruptive technologies on how to achieve business model transformation. Products and services named to each Constellation ShortList meet the threshold criteria as determined by our analysts through client inquiries, partner conversations, customer references, vendor selection projects, market share and internal research.

We are unveiling our new and updated lists today and will continue to provide valuable updates each quarter. Why so frequently? Most of these markets change frequently with mergers, acquisitions and other company changes. We will provide the latest updates to help you make decisions with the most up-to-date information available.  

Below are the lists we released today:

• B2B Marketing Automation
• B2C Marketing Automation
• Campaign to Commerce
• Cloud-Based Business Intelligence
• Cloud-Based Performance Management
• Cloud Customer Service and Contact Center Software
• Cloud Identity Management
• Configure Price Quote (CPQ)
• Customer Experience (CX): IOT Platforms
• Customer Experience (CX) Services: Global
• Customer Experience (CX) Services: SMB
• Customer Loyalty
• Data Cataloging
• Data Lake Management
• Digital Asset Management (DAM) for DX
• Digital Asset Management (DAM) for High Volume Commerce
• Digital Experience (DX) Integrated Platforms
• Digital Performance Management
• Distributed Ledger Technology Labs
• Enterprise Cloud Finance Apps
• Enterprise File Sharing: Niche
• Enterprise File Sharing: Suites
• Enterprise Group Messaging: Standalone
• Enterprise Group Messaging: Unified Communication
• Field Service Management
• Global HCM Suites
• Integration Platform as a Service (IPaaS)
• Matrix Commerce Order Management
• Meeting Management Tools
• Partner Relationship Management (PRM)
• Sales Force Automation
• Sales Productivity
• Self-Service Advanced Analytics
• Smart Services Digital Monetization Platforms
• Social / Digital Media Listening / Monitoring / Engagement Platforms
• Social Task Management: Enterprise Suites with Project Features
• Social Task Management: Niche

For more information, visit https://www.constellationr.com/shortlist.