Constellation ShortList™ for Digital Performance Management

Vice President and Principal Analyst, Constellation Research
Constellation Research

Role: Vice President and Principal Analyst focusing on the IOT of Customer Experience and how the cloud is driving more flexible, agile, and robust customer service and customer experiences. In-app or in device customer service is the future. Coverage Areas: Next Generation Customer, Digital Marketing Transformation Bio: As a Vice President and Principal Analyst at Constellation Research, Inc, Petouhoff works with Fortune 100 and 500’s Senior Leadership teams to create strategic customer experience (marketing, sales and service) solutions which engage customers, build brands and grow businesses. Combining her experience as a former Forrester software analyst, PWC systems integrator and change management consultant, Petouhoff leads executive workshops to discuss to not only…...

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Digital Performance Management provides companies with the analytics to determine if their customer experience is optimized. The Constellation ShortList™​ presents vendors in different categories of the market relevant to early adopters. In addition, products included in this document meet the threshold criteria for this category as determined by Constellation Research. This Constellation ShortList of vendors for a market category is compiled through conversations with early adopter clients, independent analysis, and briefings with vendors and partners.

Mastering digital performance management (DPM) is one of the leading challenges of the digital economy. Often referred to as application performance management (APM), it requires a joint effort between many functional departments, such as marketing, sales, customer service, ecommerce and IT. It is no longer just about IT looking at the performance of the technology stack or the management of the infrastructure or cloud that delivers customer experience. 

Today, it’s about the collaboration between the lines of business and IT to measure and manage the end-to-end transaction delivery and translate it into actionable information that a brand can use to optimize customer experience, as well as the performance of the technologies used to deliver it. When DPM is optimized, companies can deliver an engaging digital experience, maximize revenue and improve brand loyalty. DPM vendors help both the business and engineering teams to not only define conversion and revenue goals but also make sure they are reached.

They make sure the application’s performance doesn’t become a roadblock to optimal customer experiences and reaching business metrics, including conversion rates, high availability and high user experience indexes. The goal is to monitor and measure to eliminate all revenue barriers with a strong focus on digital performance to ensure that the road to conversion is quick and easy; the customer experience is smooth; and customers remain loyal as a result.

B2B Marketing Automation Makes Its Debut in the 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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In the digital era, companies are competing for buyer time and attention. The multitude of campaign channels - ranging from social media, Web programs, content marketing, advertising and events - requires companies to consider systems that can orchestrate across channels to ensure quality buyer engagement. Marketing automation provides B2B companies with an efficient way to attract, capture, engage and nurture customers, enabling marketers to deliver qualified, sales-ready leads and provide post-sale nurture for upsell and cross-sell opportunities.

Today, Constellation published a Constellation ShortList™ for B2B Marketing Automation. This Constellation ShortList helps companies choose the B2B Marketing Automation tools that will help them achieve their digital transformation goals. Here are the companies that made the Constellation ShortList:

Enterprise Solutions

• Adobe Campaign
• Oracle Marketing Cloud
• Marketo
• Salesforce Pardot

SMB Solutions

• Act-On
• eTrigue
• Hubspot
• Salesforce Pardot

Constellation advises early adopters using disruptive technologies on how to achieve business model transformation. Products and services named to this Constellation ShortList meet the threshold criteria for each category as determined by Constellation Research through client inquiries, partner conversations, customer references, vendor selection projects, market share and internal research.

Additional lists released today include:

• B2C Marketing Automation
• Campaign to Commerce
• Cloud-Based Business Intelligence
• Customer Experience (CX) Services: Global
• Digital Asset Management (DAM) for Commerce
• Digital Performance Management
• Enterprise File Sharing: Suites
• Meeting Management Tools
• Self-Service Advanced Analytics

This is the second set of lists we’ve released for this program. If you missed last week’s update, check it out here. We will be rolling out nearly 40 Constellation ShortLists authored by our analysts across a range of technologies over the next few weeks.

For more information, visit https://www.constellationr.com/shortlist, or inquire directly by contacting [email protected].

New Research: How Blockchain Security is Evolving

Steve Wilson

Former Vice President and Principal Analyst
Constellation Research

Steve Wilson is former VP and Principal Analyst at Constellation Research, leading the business theme Digital Safety and Privacy. His coverage includes digital identity, data protection, data privacy, cryptography, and trust. His advisory services to CIOs, CISOs, CPOs and IT architects include identity product strategy, security practice benchmarking, Privacy by Design (PbD), privacy engineering and Privacy [or Data Protection] Impact Assessments (PIA, DPIA). Coverage Areas: -  Identity management, frameworks & governance-  Digital identity technologies-  Privacy by Design -  Big Data; “Big Privacy”-  Identity & privacy innovation Previous experience: Wilson has worked in ICT innovation, research, development and analysis for over 25 years. With double…...

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Security for blockchains and more advanced Distributed Ledger Technologies (DLTs) is evolving rapidly.  As soon as interest in the original blockchain grew past crypto-currency into mainstream business applications, it became apparent that the core ledger would need to augmented in several ways.  

The naked blockchain by nature has to be open to all participants, to achieve the scale needed to keep out corruption of Bitcoin, but in real world business, account holders need to be subject to entry rules, and the "miners" that support the network need a governance, system.  These considerations lead to with permissioned blockchains with access controls.  Another enterprise requirement is confidentiality; most businesses need their records generally kept out of the public domain, and that means encryption or blockchain entries before they are lodged.

Access controls and encryption might be regarded as conventional security layers, but what few people appreciate is that these measures conflict with the rationale of the original blockchain algorithm, which was expressly meant to dispel administration.  

My new Constellation Research paper looks at these tensions, what they mean for public and private blockchain systems, and provides some detailed guidance for blockchain technology security: 

• Permissioned blockchains and private DLTs must implement access controls to determine who has the appropriate privileges to write to the ledger.
• Remember that any administrative agencts operating off-chain dilute the benefits of Proof of Work algorithms.
• If confidentiality is required for sensitive data written to a public blockchain, then additional key distribution and management is needed, but these can create single points of failure needing mitigation.
• Private DLTs become much more concentrated compared with pure blockchains, and need administration to protect against insider threats and to prevent fraud.  
• The quality of cryptography in all customised DLTs is critical. Algorithms must be correctly programmed and must execute without interference.
• Hardware Security Modules (HSMs) are increasingly practical, thanks to dedicated cloud HSM options emerging at cloud providers and should be considered. HSMs should be certified to Common Criteria EAL4+, or FIPS 140 level 3+.
• Software quality in general must not be overlooked. Bugs in “smart contracts” and novel blockchain cases like the “Distributed Autonomous Organization” (The DAO) have made the headlines. DLT projects must take care of the software development lifecycles. 

 A snapshot of "How to Secure Blockchain Technologies" can be downloaded here. 

New Research: Guidance on Blockchain Technology R&D

Steve Wilson

Former Vice President and Principal Analyst
Constellation Research

Steve Wilson is former VP and Principal Analyst at Constellation Research, leading the business theme Digital Safety and Privacy. His coverage includes digital identity, data protection, data privacy, cryptography, and trust. His advisory services to CIOs, CISOs, CPOs and IT architects include identity product strategy, security practice benchmarking, Privacy by Design (PbD), privacy engineering and Privacy [or Data Protection] Impact Assessments (PIA, DPIA). Coverage Areas: -  Identity management, frameworks & governance-  Digital identity technologies-  Privacy by Design -  Big Data; “Big Privacy”-  Identity & privacy innovation Previous experience: Wilson has worked in ICT innovation, research, development and analysis for over 25 years. With double…...

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Over September 26-27 I was a guest speaker at the US Department of Health & Human Services blockchain for healthcare workshop.  It was a fascinating and worthwhile exercise, as I reported in more detail here.  However, I have to say most of the presentations confirmed my view that blockchain application development leaves a lot to be desired.

Too often, blockchain-based solutions fall short of good design practice.  They typically fail to set out the problem they're really trying to solve, and instead start with the working assumption that blockchain is an inherently valuable part of the end solution.  In the current craze, people tend to treat blockchain is the end and not the means. 

There were three noticable problems with the blockchain for healthcare proposals, taken as a whole: 

1. They tended to misunderstand what blockchain does.  As I've canvassed elsewhere, the original Bitcoin blockchain only does one thing: it reaches consensus on the order of entries in a ledger.  It does not and cannot decide anything else about the entries.  And so it does not have a lot to contribute to the health IT problem of interoperability. 
2. They tended to overestimate blockchain as a database.  Several papers submitted to the workshop claimed blockchain could help federate the many disparate and siloed health information repositories, improving on the probematic Health Information Exchange (HIE) model.  However, as the workshop progressed, all parties agreed that the blockchain should not be used to hold significant health data, because it's public by nature, and limited in capacity. So if blockchain isnt going to store health data, it cannot help shift health data from its current silos. 
3. And none of the blockchain papers tackled the challenge of key management. When permissions and confidentiality need to be layered on top of the core blockchain, and the necessary key management arrangements are inplace, the distributed ledger technology actuallt becomes inconsequential.  

If distributed ledgers hold some promise in e-health, there needs to be a clear problem statement and a stronger R&D pathway, to take us from the first generation prototype blockchain, to second and third generation DLTs and beyond. 

I have published a new research report seeking to improve how blockchain-related R&D is conducted. 

The ad hoc way in which permissions and encryption is added to blockchain is just one example of overly hasty "innovation". In the rush to apply blockchain to mainstream applications, few entrepreneurs have been clear about the problems they think they’re solving.  If DLT R&D is not properly grounded, then the resulting solutions will be weak and will ultimately fail in the market.  The original blockchain was truly only a prototype; greater care is needed to reliably adapt the first generation algorithms to enterprise requirements. 

The report examines four strong research organisations in this space: the Hyperledger Foundation, Microsoft's Azure Blockchain as a Service, the banking industry joint vernture R3, and Ping Identity with its investment in the startup Swirlds. 

If your organisation needs to conduct its own R&D then the following checklist can help you stay on track: 

• Do you have the equivalent of a Double Spend problem? If the order of transactions is not critical or if it’s not really possible for assets such as physical objects to be exercised twice, then the original blockchain is probably not a natural for your use case.
• Are your assets purely digital or do you have physical assets that need mapping onto the ledger? Any mapping or translation of physical assets onto ledger entries logically needs to be done off-ledgewith authority structures that erode the pure blockchain architecture.  
• Do you need immutability? The degree of permanence provided by the public blockchains is rarely needed in conventional business, and may not be worth the cost. 
• Does your environment have administrators? Blockchain was expressly designed for a special case where central admin is banished.  Conventional business lines of command and authority structures can be at odds with the blockchain consensus algorithm. 

For more information, a snapshot of the report is available here.  

Introducing Constellation's Latest Product

Steve Wilson

Former Vice President and Principal Analyst
Constellation Research

Steve Wilson is former VP and Principal Analyst at Constellation Research, leading the business theme Digital Safety and Privacy. His coverage includes digital identity, data protection, data privacy, cryptography, and trust. His advisory services to CIOs, CISOs, CPOs and IT architects include identity product strategy, security practice benchmarking, Privacy by Design (PbD), privacy engineering and Privacy [or Data Protection] Impact Assessments (PIA, DPIA). Coverage Areas: -  Identity management, frameworks & governance-  Digital identity technologies-  Privacy by Design -  Big Data; “Big Privacy”-  Identity & privacy innovation Previous experience: Wilson has worked in ICT innovation, research, development and analysis for over 25 years. With double…...

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One of the things I love about Constellation Research is how the firm practices what it preaches.  Everything we talk about of course relates to innovation, but we are ourselves innovating all the time. For example, internally, our production process is constantly trying new ways to streamline how research gets converted into high grade publications.  

Externally in the past twelve months we opened up the Constellation Executive Network which provides new ways to access or research and analysts.  And last week we launched the latest Constellation Research product: the Constellation ShortListâ„¢ . 

The Constellation ShortList program provides technology buyers a curated list of solution providers to consider in their pursuit of digital transformation in a wide range of categories.  Products and services named in each Constellation ShortList meet particular threshold criteria in each category, as determined by Constellation Research through ongoing client and partner conversations, customer references, market share, mind share, publications, and our continuous internal research.  Each list is updated by its responsible analyst on a short cycle -- typically every three months -- so our clients always have an up-to-date guide to help them save time in competitive technology evaluation.  

My first Constellation ShortLists were published last week. 

Cloud Identity Management (CIM)  

CIM is a natural evolution of hosted identity management services, and has boomed over the past three years.  At this stage I have short-listed (in alphabetical order) CA Technologies, ForgeRock, IBM,   Microsoft, Okta, OneLogin, Ping Identity, Salesforce, and VMware.  This is a particularly dynamic category, so watch this space for new entries and exits! 

Distributed Ledger Labs (DLTs) 

DLTs are the natural progression of blockchain, expanding the vision of this decentralised secure data structure from cryptcurrency to mainstream applications.  They are highly complex, and until commoditised, buyers should stick to the large research consortia or laboratories. For now I have short-listed (in alphabetical order): R3, Hyperledger Foundation, Microsoft Azure Blockchain as a Service, and Ping Identity / Swirlds. 

Constellation ShortList™ for Customer Experience (CX): IOT Platforms

Vice President and Principal Analyst, Constellation Research
Constellation Research

Role: Vice President and Principal Analyst focusing on the IOT of Customer Experience and how the cloud is driving more flexible, agile, and robust customer service and customer experiences. In-app or in device customer service is the future. Coverage Areas: Next Generation Customer, Digital Marketing Transformation Bio: As a Vice President and Principal Analyst at Constellation Research, Inc, Petouhoff works with Fortune 100 and 500’s Senior Leadership teams to create strategic customer experience (marketing, sales and service) solutions which engage customers, build brands and grow businesses. Combining her experience as a former Forrester software analyst, PWC systems integrator and change management consultant, Petouhoff leads executive workshops to discuss to not only…...

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Customer Experience (CX): IOT Platforms are the platforms that make IoT come to life. The Constellation ShortList™ presents vendors in different categories of the market relevant to early adopters. In addition, products included in this document meet the threshold criteria for this category as determined by Constellation Research. This Constellation ShortList of vendors for a market category is compiled through conversations with early adopter clients, independent analysis, and briefings with vendors and partners.

Developing products for the Internet of Things (IoT) is a complex endeavor. Because most organizations lack the resources and skills for custom app development, successful projects require development on an IoT platform or solution. IoT data solutions offer a place to start by combining many of the tools needed to manage a deployment from device management to data prediction and insights, all into one offering. For customer experience offerings, Constellation has identified a range of platform providers, including pure-play third-party platforms, hardware vendors, connectivity providers and system integrators. Having an end-to-end ecosystem strategy means a company doesn’t have to develop their own modules, network stack or cloud onboarding.

Constellation considers the following criteria to be considered an IoT Platform for CX. One of the key characteristics is to be able to connect data from every device, sensor, website, etc. and is built on a scalable event processing engine designed to ingest and analyze billions of connected events: 

• End-to-end ecosystem strategy 
• Reliably scale to billions of devices and trillions of messages  
• Manages up to 500,000 assets and 100,000 device messages per second 
• Cloud infrastructure as a white-label subscription service  
• Web-scale processing, analytics and machine intelligence  
• Ultra-low latency  
• Augmented reality integration  
• Messaging broker supports connections using native MQTT and WebSockets MQTT.

Constellation evaluates over 25 solutions categorized in this market. This Constellation ShortList is determined by client inquiries, partner conversations, customer references, vendor selection projects, market share and internal research. 
The list includes:  

• AWS IoT  
• GE Predix  
• Google Cloud Platform IoT  
• Microsoft Azure IoT Suite  
• ThingWorx  
• Salesforce IoT Cloud  
• Lively.

For more information, please see the Constellation Research website.
@DrNatalie Petouhoff, VP and Principal Analyst, Constellation Research, Covering Customer-facing Applications

Blockchain, Healthcare and Bleeding Edge R&D

Steve Wilson

Former Vice President and Principal Analyst
Constellation Research

Steve Wilson is former VP and Principal Analyst at Constellation Research, leading the business theme Digital Safety and Privacy. His coverage includes digital identity, data protection, data privacy, cryptography, and trust. His advisory services to CIOs, CISOs, CPOs and IT architects include identity product strategy, security practice benchmarking, Privacy by Design (PbD), privacy engineering and Privacy [or Data Protection] Impact Assessments (PIA, DPIA). Coverage Areas: -  Identity management, frameworks & governance-  Digital identity technologies-  Privacy by Design -  Big Data; “Big Privacy”-  Identity & privacy innovation Previous experience: Wilson has worked in ICT innovation, research, development and analysis for over 25 years. With double…...

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Last month, over September 26-27, I attended a US government workshop on The Use of Blockchain in Healthcare and Research, organised by the Department of Health & Human Services Office of the National Coordinator (ONC) and hosted at NIST headquarters at Gaithersburg, Maryland. The workshop showcased a number of winning entries from ONC's Blockchain Challenge, and brought together a number of experts and practitioners from NIST and the Department of Homeland Security.

I presented an invited paper "Blockchain's Challenges in Real Life" (PDF) alongside other new research by Mance Harmon from Ping Identity, and Drummond Reed from Respect Network. All the workshop presentations, the Blockchain Challenge winners' papers and a number of the unsuccessful submissions are available on the ONC website. You will find contributions from major computer companies and consultancies, leading medical schools and universities, and a number of unaffiliated researchers.

I also sat on a panel session about identity innovation, joining entrepreneurs from Digital Bazaar, Factom, Respect Network, and XCELERATE, all of which are conducting R&D projects funded by the DHS Science and Technology division.

Around the same time as the workshop, I happened to finalise two new Constellation Research papers, on security and R&D practices for blockchain technologies. And that was timely, because I am afraid that once again, I have immersed myself in some of the most current blockchain thinking, only to find that key pieces of the puzzle are still missing.

Disclosure: I traveled to the Blockchain in Healthcare workshop as a guest of ONC, which paid for my transport and accommodation.

Three observations from the Workshop

There were two things I just did not get as I read the winning Blockchain Challenge papers and listened to the presentations. And I observe that there is one crucial element that most of the proposals are missing. 

Limits to what Blockchain actually does

Firstly, one of the most common themes across all of the papers was interoperability. A great challenge in e-health is indeed interoperability. Disparate health systems speak different languages, using different codes for the same medical procedures. Adoption of new standard terminologies and messaging standards, like HL-7 and ICD, is infamously slow, often taking a decade or longer. Large clinical systems are notoriously complex to implement, so along the way they invariably undergo major customisation, which makes each installation peculiar to its setting, and resistant to interfacing with other systems.

In the USA, Health Information Exchanges (HIEs) have been a common response to these problems, the idea being that an intermediary switching system can broker understanding between local e-health programs. But as anyone in the industry knows, HIEs have been easier said than done, to say the least.

According to many of the ONC challenge papers, blockchain is supposed to bring a breakthrough, yet no one has explained how a ledger will make the semantics of all these e-health silos suddenly compatible. Blockchain is a very specific protocol that addresses the order of entries in a distributed ledger, to prevent Double Spend without an administrator. Nothing about blockchain's fundamentals relates to the contents of messages, healthcare semantics, medical codes and so on. It just doesn't "do" interoperability! The complexity in healthcare is intrinsic to the subject matter; it cannot be willed away with any new storage technology.

Blockchain won't fundamentally change Personal Data practices

The second thing I just didn't get about the workshop was the idea that blockchain will fix healthcare information silos. Several speakers stressed the problem that data is fragmented, concentrated in local repositories, and hard to find when needed. All true, but I don't see what blockchain can do about this. A consensus was reached at the workshop that personal information and Protected Health Information (PHI) should not be stored on the blockchainin any significant amounts (not just because of its sensitivity but also the sheer volume of electronic health records and images in particular). So if we're agreed that the blockchain could only hold pointers to health data, what difference can it make to the current complex of record systems?

  

Blockchain's missing link: Key management

And my third problem at the workshop was the stark omission of key management. This is the central administrative challenge in any security system, of getting the right cryptographic keys and credentials into the right hands, so all parties can be sure who they are dealing with. The thing about blockchain is that it did away with key management. The genius of the original Bitcoin blockchain is it allows people to exchange guaranteed value without needing to know anything about each other. Blockchain actually dispenses with key management and it may be unique in the history of security for doing so (see also Blockchain has no meaning). But when we do need to know who's who in a health system – to be certain when various users really are authorised medicos, researchers, insurers or patients – then key management must return to the mix. And then things get complicated, much more complicated than the utopian setting of Bitcoin.

Moreover, healthcare is hierarchical. Inherent to the system are management structures, authorizations, credentialing bodies, quality assurance and audits – all the things that blockchain's creator Satoshi Nakamoto expressly tried to get rid of. As I explained in my workshop speech, if a blockchain deployment still has to involve third parties, then the benefits of the algorithm are lost. So said Nakamoto him/herself!

In my view, most blockchain for healthcare projects will discover, sooner or later, that once the necessary key management arrangements are taken care of, their choice of distributed ledger technology is inconsequential.

New Constellation Research on Blockchain Technologies

How to Secure Blockchain Technologies

Security for blockchains and Distributed Ledger Technologies (DLTs) have evolved quickly. As soon as interest in blockchain grew past crypto-currency into mainstream business applications, it became apparent that the core ledger would need to augmented with permissions for access control, and encryption for confidentiality. But what few people appreciate is that these measures conflict with the rationale of the original blockchain algorithm, which was expressly meant to dispel administration layers. The first of my new papers looks at these tensions, what they mean for public and private blockchain systems, paints a picture for third generation DLTs.

How to Conduct Effective Blockchain R&D

The uncomfortable marriage of ad hoc security and the early blockchain is indicative of a broader problem I've written about many times: too much blockchain "innovation" is proceeding with insufficient rigor. Which brings us to the second of my new papers. In the rush to apply blockchain to broader payments and real world assets, few entrepreneurs have been clear and precise about the problems they think they’re solving. If the R&D is not properly grounded, then the resulting solutions will be weak and will ultimately fail in the market. It must be appreciated that the original blockchain was only a prototype. Great care needs to be taken to learn from it and more rigorously adapt fast-evolving DLTs to enterprise needs.

New Constellation ShortList^TM for Distributed Ledger Technologies Labs

Finally, Constellation Research has launched a new product, the Constellation ShortList^TM. These are punchy lists by our analysts of leading technologies in dozens of different categories, which will each be refreshed on a short cycle. The objective is to help buyers of technology when choosing offerings in new areas.

My Constellation ShortList^TM for blockchain-related solution providers is now available here.

Introducing Workplace by Facebook

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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Today Facebook officially launched Workplace by Facebook, their enterprise version of the Facebook social network. 

Workplace, formerly named Facebook at Work has been in private beta for over a year and is being used in over 1000 companies, dozens of which have more than 10,000 people using it. Current customers include Coldwell Banker, Club Med, Heineken, Royal Bank and Scotland, Canadian Tire and Telenor. Workplace is now available for any organization to signup. 

Workplace resembles the consumer version of Facebook, with a central newsfeed of posts, groups for specific topics, and a messenger client named Work Chat that provides group chat and 1:1 video. Some of the key Workplace features include events, polls and live streaming which allows organizations to broadcast content to employees in real time. 
 

It should be noted, Workplace was the name IBM gave to their next generation messaging platform in 2003, but was discontinued in 2007.

As outlined in my research report, “Can Facebook at Work Bring Collaboration to the Business World”, one of the greatest strengths of Workplace is that most employees will immediately be familiar with how to use it. The real test comes in how seamlessly integrated Workplace can become with the business workflows that employees use to get their jobs done.

At the time of launch, Workplace does not have integrations with popular enterprise software such as Office 365, Salesforce, Workday, ZenDesk, etc. Instead, Facebook has focused their initial development efforts on the security and administration aspects of Workplace. For example, customers will be able to use single-signon via providers like Microsoft Azure AD, PingIdentity, Okta and OneLogin. While SSO is critical for getting started and gaining adoption, Constellation Research recommends that Facebook quickly develop business software integrations or partner with companies that can provide this functionality.

Workplace does allow for multi-company groups which contain people from other organizations. This is extremely important for many collaborative use-cases, but the caveat (at this time) is that each organization must be using Workplace, there is currently no guest access. 

Rather than using the common “price per user/per month” licensing model, Facebook is doing something very customer friendly and only charging for what is used. Pricing is:
$3USD for each 1-1000 monthly users
$2USD for each 1001 - 10000 monthly users
$1USD for each 10,001+ monthly users

Can Workplace Compete With A Suite?

The main collaboration battle over the last few decades has been fought by Microsoft, Google and IBM. The core of these vendors' offerings is the combination of email/calendar + content creation (documents, slides, spreadsheets) + unified communication (chat, web-conferencing). Along the way vendors offering niche services such as file sharing (Box, DropBox, Egnyte, etc), task management (Asana, Clarizen, Trello, Workfront, etc) and group chat (Slack, Glip, HipChat, Ryver, etc) have each claimed their own spot in the market, but always as a layer in addition to the Microsoft, Google or IBM stacks. 

Workplace by Facebook is not a complete collaboration suite. Since it does not provide its own email, task management, file-sharing or content creation tools, customers will still have to purchase those other products. So can Workplace succeed as a Corporate Social Intranet or Enterprise Social Network? Options such as Yammer, Jive, SocialCast, Thoughtfarmer, and Igloo have been around for years, yet none have dominated the market the way suites like Microsoft’s Office 365, Google's GSuite (formerly Google Apps for Work) and IBM Connections have. Also vendors like Salesforce, Workday, SAP, Oracle, Cisco and Infor have all added communication and collaboration features into their platforms.

In order for Workplace by Facebook to really become a critical business tool, they will need to provide deep integration with email, file-sharing, task management, and as mentioned above business process software such as CRM, ERP, HR, financial, etc. or else they risk the same fate as many social business software platforms that came before them.

So can Workplace provide enough value on it’s own to warrant being an additional tool for employees to use? If early customer interest is any indication, it would appear it can. Leveraging Facebook’s name recognition, Workplace has a big opportunity to become a leader in enterprise social software. Look at the level of attention newcomer Slack has obtained, and that was starting from ground zero. Slack claims to have 3M active daily users, it will be interesting to see how many Facebook cites in 6, 12, 24 months.

The strengths of Facebook’s name recognition and massive business partner ecosystem are certainly assets that can help their road to success. Constellation Research has already received a great deal of customer interest in Workplace (when it was Facebook for Work) and expect interest to increase with today’s official release.
 

Salesforce Einstein: Dream Versus Reality

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 has introduced Einstein as a set of platform services, but for now it seems more like a collection of acquired parts. Here’s a look at what’s real and what’s coming.

“There’s a reason they call it Dreamforce,” quipped one Salesforce partner executive at the company’s big October 4-7 event in San Francisco. “They’re great marketers, but who knows when any of this AI stuff will be real?”

There’s good reason for skepticism, given Salesforce’s habit of announcing capabilities at Dreamforce well ahead of availability, yet several Einstein “artificial intelligence” (AI) services are actually already available. The capabilities available immediately are mostly those that Salesforce picked up through its many AI-related acquisitions over the last year to 18 months. Several Einstein capabilities coming soon to the Marketing and Sales Cloud were developed organically, according to Salesforce, relying on machine learning and other technologies evolved out of the ExactTarget and Heroku acquisitions.

What’s not yet real, in my view, is the Einstein “platform services” layer depicted in the marchitecture diagram that Salesforce flashed up during several keynotes last week (see below). Salesforce insists that it’s not just a vision, but incorporating all those acquisitions will require a bit of integration work before Salesforce can deliver a consistent set of services and APIs. And only then will Salesforce be able to deliver some of the blended Einstein capabilities described at Dreamforce. Based on executive interviews and time spent at the Dreamforce “Einstein Discovery Center,” here’s a closer look at what to expect and when.

‘Discover Smarter Insights’ with Analytics Cloud Einstein

It quickly became clear at Dreamforce that BeyondCore, one of Salesforce’s most recent AI acquisitions, is expected to be among the most significant of the dozen or so deals driving Einstein – at least where machine-learning based data-discovery and analysis are concerned. BeyondCore is the engine behind the “discover smarter insights” capability in Analytics Cloud Einstein, and it’s already available.

Purchased in September, BeyondCore is a cloud-based data-discovery and analysis platform that takes in data and statistical summaries of big data and then uses machine learning to automatically spot correlations and patterns in that data. Rather than starting with hypotheses developed by data scientists, BeyondCore is designed to let business analyst select measures to investigate, such as cost, profitability or customer lifetime value. The engine then identifies and explains the drivers of a measure or combination of measures.

BeyondCore analyses and explanations are delivered in the form of text-based “stories” that are generated automatically and that answer four questions: What Happened? Why did it happen? What will happen? And how can I improve? The answers to the last two questions are predictive and prescriptive insights, respectively. BeyondCore stories can be exported as Word documents or PowerPoint presentations and the engine also generates supporting data visualizations.

To gain insights, BeyondCore connects to data sources including popular relational databases and Hadoop. When data sets are too large to load into the cloud the engine can create and rely upon statistical summaries of larger data sets. Soon after BeyondCore was acquired, a data connector was added for Salesforce CRM data, and a data output was added for Salesforce Wave, so predictive and prescriptive insights can be exported to the Analytics Cloud. Beyond Core was sold based on per-user pricing, and Salesforce executives tell me it will continue to be offered that way as an extra-cost option of Analytics Cloud Einstein.

MyPOV on BeyondCore. This is a powerful engine, and it’s no surprise it will play a prominent role in the Analytics Cloud as well as other Einstein capabilities. As for the limitations of the technology, I’d like to hear more about how quickly the engine can create statistical summaries of big data and how quickly it generates analyses. I’m told the engine can spot complex correlations and patterns across as many as 100 columns of data, but it also requires at least 10,000 rows of data to deliver statistically reliable results. You need a lot of data to make smart, automated decisions, so this capability may not be applicable to small and midsized customers that have fewer than 10,000 customers in key categories.

Recommend Products with Commerce Cloud Einstein

Several Einstein capabilities tied to the Commerce Cloud are either already available or will soon be available because they were developed or in the works at Demandware. Acquired in June, Demandware had a machine-learning based ability to automatically recommend best-fit products for customers based on their individual histories and the history of like customers. It’s a recommendation engine, and it’s a built-in and immediately available part of Commerce Cloud Einstein.

The same recommendation technology also powers an optional (extra-cost) Predictive Email service than can serve up personalized content, offers and product recommendations. Per-month pricing is based on the size of the mail list, but you can send as many personalized messages as you want. Demandware also had two other capabilities in the works that will soon be part of Commerce Cloud Einstein. Commerce Insights, a market-basket-analysis dashboard for merchandizers, is expected to be a built-in capability available before the end of 2016. Predictive Sort, a personalized product search and sorting capability, will be an optional feature, and it’s expected in the first quarter of 2017.

MyPOV on Commerce Cloud Einstein. This sort of recommendation technology has been available for quite some time. It’s table stakes for e-commerce, but it rightly belongs in the Einstein feature set, as machine learning is used to look at customer behavior data, catalog data and online and offline order histories.

Developer Cloud Einstein

Predictive Vision and Predictive Sentiment services are now available in Developer Cloud Einstein, and they’re based on the technology of MetaMind, which Salesforce acquired in April. Demonstrated at Dreamforce by Dr. Richard Socher, formerly MetaMind’s CEO and now Salesforce chief scientist, the vision engine was shown to be easily trainable by business-user types by dragging and dropping collections of images. The ease of training and implementing MetaMind’s sentiment engine was less apparent, but it’s clearly geared to straightforward natural language interaction and use by business users.

MyPOV on vision and sentiment AI. Usually you see mundane demos of such capabilities in marketing use case. (And, yes, these and other services will be available in Marketing Cloud Einstein to foster responsive and personalized customer interactions.) I was far more inspired by the example of MetaMind customer vRad (Virtual Radioligist), which is using vision services to save lives by reviewing thousands of brain scans within seconds to spot and help doctors prioritize cases of life-threatening inter-cranial bleeding. Inspirational!

What’s Coming

The list of Einstein capabilities not yet available is longer, and it includes many of the AI capabilities coming to the Sales, Service, Marketing and Community clouds. There are exceptions, such Predictive Lead Scoring and Opportunity Insights capability announced at Dreamforce. These organically developed features are currently in pilot and will be generally available as part of Sales Cloud Einstein by February. Similarly, Marketing Cloud Einstein scoring capabilities developed out of the Exact Target acquisition will be available this December. Finally, certain SalesforceIQ capabilities derived from RelateIQ are now part of Einstein.

MyPOV on the State of Einstein. It’s no surprise that integration work lies ahead before Einstein capabilities will be available across the Salesforce platform. After all, really important components such as BeyondCore have been a part of the company for just one month. Indeed, we’re at the very beginning of the company’s AI push. Salesforce not only has to turn these, and perhaps other, acquisitions into services that are consistent with its existing machine learning, deep learning and natural language processing work, it also has to embed many of these services into its various clouds and introduce feedback loops so that Einstein services can learn as new data is generated.

Will it take six months, eight months or a year or more to deliver all the Einstein services described at Dreamforce 2016? That’s unclear, and aspirations may well shift along the way. I, for one, hope that Salesforce will also introduce plenty of options for human oversight, such as rejecting recommendations or insights that subject-matter-experts deem to be off base so the technology can learn from mistakes. I didn’t see any examples of that in the demos at Dreamforce, but we have to assume that Einstein (and other early examples of AI) will be less than brilliant at launch. If humans are to gain trust in AI services, we have to design apps that place their ultimate faith in the wisdom of humans.

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Salesforce Dreamforce 2016 - It's about the platform, not Einstein

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 the Dreamforce 2016, held in San Francisco, from October 4th till 7th 2016. As usual, it was a massive affair, Salesforce claims over 160k attendees. Noticeable from last year, our conversation with exhibiting partners was more positive than last year, so Salesforce has done something right. 

 

 

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 2 slide condensation – overall and for platform (if the slide doesn’t show up, check here):
 

 

Event Report - Salesforce Dreamforce 2016 - Einstein is show, platform progress is real from Holger Mueller

 

Want to read on? Here you go: 

 

Overall

Salesforce joins the AI frenzy – No conference in 2016 without AI, so Salesforce made no difference, Einstein (like most other announcements) leaked weeks before Dreamforce. The main message is that Einstein works out of the box, no data scientist required, and of course works on all Salesforce data. But the examples were almost all based on scoring and recommendations, capabilities that Salesforce had already, or just acquired and are really the conservative bottom of AI. E.g. scoring does not pass the muster to be AI. But it works and that’s what matters. Basically Salesforce has put all predictive analytics (or I call it ‘true’ analytics – see here) under the Einstein marketing umbrella. And now needs to figure out more on what the platform is, how data will be aggregated, where that all will run, what it means for privacy and security etc. etc. [Salesforce points out that everything starts with trust at Salesforce, and that Salesforce is committed to not share data across customer boundaries. Did you catch that as an attendee / watcher? I missed it.]. But a fair start that would have looked better with less marketing hype, but this is Salesforce. (Read below what Salesforce does to enable developers to build AI applications, that is interesting).

Commerce Cloud – Salesforce has a proven track record to help CRM specialists, the humans. It’s e-commerce capabilities were less developed and the recent acquisition of Demandware has changed that picture (see here for my colleagues take on the acquisition). that. Salesforce store / e-commerce capabilities are now more beefed up and legitimate than before, so time for Salesforce customers to look at the offering. Commerce Cloud certainly is the cloud with the most product in Salesforce’s cloud arsenal.

Another Quip at productivity – Another acquisition – Quip – has been integrated with Salesforce and tries to solve the separation of business applications and productivity applications – a long, practically forever challenge for business users. With a document collaboration model that brings together both structured, unstructured and document information, including the usual side bar for collaboration / notes it certainly is a plausible approach that Salesforce customers should check out.

Salesforce unshackles itself from the past – Salesforce is one of the SaaS pioneers, and has built on a very successful platform – Force – but it is showing its age since some time and it was not designed and built for the cloud age. Since years Salesforce is dancing a limbo between products built on force (e.g. Sales Cloud and Service Cloud) vs. those built on Heroku (e.g. Marketing Cloud) (and Heroku runs on Amazon’s AWS for the less technically inclined readers). Dancing is a great activity, but not the best for an enterprise software vendor, but given the success of the force platform - not an easy thing to address and solve. But Salesforce has made a substantial commitment to AWS (see our analysis here) and at Dreamforce said for the first time that force applications will run on AWS. Likewise, developers can be building force applications will be able to move their DEV ant TST environments to AWS. Both are key developments that allow the conjecture that at some point the Salesforce platforms may again be truly combined, at the moment it looks like on AWS Cloud. And that is a key takeaway, as life gets better for all players in the Salesforce ecosystem, customers, partners and Salesforce itself.

Platform

Events have arrived – The Force platform on an object model level showed its age as it could not model modern entities such as e.g. Events. Now with a new Events object being created as a first class Salesforce object, this gap from the past gets addressed. And it is key for the future of Einstein – as Machine Learning (and maybe later AI) cannot work on just ‘business data’ alone, they need to model events that resulted from business activities and know if an outcome is positive – or not so positive. So a key step in the Salesforce entity / object model. Unfortunately, it does not find itself on the platform side, too – where due to the acquisitions that Salesforce has made, there is a large variety on design how to model events and outcomes. It would be good to see Salesforce providing a common event model on the Heroku side of the landscape, too – we should see if that will happen. But for now a good move. 

Force to AWS – Mentioned already above, probably the most important takeaways for customers. When Salesforce succeeds in running the force apps (mostly Sales Cloud and Service Cloud) on AWS, it will change its need to invest into, build, operate and maintain data centers. A very important move that should ultimately allow Salesforce to put more R&D on product development, but also help customers with more local instances of Salesforce, which helps both with performance and data residency requirements. Allowing developers to develop in force on AWS is another key step here. We will be watching.

PredictionIO comes to Heroku – This was actually the most important announcement for Einstein going forward. More developers to build machine learning applications with the help of the PredictionIO capabilities. Salesforce needs more AI applications and empowering developers, who in general are keen on this and hear from all tool makers how they can build Machine Learning / AI aps is important. Didn’t have a chance to see how easy Salesforce is making this, but this will be key for Einstein success going forward.

Kafka runs on Heroku – A consequence and productization of last year’s IoT Cloud announcement, but an important step to ingest data from other sources into Heroku, which becomes more and more the future of Salesforce Application Development – both internally and for the developer community. Let’s hope Salesforce did not have to do too many tweaks to Kafka for it to run on Heroku, so that Salesforce developers can take advantage of the high speed of innovation we see on the open source side. Future uptakes of Kafka in Heroku will write that story.

MyPOV

A good Dreamforce for Salesforce. Einstein took center stage, and certainly was a ‘must do’ announcement for Salesforce – given the announcements by the competition in the same area. But the real advancements, with short and longer term effect are on the platform side – the move to Heroku / AWS. Something that could have happened a few years ago now finally seems to take place, we don’t know what made Salesforce execs wave the start flag – but realizations like at customers that common data center based infrastructures do not scale for next generation applications may have happened in a Salesforce office, too. So it is good news for Salesforce customers that a potential end of the dual infrastructure reality is in sight, a move to AWS / Heroku can only help customers. More modern development, DevOps tools, best practices like CI / CD become tangible now, very important to make sure Salesforce not only remains an attractive and viable platform, but also to make its SaaS applications more innovative.

On the concern side, time is of the essence for Salesforce. Key competitors in e.g. CRM as well as platform have made that move already and do not have to pay the tax for a dual platform in all phases of the software development lifecycle. The problem is that Salesforce product development speed is not moving breathtaking fast, e.g. an indicator is that the Sales Cloud has not fully moved to the new Lightning user experience / platform, that was announced …. at Dreamforce… 2014. Granted, Sales Cloud is a massive product, with lots of screens, but 2+ years for a new UI is clearly in negative record territory for any leading SaaS vendor. On the flipside Salesforce is making the right moves to make developers faster and more efficient – when it happens for (paying) platform developers and ISVs, it also happens for Salesforce in house developers. How much will be built in the next 12 months will give an answer to that.

Overall a good Dreamforce for Salesforce, granted platform is not the topic 160k attendees will get excited, but platform messages work and are important (see Salesforce ‘cousin’ Workday making the platform theme the one theme of their keynote – last week) – even for end user audiences. I can’t wait the Salesforce marketing skills and spend to be unleashed for platform – maybe at Dreamforce 2017? We will keep watching.


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

 

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