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The Research Agenda for thinkJar in 2017 is Public

Over the last week or so I published the many pieces of the thinkJar 2017 Research Agenda.

Here are the five parts:

Customer Service Usage and Adoption –

Data Use in the Enterprise –

Artificial Intelligence in the Enterprise –

Platforms and Ecosystems  –

Customer Experiences and Engagement –

I got great reception, many people called or wrote to discuss this or that and what it means, why these five topics, how it all comes together, etc.  There is a method to the madness, and it’s not just because these are the most hype-full topics out there (else I would’ve added IoT and Big Data as words, maybe even analytics).  It is because (take out Customer Service, I am using that as a placeholder – but you can use any discipline: marketing, inventory, distribution, human resources, etc.) it shows the way forward for all organizations over the next decade or so.

It is because it is the way forward for all organizations over the next decade or so.  I am using Customer Service as a placeholder – but you can use any discipline: marketing, inventory, distribution, human resources, etc.  Will show you.


Come back tomorrow, but I promise you – you won’t be disappointed… (btw, come back tomorrow bc historically Tuesdays as the best read days in my blog… simple use of analytics…)

disclaimers: none at this time, except to say I am tired and stressed out like all of you about this “new democracy”… will be ok, takes time.

Research Agenda 2017: Customer Experiences and Engagement

Final post (thanks for the patience while grudging through this) in the series.

I wrote earlier this year that I was changing the model for my work and that I was going to be focused on five topics, with formal research agendas.  I shared with you first the model for Customer Service Usage and Adoption, then the one for Data Usage in the Enterprise,  followed by Artificial Intelligence in the Enterprise, and Platforms and Ecosystems.  This is the final topic: Customer Experiences and Engagement.

I know that we have been talking about Customer Experiences for a long time; Ed Thompson and I wrote “the original bible” (a stunning 48-page primer on it) back in 2003 while I was at Gartner.  Much has changed, and lots have not also in the interim.  For example, fewer people and organizations are just looking for a technology solution for the enterprise.  Yet, many still are looking for a vendor to deliver a “customer experience suite” to help them with what they have to do.

Engagement is a more modern topic (one that my good friend Paul Greenberg has chosen as the topic for his next book – due end of this year hopefully), and I wrote a detailed report in 2013 with Thunderhead about it, after interviewing some 40+ CMOs across the world.

There is, however, a misnomer that I will try to stamp out in my research on this topic  The evolution is NOT CRM –> CX –> CE.

Not by any intelligent attempt.

The topics are related, and will bring that to bear, but they are not interchangeable or even correlated.  You can, for example, launch a customer engagement initiative without even trying to understand what Customer Experience means or how you address it.  True, trust me.  Will explain more in the research I publish later this year… which is the following five notes:

  1. February 28 – CEE Market and Definitions
  2. April 4 – Trends and Adoption in CEE
  3. May 16 – Use Cases to Consider when Launching CEE
  4. June 20 – Pulse of the Market for CEE
  5. September 26 – Case Studies and Interesting Cases in CEE

In addition, of course, there is a primary research project planned for CEE that may – just may – be a quantitative project.  I originally had planned to do a qualitative project, but I think the market has matured enough by now that I can get some interesting data.  Will evaluate as we get close to the date (the launch date for the project is after the summer, there’s time).

How can you help? How can I help you?

See below? there’s a comment section.  You can put stuff in there… Or, you can drop me a line and let me know what interesting data, facts, use cases, case studies, and others you have around the topic.  Or, if you have any questions — just let me know.  Glad to answer anything, or help, or — whatever helps me further the understanding of the topic.

Anything I missed?

disclaimers: in 2013 (and later) I worked with Thunderhead and under their auspices wrote the engagement report I referenced above.  I ran the entire project independently and wrote what I saw in the market, regardless of fit for their message.  they sponsored me on that project and a few others, and I am forever grateful.  if you want to see the report, which I cannot post due to distribution rights, please let me know and I can get out a courtesy copy.  everything else that needs to be disclaimed has already been done in past posts.  thanks for reading.

Research Agenda 2017: Platforms and Ecosystems

The fourth installment of the research agenda for 2017 (and beyond) is the basis for all the others to exist: Platforms and Ecosystems.

I told you a few weeks ago that I was changing the model for thinkJar (my company, in case you just know me – or neither) to publishing more structured research.  Part of that commitment is creating a research agenda – but had to be something I can manage as a solo practitioner.  Thus, I have created the agenda for the five topics I will cover, and have been publishing them here.  So far,

Customer Service Usage and Adoption (CS)

Data Usage in the Enterprise (DU)

Artificial Intelligence in the Enterprise (AI)

Platforms and Ecosystems (PE) (this one), and

Customer Engagement and Experiences (CEE) (tomorrow)

With that bit of administration – let’s focus on Platforms and Ecosystems.

This should be a simple one, the cloud has three layers – Infrastructure, Platform, and Software – and this is where we deal with the middle layer.  I have written extensively about the cloud before, and it is all summed up in my wonderful eBook “Defining a Pure, Open Cloud” (link to PDF) where I make the statement that I have sustained since the early 2000s, platforms are going to rule the way we use software in the enterprise.

That is still the case, and that is what I am putting into research and emphasis into this new year.  For platforms to operate the proper way, ecosystems are essential.  Much to the chagrin of — well, enterprise software vendors, there won’t be a simple business that can operate on a single platform; relationships between platforms become ecosystems and ecosystems are what deliver value in the cloud.

Pure, Open, and Simple.

The research I want to publish this year (and into the future) will focus on this: how can an organization embrace an open platform and create an ecosystem? To this end, I want to attack this from the following perspective:

  1. March 7 – Definitions and Market Structure
  2. April 11 – PE Trends and Adoptions in the Enterprise
  3. May 23 – Use cases for PE
  4. June 27 – Pulse of the PE Market
  5. October 3 – Case Studies and Examples in PE

How can you help? How can I help you?

If you see something – say something!

But, no – seriously folks…

If you have an interesting story to tell, a point to discuss, a something to contribute – or any questions about the topic, please drop me a line.  Any questions not related to the topic, the answer is 42 – but you already know that.

Tomorrow, the last installment of my research agenda: Customer Experiences and Engagement.

Anything to add?

disclaimers – what can i say? vendors are not guiding my research? said that already, this is complicated? said that too, you cannot get there from here without help? you know that.  bottom line, i write my own research, do my own primary research, and everything i said is backed up with data and years of doing it.  don’t try this at home.

Research Agenda 2017: Artificial Intelligence in the Enterprise

This is the third installment, and likely the one that’s more ambitious and controversial.

I wrote earlier this year that I was changing the model for my work and that I was going to be focused on five topics, with formal research agendas.  I shared with you first the model for Customer Service Usage and Adoption, then the one for Data Usage in the Enterprise, and I am somewhat hesitant to offer this third installment: Artificial Intelligence in the Enterprise.

Back in the early 1980’s, I got my hands on the (then) newly-published “Encyclopedia of Artificial Intelligence”.  It was three volumes filled with (almost) everything we had learned about AI until then – almost 2,500 pages (and has been updated many times since – I think it is in the fourth edition).  I read, devoured, the contents and then set out to find out more.  I lived in Argentina and I was in HS – not easy to find computers or programs that were made available to kids, but managed to find some things: compiler design classes (YACC), programming classes (Javelin, a 4GL expert-systems language), and some other conferences and seminars.  Later in college, I took classes on building neural networks (Pascal and C, tried but not enough you can do in a 10-week class) and around symbolic systems.  Did other things along the lines of linguistics (I know, the irony – the foreigner working on linguistics – right? most of the top experts were foreigners at that time), psychology, cognition,  learning,  decision making, expert systems, and analytics along the way.  All in all, have been around this world for some time.

The hype surrounding AI these days is deep – even deeper than it was for Big Data (hear much about that these days? yeah, not as much… not as much). The “market” for AI is nowhere even beginning to set (like the one for Big Data – but that is another research topic) and there is a ton of potential – most of it latent for the past 40+ years while waiting for more power and faster speeds from computers (similar story to the world of Data Usage in the Enterprise).  I want to bring a lot of that potential to the surface and hopefully see it realized.

I am covering AI as a starting market not because the concept or technology is new, rather because I see it as a starting point to where the enterprise can enter and get value out of it.  Until now vendors used decision systems, analytics, NLP and linguistics and other technologies as part of what they did and never thought of using AI (there was a time during the 1990s when we used “Fuzzy Logic” ‘member?) for their pitches – but now they all do.

What is an enterprise to do? How do you distinguish between ML offerings from an IaaS provider and a chatbot from a .ai  vendor? This is where I will try to show some structure.  The notes in my research agenda, and the deadlines – which are all subject to change and to be added to as the year goes by – are:

  1. February 21 – Artificial Intelligence in the Enterprise Market
  2. March 28 – Trends and Adoption for AI
  3. May 9 – Enterprise Use Cases
  4. June 13 –  AI Pulse of the Market
  5. September 19 – Interesting Examples, Case Studies in AI

In addition, I will be conducting a qualitative study on AI (talking to interesting people in the field, as well as to practitioners, to get a better perspective of what’s going on; will publish findings on June 26.

How can you help (or how can I help you)?

If you have an interesting case study, if you are willing to be interviewed for a “lessons learned” interview, if you have some wisdom or insight you’d like to contribute, if you would like to discuss the market — or if you are looking to sponsor the research (wink-wink) simply contact me.  You can also leave a comment below if you prefer.

(part four of this five-part research agenda publishing is coming soon and will display the same information for the topic of Platforms and Ecosystems)

disclaimer: no disclaimers at this time, sorry – writing templates and prepping to cover 5 research topics has taken all the humor away… sigh (it’s so bad, i am even reusing disclaimers) — but i have to say .ai and IaaS are not my clients, they are mentioned as distinguishing factors in conversation.  Any coincidence with an actual vendor, totally and purely koinki-dinki.

Research Agenda 2017: Data Usage in the Enteprise

A few weeks back I told you I was changing the model for thinkJar, then I shared with you the agenda for customer service.  This time, I am presenting you with the agenda for Data Usage in the Enterprise.

First, the question everyone asks – what is Data Usage in the Enterprise.

I came to this name because I am not very creative with TLA (three letter acronyms, if it has to be said) to be honest (or TBH – to stay on topic…).  I cannot think of something like “Enterprise Data Usability  – EDU” o “Enterprise Data What-To-Do-For-Value – EDW” or anything like that.  I mean last two times I had to do TLA I ended up with EFM and CIH – neither one of them lighting the world on fire…

Reality has four facets:

  1. There is an ungodly amount of hype surrounding the use of data in the enterprise – from Big Data to Analytics and even ending up in AI these days.  Everyone is calling it something different — yet…
  2. The need for data usage in the enterprise has not changed much over the past few years (I’d even say decades, but we have a different battle).  What has changed dramatically is…
  3. The power and speed at which we can process, store and use data have grown dramatically.  When Gartner was doing RTE (real-time enterprise — see, TLAs are not good) back in the early 2000s we lamented that the lack of processing power and speed was the killer for the RTE.  However, in spite of this growth…
  4. Enterprises continue to have similar needs: create a data management model to make data-backed decisions efficiently.

And that is what Data Usage in the Enterprise is: a summary of a market that has many vendors aiming to help customers make sense of their data needs, and service those to create better decision solutions.  Everything else is just plain hype.

I will focus my research on what I considered a semi-mature market (virtually all organizations have more than three “analytics” systems in place today) and cut through the many hype-tastic labels to give you a useful model of what you should be looking for, where, and why.  I am working this year the following pieces (with due dates, which could change based on external factors):

  1. February 14 – State of the Market: Data Usage in the Enterprise
  2. March 21 – DU Market Drivers and Inhibitors
  3. April 25 – Latest and Greatest Lessons Learned: DU
  4. May 30 – DU Implementation: A Case Study
  5. September 5 – DU Implementation: A Case Study

I may also, from time to time, write something that is not on the agenda as it fits into the research (already got some ideas, but always welcome more)… How can you help (or how can I help you)?

If you have an interesting case study, if you are willing to be interviewed for a “lessons learned” interview, if you have some wisdom or insight you’d like to contribute, if you would like to discuss the market — or if you are looking to sponsor the research (wink-wink) simply contact me.  You can also leave a comment below if you prefer.

(part three of this five-part research agenda publishing is coming soon and will display the same information for the topic of Artifical Intelligence in the Enterprise)

disclaimer: no disclaimers at this time, sorry – writing templates and prepping to cover 5 research topics has taken all the humor away… sigh.

Research Agenda 2017: Customer Service Adoption and Usage

A few weeks back I told you I was changing the model for thinkJar.

This is the first of five posts where I will give you more details on what I am working on this year.

First, and in its sixth year, the Customer Service Adoption and Usage report.

The Customer Service market is very much settled.  There is little innovation coming out the market and we are running a steady 10-12% growth in market value year/year.  We also have methodologies and models that are pretty much known by anyone and the major issues and trends in the market deal with how to optimize it, not just how to use the solutions in the market.

Due to the market maturity, I focus my research more on the lessons learned and what’s working aspects than in the “new shiny bead” solutions for it.  I am working this year the following pieces (with due dates, which could change based on external factors):

  1. February 7 – State of the Market: Customer Service
  2. March 14 – Customer Service Market Drivers and Inihibitors
  3. April 18 – Latest and Greatest Lessons Learned: Customer Service
  4. April 24 – Customer Service Adoption and Usage Report: Findings and Analysis
  5. May 30 – Customer Service Implementation: A Case Study
  6. September 5 – Customer Service Implementation: A Case Study

How can you help (or how can I help you)?

If you have an interesting case study, if you are willing to be interviewed for a lessons learned interview, if you have some wisdom or insight you’d like to contribute, if you would like to discuss the market — or if you are looking to sponsor the research (wink-wink) simply contact me.  You can also leave a comment below if you prefer.

For anything else, there ain’t no Mastercard — you are going to have to wait.

(part two of this five-part research agenda publishing is tomorrow and will display the same information for the topic of Data Usage in the Enterprise)

disclaimer: the bad joke about not having Mastercard has no relationship whatsoever to the company and is a bad take on the commercials they use to run in the 1990s.  if it offends anyone, who is not with the Mastercard corporation, nothing I can do. if they’re with Mastercard, apologies and let me know – will take it down and apologize before you involve your lawyers (but after we discuss your improper sense of humor maybe).

A New Model for thinkJar

In the perpetual search for a better model to share research, news, reviews, and more I am “formally” changing what I do.

I am, for the first time since Gartner, creating a research agenda.

There are five topics in it right now:

  • Customer Service Adoption and Usage
  • Data Usage in the Enterprise
  • Artificial Intelligence in the Enterprise
  • Platforms and Ecosystems
  • Customer Experiences and Engagement

There will be primary research aligned with both (quantitative for more established markets, qualitative for emerging markets) and  “reports” like drivers and inhibitors, case studies, lessons learned, business issues, executive conversations, and more.


The dates and titles will be published (in my *new* website – coming in January) so you have an idea what I am working on, but one thing will remain clear – this is not about covering vendors or promoting vendors, or comparing vendors, or selecting vendors.  The goal on all this is to provide a strategy-focused, market-based perspective of business and technology issues – not a vendor-influenced view of how to sell software.  This is about feeding strategies and aligning technology and business.

Why am I doing all this?

Two reasons: I am overly tired of hype; I’m going to focus on finding and publishing real information (call me the anti-fake-news-for-the-enterprise crusader if you may), but more importantly because I sense that this information will be, for the next 2-3 years, far more valuable than thought leadership and visions for the future.

It’s time to GSD.

Adding my 2 drachmas to it.

disclaimer: there are commercial aspects to this model, and it’s not a subscription option.  everything I write will be published for free either here or my LinkedIn page. I very so much appreciate my clients that over the years have paid me to do research just because they think I  bring a different perspective – and would like to continue that.  

As a shameless plug, if you are and vendor and interested in learning how you can support my “research habit” for the topics above, please drop me a line.

Democratizing Large Enterprise Chatbots for Small Businesses

I must confess, I am a bit of a nut about chatbots.

I have been doing AI and related technologies (including NLP and bots-like tech) for quite some time and was a firm believer in the early waves of chatbots (back then we called them virtual agents – this is circa 2000 and was just starting to enter the realm of large enterprises).

Still am a firm believer, despite the bad name that they engendered through the early parts of this century due to lack of quantifiable results.  I mean, the worked – they just didn’t register well enough with the metrics we needed to justify them (read: too complicated, expensive, and cumbersome to maintain).

As we evolve with these technologies I am more inclined to believe that it is one the viable paths to get to full automation (which continues to be my dream for customer service) – or close to it.  But, to get there we need to simplify deployments and the maintenance.

This is why I was excited to hear that one of my clients, NoHold – one of my oldest and dearest clients with good virtual assistants / chatbot technology, was thinking of doing that  – making it simpler, cheaper, and easier to deploy them.  We had been talking for a while about their plans and the research they prompted via an inquiry eventually became the latest linked-in blog post I wrote: Chatbots for the Small Business.

In that post, I claim that simplicity and democratization of the resources inherent to chatbot’s world can be used by small business easily to fulfill their needs.  I also make the statement that if this works for small businesses then anyone and anything can leverage chatbots.  My hope is that we can all, for professional or personal reasons, create and manage an army of chatbots that, easily and with limited or no maintenance, will allow us to focus on the real needs we have and simplify the easy interactions with others.

psalbert-noholdToday, NoHold launched their product.  And I must confess I am interested in it.  Very.  Three reasons why:

  1. Democratization of Chatbot Technology.  This the most interesting point to me: bringing the complicated components necessary to run chatbots in a simple manner to all organizations.  In my past post, I looked at a handful of companies that are working on that – but this launch is a tad different.  NoHold has lengthy (read, over 15 years) doing this and they have evolved not only their chatbot tech but also knowledge management components to deliver complex bots. Their new QuickSmart platform, a subset of all those components, emphasizes the ease of deployment and lack of training for a chatbot. It has all the large scalability and powerful language and intelligence processing elements that are provided in a simpler turn-key (or almost solution).  There is no easier way to democratize technology than to make the complex simple and to allow organizations access to high-tech that would otherwise be unavailable to them.  This is one of those moves.
  2. Platform Based.   For small businesses, the ability to do little and get lots of value is a preternatural pursue;  having limited resources to begin with they are always looking for better ways, and cheaper/faster/easier/simpler ways, to accomplish what they need.  Using platforms, and their inherent ability to both leverage other elements as well as easily customize and personalize the outcome, is a natural – and one of the reasons cloud computing has been growing steadily among the SMB crowd.  From their early days, NoHold had relied on their SICURA platform to deliver ever-easier implementations of chatbots for their customers and now they have extended that modus operandi to their new QuickStart platform for small businesses.  This means that SMB organizations can now – for the most part, there are some differences – create and deploy similar complexity chatbots in their service setups.  This is not only good for the SMB, but also for the large enterprises that benefit from the lessons learned in using platforms more efficiently.
  3. No Coding.  SMB needs are easy and simple.  The lack of resources becomes an insurmountable issue if, as with most enterprise chatbots, a few months of work are required to deploy.  Removing the need for complex configuration and simplifying or eliminating coding is the easiest way to serve the needs of the small business – but also the needs of large enterprises that may have business workers creating chatbots as needed.  NoHold QuickStart uses word documents, with minimal formatting and structure, to create a chatbot.   This is a tremendous advantage over the traditional way to train and implement a chatbot – where mostly a set of Q&A is loaded one by one via an interface – and one that can leverage documents already created by the company for other purposes.  This is a critical sine-qua-non in my opinion for small businesses to embrace this technology as — well, anybody in the business world knows how to use Word.

There is a lot more to cover in this release: more features, more functionality, more use cases, and more examples – but I think the points above are what make my interest in this launch so.  If there is a way to leverage existing, complex technology and deliver a simplified model of the same to democratize access to the technology – to me that’s a good day.

Read more about the launch here, and read more about the product here.

disclaimer: NoHold is a client, one of my oldest clients, and I have received compensation along the way to help them with this and other launches.  I was drawn into drafting my Linked-in post based on research I had to do to answer their inquiry.  I am solely responsible for this content and the opinions expressed herein and no one at NoHold attempted to tell me what to say or do.  if you disagree with my position, that’s good if you have the data to back it up.  if you agree, that’s better.  I do believe we are just tapping the surface on what we will be able to do with chatbots and will continue to cover this as it becomes available – a large part of my research going forward is about artificial intelligence.  if you have any worthwhile announcement, launch, or something interesting to share with me in this field – please contact me.

ps – since i wrote this I also saw coverage in Forbes and TheNextWeb.

The Year of Respite

According to Google’s definition (which I am sure comes from a dictionary – but not even my kid knows what those are anymore) respite is an ancient word that comes from a latin root meaning refuge or consideration.

The proper definition is below – you can read it, but I’ll save you the trouble – it’s about taking a moment to rest, or to get relief, from something hard or difficult.

((definition of respite))

As John Oliver said in the November 13 show when talking about the election results (and I won’t even talk about that) – 2016 has been a “difficult” year (he used other words that Siri would’ve translated to Ducking Shorty) — and that applies to our jobs as well.  We are at the end of a 6-to-8 years stretch overflowing with hype and “new things”.

The year 2010 shall forever live in infamy: the demise of global economies peaked and the hard job of rebuilding began.  Investments in Enterprise Software were nil or as close as possible to that.  The pace of innovation skyrocketed.  The technologies and tools we discovered since the turn of the century were put to the test in “credit card” or skunkworks-style projects that virtually all organizations had under way for social, collaboration, and data use.

As social began to slow down “Big Data” began to boom – and virtually anything that could be hyped was.  We hyped Big Data, Analytics, Cloud, Artificial Intelligence, Platforms, Mobile (remember BYOD?), and just about anything else.  We saw the rise of concepts like Citizen Programmers (or Employee Programmers if you prefer), Digital Transformation, Self Service and full-on Business Automation, Predictive and Prescriptive Analytics, and Collaborative Enterprises with Communities.

In other words, we fully dedicated ourselves to anything that may have an impact on the organization going forward as we knew that the lull in investing was temporary and we would go back to doing something — just didn’t know what and wanted to be prepared; we continuously sped up until this year.

And it was exhausting.

My clients are realizing that they cannot implement everything we hyped over the last few years; none of these projects are 6-month to one-year projects.  These are long initiatives (cloud migration can take up to a decade, no digital transformation initiative at a corporate level will be done under five-to-seven years, artificial intelligence? try 20+ years) that may yield some results in the short-term – but only to whet your appetite for more.

Just learning what data you need, where it comes from, and how it can be used can take a couple of years of trial-and-error – and then you still need to implement your findings and use the data for something useful.

And this is where we are today.  2017.  The year of respite.

This is the year when everyone is asking the same questions – not merely “what is that?” or “how do you use it?” but more like “show me the value it can bring to my organization and my customers in the short-, near-, mid-, and long-term” or “tell me what are the implementation strategies that have worked and how I can adapt them to my organization”.  There is also “help me understand the necessary elements and changes I need to undertake to take advantage of that technology or tool”.

And my favorite – “where do I start?”  Or rather, “where did Acme start to succeed at technology X?”.

This is not the year where I am going to become famous (infamous?) by concocting a new “vision” or “business model”(like my lemniscate back in 2010) or writing about “thought leadership” that describes where we are going to be in the next 10-20 years.  We realized that where we are going to be in a decade’s time will be very different for each and every one of us.

I don’t want to sound the alarm – we are not doomed; we reached the year of respite: the time when we stop looking at what’s possible and how hard it would be to change it and instead we focus on how we are going to get these things done.  The focus is in doing, not in talking.

Are you ready?

( this is part one of a two-part series – the next one will tell you what I am doing in the next few months/years to help you get ready)

disclaimer: Acme has never been, is not, and likely will never be a client.  It should go without saying, but these are my experiences and thoughts and they may differ from yours.  In which case, we both know you are wrong and should embrace mine.  If you don’t, I cannot be held liable for your failures – capisce?

Why Salesforce Should Buy Twitter

Bear with me before dismissing me as crazy — I love the idea.

Here is the explanation, which SFDC may dispute. Read it in its entirety before pronouncing judgment.

Twitter is not a social network. It has failed dismally at it. Facebook has eaten their breakfast, lunch, and dinner and drank their milk at it.

At the beginning Twitter was cobbled together in a rush to launch at South-by-Southwest.  It grew, haphazardly, showing a ton of problems and a total lack of scalability (as did Facebook at the same time).  Then it was shut down temporarily and emerged a solid,  well built notifications, communications, and distribution network that can handle massive volumes – greater than any enterprise software application ever had to handle.

The gap between “fail whale era” and today is yuge. failwhaleRemember??? **

Today, it’s a stable, functioning, scalable, publish-subscribe,  notification network can handle live video in volumes that were previously unthinkable.  There is no well established, viable revenue strategy in place today — but….

SFDC wants to grow 10+ times larger than it is today – maybe more (I hope more, and this is my assumption).  Oracle could claim they are the first vendor to reach $10bb in pseudo-cloud revenues if they get there first, but SFDC can claim to be the first one to get to $100bb in actual-cloud.  I may be overextending myself – but I believe it is totally doable with some tweaks to their technology, including the underlying infrastructure.

A few years back SFDC launched Salesforce1 – which I claimed was their first move to become a three-tier, open-cloud, platform-based enterprise software solution.  Last year they introduced Thunder (which in spite of their term of “IoT Cloud” is a data abstraction layer that allows them to process massive amounts of data in real-time), and this year they introduced Einstein – a platform play to give all their applications access to Artificial Intelligence and Machine Learning (eventually).

Yet, among these many moves to strengthen and grow their platform the underlying architecture  has not evolved equally.  Since the times of the Social Enterprise and the introduction of Chatter – when SFDC changed the paradigm of what it is to work in an enterprise application – the volumes keep increasing… yet the architecture did not.  Chatter volumes are very large, but I doubt (and will likely be told I am wrong) that it can handle the volumes associated with a 10x growth spur.

If they do buy twitter (we can argue price and value later, as we did with WhatsApp – which proven their $19bb value to Facebook many times over my initial apprehension), they have to shut down the crappy social network. They must get rid of a network where most of the ugliest things in life happen (the Kardashians can move to Snapchat, Trump will eventually fade away) and let them find a new home (maybe they move to Facebook and we either can get them under control or shut down Facebook and get rid of the privacy invasion… double bonus!)

Why shut it down?

So they can begin work immediately to make the underlying infrastructure of twitter the replacement for Chatter as a distribution network.  It should take  +/- 18 months (I have not done a technology due diligence, may be way off here – I am not inside and need more details before making this assessment).

SFDC is then a platform-based solution with a world-class distribution and notification networks, a data abstraction layer to match it (thunder/IoT cloud), a soon-to-be-set-of-services for AI (Einstein), and the potential to become the enterprise software platform it needs to be for the next generation.

From the technology perspective it adds a layer of infrastructure that they need (I’ve heard some things about thunder being able to handle the load for chatter, etc. – have not explored it sufficiently but my doubts are in handling the growth not at today’s levels) to match their hopes for growth.  A killer, scalable, massive pub-sub network as the underlying infrastructure for a platform for enterprise software could be the ticket to that 10x (or more) growth – and be worth a ton.

Dismiss me now, I am done.

disclaimer: these machinations are what happen when I am asked what in the surface seems like a simple, inane question – and one that everyone expressed to be impossible and dumb.  Is not that I want to be contrarian, but I wanted to use a different thought process — what if we went beyond the surface? that’s usually how i approach things — looking for their potential, not the face value. I don’t know all the details on the architecture for both and this could be a horrible idea.  I will eat crow if that turns out to be the case… but it would make for a wonderful differentiator if it isn’t.  You know the rest: SFDC is a current client and they paid my expenses to attend Dreamforce; oracle was a client in the past; twitter was never a client; any other vendor implied or any other relationship assumed is a mere coincidence and even if they weren’t they don’t signify endorsement or agreement or influence.  I have enough friends and “frenemies” at SFDC that some will laugh and some will nod.   Either way – mistakes are mine, I own the opinion and it is not influenced by anything other than a long week without much sleep and too much meat (hope my doctor does not read this).

** image credit: By Source, Fair use,