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 – http://estebankolsky.com/2017/01/research-agenda-2017-customer-service-adoption-and-usage/

Data Use in the Enterprise – http://estebankolsky.com/2017/01/research-agenda-2017-data-usage-in-the-enteprise/

Artificial Intelligence in the Enterprise – http://estebankolsky.com/2017/01/research-agenda-2017-artificial-intelligence-in-the-enterprise

Platforms and Ecosystems  – http://estebankolsky.com/2017/01/research-agenda-2017-platforms-and-ecosystems/

Customer Experiences and Engagement – http://estebankolsky.com/2017/01/research-agenda-2017-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.

How?

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).