deal architect

Why the changes at SAP may be timely for the industry

Since Thursday evening I have heard from many if I am surprised by the changes at the top at SAP

My answer is "only slightly"

From Sapphire in May, I wrote after some of the changes earlier in the year

"Nick Tzitzon, EVP of Marketing and Communications had told me "we have individuals in some cases who collectively have worked for SAP for 100 years, and who decided that it was time to try something else in their career. Does this not happen in every company?" Hasso Plattner was even more emphatic when he told a few of us that several customer executives have marveled at the smooth transition. He told us SAP is much "younger" now and he has never been more proud of the company. Watch this keynote and see the energy of execs like Christian Klein, Ryan Smith, Jen Morgan and others."

At the event, Bill McDermott had asked me what I thought of the new-gen of executives.  In background, I had heard from many customers as I was writing SAP Nation 3.0 that their legacy ECC (and PeopleSoft and Baan) systems were increasingly a recruiting liability as they tried to hire younger tech talent. So, SAP's changes were in line with what many customers were experiencing. 

However, there are more executive changes since at SAP. So people ask did McDermott need to leave along with Rob Enslin and others this year? Who exactly are Jennifer and Christian? What's wrong at SAP?

Let me try and address them. I have not talked to Bill since Thursday but his decision to leave after 10 years as CEO is really not that dramatic. According to Korn Ferry, the average Fortune 500 CEO tenure is less than 5 years.

People speculate he wilted under pressure of activist investors. Frankly, as I wrote in the book, the last 3 years have been quantum more demanding.

"That (McDermott HBR) interview was conducted before President Trump came to office. I imagine there are now additional challenges in being an American CEO of a European company."

About Jennifer, Fortune included her in its list of the 50 most powerful women in the world. They described her meteoric rise at SAP "going from head of regulated businesses (till January 2013) to overseeing all of the Americas, Japan, and Asia Pacific in just five years.”

In my book, I cataloged the Digital Transformation that Christian has led within SAP. This extract should give you some dimensions of the complexity

"We are tackling the whole value chain at SAP. We are looking end to end, looking at marketing, sales, finance, service and support, cloud operations and the cloud infra- structure. That’s the first aspect of our transformation. Next, we are infusing machine learning, IoT, predictive analytics and these new technologies to bring the intelligence into play. It’s about adapting business processes to the new digital age. It’s about fixing the data architecture. Last but not least, it’s about change management, and taking the people with you. In the end, everything that we do in the cloud impacts our 90,000 employees and our 400,000 customers.”

They are both impressive executives. The Co-CEO arrangement is unusual, but not at SAP. McDermott was co-CEO with Jim Hagemann Snabe for several years. They replaced Léo Apotheker, who himself was once an co-CEO.

Honestly, I think the industry needs a jolt. While the book overall is positive to SAP, it highlighted the under-achiever performance of the industry in the last two decades. I mean that to include SAP and Oracle and Salesforce and Workday and Infor and IBM and HP and many others.

Amazon shows authors "popular highlights" that a cluster of readers mark on their Kindles. Chapter 2 of the book looked at the disappointing performance of enterprise vendors. The majority of the highlights so far for my book are in this chapter - see some extracts below, courtesy of Amazon's algorithms

Page 32

In the meantime, SAP and its competitors, who mostly focused on each other while trying to one- up each other and impress Wall Street, have missed out on large opportunities emerging around them. (the chapter discussed cloud infrastructure, smart products, digital advertising, IoT and many other markets that new entrants like Amazon, Google, Foxconn, Siemens and others took advantage of)

Page 40

In October 2018, Oracle co- CEO Hurd predicted that 80% of business applications would be in the cloud by 2025. Today, even after two decades of cloud computing, industry and geographic coverage is spotty — by my estimate, less than 20%.

Page 45

More alarmingly, we are starting to see customers get tired of waiting for vendors and go back to building their own replacement systems. With microservices, open source and other options, we are seeing the pendulum swing away from buy back to build. That’s a stunning reversal from the 1990s.

Page 53

However, they don’t have enough process visionaries who can dream up next- gen shop floors, emergency rooms or warehouses. So you see the industry continue to invest in infrastructure, platforms and tools.

 

If Jennifer and Christian only focus on financial performance, they will do little to change the industry trajectory. We desperately need them to aim differently. Much as I respect what McDermott did, I am looking forward to the next era at SAP with that hope.

A tumultuous couple of decades

As I was filing our quarterly Form 941 last week, it hit me that we have entered the last quarter of this decade. As I flew into LaGuardia on Monday, it took me back two decades to my time at Gartner. In October 1999, there was nervousness about the damage Y2K could cause, but also euphoria at the phenomenal few years many tech vendors had enjoyed. The introduction of the euro promised another bonanza for the near future. Enterprise vendors, particularly two sectors that I followed - enterprise applications and service providers - had reason to feel very good about themselves.

In New York this week I heard an optimistic Mike Salvino , the new President and CEO of DXC, at a HfS event. I heard similarly optimistic (for different reasons) Cognizant executives at one of their events the day before - they also have a new CEO. I also kept up with the celebratory mood at HR Tech in Vegas virtually.

At Gartner, I had followed SAP, Oracle, PeopleSoft and their SI partners. CSC, EDS and HP Services were players back then.  They continue to live on in the form of DXC, founded a couple of years ago. Cognizant had revenues of less than $ 100 million in 1999. It has grown like a rocket with over $16 billion in revenues now. HR Tech was only 2 years old in 1999. But the backwaters of the HR function had led the client/server applications market. This time around, it has similarly led the cloud applications market.   

There is lots to appreciate and applaud.

However, the even bigger lesson of the last couple of decades is the many missed opportunities. Outsourcers missed out on the cloud infrastructure market. It was theirs to lose and so far, players from left field like Amazon and Microsoft have benefited. After 20 years of cloud applications (Salesforce was born in 1999), application vendors can barely cover 20% of the country/industry grid. At the HfS event last week, the biggest attention was on robotic process automation (RPA). Hardly any talk of Amazon's Kiva warehouse robots, or UPS's drones or other operational automation. Or automating the labor intensive outsourcing space. CRM vendors completely missed out on the digital advertising market. Google, Facebook, and digital agencies have made out like bandits in the vacuum. SaaS vendors had an amazing business model which bundled multiple contracts - the license, the maintenance, the hosting, the application management - into one. Then they got greedy like their on-premise competitors. I saw Bruce Rogow, my former Gartner colleague in New York and he told me he sees in his travels CIOs struggle with new opex/capex funding paradigms. Instead of delivering a knock out punch, SaaS vendors mostly co-exist with their on-prem competitors. Outsourcers saw every one of their clients develop smart products and let contractor manufacturers like Foxconn and Flex dominate that market. They have let GE, ABB and others generate excitement about the IoT space.

Vendors ask me, what's next? Blockchain, machine learning, quantum computing, sure. But much bigger opportunities will persist from those missed in the last two decades. A new generation of vendors will move in. Vendors will cannibalize each other. And here is the most important nugget. Customers learn from their mistakes - I am seeing that in the new wave of SAP related services. In writing my recent book, I saw much more caution about using SIs as they implement S/4HANA. There is a "fool me twice, shame on me" vibe.

So, forget about doing business as usual. The best thing every vendor can do as we enter a new decade is to be introspective, even if Wall Street is pleased with your performance. Ask the tough questions - how the hell did we miss as many markets as we did? And how do we re-organize to make sure and not miss so many markets again?  

Exciting times ahead.

TAMyopia

When I was at PwC in the early 90s, I remember we used to throw out statements like "we have 40 to 50% market share in the SAP (or PeopleSoft) implementation market" When I got to Gartner I saw their competitors were saying similar stuff. So, I started telling them - "no, actually you have less than 10%" . The tune of at least some of them turned to "we have 50% market share of deals we choose to chase". Choice - certainly their prerogative which segments or verticals they chose to focus on. But it's not a reflection of Total Addressable Market.

One of my favorite advisory projects is helping vendors challenge their previous definitions of TAM.

I have seen plenty of dysfunctional behavior from technology vendors of every shape when their TAM definition is narrow  - salespeople chasing deals convenient to them, not what the leadership would have approved, marketing claims to deliver functionality in areas which their R&D had no intention of developing in any meaningful manner, CFOs and investment committees which don't approve enough R&D and Capex needed to keep their entities competitive.

In my recent book, I outlined several trillion dollar markets that many technology vendors have missed since the late 90s. Those included smart products, digital marketing, IoT, cloud infrastructure, various verticals - - Google, Facebook, Amazon, Foxconn, GE, Siemens and a whole bunch of vertical players have thrived in those markets even as many technology vendors ignored them. Download the chapter from my recent book  in link here if you want to read about all the missed markets.

Many of those markets were brand new ones or what in MBA school we called adjacent markets so you can understand why so many vendors ignored them - even as they now wish they had entered those markets. But even in their core focus markets, I have seen many a myopic definition of TAM. Here are a few examples

a) Automation and Outsourcing

When I wrote Silicon Collar three years ago, I chose the title because it felt like traditional definitions of white, blue, pink collars were dated as technology was reshaping every job, everywhere. We all wear a Silicon collar these days.  I had examples of robotics, drones, machine learning, CGI, exoskeletons, robotic process automation (RPA) and other automation in the oil patch, in accounting, in films, in winemaking, garbage collection and many other sectors. I wasn't dreaming this up - executives at sophisticated companies were in the book describing their experiences.

So, it has been a bit disconcerting to see so many outsourcers have glommed onto RPA as their major (in many cases only) focus when it comes to automation. Even more cynically, most outsourcers do not take seriously repeated customer expectations to automate their own labor intensive services.  

b) Data Centers and networks

I cannot believe how envious many vendors are of AWS's and Azure's recent success. On the other hand, I cringe when I hear software vendors and many outsourcers say "glad we didn't have to invest billions in Capex". The reality is less than a decade ago HP(with EDS), Oracle (with Sun), IBM, Accenture, Verizon, Deutsche Telecom and other telcos populated and ran data centers and networks for the world. The emerging cloud infrastructure market was theirs to lose and they have done so - spectacularly, so far.

Many SaaS vendors view the cloud infrastructure market as a bonanza - let someone invest the Capex, we will just rent it for cheap. It's not really that cheap and increasingly they are being expected to support multiple clouds. Zoho has continued to invest in its own data centers and passing along those savings to customers. The time will come for other software vendors to similarly hedge their bets. They will have to expand, or at least, morph their market definition of infrastructure.

c) "Glo-ver" in SaaS

I have increasingly been pointing out a huge under-achievement in the enterprise applications market. After two decades of cloud and in-memory applications, only about 20% of a grid across countries and industry processes has a decent choice in contemporary architecture. Myths persist such as "verticals are a sink hole". Sure if you dont do it right, but go see how much lock-in revenue and margins SAP has enjoyed with its vertical investments in the early 2000s.

I have accelerated my coverage of applications and executives who are focused on the white spaces - see some of that coverage summarized here in a trend I call Glo-ver  expansion in focus.

Back to helping vendors challenge their previous definitions of TAM.  Those are fun projects when the vendor is willing to open up. I must be honest though - many vendors look to influence analysts, not use them much for intelligence. So, they would rather posture they have "50% market share in deals they choose to chase".  

Seriously, I hope that changes. Too many vendors are defining markets too narrowly and finding their competitors have done the same and a result too many are chasing after the same, narrow markets. That is a sure path to commoditization.  Trust me - I have seen it repeated many a times over the last three decades. There is little safety in those numbers.

Occupation: Advisor, Analyst, Author

In my four decades of global travel, I have always hesitated about the correct occupation to list on a landing card or tell a passport officer (apparently I am not the only one who agonizes about that answer). At PwC, the answer used to be consultant or advisor, and I am sure I noticed a couple of immigration officers smirk at that. At Gartner, the answer was analyst, till an officer asked me "so why are you not using your diplomatic passport?" and I had to say "not that kind of analyst". I felt the most welcome ever entering Canada a few years ago. The immigration officer practically hugged me when I said I was an author. That did not last. Couple of trips later, in some other part of the world, that answer triggered the question "so how many copies of your books are you carrying?" Ergo, let's figure out how much customs duty you owe.

I should say AAA - as in advisor, analyst, author as my professional bio describes me. But then they would assume I worked for the automobile association.  Can you imagine how long I would hold up the immigration line?

AAA confuses far savvier clients of mine. Many of them see my 7 books and assume I am an academic. My accountant does not understand all the different revenue and spend categories that apply to each. Vendors see analyst and wonder why I don't do Magic Quadrants. Still others have no idea I write the New Florence blog - 15 years and 6,000 posts later. The Deal Architect name has led to inclusion on mailing lists for architecture magazines.

But deep inside I know if I were to unbundle AAA I would probably not get a nice reader review like this

'Mirchandani not only does his homework but seems to have first-person access to everyone who’s anyone in the technology sector, so the quotes peppering the text are all germane and keen'

Even better are those from my advisory clients who say "Thank you for stopping us from making a $20 m mistake" or others who say "you opened our eyes to all the white space in the market we were not addressing"

They don't get hung up on monikers. They do ask me how I manage any conflicts of interest. Otherwise, they just know the blend of AAA brings them a very different perspective and much higher payback than what they get from others. 

Curious - would love to hear from readers what unusual experiences you have had with immigration and customs.  

Plex ML project - something every software vendor/systems integrator could emulate

Machine Learning is a hot topic. Every software vendor is talking about ML and AI in their products. Plex applied ML to something very different - an understanding of how their customers configure parameters in the Plex system. This conversation with Jerry Foster, CTO, describes the project.

It has always bothered me that after millions of ERP, CRM and other enterprise projects and upgrades, we cannot get predictability and savings from automation in these projects. I think every software vendor and SI can build on this exercise around other SaaS products to automate configuration and related templates. Another interesting area Jerry describes is how rethinking customizations and the UX can dramatically reduce configuration change effort.

 

When your competitors talk about machine learning, it’s typically in terms of customer data and different functional areas like accounts payable. Your project looked at implementation configurations, which is a unique approach. Can you share your perspective on Machine Learning?

Just like with any transformative technology, everyone likes to ask, "What is your machine learning strategy?" or "What's your IoT strategy?"  But you can really go down a rabbit trail because those technologies are so big. So we've always tried to ask “What problems are we going to solve for our customers?” which could even mean solving an internal challenge that ultimately helps our customers.

At Plex, we didn't want to just start building Machine Learning models and doing them willy-nilly. Instead, me and my team discussed areas where we could apply machine learning efforts in order to solve a problem while also gaining our own internal knowledge on what a ML project entails.

Can you share some background on how this specific Machine Learning initiative began?

Last year, our VP of Services told me that about 15% of incoming customer care tickets deal exclusively with the configuration setup of our system.

Because the Plex Manufacturing Cloud is a Software-as-a-Service product, everyone is using the same codebase, which means we use configuration settings (also known as customer settings) that determine how the software behaves and functions.

Settings have business flow and visual ramifications. For example, some of those settings determine actual workflow, which may be different for a food and beverage customer versus a discrete manufacturing customer.  While others are as simple as a setting that will show certain columns on the screen and, if it's turned off, they are no longer visible. 

Because the system is so full-featured, there are a large number of settings so it would be unreasonable to expect our customers or even our Customer Care team to fully understand all of the interdependencies of the settings and the unintended side effects from turning settings on and off.

Obviously, when engineers build these systems, they try to take that into account.  They will add a new setting and see how it works with all other similar settings.  But over time, you can't ascertain all of the effects of all of those settings.

Our customers are trying to figure out how to optimize their settings, so when I was pondering that stat – 15% of our tickets are related to configuration settings – I realized this would be a great place to build a machine learning model that could help us understand what the net effect is.

Our desire was to ask “How could a customer use this model to compare it to their settings?” and be able to say, "Here are the settings you should change in order to have the optimum configuration.”

Once you identified your objective, where did you start? What technologies did you use?

We put together a team of engineers and gave them this assumption that there is a data set that could be analyzed to determine whether or not a customer's settings are configured correctly.

The team came up with a framework and a set of tools, which included Azure Machine Learning Studio because, in our opinion, Microsoft has done a really good job providing the necessary tools to very quickly get up to speed on a machine learning presentation.  We also used Neo4j as our graphing database and a system called Notebook, which is a way to put together any Python scripts that you are using to augment your artificial intelligence project. 

From there, the team put these tools together and started building out their models.

Once you had the tools, what was the process like?

 There were actually three phases to the project.  The first is feature discovery, which is basically asking “What are the data sets that we need to test our assumption?”  The second phase was building the knowledge capture system, which actually gathers all of the data from the feature discovery.  Then, of course, the final phase is a building a recommendation system.

The first phase – feature discovery – was determining which data sets were the most important.  One thing I learned is that feature discovery is just as much art as it is science.  You really have to have people on your team that know the data.

We put together a set of data that we could use to test our thesis and we started to build some models around that data.  We found that once we had a set of features and a set of data points, it wasn't enough to determine what data points to use, we needed to establish just how much data to use as well.

For instance, if we were too restrictive in our data sets that we were using, the models couldn't make any inferences from that.  It was basically regurgitating what you put in.  But if we were too broad in the amount of data, data sets, and features that we were putting into the model, it just came up with all sorts of crazy inferences and dependencies that had no meaning.

We had to keep refining, which we is a big part of the machine learning process. We really had to learn to accept that there is an iterative nature to determining which data could best give us the results that we were looking for.

Once we had a set of data points and features that we felt were representative and returned some good initial results, we built a knowledge capture system using Neo4j, Excel spreadsheets, and a data factory from Microsoft. This system pulled in the data, adjusted it, and used the models that we had built in ML Framework to start analyzing the data. 

The next part was actually consolidating and analyzing the data using mainly Azure ML as well as some Power BI and Excel along with Neo4j for some of the visual representation.

Can you explain how this applied to the configuration setting problem you were trying to solve?

As I mentioned, our customers turn settings on and off in order to configure the system to meet their needs.

To make that process easier, Plex has a set of customer setting templates – 12 or 13 primary templates based on size, industry, facility count, etc. – that we use to categorize our customers, which then informs new customer implementations. When a new customer is brought into the fold, it’s more efficient to associate them with an appropriate template and copy the template settings to their instance.

For this project, we wanted the algorithm to pretend we didn't have those templates and determine on its own how it would group our customers. We fed the data into the model and it begain to cluster our customers and match them with a particular customer setting template. Using a mechanism called similarity analysis, the model then made recommendations based on the delta between the customer and neighbors using their common template as a baseline. 

Can you show me how this played out in the model?

The image below is from Neo4j and it actually shows the clustering of customers matched to a particular template.  It first came up with its own clustering, and then it attempted to match those clusters with our templates. 

This next image shows the model’s clusters.  It identified 14 broad groups completely disconnected from our templates. 

Then what we told the model to do on this next screen was to try and match its clusters with our templates, and it actually grouped our templates into those four groups that you see highlighted right there: 0, 7, 9, and 11. 

So the model reclassified your templates?

Yes, the machine learning algorithm essentially said four of the groups it had defined (Group 0, 7, 9, and 11), could match up with one or more of Plex’s existing templates. 

What else does this tell you?

All in all, what you see here is basically six of its groups matched our templates. As you can see on this next image, the model matched two of its groupings – 3 and 8 – with a few templates that we don't typically use as much. The model also has two groupings, 2 and 6, that it couldn't match with any of our templates.

One of the main findings coming out of this is that we are basically missing two templates.  In other words, there is a set of customers that, when they onboard as a new customer, we're assigning them to a template that, although it may not be bad, is probably not the optimum template that they should be getting.

This final graph illustrates a finding that was consistent across the board : the number of times our customer settings were changed dropped off a cliff in about 2015.  That was a completely unexpected finding from the study.

 

This finding validated the work that we've been doing since 2014 to reduce our overall dependence on settings, including our new user interface, which is much less settings-dependent than Classic. It gave us confidence we were on the right path.

This is also really cool because it wasn't actually in our initial hypothesis. It wasn't what my team was trying to find or determine, and it was just an unintended side effect of the work they were doing.  Of course, one of the advantages when you start doing a project like this is you start looking at your data in detail and you come across insights that you had never expected.  This is something that the initial algorithms that we were putting together showed us that wasn't even part of our initial thesis.

Those were the two main results: identifying configuration setting templates that we had missed, and showing us that the work we had done in reducing the number of settings had paid off. 

What action will you take from here?

One of the follow-throughs from this, obviously, will be to look at the model’s groupings that do not identify with a Plex template and ask what are the characteristics of the customers in those groups?  Why did we miss those?  One of the main follow-ups that we're doing right now and in upcoming months is trying to determine which customers are in those groups and what are the characteristics of those groups so we can come up with a proper template for those customers.

The second action is to follow through with the recommendation system because that's really where the productization of this is going.  If I'm an existing customer, I want to know if my settings are optimum. I want to know, based on all the customers that are like me, which of my settings are different, and why do I have those settings different than all of the other customers who are like me?

We already have the "like me" part done.  Now, we actually have to productize that so we can provide a tool, and we want to provide that tool in two different contexts.  One is an internal tool for our support team so that when they get an incoming call that says, "Hey, I've got a problem," they can run this against the settings optimization tool and say, "You know what?  You might want to check these settings.  I've noticed that they're different from you, but all other customers like you have these settings set differently."  Then, of course, the next iteration or the second context would be a self-service tool that the customer could just go onto the Plex menu and analyze their settings. 

Were you happy with the results of this project?

Overall, I was very pleased with the outcome of the project and how the team worked.  It was exciting to see.  One of the interesting things about where machine learning has come from where it was five, ten years ago is that the tools are so strong that you don't need a team of data scientists.  You need a team of smart engineers and a couple people who know the data. 

Yes, a data scientist is obviously going to help, but to get 70% or 80% of the way, in my opinion, you don't really need a data scientist.  All the things we did here, there was no data scientist involved.  That, to me, was a real encouragement.

The last takeaway I'm taking from this is, once you start to realize what's behind machine learning, you actually start to use that as a mechanism to solve problems.  In other words, it's not just this machine learning project that the labs did.  It's how we can analyze any problem moving forward.

It's going to be a long journey, but it's very exciting.  I'm really excited to see where we can go with this.

Where have all the process visionaries gone?

A recent visit to an Amazon Fulfillment Center was eye-opening. They have completely redefined the old warehouse pick, pack, ship mindset. It's now stow, pick, pack, verify, and in their lingo, SLAM and "chaotic storage". Kiva robots and miles of conveyor belts bring work to employees not the other way round. Scanners, scales, sensors monitor and facilitate the worker - what size box to assemble, cut right length of packing tape, apply shipping labels etc. Read more about my visit here. 

I was recounting the visit to a software CEO. He said "Amazon is amazing". And I responded - "it's not just Amazon". I told him about companies I have worked with and written about that have dramatically rethought their processes with all kinds of modern technology - shop floors, cashierless stores, return logistics, transportation routing, payment processing and on and on. He asked me which vendors they work with. And I named a few then stopped and said "In the end, these customers are themselves driving the vision about the new process with a wide range of technologies. Rarely is there a single vendor or product".

The realization sunk in that true process innovation is not really being bought "shrink wrapped". Companies which are taking a "systems approach" - adding sensors, robots, flexible workflow, RPA at front, center, back of any process - are getting stunningly better results.

Of course, plenty of SIs will continue to talk about "best practices" embodied in packaged software, and many customers will say they are satisfied with "good enough" processes.

Most software vendors have in the move to cloud computing hired good architects who have helped them think about multi-tenancy and platform technology. They have not invested similarly in business process experts, especially those unique to individual industries or geographies. And even with the underlying technology they have waited for consumer tech to innovate with UX, AI etc. A few of their "good enough processes" added together could translate to several basis points of margin lost every year.

Jeff Bezos, CEO of Amazon has been known to say "your margin is my opportunity". I used to think it was his way of targeting fat, dumb, high-margin competitors. After visiting his warehouse, I think he also means to target companies which are not focused enough on margins and instead waste them in process inefficiencies.   

Maverick idea: Shift some marketing dollars to product design

(Sir) Jony Ive recently left Apple to start his own design studio. On his heels, Marcus Engman with a similar role at Ikea is doing the same, and he has an interesting value proposition “I want to show there’s an alternative to marketing, which is actually design ..And if you work with design and communications in the right way, that would be the best kind of marketing, without buying media.”

He is definitely on to something. Look at all the money we have poured into modern marketing and the poor results

1. email marketing has led to all kinds of spam and phishing scandals
2. telemarketing has turned into nuisance calls, robocalls and scams
3. TV advertising has been abused to the point where the FCC does not even try to limit number of commercial minutes per hour anymore - other than for kid's programming
4. digital marketing is turning into a privacy nightmare

BTW, there is a naive view that GDPR and EU regulators will somehow make 4) more viable. To which I point to how poorly regulators have done trying to regulate 1, 2 and 3.

Nineteenth century retailer John Wanamaker is credited with saying “Half the money I spend on advertising is wasted; the trouble is I don't know which half.” We spend a lot more on advertising these days, and have much better tools, but the results are only marginally better.

The sad thing is enterprise vendors have for over a decade now missed the move to smart products, a trillion+ dollar market I covered in my 2012 book The New Technology Elite. Contract manufacturers like Flex and Foxconn and design firms like IDEO and frog were the big beneficiaries of a focus on smarter, better designed products and services.

Enterprise vendors brag about CRM, but the reality is most also missed out on the digital advertising market. Google, Facebook, now Amazon have mostly benefited.

Will they change their tune and encourage customers to try the balancing act Engman is preaching?

The "Glo-ver" phase in enterprise apps

I don't mean to pick on NetSuite but it is one of the oldest cloud apps vendors - born as it was in 1998. Over 21 years, you would think it would have a fuller global footprint. Especially since it had an opportunity for global subsidiary implementations in a two-tier ERP world.  Look at its site and see how few countries it supports functionally. Look closer and even there see how many functional areas are covered by partners. Some analysts say since Oracle's acquisition in 2016, its geographic reach has grown. Yes, likely in terms of sales reach, but in functional footprint - not sure it has.  

Same thing with Infor. For nearly a decade it has talked about emphasizing vertical functionality. But it has largely spray painted its financial, crm and hcm features with some industry terminology. It has not developed operational books of record - patient related functionality in healthcare, merchandising in retail etc. 

It's not just Netsuite or Infor. Most other cloud vendors are largely English speaking-centric and horizontal in functionality. Hey, cannot blame them - they are doing fine. Wall Street is generally happy with their performance.

In the meantime, however, the world has changed dramatically. Customers are not waiting and they are finding plenty of solutions to fill geographic and vertical niches. They are going to startups, SIs and are even filling the gaps themselves.

In the last few weeks I have profiled some of these extensions

• Mike Laven of CurrencyCloud covered fast moving trends in Fintech
• Larry Bridge of Cognizant talked about trends and solutions in healthcare 
• Uptake execs talked about assets and dealers in heavy industrial sectors 
• Raju Vegesna of Zoho talked about coverage in places like Japan, UAE and Nigeria which most vendors usually ignore 
• Mike Laven elaborated on how China and India are reshaping global financial markets 

Expect my coverage of global and vertical trends to accelerate. The applications industry is rapidly moving into the phase of "Glo-ver" extensions - global and vertical extensions to core horizontal functionality.

Cloud Applications - A long, winding road dotted with Bystanders

Frank Scavo's Computer Economics has a nice post about the the long, winding road - the slow customer march to cloud applications. Bill Kutik pointed to similar slow movement more specific to HCM cloud applications in a recent LinkedIn post.

My recent book and my advisory work provide an additional perspective to their data. I identified a large group of what I called Bystanders in the SAP customer base. They have not taken advantage of its growing portfolio of new products - S/4, C/4, cloud properties, SCP, SAC, Qualtrics etc. They continue to stay with ECC, BOBJ, B1 and other 20-25 year old products. I did not call them laggards or anything else pejorative for reasons that will become clear as you read further. I estimated 60% of SAP's 400+K customers are in that group, especially those in Europe. If anything SAP is ahead of its on-prem peers. S/4 is maturing nicely and its coverage in functional and geographic areas is much wider. The competitors have even more Bystanders in their customer base.

There are three big gating factors for their lack of movement

Not enough functionality

I have heard from countless customers they do not see enough from individual vendors in the cloud, and they are afraid to add to their patchwork of already too many vendors. After 20 years of cloud applications, we have too little global or industry coverage. So, you have vendors like NetSuite brag they have users in 200+ countries and they support over 190 currencies, but their own site shows detailed functionality for less than 20 countries. You have vendors who claim to "do ERP" but have zero shop floor or warehouse or operational capabilities in the cloud. I got a nasty note from a vendor executive asking why I considered "back office" functionality "boring". I responded "I was trained as a CPA so boring is a strong term but I am amazed the industry talks so little about operational systems and vertical functionality compared to all the talk of hcm, crm and core accounting."

Too expensive and risky a migration

A number of customers have tested the waters and many have been shocked by the proposals they have seen from SIs. Many SIs are behaving like it's 1999. Just because the S/4 or Workday software is relatively new, it does not justify a premium in consultant rates. The functionality is not dramatically different. Also, SI delivery models have not evolved much. Just not enough automation in data conversion, testing and other phases of projects they do over and over at clients. Finally, there is their age old FUD of  "oh, there is so much change management". And yes, they still cannot show predictable outcomes. The muscle memory of the overruns and failures of the last wave of ERP and CRM remains with too many customers for them to sign up. I was actually pleased to catalog many case studies of ECC to S/4 migrations in my book which minimized the organizational change and the SI role - they stuck with SAP GUI, stayed on-prem etc.  They are postponing innovations, but frankly reducing exposure to the "change management" and other SI premiums.  

Cloud economics are just not as attractive

In wave 1 of the cloud, the economics were compelling. The cloud collapsed 4 contracts from on-prem world - the software, the hosting, the AMS and that of SIs who did frequent upgrades. Multi-tenancy and simplified contracting explained why it was so attractive to look at a Salesforce or a SuccessFactors relationship. But in the last decade, reality has set in. I have heard from a number of customers their incumbent infrastructure is fully amortized - the cloud only adds to that cost layer. I have heard from others who have sharpened their pencils, that buying their infrastructure is still cheaper than subscribing in the cloud when it is annualized. Then there is the expectation that comes from their outsourcing experiences - when you hand someone a 3 or 5 year contract you have the right to expect CMM Level 5 or Six Sigma continuous improvements. Instead, too many SaaS renewals are turning into unpleasant head-butting experiences - no improvements, just massive cost increases. 

Most customers I have talked to would love to modernize. Many, in fact are frustrated with the status quo. ABAP or Peoplecode are not exactly recruiting assets any more. Their significant customizations and technical debt are growing compliance and security risks. They realize they cannot leverage multi-tenancy, machine learning and other advances with their current set-up. But they are nervous. They want lots more operational functionality, and they want better economics and predictable results.

In the book, I called on SAP  to "tilt its bell curve" and jar the Bystanders into action. Other on-prem vendors need to do that even more in their customer bases. Pure cloud vendors continue to have the opportunity to replace incumbent vendors. But they need to tackle the three big issues I highlight above.

Just calling the Bystanders "laggards" or something else demeaning is to ignore the reality of the state of cloud applications.

Tim Cook - Book Review

I read the Leander Kahney book on Tim Cook on the long Emirates flights to and from India. In India, I had a conversation with an Apple consumer like me (not really that knowledgeable about the operations of the company). His point was India should require Apple make massive investments in return for access to their large market. I asked him if the US should expect the same of TCS, Infosys, other Indian outsourcing firms for similar access to their largest market? He ignored my question. Then we got into Apple needing to diversify production away from China given recent trade tensions. I asked him how India would improve its infrastructure to support Apple's finely tuned supply chain. Again, no good answers. Raise our amateur conversation a few levels of abstraction higher. It would highlight how Tim Cook is one of the most geopolitically influential executives in the world these days.

And if rumors of the Apple Car are true, Germany, Japan, S. Korea are just as interested in what he is going to do. So I thought it was odd that Leander's book does not look much at these global crosscurrents in his new book. Instead, I thought Kahney spends way too much time on race relations in Alabama where Cook was born and raised, and his sexual orientation. In that sense, I had the same reaction as I did to Walter Isaacson's book on Steve Jobs - bit too much focus on personal background and idiosyncrasies, less on the gears and timing belts of the Apple juggernaut.

Unlike Isaacson, Kahney did not have appear to have direct access to the subject. However, from the citations he did have access to a handful of Apple executives, and with his long time blog The Cult of Mac he has a slew of contacts in the Apple ecosystem. I find when I write books, I have to take off my blogger hat and not focus so much on newsy, gossipy stuff and focus instead on complex issues that corporate executives are interested in. I don't think Kahney does enough of that. So much of the book is actually cut and paste of lots of historical reporting about Apple. I wish he had spent time in China crawling through Foxconn, Fedex and various component vendors that make the remarkable Apple supply chain work. How it is orchestrated with last minute design changes, how it is mostly kept secret etc. 

I do like that Kahney addresses the silly notion that Cook is not innovative. By launching Apple Pay and Watch he has bought Apple a toehold in massive emerging digital payment/commerce markets and wearables/healthcare markets. And if Car becomes a reality, he should do  the same in rapidly changing mobility markets. Just wish Kahney had explored those a lot more about how those products are bought to life, and how their complex supply chains are choreographed.

You will likely understand Tim Cook a bit better as a human after you read the book. I found it did not really add that much to my understanding of him as a business executive or that of Apple's operations.