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You Know What’s Even Better Than MRR? POGR. Plain Old GAAP Revenue.

By on October 23, 2017

There’s a trend that’s accelerated in the past year or so with companies I meet with, advise, track, mentor, have invested in, etc.

The trend is that I have no idea what their revenues are 🙂 

What I mean by that is that in an effort to present all different types of revenue as “recurring”, the whole meaning of MRR has been corrupted.  On the one hand, I’ve seen many startups start to include non-recurring revenue in “MRR”.  Hmmm.  Which of course you can’t, since the the second R means Recurring.  On the other hand, I’ve seen companies not give themselves enough credit for all their revenue streams.  And really worst of all, I’ve seen so many startup craft new metrics I simply can’t understand.  XYMRR.  This-AND-ThatMRR.

There’s nothing wrong with any of this, as long as you do one key thing:

Start with real, recognized, GAAP revenue.  In other words — just tell me, tell everyone, how much money you made last month and last quarter.  Period.

As a case study, let’s look at SaaStr Annual 2018 and 2016 speaker Atlassian (see our deep dive with President Jay Simons here).  Atlassian just blew out the last quarter, and its market cap topped $10 billion as result!  How did Atlassian do?

$193 million in GAAP revenue last quarter.  Revenue.  And $62 million of free cash flow.  Cash.  Not too shabby!

Start there.  Always start with the very top line and the very bottom line and make sure they are right.  Real, honest, GAAP revenue.  And real honest cash out-the-door (or in Atlassian’s impressive case, in-the-door).

Now if you don’t know Atlassian well, it gets more interesting.  Atlassian’s growth has been turbocharged by its move to the cloud and recurring revenue.  This segment of its revenue is growing 67% year-over-year, and the other segments are growing much more slowly.  But its roots are “on prem”, and a huge chunk of its revenue is still from perpetual licenses, installs, and services revenue:

Has that hurt Atlassian?  Doesn’t seem like it.  Yes, Wall Street (and thus VCs and thus press) wants recurring revenue.  Recurring revenue is more predictable, and done right, more profitable.  But it doesn’t have to be all recurring revenue.

Many trends are giving rise to more types of mixed-revenue in SaaS and SaaS-ish companies:

  • Many startups have a hardware component.  Is that MRR?  No.  But it’s revenue.
  • Many of you of course will have services revenue.  Should you hide that in the numbers?  No.  It’s real revenue.
  • And more and more of you use dynamic, pay-as-you-go-pricing.  It’s not bad.  Slack and Twilio do it:-)   But is it MRR?  Well.  Maybe.  If you have an 80%+ likelihood it recurs, it’s generally deemed recurring.  If not — it’s simply variable revenue.  It’s still money-in-the-door, and it’s still great.  Indeed, there’s much more variable revenue in SaaS business models these todays.

What’s more important is if these other sources of revenue are low or zero margin.  Services revenue done “right” often have 40%+ gross margins.  The same with hardware as part of a service.  If they are give-aways, by contrast, the gross margins are often zero or negative.

In any event, report it all.

But start with one key metric — Revenue.  And please, keep it simple.  Make sure everyone in the company and every investor knows exactly how much money you bring in each month.

Then, break it down into clear, simple segments.  Hopefully, Recurring Revenue and “MRR” is your largest segment.  Yes, MRR is King.  Recurring revenue drives the highest multiples and the most investor interest.  But Revenue and Cash are what really matters at the end of the day.  Whatever it is, just break it down and stack them up like Atlassian does.

It’s OK.  What isn’t OK is if no one really understanding how the business works.  That just confuses everyone, and in the end, will drive you to outcomes and expenditures that aren’t the right ones, or at least not the ideal ones, for your business.  Keep the made-up-acronyms for secondary analyses.  Don’t use them as core metrics.

 

(Cross-posted @ SaaStr)

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Of Halloween and Enterprise Software

By on October 23, 2017

This time of the year, I buy several packages of assorted candies. Keeps the neighborhood kids who come around on Halloween happy. I happened to check the contents of one pack – it had Hot Tamales, Chewy Lemonheads, Tootsie Rolls, Abra Cabubbles and who knows what else.

Would a 4 year old ask for those? Would their parents want their kids to have them?

And it hit me, enterprise software has become a bit like those assortment packs. I have asked scores of vendor executives why everyone is fixated on analytics and AI and not on functionality. They respond users cannot absorb our pace of feature delivery. In contrast, most customers say they see lots of me-too functionality and very little industry functionality.  They are offered pretty analytics. Or tools which invoke grand wizards like Coleman and Pacioli and then underwhelm.  And told to look for functionality from partners. Like the candy bag – too much of what customers don’t need, too little of what they would like.  Then you have the other kind of bundling. You want SAP S/4? It only comes in the package with HANA. You want Oracle autonomous database 18c? Get ready to also sign up for Oracle’s infrastructure cloud. Or as Larry Ellison says tongue in cheek “only if you are willing to be pay less than you would pay Amazon”. You want Salesforce’s AI Einstein? Sign up for the package with IBM Watson. Take two AIs and call us in the morning.

It reminds me of the quote from Forrest Gump “Life is like a box of chocolates. You never know what you’re gonna get.” I don’t know – for what companies pay for enterprise software they should be allowed to be a lot more picky. It seems vendors are driven more by marketing advantage with the latest buzzwords than customer needs.

I have a better idea. We should move to a concept around another holiday – Fukubukuro. Around their New Year, Japanese stores sell a  “lucky bag” with all kinds of mystery items. But they do so at ridiculously low prices.

(Cross-posted @ Deal Architect)

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Docker gets pragmatic, embraces Kubernetes

By on October 23, 2017

So it happened. Docker announced support for Kubernetes, making the container orchestration platform a full citizen alongside Docker Swarm as a deployment target. The keynote demo was pretty sweet – showing how a Docker user really didn’t need to make a choice any more about which orchestrator to use.

As Solomon Hykes said:

“It’s not a fork, it’s not a wrapper, it’s not a half integration shim thing. it’s full support”

Docker’s decision to offer native support for Kubernetes makes obvious sense from both a business and a technical perspective.

Kubernetes is crushing it – it is now the defacto standard for container orchestration, given support from pretty much the entire vendor industry from Amazon Web Services, to IBM, Google, Microsoft, Oracle, Pivotal and Red Hat. Given that Kubernetes is the platform being chosen for deployment and operations it is also where the money is going to flow. Docker couldn’t afford to shut itself from the business opportunity. It has to be galling to be the company that kicked off the container revolution, only to see Red Hat emerge as a competitor with OpenShift, because of its Kubernetes story, packaging expertise and customer footprint. Latterly Pivotal has thrown its hat into the ring with Pivotal Kubernetes Services.

Meanwhile, technically speaking, integration was never going to be too painful. Kubernetes and Swarm are cousins, if not siblings. As Hykes said in a session after the keynote:

“Orchestration is a commodity, over time… as we expect it to be. My observation is that all orchestrators look more and more alike. the only two that matter are Swarm and Kubernetes. Over time no one will care which orchestrator is winning”

Sounds a lot like convergence doesn’t it? Docker’s decision makes a great deal of sense. It’s easy to view embracing Kubernetes support as a zero sum game, where Docker loses because Kubernetes wins. I am not sure that makes sense.

It’s Docker’s job to do the best job of packaging container stacks to make them easily consumable, even if the technology comes from outside. The last thing prospective customers want to see are religious wars. As Brendan Burns, one of the original creators of Kubernetes, now at Microsoft Azure, said in the hallway after the event, Docker makes tools that people like to use. Tim Hockin, Principal SW Eng, Kubernetes & Google Container Engine, agreed that what matters was user experience. And whatever your opinion of Docker, you can’t deny it’s very good at creating tools that developers like. Before Docker, containers were painful. Now – it’s a new industry.

But it’s also a community. It was very very clear talking to Burns and Hockin the high regard they have for Docker and Hykes. They weren’t crowing at all, but rather happy that a point of contention had been taken off the table.

Docker’s focus should absolutely be on creating tools that paying customers want to use, not trying to outflank Kubernetes. And today it announced that it’s doing exactly that. I will be saying more tomorrow about paying customers when I discuss Docker’s Modernize Traditional apps (MTA) program and news from the day two keynote.

 

full disclosure: AWS, Docker, Google Cloud Platform, IBM, Microsoft, Oracle, Pivotal, Red Hat are all RedMonk clients.

 

(Read this and other great posts  @ RedMonk)

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OpenWorld 2017 and beyond

By on October 23, 2017

I spent the first week of the month in San Francisco at Oracle’s customer event, OpenWorld 2017 and when I wasn’t drinking from an information fire hose, it was alternately fascinating and exhausting. There were major announcements in database, blockchain, AI, cyber security, and other stuff I have only a tangential association with. For instance my eyes glaze over when they start talking about bare metal servers and going serverless so I’ll limit these observations to database and security with a few dashes of other things.

But primarily this OpenWorld was a coming out party of sorts in which the culmination of billions of dollars of investments in advanced technologies became unavoidably visible. From here on Oracle might still support its legacy customers but make no mistake, they are legacy and the future is at minimum about moving the datacenter to the cloud and moving your traditional licenses there. Oracle even has a name for that practice BYOL or bring your own lice

Oracle is making that task as easy as it can be and I spoke with customers who were doing it or just finishing who were pleased with the experience. We don’t often think about it if we keep to the big picture but there are a lot of old systems in the enterprise that need to be replaced because they no longer support their missions. There is no better indication than that for determining that the next few years will be full of stories about cloud migration.

The key to successful and necessary transfer to the cloud is Oracle’s autonomous database, 18c. The new product will be available by end of year and is supposed to self-provision, self-maintain and self-patch while running thus eliminating the need for most downtime. CTO and founder Larry Ellison said that it takes an average of 13-14 months from when a patch is available to when it is installed in a majority of customer shops. If a patch covers a security problem that leaves bad guys a huge amount of time to steal data, which has become epidemic.

As Ellison said in his opening night keynote, “People are going to get better at stealing data, and we have to get better at protecting it.” Fair point. The database will be a big part of that protection along with some semi-autonomous security software (that will become autonomous soon enough). Both products rely on Oracle’s AI and machine learning tools as well as advanced database hardware.

That’s the catch, but it seems eminently reasonable: you have to be on Oracle gear to get the full benefits of the software’s power. Actually it’s less of a catch than you might think. While there are enterprises that are big enough to need and to purchase the hardware, many if not most, customers will receive the full benefits of Oracle’s technology as consumers of its cloud services. So many of these announcements can be rightly seen as further inducements to move to the cloud.

Ellison is fond of saying, “You can get all of this, but you have to be willing to pay less.” That’s fair if you’re looking at the monthly or annual subscription charge but one suspects that over time many companies will be paying more overall though there’s the issue of refresh that many companies avoid but which are a standard part of the cloud. We’ll see, in the end you get what you pay for.

Oracle also announced a foray into blockchain, the distributed ledger technology that provides greatly enhanced security, speed, and transparency to inter and intra-company transactions. For instance you will soon be able to use blockchain to track the provenance of parts in a supply chain, and one can only hope that credit reporting adopts similar safeguards in the future. We can also hope to track some customers and their purchases that way, especially in a B2B setting to facilitate sales.

Lastly, there’s AI and data. Whether it’s called AI or machine learning, the technology requires lots of data to train a model to be useful in predicting the future. Most enterprise data is deficient in one or more axes of data on hand so Oracle’s solution has been to provide clean data to augment private data and deliver the big picture view that’s needed in sales, marketing, service and a lot more. Oracle also introduced a new set of IoT applications aimed at specific business outcomes. It’s impossible not to say more about this later.

But to summarize, Oracle’s long-term investment in cloud technology has begun to pay off. We’ve seen this in the company’s earnings reports over the last year and OpenWorld was a kind of coming out party for numerous solutions at literally every level of the software (and hardware) stack. The company will be a formidable competitor in the years ahead as its legacy base is up for grabs. They’re all going to move to the cloud at some point and Oracle wants to keep them. Other vendors with good solutions are competing at every level from Amazon and Google to Microsoft, SAP, and Salesforce. This will be fun to watch.

 

(Cross-posted @ Beagle Research Group)

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Salesforce gets granular

By on October 20, 2017

If you need an example of digital disruption, you can’t do better than the retail banking industry. A byzantine collection of rules and regulations plus the overhang of many legacy systems have conspired to prevent banks from becoming more involved with their customers. And even innovations like the ATM from several decades ago, only serve to distance banks from their customers. This leaves plenty of opportunity for upstart technology vendors to disrupt the applecart. All of this combined with a generation of potential customers who were raised on digital products and services puts an important demographic up for grabs thus setting the stage for disruption.

Not long ago Salesforce recognized the dynamics in play and moved to develop vertical applications for selected industries including banking where large institutions need to address the requirements of large customer bases. The result is the Financial Services Cloud for Retail Banking, announced last week.

Some of what Salesforce delivers in its Financial Services Cloud will seem revolutionary to many bankers. Things like a rich assortment of applications for all phases for banking from loan origination to customer management. But a good chunk of the benefit comes directly from being a cloud solution with easy onboarding and updates scheduled three times per year.

Where the Salesforce Financial Services Cloud differs from many banking products is in how it brings together banking products resident in its AppExchange that together deliver concerted solutions. For example, nCino tells a powerful story of reducing loan origination time by orders of magnitude not by building a closed system but by integrating other platform products.

Vlocity, another partner with strong banking apps based on the cloud platform is another example. It provides intelligent agent and omni channel services for financial services and insurance. In these and other relationships you can see the Salesforce go to market approach solidifying. Partners bring expertise, which can be as big as loan origination or as specific as document signature capture. All of it goes into a solution that customers can craft for their specific industry needs.

As I’ve noted many times before, when companies get to the multi-billion dollar revenue level, they can’t expect to sell the same products in the same ways they did when their businesses were much smaller. To show growth a business needs help and that means acquiring other complementary businesses and selling in concert with partners that can add specificity to the core product. Salesforce has done both and I look forward to hearing more about vertical strategies at Dreamforce.

(Cross-posted @ Beagle Research Group)

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NASSCOMBPM 2017: The Indian BPM industry graduates from from biryani to bhuna, but needs to stop celebrating the past

By on October 19, 2017

Rohit Kapoor (right), Chair of the NASSCOMBPM Council, hosts his first summit in Bangalore

It was good times revisited in Bangalore last week, where the Indian-heritage BPM (BPO) services firms gathered for their annual reality check, courtesy of NASSCOM, India’s leading IT and BPM services body.  We were pleased to get the chance to meet with many of the industry’s finest and have some frank exchanges on what this industry must do to stay relevant and keep eking out a growth curve.

And, similar to the recent HfS summit in Chicago, the conversation has moved rapidly along in recent months with a genuine buzz of excitement about the future.  In short, most folks are stepping out of the comfort zone and attempting to embrace the emergence of technology-driven value levers as part of the very fabric of the future of business process services. However which way we look at this, we’re becoming a data services industry that supports the inexorable enterprise drive towards Digital Operations.

The NASSCOM folks fondly remember how we once described the Indian-heritage BPM (BPO) industry rather like a “biryani” – (a mixed rice dish comprised of many different ingredients that also has many different regional variants across India).  At that time, three years ago, we called out all the ingredients the industry needed to embrace to reach that next level, namely partnerships, platform plays and RPA.  Fast-forwarding to last week’s BPM bonanza in Bangalore, we can proclaim that a pretty tasty biryani is being regularly served up, and now we are enjoying one of my favorite dishes, the “bhuna”. Bhuna is a cooking process where spices are gently fried in plenty of oil to bring out their flavor. The Indian curry dish “bhuna” is an extension of that process where meat is added to the spices and then cooked in its own juices which results in deep strong flavours but very little sauce.

Much the same can be said of the current state of the Indian-flavored BPM industry, where the baseline delivery of process services have been nicely spiced with disciplined execution, competitive pricing, RPA capabilities and process standardization, and the added meat is providing new levers of value with emerging intelligent automation and analytics capabilities.  The missing potatoes are the added elements of risk that BPM services providers need to take to fix the data underbellies of their clients so they can truly start to benefit from the fruits of having digital operations to support their clients.  We’ve reach the era where real data transformation is the missing ingredient that can really take this industry to the next level.

Having met with most of the key BPM service providers with key Indian delivery ties at the annual NASSCOMBPM summit in Bangalore, these were my takeaways:

The mood is upbeat as the Indian BPM leaders are (largely) facing up to reality and embrace the opportunity.  Over the last couple of years at the annual NASSCOM reality check, we witnessed heated arguments regarding the viability of the long-term viability of BPM.  Several service provider leaders have defended the value of the traditional labor arbitrage model, while others have called for a reality check that the old model is dead and we need to conform to a very different world of outcome-driven, automation-centric models that aggressively cannibalize obsolete engagements. In short, the underlying feeling has been one of doom and gloom, and we’re simply waiting for this impending nose-dive for the industry. This year, most people seem to have taken a crash-course in reality and have realized the answer lies somewhere in-between – the old model provided a valuable lever to drive out costs and tee up the next phase of engagements – and the current crop of service providers should be smart enough to invest with clients to derive value out of several additional value levers, in addition to providing affordable, global talent.  Yes, it is possible to advance the traditional labor-driven model into the digital age without making too much of a sacrifice on the profit margins.

The mid-tier service providers are showing the way.  As the large FTE monster deals dry up and we’re left with smaller engagements that require less staff, more automation and better analytics-driven outcomes – and most of the mega providers simply cannot afford to scale-down to be competitive.  The likes of EXL, WNS, CSS Corp, Sutherland, Concentrix, Hexaware et al are all competing for the smaller, tastier deals on the table that require real attention and focus.  Plus most clients are now eager to explore relationships with smaller competitors who roll out of the red carpet for them… the crux of the issue here is that the mega-deals of the past are not coming back, and if the large providers cannot accept this, they are going to be left defending that they have for the next decade, as opposed to growing their businesses and investing in their capabilities along the way.  This is going to vet worse before it gets better – and everyone knows it.

Most of the BPM providers are still confusing everyone with their “strategies”.  I challenged nearly every service provider to articulate their value proposition in three bullet points.  They all failed miserably.  Being all things to all people is the recipe for bland failure in this attention-deprived world, and spinning the same marketing mush as everyone else has become the disease of our industry for the past couple of decades.  It’s time to crush the vernacular and talk openly about what we all stand for.  Sadly, I do not expect anything to change soon…

This industry desperately needs new blood.  As per the last 10+ years I have been coming to this event, we’re subjected to many of the same folks spinning the same old stories and viewpoints.  We need to meet more of the emerging talent and hear from some of the newer thinkers.  They do exist and need to be blooded… otherwise, we come across like an industry without a succession plan.

There is a chasm between BPM and IT services, which is a real missed opportunity for India.  The divide between the process people and the tech people in India seems to be greater than ever, at a time when automation and digitalization are closing that gap.  India needs to embrace hybrid delivery and present a united front between BPM and IT at its flagship events.  The RPA and AI discussion at the BPM level is so much more real and actionable than when the techies discuss it… As we discussed here, the worlds of software, business operations and services have always been chasms apart – different mindsets, vernaculars, conversations, ideas of what constitutes value – and vastly different cultures.  NASSCOM needs to help bring together these mindsets and drive the conversation forward as a unified industry, not several soloed sub-industries.

There is a lack of exciting up and coming digitally-centric BPM firms appearing in an industry in real need of some disruption.  While the mid-tier BPM firms are clearly enjoying something of a modest renaissance period, there really isn’t much coming up behind them – at least they are not on display ay India’s flagship BPM summit.  Where are the Indian digital pureplays and fintechs?  Sure, we met some exciting Indian RPA software firms, such as Intellibot and Option 3, but there seems to be precious few emerging human-plus-tech process delivery firms emerging to get us excited.  We need these firms to be wheeled out, wrapped in cotton wool and showcased as the future of Indian services.

Bottom-line: NASSCOM needs to deliver a stronger message of reality and guidance to support its industry

While NASSCOM has performed an amazing job over the years putting India on the IT and BPM services map and providing a vehicle for networking and information sharing, the message today is largely avoiding the harsh realities the industry is facing. The industry is going to suffer some labor shrinkage over the next few years (we predict a 450,000 decline by 2022 in India’s IT/BPM service industry alone by 2022).  NASSCOM needs to be at the forefront of recreating the next phase of India’s services capability and confronting the legacy model, not celebrating what’s left of it’s glorious past, but embracing its less certain, but high-potential future.

(Cross-posted @ Horses for Sources)

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How much will we trust AI ?

By on October 19, 2017

Most of my adult life was spent doing consulting work for clients around the world on topics like information management and analytics. For last few years, I have also been involved in IOT, AI etc. When I look back – one thing has been common for all “data projects”, and that is the importance of TRUST…or lack there of. Billions of dollars have been spent creating BI systems ,data warehouses, data lakes, cubes, reports, workbooks, blah blah – and yet at the end of the day, spreadsheets still prevail at EVERY SINGLE COMPANY I know !  I wrote about this in the past.

excel-spreadsheet

But why ?

There are good reasons why users don’t trust data they get from BI, like

  1. In most cases, users cannot see how it ties back to source systems
  2. Transformations and enrichments to the data are not transparent to users
  3. Users do not know how well the system has been tested
  4. The tools may not be as intuitive as a spreadsheet….and many more

Every single one of these problems in “Classic BI” has a solution – which either a product or a service can solve. An IT expert can probably show what transformations happened, or create some reconciliation reports for example. Or a data lineage tool can trace back from report to source. So with some additional cost, we can minimize the trust issue – though the cost may eventually become prohibitive to build trust at scale.

Past is set in stone …most of the time, at least 🙂

The reason we could do all of this is because we are essentially looking at things that already happened – which are kind of “set in stone”. And the information we got from such systems had finite values as answers like “sales in north america was $10M” which is based on basic arithmetic . If I asked the same question again tomorrow, I will get the exact same answer of $10M. If I did not – I would know right away that something bad happened. If a bad decision was made –  for the most part, it is possible to trace back and validate the data and confirm if it was indeed bad, and prevent it from happening in future.

Now lets look at the world of AI .

To avoid a religious war on terminology – please allow me to use AI in its most generic sense as an all encompassing thing that includes what we call data science, machine learning, cognitive etc. Definitions matter – but for this post humor me and pretend all the right things are covered when I say AI.

Just like with classic BI, we use a lot of data and transformations. However, the fundamental idea now is that we are not just reporting on what happened – we are trying to make the best educated guess on what will happen in future. We are not in the world of “only finite answers” here – instead of the exact sales that happened this quarter in North America, we are often trying to find what are the odds that sales will be greater than $10M next quarter, for example.

Enter the trust issue

On one hand, it is quite useful to have a system that makes such predictions for us so that we have a window to the future. On the other hand, if we do not trust the answer – it is a lot more difficult to explain how the system arrived at the answer. And if the system told me today that I had 80% chances of hitting $10M for the quarter, it could very well tell me tomorrow that I only had 50% chances of making that $10M number.  I and everyone else in my team might think the system is foolish because we can see the math to get to the $10M number we want . Lets say the quarter finished and we did exceed the $10M number – this still does not mean the system was either right or wrong. That is the beauty (and pain) of how probability works !

Can’t the creator explain the creation ?

I am often asked “Can’t you just ask the programmer or data scientist who built this to explain how the system predicted?”. Yes I can – and some times that is all it takes to get the answer. But many a time, they may not be able to give that answer with the precision you expect. AI systems are learning systems (with or without human help) – and they learn and get smarter mostly by going through a lot of data, as opposed to just crunching logic fed by a human. By the time I asked the $10M question the second time, the system might have learned something from a new pattern it detected.

AI can piss you off

A sales forecast, in the larger scheme of things, is probably not going to change the world for most of us. However, if we think of other scenarios like say salary planning  or promotions where an AI system scores everyone in a team on a complex set of parameters and makes recommendations – it is hard to accept a decision that cannot be explained in an easy to understand way. The system may be totally right – or it might have all kinds of bias built into it with the model, or the data it trained with. It might have false positives and false negatives. There are techniques to minimize all these problems – BUT If it cannot explain its results to us as users – how will we know for sure ?

Can you trust machines ?

There is another version of the trust issue – when machines need to make choices that affect us. Lets say you are a factory supervisor driving in a self driving forklift that is picking up a heavy load from one top shelf and putting it in another top shelf, while your workers are walking below its arm. The machine probably has visual recognition capabilities, and can crunch lots of parameters from data and make good decisions. Lets say one day, the machine detects the load is too much to bear and it has two options – flip on the side and injure you, who is sitting inside or drop the load and injure your workers. What should that machine do ? And if you don’t know what the machine will do – or at least know that you can over ride it – will you work with that machine ?

AI – its just like us, except it isn’t 

I also get asked “Well, AI is supposed to think like a human, so why can’t it explain its thought process like we can?”. This is an excellent question and it presents two issues. 1. We don’t all think alike – even in the fictitious forklift example, I am sure different people will choose differently.  And 2. We often take a decision, and find an explanation for the decision later if someone asks. We can’t always explain our decisions very well either to someone else except for simple cases. And finally, we make poor decisions too. So mimicking human thinking as-is perhaps is not the best way to think about AI either 🙂

AI is everywhere, and mostly harmless

I am of course not generalizing that all AI scenarios run into a trust ( or ethical or moral) issue. Many don’t – for example an AI algorithm might predict how much longer a device will work before battery runs out. I doubt I will have a trust issue if I see it work approximately well for first few times. And there are several of those kinds of “little” AI solutions all around us – and many might not ever be visible to us. We just take them for granted ! Even in the sales forecasting or promotion examples – over a period of time, we may trust what the system tells us. But the trouble is – will we give it enough time to let it work long enough to earn our trust ?

So what can we do, really ?

Just like other projects, AI projects need some basic education and expectation setting for stake holders before we embark on them. Unlike basic math, and if-then-else logic – statistics concepts needs a bit more hand holding. People tend to use terms like confidence, significance, sampling etc loosely and it is very easy to set wrong expectations with stake holders even with the right intentions. And then there is the issue of trust, and its ethical and moral considerations. It is important to discuss these thoroughly upfront, and during the projects . When done right – and transparently – AI can and does add significant value to us. Its on all of us in this industry to make sure we let AI earn trust the right way !

 

(Cross-posted @ Vijay's thoughts on all things big and small)

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Oh hai Jenkins, oh bye Hudson, let’s keep an eye on Concourse

By on October 19, 2017

While it didn’t garner much if any attention, at OpenWorld Oracle publicly deprecated Hudson, its fork of the Jenkins continuous integration server. It might be more proper to consider Jenkins a fork of Hudson, given Jenkins was originally developed as Hudson at Sun, back in 2004. But Jenkins is the living project and community, led by original creator Kohsuke Kawaguchi, CTO of CloudBees. Jenkins has shown sustained strength, and is the defacto standard CI/CD server for enterprises.

Oracle has been doing some sensible house cleaning of late, for example open sourcing Java, and it was not a great surprise when Thomas Kurian, President of product development at Oracle, showed a slide in his keynote last week which featured Jenkins, but left Hudson out. Oracle may not have officially announced it is end-of-lifing Hudson before, but it’s a safe assumption. Oracle had already handed over parts of Jenkins to the Eclipse Foundation, but it’s not clear who would be the major committers or contributors without Oracle investing. It doesn’t appear to be under active development.

Jenkins though continues to find new friends. Consider for example, Microsoft Azure and Kubernetes – no Visual Studio Team Services only thinking here.

All that said, Jenkins, like almost all software that’s been around for a while, particular enterprise software, had become somewhat complex and unwieldy. It faces competition from new platforms written from the ground up to run on new platforms taking consideration of all the latest in thinking about development pipelines and developer experience. Let’s face it, in 2004 GitHub didn’t even exist. Post GitHub though, sleek hosted CI/CD apps – such as Codeship, CircleCI and TravisCI – have garnered a strong following. Atlassian is also a competitor with Bamboo. Jenkins hasn’t stood though – with it’s BlueOcean UI overhaul it now has a modern UI, with visual tools for building and managing pipelines, and native GIT support.

A notable new entrant in the field however is Concourse, written by Pivotal engineers as a side project, partly out of frustration with Jenkins. Concourse is designed to be fairly simple, with 3 base primitives (tasks, resources and jobs) and a declarative configuration file. Config as (YAML) code. The command line interface, Fly, is clean.

For Pivotal it’s a line to walk – many of its customers use Jenkins already, so it hasn’t pushed Concourse as aggressively as it might. It wants its sales people to focus on the Cloud Foundry and Spring products. Pivotal also avoids offering hosted software, instead working with Amazon Web Services, Azure, Google Cloud Platform. On the other hand, given that Pivotal is selling the idea of writing better software, with a strong test automation story for continuous integration and deployment, and teaching it’s customers to do so, it does make sense it would sell and support it’s own opinionated CI/CD platform.

So – it was interesting to see the good people over at Engineer Better, a UK-based services company specialising in Cloud Foundry, yesterday announce AirplaneCI, a hosted ConcourseCI play. If AirplaneCI takes off, then Engineer Better could be a nice tuck-in acquisition for Pivotal. Either way, it’s a neat win for Concourse.

If you want a lot of customisation with your development processes, and plugins to every developer tool you can think of, Jenkins is still very much a natural choice, which is why it’s the industry standard.

 

related post – The race to own the pipeline. Atlassian ain’t playin–introducing DevOps Marketplace.

 

disclosure: Oracle, TravisCI, Microsoft and Pivotal are all clients. All research here is RedMonk’s own, though.

(Read this and other great posts  @ RedMonk)

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Gartner’s Top 10 Predictions For IT In 2018 And Beyond

By on October 19, 2017

  • In 2020, AI will become a positive net job motivator, creating 2.3M jobs while eliminating only 1.8M jobs.
  • By 2020, IoT technology will be in 95% of electronics for new product designs.
  • By 2021, 40% of IT staff will be versatilists, holding multiple roles, most of which will be business, rather than technology-related.

These and many other insights are being presented earlier this month at the Gartner Symposium/ITxpo 2017 being held in Orlando, Florida. Gartner’s predictions and the series of assumptions supporting them illustrate how CIOs must seek out and excel in the role of business strategist first, technologist second. In 2018 and beyond CIOs will be more accountable than ever for revenue generation, value creation, and the development and launch of new business models using proven and emerging technologies. Gartner’s ten predictions point to the future of CIOs as collaborators in new business creation, selectively using technologies to accomplish that goal.

The following are Gartner’s ten predictions for IT organizations for 2018 and beyond:

  1. By 2021, early adopter brands that redesign their websites to support visual- and voice-search will increase digital commerce revenue by 30%. Gartner has found that voice-based search queries are the fastest growing mobile search type. Voice and visual search are accelerating mobile browser- and mobile app-based transactions and will continue to in 2018 and beyond. Mobile browser and app-based transactions are as much as 50% of all transactions on many e-commerce sites today. Apple, Facebook, Google and Microsoft’s investments in AI and machine learning will be evident in how quickly their visual- and voice-search technologies accelerate in the next two years.
  2. By 2020, five of the top seven digital giants will willfully “self-disrupt” to create their next leadership opportunity. The top digital giants include Alibaba, Amazon, Apple, Baidu, Facebook, Google, Microsoft, and Tencent. Examples of self-disruption include AWS Lambda versus traditional cloud virtual machines, Alexa versus screen-based e-commerce, and Apple Face ID versus Touch ID.
  3. By the end of 2020, the banking industry will derive $1B in business value from the use of blockchain-based cryptocurrencies. Gartner estimates that the current combined value of cryptocurrencies in circulation worldwide is $155B (as of October 2017), and this value has been increasing as tokens continue to proliferate and market interest grows. Cryptocurrencies will represent more than half of worldwide blockchain global business value-add through year-end 2023 according to the Gartner predictions study.
  4. By 2022, most people in mature economies will consume more false information than true information. Gartner warns that while AI is proving to be very effective in creating new information, it is just as effective at distorting data to create false information as well. Gartner predicts that before 2020, untrue information will fuel a major financial fraud made possible through high-quality falsehoods moving the financial markets worldwide. By the same year, no significant internet company will fully succeed in its attempts to mitigate this problem. Within three years a significant country will pass regulations or laws seeking to curb the spread of AI-generated false information.
  5. By 2020, AI-driven creation of “counterfeit reality,” or fake content, will outpace AI’s ability to detect it, fomenting digital distrust. AI and machine learning systems today can categorize the content of images faster and more consistently accurate than humans. Gartner cautions that by 2018, a counterfeit video used in a satirical context will begin a public debate once accepted as real by one or both sides of the political spectrum. In the next year, there will be a 10-fold increase in commercial projects to detect fake news according to the predictions study.
  6. By 2021, more than 50% of enterprises will be spending more per annum on bots and chatbot creations than traditional mobile app developments. Gartner is predicting that by 2020, 55% of all large enterprises will have deployed (used in production) at least one bot or chatbot. Rapid advances in natural-language processing (NLP) make today’s chatbots much better at recognizing the user intent than previous generations. According to Gartner’s predictions study, NLP is used to determine the entry point for the decision tree in a chatbot, but a majority of chatbots still use scripted responses in a decision tree.
  7. By 2021, 40% of IT staff will be versatilists, holding multiple roles, most of which will be business, rather than technology-related. By 2019, IT technical specialist hires will fall by more than 5%. Gartner predicts that 50% of enterprises will formalize IT versatilist profiles and job descriptions. 20% of IT organizations will hire versatilists to scale digital business. IT technical specialist employees will fall to 75% of 2017 levels.
  8. In 2020, AI will become a positive net job motivator, creating 2.3M jobs while eliminating only 1.8M jobs. By 2020, AI-related job creation will cross into positive territory, reaching 2 million net-new jobs in 2025. Global IT services firms will have massive job churn in 2018, adding 100,000 jobs and dropping 80,000. By 2021 Gartner predicts, AI augmentation will generate $2.9T in business value and recover 6.2B hours of worker productivity.
  9. By 2020, IoT technology will be in 95% of electronics for new product designs. Gartner predicts IoT-enabled products with smartphone activation emerging at the beginning of 2019.
  10. Through 2022, half of all security budgets for IoT will go to fault remediation, recalls and safety failures rather than protection. Gartner predicts IoT spending will increase sharply after 2020 following better methods of applying security patterns cross-industry in IoT security architectures, growing at more than 50% compound annual growth rate (CAGR) over current rates.The total IoT security market for products will reach $840.5M by 2020, and a 24% CAGR for IoT security from 2013 through 2020. Combining IoT security services, safety systems, and physical security will lead to a fast-growing global market. Gartner predicts exponential growth in this area, exceeding more than $5B in global spending by year-end 2020.

Gartner has also made an infographic available of the top 10 Strategic Technology Trends for 2018, in addition to an insightful article on Smarter with Gartner.  You can find the article here, at Gartner Top 10 Strategic Technology Trends for 2018.

Sources:

Gartner Reveals Top Predictions for IT Organizations and Users in 2018 and Beyond

Smarter With Gartner, Gartner Top 10 Strategic Technology Trends for 2018

Top Strategic Predictions for 2018 and Beyond: Pace Yourself, for Sanity’s Sake (client access reqd)

 

(Cross-posted @ A Passion for Research)

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The road to Dreamforce, Salesforce IoT strategy

By on October 19, 2017

Salesforce is a large and well-disciplined development company. They’re pretty good at marketing too. They continue to innovate in all product areas from core platform to applications and the one criticism I’d make is that it’s information overload at times. Reminds me of Oracle.

Lately they’ve made a big deal of adding their AI product, Einstein, to every application area to provide insights and stack rank options. Given that this is the first year of Einstein, it’s not surprising that the foundational layer looks similar across all of Salesforce’s disciplines; in cloud after cloud we some version of next best… . I expect that we might see examples of this at Dreamforce.

But back to discipline. Salesforce is paying a good deal of attention to the applications level these days in addition to spiffing up its platform and it’s noticeable in banking and finance as well as in the IoT but the differences in approach are what’s most interesting.

In IoT, the company is taking a very different approach than in finance. The company recently announced IoT Explorer and the ability to develop IoT applications quickly. App building tools are a part of the platform so it’s not much of a surprise that more agile development is coming to IoT though it reveals an interesting take on company strategy.

IoT has a lot of moving parts right now and the natural inclination for some might be to try to dominate the end to end process from intelligent devices to connectivity to IoT platforms and finally on to business applications. But three of these four areas might easily not bring in a nickel for a company like Salesforce. Specialized vendors that Salesforce would have trouble competing with dominate devices and connectivity. IoT platforms is a similar area with companies like Amazon Web Services dominating. So Salesforce wisely didn’t try to compete and is dedicated to supporting the leading platforms leaving the business application area as its chosen place to compete.

By focusing on the apps, Salesforce is positioning itself as the key to capturing value from investments further up stream in devices and platforms. Typically a business might make independent decisions about those areas and the business app company better be able to work with whatever is in place and that’s the strategy.

Given the state of the early IoT market, it makes good sense to go with an app development strategy. It’s too early to have standardized apps that vendors might want to buy off the shelf so having a quick and efficient development tool set makes all the sense in the world.

On the other side of the industry there’s financial services and banking, two mature marketplaces with specific application demands and here the strategy is less about new development than it is about using the platform to make clean and effective changes to process oriented operational systems. So in banking and finance Salesforce can deploy its many platform based tools as well as partners like nCino and Vlocity to produce systems that cover the institution’s critical processes, like loan origination and customer service. Again, Dreamforce might have more to reveal here.

(Cross-posted @ Beagle Research Group)

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