Sunday, November 01, 2015

If 87 Unicorns Fell in the Valley, Would they Make a Sound?

This post was also published in VentureBeat.

A lot of noise has been made about the inflated valuations of Aileen Lee’s unicorns and the amount of money they have raised. There are rumors that uber-unicorn Uber is now raising an additional $1 billion, in order to continue to fuel growth financed by losing a rumored two dollars for every dollar in revenue. On the one hand, that seems irrational; but on the other hand, loss-fueled growth is how companies like Amazon became the behemoths they are today.

In the broader context of Silicon Valley technology companies, the unicorns in aggregate form barely a ripple in the fabric of space-time. According to CBInsights, there are 87 Unicorns in the United States, with a combined valuation of $312 billion. Cross-referenced with Crunchbase, those 87 have raised a cumulative $48 billion, with over half of that amount invested in the top 14 unicorns.

What if all of these unicorns vanished to Candy Mountain tomorrow? What would the ramifications be across the various ecosystems tied to unicorn mania? $48 billion is a lot of money to lose, even for the well-funded and diversified investors that have poured money into unicorn mezzanine rounds. However, consider that Fidelity, TPG, and T. Rowe Price alone manage over $6 trillion in assets, so even if they had invested half of what has been put into the unicorns, it would be less than half a percent of their assets, well within the volatility range of their much more stable asset classes. These asset managers are much more concerned about Chinese power consumption data than they are about unicorns returning to mythology.

The late stage unicorn investors, which traditionally invest in tech IPOs, have put anti-dilution and liquidation preferences into these private rounds, ensuring that they will very likely get their money back out at a minimum. The founders, early stage VCs and executives have sold portions of their stakes for cash in these rounds and in secondaries. VCs can complain about overinflation and Kind bars being handed out, but is it really out of altruism for the startups, or because they are shut out of the early stages by Angelist and micro-VCs and the later stages by hedge funds and public market investors?

If the asset managers aren’t biting their nails and wringing their hands about the future of these unicorns, what about the general technology sector? To put the $48 billion invested into unicorns into the technology sector context, consider that two months ago, during the Chinese market jitters, Google’s market capitalization plummeted $54 billion in a week. Google’s investor exposure is more similar to the dot com, with main street investors in the stock. The world did not end. Rent prices in San Francisco did not plummet. Kind Bar inventories at Whole Foods and Safeway did not explode. In the grand scheme of things, it’s just not that much money.

So who could potentially lose here? If investors, founders, and the general technology business isn’t impacted, who is? The employees of unicorn companies who were enticed to startups with multi-billion dollar valuations. There is a good chance many will get squeezed down as the inevitable “IPO is the new downround” public offerings happen when some unicorns fully tap out private markets. But how many people will that actually impact? Even if all the unicorns are forced to rightsize and lay off staff, they are still startups and likely employ fewer people in aggregate than the 33,000 employees HP just announced it will lay off.

There were 39 unicorns when Aileen Lee wrote her original article in 2013. There are now over 140 unicorns worldwide. Some unicorns have hit escape velocity. Others seem more like they are headed to the endangered species list once fundraising windows close and their business models are exposed. Either way, the large late stage investors who have put multiple bets on the roulette table will come out unscathed, so perhaps the unicorn angst is isolated to the Silicon Valley echo chamber.

Wednesday, July 15, 2015

How Twitter Lost the Stream Wars

This post was also published in VentureBeat.

Unlike other category-defining Internet companies, Twitter has struggled to meet both user growth metrics and Wall Street’s expectations. There are a lot of possible explanations for Twitter’s user growth problems, but they really boil down to one simple thing: As the content shared into streams grows exponentially, the streams have to get smarter in order to remain relevant to users.

Twitter presents cards in a straight reverse chronological stream that shows all content. The more people you follow and the more you use Twitter, the worse the Twitter experience becomes.

Facebook took a very different tack. Back in 2008, Mark Zuckerberg established Zuckerberg’s Law of Information Sharing, which predicted that the rate people share information like status updates and photos would double every year. In 2009, Facebook acquired Friendfeed for $50 million, integrating a team that was using content shared from external sites to learn what users liked and didn’t like. In 2011 and 2012, Facebook poached data science teams from across Silicon Valley to build an increasingly intelligent rules engine called EdgeRank that figured out what posts to show to which user in what order.

Users complained every time there was a change, but Facebook’s relentless focus paid off. Now in 2015, Facebook’s stream automatically notices how long it’s been since you’ve last looked, what types of content you’re interested in, what you like, what you click, and figures out who your close friends are to showcase their content. The Facebook ranking algorithm is constantly tweaked and optimized by an increasingly large machine intelligence team. On Facebook, the more people you follow, the better the experience gets as it increases signals to the stream algorithm. The Facebook stream has become so good that brand content is increasingly filtered out, so Facebook has just added a SeeFirst option that lets people opt-in to brand content.

Google also foresaw that the exponential deluge of information would overwhelm users and in 2011 began to work on Google Now to predict what people would be interested in as a stream of cards; it subsequently shut down other personalized Google attempts like iGoogle. Google Now launched in 2012 and after three years of iterations offers an extremely advanced interface that infers things you need to know, ranging from where you parked your car to fresh information about items you have searched. Google has been very proactive about placing Google Now before you search and answer cards above search results.

Conversely to Facebook and Google, Twitter has stuck with a straight temporal stream that shows all content no matter how irrelevant. Attempts to overlay features such as the Discover tab and a “while you were gone” view did not change how the main Twitter stream works: a torrent of information that quickly slides both interesting and silly posts into obscurity. Attempts to introduce threaded conversations created replicas of conversations on the same stream.

Twitter’s Dick Costolo recently lamented being too focused on short-term thinking to appease Wall Street. Exhibit #1 was the relentless effort to have users follow more people on Twitter. The early product team at Twitter discovered patterns that indicated if you followed at least 30 people, you were likely to remain engaged. So they redesigned the product to drive this behavior. Somehow this blossomed into a constant effort to get every user to follow more people. Yes, it was an easy engagement number to show Wall Street. However, the more people you follow on Twitter, the worse the experience.

With mobile usage surpassing desktop usage, the constraints of a mobile screen make stream optimization critical. The most relevant and actionable cards need to be on the top.

Although many commenters seem to think that switching to a Flipboard or Nuzzle style view would help, Facebook, Google, and others have proven that a stream interface with cards really performs. The real problem Twitter needs to solve is ranking whose posts are important to whom and how well the content is received. Twitter has been acquiring some machine learning teams, but is it too little too late? Perhaps not.

The first step is to remove the bad actors, as Twitter is a veritable bot farm. The second step is sorting the Twitter stream by relevance, with an option to switch between relevant and temporal posts, just like Facebook did years ago. After a bit of weening, nobody cares about the real time feed anymore. Once this switch is made, there is plenty of runway to iterate with users and test what works and doesn’t work. The third step is to aggregate similar posts together so that there is context and the stream doesn’t overflow with similar content.

Twitter has become the newswire of our generation, with everything from breaking news such a revolutions, interesting content, and celebrity crosstalk. Twitter just needs to be sorted into a modern stream, and the user growth and Wall Street accolades will follow.

Sunday, June 14, 2015

Regulating the Sharing Economy: How Uber et al Will Soon Face New Rules

This post was also published in VentureBeat.

Uber’s fifth year anniversary highlights that sharing economy services have rapidly become persistent and mainstream. Uber facilitates over one million rides a day. It’s estimated Airbnb will make $6 billion in gross bookings in 2015.

Legacy regulatory environments were first ignored and then fell by the wayside or changed as people recognized that a customer-reviewed driver in a personal modern car was just as good, if not better, than a sociopath in a yellow Crown Victoria. However, Uber drivers are now congesting major cities, and neighbors of Airbnb-listed rentals don’t want to live adjacent to what is effectively a hotel.

As the adage goes, bureaucracies are created to solve a real problem, actually address the problem, and then simply sustain themselves. There is no doubt that taxi commissions and hotel regulations have far exceeded that threshold. Jurisdictions like Las Vegas that forbid street-side pickups in conjunction with hotels that only allowed a single cab to load at a time conspired to make changing locations incredibly difficult for consumers. Regulations mandating that the rack rate for a hotel room be placed inside a room make no sense in an Internet-enabled world.
However, as sharing economy services have reached scale, the need for regulatory intervention is increasingly becoming apparent, in numerous areas:


Having no upper bound on the number of vehicles available is leading to massive congestion in the downtowns of major cities. For example, San Francisco has 22,000 possible Uber drivers, and its legacy taxi industry is limited to 1,900 medallions. Clearly San Francisco needed more taxis, with the medallion system enriching existing owners with artificial scarcity rather than reasonably limiting the number of vehicles permitted to ferry passengers. On one hand, in aggregate there is less traffic in San Francisco at key intersections, very likely due to a combination of Waze distributing the load to side streets and people using ride sharing rather than driving their own cars. On the other hand, during surge times downtown is at a virtual standstill. Ask any ride sharing driver and they will tell you it’s Uber and Lyft drivers. In the near future, cities will likely add caps to the number of concurrent Uber and Lyft drivers as part of a mandatory regulatory license. Such caps will likely create scarcity and increase rates.

Bad Drivers

Uber drivers are now as notorious as taxis for illegal U turns and arbitrarily double parking when they could just as easily pull over. Due to driver ratings when carrying passengers, they are better drivers than taxis, but only marginally better. Taxis had very visible markings that allowed consumers to have a recourse. It is not unreasonable that the companies provide visible identifiers that noncustomers could use to rate the drivers. Yes, a "How's My Driving" sticker! This will go a lot farther than trying to see an optional decal in the windshield and capturing a license plate number.


There is anecdotal evidence that it's very difficult to get an Uber in poor and underserved neighborhoods. With Uber’s driver opt-out model redlining is going to be par for the course for many drivers. As part of their license, taxi companies were regulated to not redline neighborhoods. However now that taxi companies have become eviscerated by Uber and Lyft, good luck getting any type of ride from underserved neighborhoods. Uber will likely have to create a market-based incentive to address this problem, by perhaps giving the drivers the full fare.

People with Disabilities

– Taxi companies are obligated to accommodate people with disabilities, with specialized wheelchair capable vehicles. Uber is currently being sued in multiple states due to issues with the Americans with Disabilities Act. However, much like with redlining, the evisceration of legacy taxi companies will likely lead to regulations that will force Uber to add drivers with specialized ADA vehicles to its fleet.


Uber claims that the driver’s personal insurance will cover injury to passengers. However the insurance companies disagree and have created specialized products that drivers must buy. Uber provides a $1 million backup policy, but there is a lot of confusion as to when it kicks in. States like California are starting to institute stringent insurance requirements that define liability during gray areas like when a driver is waiting for a ride request.

Vehicle Standards

– Uber has been largely self-regulating on vehicle standards by insisting on modern vehicles, all of which have numerous mandatory safety features. There is no longer a need for ridiculous standards such as two way radios and credit card swipe machines in the age of the app. Some jurisdictions may mandate a fire extinguisher for paid rides.


In order to prevent surprise fares, most jurisdictions have mandated visible meters that continually update during a trip with the current fare. The Uber app lets users see a fare estimate if a destination is entered – it could just as easily show a somewhat current fare. Uber will likely add this feature on its own as it is useful; but if not, it will likely be mandated soon enough by various jurisdictions.


Tax authorities are even going after yoga studios that have regular teachers. Chances are that they will soon force Uber and its ilk to pay employment taxes for its workers.

There will, of course, be jurisdictions that overly regulate ride sharing services, much like some jurisdictions like New Jersey still mandate that gas can only be full service in order to preserve gas station attendant jobs. Most jurisdictions will likely fall into a reasonable middle ground.

It is becoming clear that sharing economy companies are not necessarily going to self-regulate and will increasingly be subject to external regulation now that they are hitting scale. Let’s just hope that the cycle does not repeat into yet another self-profligating bureaucracy that simply recreates all of the same problems companies like Uber were built to address.

Sunday, May 31, 2015

How Tech is Leading us Back to a Village-style Life

This post was also published in VentureBeat.

There has been a lot of discussion about how the acceleration of technology is decimating the middle class and traditional jobs. But there has been very little discussion of an emerging trend where individuals are opting out of these same jobs people fear will disappear.

Driven by a post-scarcity economic model whereby you can live very frugally if you choose to, some workers (mostly college-educated and urban) are opting out of the now traditional work structure and choosing their own path. As Chelsea Rustrum puts it in her book It’s a Shareable Life, “You can live a life dictated by choice, passion, and freedom — a life where your … experiences are of the highest value.”

They are opting into alternative, passion-based professions that have gained popularity and acceptance, such as craft beer producer or yoga teacher, and that have flexible hours. Twenty years ago, if Bob, the valedictorian, showed up to his high school reunion and said he was starting an artisanal coffee shop and manually roasting his own beans, most of the attendees would have laughed and asked each other, “What the heck happened to Bob?” Today, Bob is admired as one of the few that are beginning to embrace the lifestyles of a hundred years ago. Yes, machines can manufacture pretty darn good coffee. But Bob likes to hand roast coffee, and people like to drink it.

The economics underlying this shift are of course driven by technology, which has progressively driven down the cost of commodity goods and enabled the easy sharing of capital assets. However, in an ironic twist, technological progress and abundance are ushering in a very retro lifestyle.

Housing, dining, and even employment are being unbundled into pre-industrial age configurations. Shervin Pishevar, an investor who funded Uber, posited this when he noticed that village services could be implemented at city-wide scales. But perhaps what is actually occurring is the reverse; the cities and services are decentralizing themselves into villages and village-like urban neighborhoods.

Some of these trends are already well established, while others such as food carts are of course small micro-trends amongst relatively wealthy city-dwellers.

1920s 2000s 2010s Breakout Company
Goods Local artisans Amazon Local artisans Etsy
Coffee Local artisans Starbucks Local artisans Blue Bottle
Barber Local artisans Supercuts Local artisans StyleSeat
Cities Villages and urban neighborhoods Suburbs Villages and urban villages
Personal Transport Hitch a ride and pay Own a car Hitch a ride and pay – Uber and Lyft Uber
Commuter Transport Small shared vehicle Mass transit Small shared vehicle Chariot
Hotel Rent a room in a guest house Hotel Rent a room in a guest house AirBnB
Housing Small houses McMansions Small houses and microapartments
Spirituality Church Consumerism Yoga and meditation CorePower Yoga
Work Independent craftspeople Companies Independent contractors oDesk
Trade Barter Paypal Barter and apps
Food Local store with local food and neighborhood delivery Safeway and factory farms Farmer’s markets and local food delivery FreshDirect
Entertainment Local artists Pop stars YouTube Stars and local bands Maker Studios
Restaurants Small restaurants, many home-based Chipotle Food carts Munchery
Schooling Schoolhouse, Home schooling, Trade apprenticeship Factory schools Charter schools, Home schooling, Trade schools AltSchool

Many of these new services offer very predictable quality due to built-in recommendations or via TripAdvisor or Yelp. Others are very haphazard, like a Burning Man camp during its heyday a few years ago. You can’t buy your way into a top restaurant when it’s a food cart whose owner has everything she needs. She’s much more incentivized to trade her services for a private yoga session, or just simply offer her food to people she already knows and likes.

The “return to the village” trend is, of course, limited to a small population that can afford to spend their time on personal pursuits and eschew higher wages. This privileged demographic could certainly suck it up and work 12 hours a day, be online every weekend, and live the materialistic American dream – but they now have the luxury of trading less time for less wages, while still meeting their needs and leading excellent lives.

In parallel to the great migrations of the Depression era, young, educated people are flocking to cities like Detroit and Buffalo to begin a new kind of life. While the 1 percent worries about the new home construction index, others are taking advantage of relatively empty cities and abundant, inexpensive housing. The recent unbundling of healthcare from traditional career-track jobs is only making the opt-out path even more attractive.

People spent extremely long hours at work well before the industrial revolution. However, research shows that they actually spent far fewer hours actually performing work due to limited light, a lackadaisical work ethic, and numerous religious observances. The shift-based work day schedule developed during the industrial age has lasted well through the information age, and has extended into even longer hours for most knowledge workers. What happened to John Maynard Keynes’ prediction of a 15 hour workweek where people’s needs could easily be met with very little work?

While we’re still a long way from a post-scarcity economy, we are already at a point where a large portion of the population no longer works the traditional 40+ hour work week, and it has become increasingly difficult to find service workers that can reliably perform monotonous jobs. Perhaps, in the near future, the time-for-wages equation will shift positively and benefit all Americans, well beyond the privileged few that can choose to opt-out and return to a village lifestyle. A world where workers will be empowered to dictate their own hours, their own wages, and most importantly, their own freedom to explore their passions. A massive shift that opens up the opportunity for numerous peer-to-peer services and networks.

Tuesday, May 12, 2015

Push Comes To Shove: The New Way We Interact With Information

This post was also published in ReadWriteWeb.

Since its inception in the 1960s, the modern computer has offered humans the same “pull computing” paradigm: make a query, get a response. Or, as we often experience it: Go to the haystack, try to find the needle.

But that’s quickly changing. As software grows more intelligent and learns more about our preferences and behavior, it seemingly gets to know us. That knowledge makes software more valuable because it means that it can deliver things to us, perhaps even before we know we want it. We are at the start of the era of push computing.


With push computing, a computer is no longer just a question-and-answer service; it’s expected to proactively figure out what’s interesting to you and deliver that data. On mobile, that’s often an actionable stream of cards and timely notifications of important items.

Push computing represents a major shift in architecture from the pull relationship computers have long maintained with users. Computing interfaces have evolved from green screens to GUIs to HTML5 to apps, but most applications have the same workflows and address the same needs in a pull-based fashion.

Outside the view of users, however, software delivery has steadily evolved toward a push-type model. Just consider how far we've come, from the hosted timesharing of mainframes and minicomputers to dedicated Unix servers to the PC floppy disk and CD and finally to the increasingly prevalent “software-as-a-service” we see today.

Over the past few years, push computing has also begun to infiltrate the interfaces of key consumer apps. Of course, as Chris Dixon recently pointed out, some Internet services are further along than others. Facebook, for instance, has mastered intelligent news feeds of cards and relevant notifications while Twitter delivers a straight temporal stream that grows more overwhelming the more accounts you follow.

Don’t Push Me

Not all pushes are the same, after all, and companies have to think carefully about the information that is important to push, when and why it‘s pushed, and how they expect users to react.

Major players are also trying to figure out how to make push a central part of the mobile OS. As I wrote a few months ago, Google is aggressively recasting itself as a push player with Google Now and answer cards in search. Apple is decidedly in the pull camp, as Siri is rarely proactive, although the iOS notification manager is well ahead of Android’s. Push has also become the backbone of successful mobile apps powered by real time infrastructure such as PubNub and Amazon’s Simple Notification Service.

Machine learning is key to the success of contemporary push-based services. Notifications and cards should only presented to users if they deliver relevant information users can act on easily.

Previous attempts to provide user notifications via email failed because email notifications are typically irrelevant and spammy. We’re all well trained to avoid spam like the plague, so users typically dumped all notifications into an email folder and never looked at them at all. Email is also inherently less actionable because a user has to click on a link, log into an application, and then perform an action.

For push to work, it’s crucial for applications to make their notifications actionable, friction-free, and rooted in sophisticated machine learning. Early efforts like PointCast to push information were too static and overloaded networks with continual updates.

Getting Pushy At Work

While push got its start in the consumer realm, the case for business-based push is in many ways much stronger. Enterprise systems manage discrete events that often require urgent action. For example, a sales opportunity might be closing in a CRM system, a complaint from a customer you cover could pop up in the service system, or the HR database could flag you about a new hire you need to onboard.

Conversely, the relative importance of events in consumer apps are much more nebulous. To deliver a superior experience to users, Google Now must continually learn, confirm and re-confirm details about where you live, where you work, your calendar, your travel arrangements, your preferences. Peoples’ lives and environs are constantly shifting, making it hard for the new generation of consumer apps to keep up.

What is more difficult about enterprise events is that they must be extremely secure and the data is often locked away in a variety of data siloes.

As users increasingly expect their services to be intelligent and proactive, push computing is making its way not just to mobile, but also to desktops and laptops by means of browser notifications. The new generation of push software is ushering in a new way for humans to interact with technology, and in the case of the Internet of Things, for technology to interact with itself in the form of networks of “smart” devices.

But as digital data becomes more voluminous, our systems have to get more intelligent. They have to filter, analyze, and deliver information to users—and then only when they need to know it or act on it. The goal should always be simple: for the haystack to bring you the needle—whatever it is—before you even start to look for it.

Saturday, May 09, 2015

Goodbye, SaaS — Hello, Containers-as-a-Service

This post was also published in VentureBeat.

When Salesforce’s Marc Benioff first started pitching on-demand CRM software, people thought he was insane and were convinced software-as-a-service would never work. Although we are now living in a SaaS heaven with all of the benefits of software that is always available and up-to-date, we are also beginning to see the SaaS hell naysayers were warning us about.

When selling Salesforce to a mid to large organization, Salesforce expects multi-year contracts with pre-negotiated user counts, exactly like the on-premise predecessors it ridiculed during its early days. The whole idea of “pay for what you use” has been subsumed by the realities of the sweet cash flow dynamics of a traditional enterprise sale, which ends up as shelfware when customers over-provision.

Compounding this issue is that the expense is accounted as an operating expense that affects EBITDA, a key Wall Street metric, while on-premise software was accounted as a much more palatable capital expense.

There are some other cracks in the SaaS armor. In a world of “big data,” enterprises are starting to realize that SaaS solutions do not offer unfettered access to their own data. Salesforce’s API access to your own data is metered and hinged off of user counts or API purchases – an enterprise has to take out its wallet and pay these vendors for scaled access to its own data. In a world of extreme security consciousness among CIOs, security is fully delegated to the SaaS provider. The multi-tenant model shares data infrastructure to the benefit of the vendor, not the customer. Integrating various SaaS silos has become so complicated that the field now has dedicated systems integrators like Appirio.

SaaS has become the orthodoxy du jour, with an ecosystem ranging from accelerators to post-traction venture funds focused solidly on SaaS. After 15 years of SaaS, you do have to ask, what’s left to SaaS-ify? In many segments, we are now on the third or fourth iteration of software that offers essentially the same workflow, such as Namely and Betterworks on the heels of Workday. The latest entrants are forced to target verticals in sluggish industries like construction and energy. So the question is, what’s next?

Containers and Containers-as-a-Service

There has definitely been a lot of buzz about Docker containers. The ability to separate an application, microservices, and their configuration from the underlying Linux operating system is very attractive. Orchestration layers built on top of containers such as Docker Swarm and Google’s Kubernetes make it easier to manage and scale clusters of containers.

The three major cloud providers, Amazon, Google, and Microsoft, have all added CaaS (Containers-as-a-Service), allowing any Docker container to run on their platform, filling a void between IaaS (Infrastructure-as-a-Service) that requires a lot more system administration and configuration, and PaaS (Platform-as-a-Service) that is typically very limiting in terms of language support and libraries.

Containers have been around for quite a while. As Bill Coleman, the former head of Sun Micrososytems’ Software group, recently reminded me, Solaris offered containers in 2005. What’s changed is that the new generation of Docker-powered containers have widespread support and are easy to learn. Now that there is a standard way to manage and deploy applications, there is the potential to reinvent how cloud software is delivered.

The Potential for CaaSi to Fix SaaS

Imagine a world where when you can purchase software or rent an application, then run it in the public or private cloud of your choice. Just like SaaS, the software would be automatically maintained by the vendor. But you would own and control all of your data, including the access by the vendor.

Until recently, this would have seemed like a pipe dream due to the intricacies of hosting, managing, and updating the software. Now, we are almost there. With some small incremental improvements, CaaS can evolve into CaaSi – Containers-as-a-Service for ISVs (Independent Software Vendors). Whether in a public cloud or in your own private cloud, the vendor would have the access and keys to manage and update the containers on your schedule rather than theirs. The vendor would not, however, have the ability to access your data without your explicit permission.

With the new CaaSi model, software customers have the best of the on-premise world combined with the best of the SaaS world. When customers buy software, just like with on-premise software, they have complete visibility into the hosting costs, full ownership of their data, tightly controlled security, and also the flexibility to use capital expense accounting. Just like with SaaS, they have the ability to scale as needed and receive automatic updates from the vendor.

Given such a huge transition on the horizon, it is no wonder that Docker is a newly minted billion-dollar unicorn with companies like CoreOS and Mesosphere battling over the best implementation of Google’s Kubernetes. In order to build out a CaaS/i future, CaaS providers need to add better support for immutable infrastructure by maintaining the separation between application containers and their underlying data, along with the delegated management of containers, usage metering for billing and abstraction for services such as logging and monitoring.

One highly material benefit that customers receive from SaaS is the network effect – the ability of the vendor to analyze in aggregate how all of the users are using the system and accelerate that usage in new features. In order to provide a similar level of functionality in a CaaS/i world, customers would need to opt-in to anonymous collection of their data usage in order to receive the same benefits of the analysis. But rather than a drawback, perhaps this is the point: the vendor should have to ask permission in the first place, and the customer should control their sensitive data.

A few startups are kicking off this trend. My own company, Sapho, is enthralled about the ship-as-a-container option and has launched that alternative., an orchestration backend, is only available as Docker containers. And Replicated and Infradash are providing the infrastructure for an independent software vendor to ship and manage Docker containers. The coming year should see a lot of activity on this front.

Friday, February 13, 2015

Small Screens, Big Decisions: How Mobile is Forcing Businesses to Rethink Software

This post was also published in VentureBeat.

One of the biggest problems in software is feature bloat. Even sprightly new companies that base their competitive strategy on minimalism and ease of use can eventually fall prey to the same force that bogged down their predecessors.

For example, one could argue that thanks to feature bloat, Salesforce is more complicated today than Siebel was when Salesforce first promised a simple, cloud-based solution. The promise of simplicity must quickly meet the business reality of closing deals or improving productivity — and for many software vendors, the promise of simplicity gets lost as they add customers, all of whom have feature requests.

Feature bloat happens because as software vendors add diverse customers, they end up adding features that are rarely used or only applicable to fringe use cases — many of the same ones that plague their incumbent competitors — ultimately slowing down the software and adding unnecessary complexity.

A new vendor that adds 10 features for each thousand customers ends up with quite a bit of bloat. It’s very common for enterprise software vendors to add “appeasement features” (those features added to make a sale or appease a customer) even if it’s known that a sizeable chunk of the user base will ignore it. One classic XKCD comic about a university’s home page mocks how rare it is that a home page shows the items users actually need. Recall the last time you used a copy machine; did you use any of the hundreds of options, or did you simply hit the big “Copy” button?

The trouble is that feature bloat isn’t just a traditional software problem anymore. As workers mobilize and take business software with them on their devices, feature bloat can follow, resulting in heavy and cumbersome mobile apps. For instance, Workday’s mobile app piles on hundreds of new features, very few of which users actually requested. Software bloat is particularly noticeable and problematic on mobile devices because they’re small and their screens can only fit so much, in spite of the recent trend of phablets and larger phones. Show too little, squeeze too much, or make it difficult to navigate, and the app becomes virtually unusable and engagement plummets. We’re often left thinking: Where’s the big “Copy” button?

Knowing that space is limited and users increasingly demand a superior mobile experience, app developers have to put a great deal of thought into what to show and how users will interact with it. Consumer apps, for example, are unbundling themselves into easy-to-use single purpose apps ranging from Instagram to SnapChat to Tinder — all of which are able to maintain incredibly high user engagement.

Thus, small screens present a beautiful challenge: As businesses develop mobile apps, they have a unique opportunity to prioritize what is shown on a device screen and determine what is actually usable and actionable for users. At last, a forcing function to limit the feature bloat of enterprise software, at least on mobile devices.

Too Much Information, Too Little Space

Recently, one of our customers at Sapho complained that one of their mobile business apps was taking a long time to load. Upon investigation, we discovered that they had replicated a desktop web experience on mobile. Their 16-column Excel-style table with hundreds of rows was being shoe-horned into a “mobile-friendly” app. “Mobile-friendly” it was not; the sheer volume of data displayed made it completely unworkable, from the loading process to viewing and editing.

This customer is not alone; plenty of enterprises create mobile apps with the intention of replicating the desktop experience on mobile. The result is the opposite of their ultimate goal — to enable users to be more productive away from their desktops — because the unwieldy app becomes difficult to access and interact with.

Choose Your Most Important Data

The first big decision for an enterprise mobile app is to choose which data to bring to the mobile experience and then accept the fact that the rest is best left behind on desktop. Not all data is valued in the same way, and mobile presents distinct use cases. Did the Sapho customer need all 16 columns of data on its mobile app, or could it have selected the six that its users truly wanted on the go?

Consumer services have been faster to catch on to this. The website of a weather service might display a wealth of information: granular data on sun and moon cycles; the exact time of today’s civil twilight, nautical twilight, and astronomical twilight; and historic average rainfall for each month of the year. The same company’s mobile app would narrow its focus, showing the user a smaller and more mobile-relevant subset of information about weather conditions at the current location for the current week.

Rethink Workflow

The second big decision is to rethink the user’s workflows as they interact with data, redesigning the basic architecture of the information itself for a fundamentally mobile experience. If desktop software is a meal, we need to course it out and cut it up into manageable bites if it’s expected to be consumed on mobile. In the case of our Sapho customer, we moved the data into selection lists that let users delve deeper as needed — as opposed to showing one big spreadsheet — resulting in dramatically decreased load times and enabling users to navigate and engage with the data quickly and easily. Think about which areas of the software are used most, from the data itself to the editing tools, action buttons, text fields, banners, and more, and ensure that the most-used areas are front and center while less-used areas remain hidden.

Build in Notifications

The third big decision is how to incorporate notifications for the enterprise mobile app. The best way to remove features from an app is to make it intelligent and predictive. The current enterprise software paradigm forces users to remember what they need from an app and then perform an action. It’s the only paradigm we’ve ever known. But just as iTunes and Amazon make recommendations based on your previous spending behavior, enterprise software must learn about its users and deliver relevant information to them. Users on mobile increasingly expect software to process on their behalf and notify them of important events, and the next generation of enterprise apps should offer notifications that let users know when something important happens in their business.

The thoughtful prioritization of data and its organization into new workflows with notifications create the foundation for a rich and powerful mobile experience, offering businesses the opportunity to finally harness enterprise software for an entire organization. IT organizations and vendors have the opportunity to renew a conversation about how to make the big decisions that make sense for their company; the big decision that will finally make small screens valuable across the organization.