Brand Management in the Age of Twitter

In days gone by, a brand interacted with consumers either through retailers or tightly controlled feedback channels.

Brands were separated from their customers and marketing was a “Mad Men” sort of process of smoked filled rooms and catchy slogans and jingles like “Intel Inside”.

The rate of change in the past few years in the way that brands interact with their customers has been exponential with the rise of review sites like Yelp, Trip Advisor, Angie’s List and Twitter with the powerful elegance and beauty of the hashtag.

The hashtag has been the most powerful shift in the loss of B2B anonymity. In the past, if you wanted to complain about bad customer service or a tainted burrito, you were relegated to the black holes of customer service or the complaint department.

Now with a few clicks on your phone, you can bring once powerful brands to their knees as witnessed by Chipotle with a record loss of over $1B in market cap after a sick staffer spread the norovirus around a suburban Washington DC, store, sickening more than 130 customers.

Now powerful tools from Salesforce, Adobe, Lithium and others enable brands to monitor and respond with immediate damage control to prevent billions in loss or increased distribution to capitalize on market inefficiencies.

But the power of the hashtag is not just about angry consumers.  While monitoring its twitter feed shortly after the release of a new LeBron James shoe, Nike noticed that these shoes were being bought up in droves in the Midwest and sold at a 500% mark-up on eBay.  They quickly responded and stepped up production.   When Nintendo was trying to figure out what to do with old outdated games like Donkey Kong, they bundled them onto an old console and listed them for $99. These caught on like wildfire amongst 40 year old gamers looking for childhood nostalgia. Nintendo cranked up production and the price to meet this overwhelming market demand.

Twitter and the power of the hashtag has further accelerated the swing of power from the brand to the consumer.  

Next blog: The power of AI and ML in the new age of brand management. 

Machine Learning is Already in the Palm of your Hand

When most folks think about Machine Learning and Artificial Intelligence, they generally think it’s futuristic science fiction.  iRobot kind of stuff where androids take over the world and a courageous scientist must hack into the central computer to save the planet and restore humanity back on top.

But sometimes some of the best new tech comes disguised as just something really convenient.

You see if you did the iOS10 upgrade to your iPhone, you’re using machine learning right now and probably didn’t even know it.

The machine learning algorithm used by iOS runs natively on your iPhone.  You don’t have to upload your pictures to some High-Performance computing cluster in the sky.  When you take a picture, a lot of really cool things happen.  iOS tags that photo with the location and time.  Simple stuff given the GPS.

But iOS also runs each photo through about 10 Billion (According to Apple) calculations so iOS can recognize faces in your photos and group by person, but it also has advanced object recognition, making it possible to find images of any number of different things. Your little iPhone knows if you have taken pictures of food, cats, dogs, trees and automatically tags meta-data to your pictures for instant retrieval by those search criteria.


OK joke time, Question: What did Will Rogers say to the computer scientist? 


Answer: I never Meta-Data I didn't like.  Ugh. Sorry about that.

In my prior Blog The Fourth Industrial Revolution is Here and Why Google is Getting it Right I touched on how Google’s image and Video API’s were using the virtually unlimited horsepower of the Google Cloud to dissect videos and images pumped up to the Google Cloud to detect Great Danes from Poodles but to classify both as dogs.

Well with iOS10, your iPhone can do pretty much the same thing.

Here’s how to access it.  Go to your Photo Collections and click on the Search Icon. 

Up will pop a search bar and keyboard.  Simply type in the text of the image you want to search for.  In the first case, “Cats”. and IOS will use the predefined meta-data tags automatically associated with the images.  iOS gathers up “Cat” related images and presents you with the below collection.

Ridiculously simple. Machine Learning for the masses and the democratization of what was once highly advanced computer science in the palm of your hand.

Transforming from a Product to a Platform Company

For 20+ years I’ve dedicated a large part of my career to stewarding the evolution of organizations as they transition from a product focused company to a platform.

Recently I’ve been asked about my definition of a platform? Why is it an important step in the evolution of a company and what is the role Business Development plays in that transition?

I’ll do my best to answer those questions in this Blog.

I got hooked on the movement early on when the godfather of multi-tiered architectures,

Alfred Chuang said: “The strength of any platform is defined by the number of partners standing on it”.  At the time, Alfred was CEO of BEA Systems a young, scrappy Java App server company. Alfred knew that to outpace IBM in the JVM market, he had to assemble the best set of partners building on top of his platform.

As we look at the rise of a few notable platforms in the past decade, patterns begin to form.

Facebook – When Facebook broke away from challengers like:  MySpace, Friendster, AOL, etc. there was a few noteworthy differences.  Building your online profile in Facebook is a simple point, click and upload some pictures into a predefined template. The customization model in Facebook is a simple process. MySpace on the other hand, had limitless combinations of presentation options and was

customized via HTML Tags. Great for geeks.  Bad for grandma.  The second compelling feature of Facebook is the ability for third party apps to target the growing community of users with very specific messages based on massive amounts of personalization data. Personalization data, on not just where you were born, went to school, your major, where you work, where you’ve lived, visited and what you ‘Like”, but your entire network of fiends, their profiles and so on and so on.  The flywheel effect kicks in when you can invite partners to build an app on Facebook.  Apps like Spotify, Airbnb, LiveNation etc.  The fourth key attribute that Facebook also has the pole position on is the control point as they stand as the gatekeeper to all your news feeds, interactions with friends, life chronology etc.

iPhone – One of the greatest platforms on the planet.  The commonalities to Facebook are resounding.  Easy, standardized, custom development model.  Massive installed base of loyal customers.  A multitude of persona’s and usage models.  Now every company must have their own customized iPhone app to: 1) Access the throngs of iPhone enthusiasts who crave the standardized UI and User Experience( UX) that is uniquely iPhone. 2) take advantage of the personalization data that is captured by iPhone e.g. your location, places you visit, place you call home, who you communicate with, your browser history, who you transact with etc.

iPhone has a very rigid app development model that takes advantage of platform services like a camera, GPS, microphone, motion accelerometers.  Using the platform features in a structured, certified dev environment has enabled 1000’s of aps in gaming, travel, finance, music, sports, affinity groups and commerce.

To recap the key attributes of a platform:

• Community – Scores of happy users of an anchor tenant app.  You must have this first to entice developers.
• Customization Model – An app dev environment enabling developers to consume rich services served up by your platform.
• Third Party Apps – A wide range of apps built on the extensible customization model consuming platform services.  These apps are often certified by you to make sure they are addressing all of the platform features in a correct manner.
• Control Point – A controlling layer that serves as a centralized information bus for either data, experience or both. When developers use your control layer to access critical data or drive the UX, your platform becomes ultimately sticky.

Transform your Company

Most companies start with a great product idea.  This singular product takes them to being a successful product company with traditional engineering, marketing, and sales. The total addressable market (TAM) of that company is gated by the market size of that one product.  Case in Point – VisiCalc.  Before there was Excel there was VisiCalc from VisiCorp.  This early spreadsheet program, first released in 1979, was originally exclusively for the Apple II.  It was seen as a the “Killer App” for the Apple II. In effect the reason to be for this new compute platform.  People were known to buy the $2,000 Apple II to run a $100 VisiCalc Spreadsheet program. VisiCalc was originally marketed to financial professionals who were looking to use the Apple II. Sold through boutique computer stores, the small TAM, and narrow channel resulted in about 700,000 units sold in six years.

In 1988, Microsoft released Office.  It included Word, Excel and PowerPoint as a bundle. Really it was just marketing three separate disks, installs and apps as one suite.  It had a customization language in Visual Basic for Applications (VBA), enabling user-defined

functions (UDFs), automating processes and accessing Windows API and other low-level functionality through dynamic-link libraries (DLLs). It joined and replaced the abilities of earlier application-specific macro programming languages such as Word's WordBasic. It can be used to control many aspects of the host application, including manipulating user interface features, such as menus and toolbars, and working with custom user forms or dialog boxes.

Suddenly the TAM that Microsoft was addressing was much broader.  Not just financial pro’s, but writers, presenters, collaborators. This new set of knowledge workers demanded a suite of apps that were integrated and had dynamic data transfer for building graphs in Excel and having them auto appear in Word or PowerPoint presentations.  The ability to customize and control all of the apps via VBA brought in third party app developers eager to build apps on top of the Microsoft platform. Combine the power of the platform with multi-channel sales e.g. resellers, online, direct, Fast forward to today.  Microsoft has sold over one billion licenses of Office.  That’s 1 for every 7 people on the planet.

Business Development


An interesting organizational change happens when you decide to shift from being a product company to a platform company.  Business Development (BD) takes a center role in the org. As a single product company, BD was probably focused on providing leadership for Systems Integration partners (SI’s) to assist in successfully deploying your product for customers and maybe integrating your product with existing or third party apps.  An

important function but one that changes dramatically as you become a platform company.

To validate you as a platform company, BD takes on much broader functions:

App Developer Communities – This is a 1:Many function where platform API’s are exposed and supported by partner oriented engineers.  Engineers who are versed in your platform and the multitude of partner app use cases that can delivered by using your platform service.  Exposing your services into popular dev communities Integrated Development environments (IDE’s) like Visual Studio, Eclipse, and run-time execution environments e.g. EJB’s, Ruby, Google App Engine, AWS AMI’s and Lambda, Containers, etc. making sure your platform Software Development Toolkit (SDK) supports the permutations of IDE and run-time environment.

Co-innovation -  This is a 1:Few function where select ISV’s are identified as lighthouse apps to integrate to, build on top of and even OEM your platform because of its unique control point in the stack or UX. The key is to define and co-innovate on a joint solution that is unique to your and only your platform and then keep that integration fresh and ahead of the other platform competitors. The BD teams engage with these ISV’s for a more intimate discussion that probably includes some of the deeper engagements described below. Many times these ISV’s are singled out by the product or sales teams because of their prominence in a market space, geography or industry vertical.  E.g. Schlumberger in Oil and Gas, GE and Siemens in healthcare etc.

Co-Marketing – Once a joint solution is defined the 1:Few Go-to-Market (GTM) plan put in place, invariably contains Co-Marketing. Doesn’t matter if you’re a 10 person start-up or IBM, your contact lists and the numbers of times you can hit these before people opt-out is finite. Partners often have different installed bases than you. Partners leverage each other to market to those installed basses.  Leverage is garnered by leveraging each other’s brand equity.  People are much more likely to respond to a Call to Action (CTA) from a brand they recognize than one unknown to them. Exhibiting at each other’s users conferences and trade  shows brings in a whole new audience.  Even something as simple as a joint press release can be impactful in an over-saturated world.


Co-Selling – This is when things get interesting.  Now that the sales teams have something that is unique, compelling and differentiated to discuss with customers, it’s time to do the account mapping and joint selling.  This is generally done on a grass roots level region by region, rep by rep, account by account. Quota buckets,  accelerators, Sales Promotion Incentive Funds (SPIFS) all determine

how reps focus their energies. As the life blood of their W2, reps are extremely protective of their accounts, so letting another partner in for a pitch is often an exercise fraught with risk.  But as is often the case in many B2B relationships, knowledge displaces fear and replaces it with trust. Understanding how your counterparts are goaled and compensated is critical.

Co-Delivery – 3:few. This is the final mile of a true GTM partnership.  When two product companies come together, what’s often overlooked is actually the most important aspect and that’s customer success.  Solutions, especially joint solutions require specialized expertise and focused alignment with key SI’s that are well versed in the joint solution and have the same obsession towards customer success as the ISV’s.  The further they get away from your own core product, your own Proserv org's expertise will diminish. Partners aligned for your mutual success are key here.
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The rewards for making the difficult transition from a product company to a platform company are significant.  With all its interconnections and eco-systems supporting it, your platform becomes infinitely stickier, customers are far less likely to displace your platform for a whim of pricing or functionality.  Your long-term revenue and market cap will reflect the widened TAM.  Just ask Facebook, Amazon, Google, Microsoft, Apple and the host of other iconic brands that have successfully made the turn and have dominated.

Software is eating the World or What Happened to my Phone Company?

The story of CenturyLink is a common story in the evolution of the Telco industry.  You can substitute Verizon or AT&T and it’s pretty much the same story.

In 2000, then CenturyTel acquired 300,000+ residential service lines from GTE. In 2010, CenturyLink acquired Qwest Communications. This new combined entity became the third largest telco in the United States with 17M access lines 5M broadband customers and 1.4M video subscribers. Traditional voice and data services.

Right around the same time in 2011, CenturyLink acquired Savvis. Savvis sold managed hosting and collocation services with more than 50 data centers (over 2 million square feet) in North America, Europe, and Asia, automated management and provisioning systems, and information technology consulting. At the time, Savvis had approximately 2,500 unique business and government customers.  Not so traditional Telco stuff as this was pure B2B.

But faster than you can say, “Gotta Keep up the Jones'” the corporate development groups at Verizon snatched up Terremark and Time Warner Cable purchased NaviSite. 

Both were Colo/Managed hosting providers like Savvis. What does a managed hosting and Colo business have to do with selling phone lines, video and broadband?  Not a whole lot.  These services are sold B2B and are the IT version of parking your car in the neighbors garage.  He may wash it and change the oil sometimes, but it’s still pretty much your car sitting in your neighbors garage.  He pays for the roof overhead, but that’s pretty much it.

A Short primer on Colo vs Managed Services vs. Cloud.

Collocation You purchase and own both the hardware (servers) and software that will host your web presence.  You are responsible for buying, setting up and configuring both. This is the easiest market with the lowest barriers to entry.  Read, Even the Telco’s could do it. 

Managed Hosting The hosting provider ‘managed’ everything that was required to give you an online presence for your website.  I’ll wash, wax and change the oil on your car regularly.

Shared hosting account – Multi-tenancy.  Which just means that many customers such as yourself shared space and resources on a single server, which has been designed to host multiple accounts simultaneously.  I’m renting you a zip car.

Dedicated hosting account, often referred to as a dedicated server.   These are your servers.  Kind of like Colo.  Customers concerned about security love this.

Public Cloud, Everything from Compute to Storage to database is virtualized and multi-tenant, spread across every region of the globe for disaster recovery with multiple zonal fail-over in each region to accommodate data sovereignty rules. I’m renting you a car that I can rent many times.  You never have to buy a car again.  Oh and I also make the car.  

More on that in a minute...

AWS Envy

You see five years ago CenturyLink and other Telco’s saw the rise of Amazon WebServices and believed they could compete head-on against AWS in the public cloud market. As noted above, that ambition inspired CenturyLink to buy Savvis for $2.5 billion in 2011. Around the same time, Verizon acquired Terremark and Time Warner Cable purchased NaviSite.

Why did this seem like such a good idea in 2011?  Back then, all these phone companies thought that they needed to compete with AWS was the physical presence of massive data centers.

But they grossly underestimated one key fact in AWS’s success.  Yes while they do own their own data centers for the most part, the reason why AWS is successful is because they are complete stack of truly virtualized, multi-tenant services based on commodity infrastructure and in the case of AWS, as opposed to Amazon.com, AWS writes the books. In other words, Just like GCP and Azure, AWS makes what they sell.  They are the manufacturer of: Aurora DB, Dynamo DB, Redshift, S3, Glacier and the swarms of other AWS products.  Their hardware COGS e.g. servers, storage and networking is on the cheapest commodity gear with smart software for fail-over and high availability.

The Telco’s are not software providers. And as Marc Andreessen so eloquently pointed out “Software is eating the World”.

Amazon’s AWS revenues continue to grow rapidly, surging 42 percent to $3.66 billion in the company’s Q1 2017.

Fast forward to present day to close on the current state of the Telco's. 

Verizon has essentially abandoned its public cloud and will instead focus heavily on private clouds as well as secure network connections to Azure and AWS just like Equinix.

From the Verizon CEO in August 2016.  “CenturyLink’s Colo revenue is not growing, and the telecommunications giant is looking for ways to avoid investing more capital in the segment. CenturyLink is looking for “alternatives” to owning its nearly 60 data centers around the world that support colocation, managed hosting, and cloud services”.

On Nov 04, 2016 CenturyLink sells Savvis to Private Equity for $2B.

Even the once mighty Rackspace (Never a Telco but an arch enemy of AWS) waved the white flag on March 8th 2017 when they announced a strategic relationship with Google Cloud to become its first managed services support partner for Google Cloud Platform (GCP). Google Cloud and Rackspace are collaborating on some new managed services offering for Google Cloud Platform customers that will launch later this year.

The managed support services will offer GCP customers added cloud architecture support, on-boarding, data migration expertise, as well as ongoing operational support for customers, to help ensure optimal cloud application performance. 

They’ve offered the same services for AWS for some time now.

That’s akin to Ford getting out of the car manufacturing and selling business and instead doing specialized services for Tesla. 

The cloud world has changed dramatically in the last 7 years.  Colo, Hosting, Managed Hosting business have all but dried up and most of these orgs are divested or reinvented themselves as services companies for the big three GCP, AWS and Azure.  You see, cloud computing takes enormous scale and tremendous software DNA up and down the stack. Without both assets, time to reinvent.

Machine Learning and Why my Wife does not need to find a New Hobby

In my last Blog, The coming of the Fourth Industrial Revolution and Why Google was Getting it Right, I wrote about why prior technical breakthroughs in industrialization e.g. steam to electricity, vacuum tubes to integrated circuits, process efficiency to Machine Learning and Artificial Intelligence had been sea changes not because of their technical breakthroughs but because of the societal changes that were made possible because of these giants steps in tech.

I got a lot of questions coming off this last blog, primarily “What is machine learning and why is it all the rage?”


Let me start by saying human to human interactions in the modern world are ambiguous and messy, Human brains have been conditioned throughout time to make sense of this messiness and come to in most cases, sensible conclusions.  Take the very human ability to detect sarcasm in a conversation. We can do this because we pick up on the subtle clues like: prior knowledge of the speaker, inflections, situation or subtle body cues like: eye rolling and smirking.

One would think that the ability to detect sarcasm is a learned behavior that machines would never be able to perceive.  Think again.  By the way, the above image of Gene Wilder was found by searching for facial sarcasm.

Blame your grandparents, but even when people age, your facial bone geometry remains the same.  The angles and distances between key bone structures on all us homo sapiens can be broken down into math.  Wait, math? Billions of images, subtle mathematical facial changes, when combined with speech patterns? That spells sarcasm? Sounds like a job that super-fast computational algorithms accessing billions of data points spread over a virtually unlimited farm of servers would be good at.

So I can auto-tag my selfies.  Who cares?  Well, how about an algorithm that can provide early warning detection to parents and mental health professionals that subtle changes in a person’s facial expressions combined with sentiment in their postings, on-line behavior and inflections in their speech from billions of data points derived a propensity for suicide.   What if we could detect that propensity years in advance of an event?

School counselors are hopelessly outnumbered and overwhelmed and parents are simply not mental health professionals. The saddest thing I’ve heard from a parent whose child has taken their own life was “We never saw it coming”.

Technology has always elevated mankind.  Masses of people were once relegated to manual labor.  From building the pyramids to coal mining and factory work.  A modest technical insertion from something as simple as a fulcrum to a steam engine to manufacturing automation was introduced and mankind is elevated.

One of my favorite examples is from right around the turn of the century when elevators had an attendant who pushed the buttons for you when you entered the elevator.  Well along came automatic elevators which did not require an attendant. People would enter the elevator and quickly exit because they thought it was not safe. 

According to the US Department of Labor Statistics, 22,360 people work in the US fruit canning and preserving industry. Many of these workers are hand sorting fruit based on size, color and quality.  Human kind is better than that.

Which brings me to my wife. She loves to do jigsaw puzzles.  She can sit for hours doing these things.  Being the student of behavioral and computer science I am, although I could never have the patience to do one myself, I can spend hours watching her do these puzzles. Like all good jigsaw puzzle solvers, she sorts them by color, geometry and then proceeds with edges and common colors.  She then works her way to the middle and eventually solves the puzzle, sans the illusive peace that the cat may have made off with.

But when she’s done, she lovingly admires her finished masterpiece for a short while before breaking it apart and putting it back into the box.  That’s generally when I sit in amazement thinking that a machine learning, image and pattern recognition algorithm could have done that puzzle as quickly as you could read this sentence.


But then I realize I’m totally missing the point.  That some experiences are better left for the human condition. The mental rush and warm feeling of accomplishment is what makes us human.   Technology frees us to from having to perform menial rote tasks as acts of survival.  How we choose to spend this gift of freedom is up to us.

The Fourth Industrial Revolution is Here and Why Google is Getting it Right

Industrial revolutions are about harnessing the power of the day to drive a breakthrough in business or social conventions to produce a sea change in our everyday lives.

The First Industrial Revolution used water and steam power to drive industrial activity. We saw the rise of the locomotive that enabled rapid transport of people and goods. Suddenly commerce could span the once insurmountable mountain passes and open prairies that kept civilization in pockets. Mechanization of the farm also meant less people were needed in food production, freeing people to explore 

more academic topics. 
The Second used electricity to fuel the rise of the modern factory and around the clock productivity aided by multi-shift workers and the light bulb. This in turn drove further urbanization and accelerated moving people off an agrarian lifestyle and the rise of centralized cities and a suburban lifestyle.
The Third used microprocessors and information technology to automate production planning. This saw the rise of process efficiency. Former manual processes were now handled by computers freeing up people to again rise above these manual task e.g. ERP, Supply Chain and CRM efficiency, ATM’s, Self-checkout, ecommerce.

Now a Fourth Industrial Revolution is here.

This new revolution is happening because of a few things that have come together in recent years.

The rise of hyper scale cloud computing.  At the click of a mouse I can rent computing power that 10 years ago would have taken millions of dollars of investment, floor space, people and power to access.  The democratization of compute.

IOT – Sensors are everywhere. These sensors are continually exhausting unparalleled amounts of data. Whether it’s bio-informatics as simple as your Fitbit to genomic sequencing to predict your propensity for thyroid cancer and formulate a targeted therapeutic and improving outcomes that just a decade ago were trial and error guess work. Or IOT sensors on each stalk of corn in a farmer's field driving higher yield and eliminating crop loss from over/under watering, fertilizers and pests.

And lastly advanced algorithms in Machine Learning and Artificial Intelligence. In March of 2016, the world was stunned when Google DeepMind defeated a legendary Go champion Lee Se-Dol from South Korea.  Most AI experts thought that type of deep neural networking was a decade away. Go is a simple game and take only a few minutes to learn, but the possible combinations of moves are endless. The number of potential board positions is: (That big number over there) a number greater than the number of atoms in the universe. That's a lot of atoms.  Because there are so many different combinations, Go is the “Holy Grail” of AI.

Which brings me to why Google is getting it right.

Cloud computing takes scale.  Check

A hyper scale network free from the public internet.  With a private fiber network in 70 points of presence in 33 countries.  Check.

But AWS, Azure and even IBM and Oracle would argue “me too” on the above.

So why is Google poised to be the center of the Fourth Industrial Revolution? 

YouTube, Vision API, Video API ,Tensor Flow and the democratization of AI that's why.

For instance, every minute of every day, 300 hours of Video are uploaded to YouTube.  1.3 Billion people watch 5 Billion videos every day. (That’s democracy in action) When you search for “Dogs” on YouTube, you’re not just searching meta-data tagged manually to a video. YouTube uses the open source Tensor Flow library to find all images of a dog embedded in ANY video.  Even if the video has nothing to do with a dog or a dog just happen to stroll across a few frames of an hour long video about baseball.  But how did the algorithm know what a dog looks like? There’s lots of different dogs out there and coding a traditional algorithm to cover all dog conditions would be ridiculous.  Google Machine Learning and Google compute with its ability to process billions of references to hone the algorithm to statistically predict that image is a dog. That’s how.

So how does the ability to find a dog in a video spell a sea change for mankind? 
It’s not just about the videos or the images or the audio, it’s about combination of massive amounts of compute on demand, a super-fast network, the advanced ML algorithms and the ability to unlock immense amounts of intelligence from the unstructured and machine generated sources.  Couple this on top of the open source nature of the Tensor Flow libraries and you have the flywheel effect of Billions of people, data sources and compute engines. (More democracy in action)

Imagine next generation apps to predict fraud, money laundering, powering self-driving autonomous cities that optimize people’s needs to get somewhere with the capacity constraints of the freeways. But this time, everyone is humming along at 80 MPH with zero accidents.

Insurance claims being handled quickly, paid efficiently with minimal fraud because the corpus of accident video, image and report data has seen this exact scenarios 1000’s of times before.

Applications were once constrained by how fast a human could type or a printer could print. That’s all changed.  Say you want to learn how to replace the transmission in a delicate piece of engineering like your late model BMW. Wouldn’t you much rather learn by watching a video specifically customized for you with the aggregated relevant clips from the largest video library on the planet with each step by step procedure and pitfalls?  A video tailored perfectly to your experience level and perspective.  The alternative would be a dry manual written from one perspective and for a mass audience without regard to your needs and reference points. Life is good when the worlds data and compute resources come together.

The platforms have arrived and are getting more powerful by the minute.  The application possibilities are gated only by our creatively.

Affinity establishes community, community builds trust, trust lubricates commerce

People establish trust based on commonality.  Facebook recently launched a competitive offering to Craigslist.  But just like eBay is an efficient market for sellers due to its high liquidity of buyers therefore creating the most efficient market for sellers driving to the optimal price, Facebook did something really smart as they are encouraging people to build localized groups where buyers and sellers of personal items can meet. But unlike Craigslist, Facebook uses their built-in community to create trust to drive to an efficient market.

I like selling things.  There I said it. But truth be told, I’m always selling something.  In my personal life, I’m constantly listing things on Craigslist, eBay and now Facebook. It’s the thrill of fishing with multiple lines in the water.

I’m fascinated by and a perennial student of different selling models and dynamics. Facebook is my latest fascination.  You can create a Facebook affinity group and sell, trade or barter stuff within that group.  Why? Because affinity establishes community and community builds trust and trust lubricates commerce.

As an experiment, I’m selling the same item in the venues of: Craigslist and Facebook.  Although Craigslist is better organized and categorized and would therefore intuitively drive more people in need of my item to me, the power of Facebooks affinity crushes Craigslist. I posted a personal electronics item on Craigslist about a week ago.  I’ve received 2 inquires. Poorly qualified, not ready to buy now. Craigslist trolls. Facebook on the other hand, as soon as I posted the very same item, I was inundated with 15+ Facebook personal messages from people ready to buy my item right now.  Why?

The Facebook selling group is called “Danville for Sale or Free”. It is by invite only, gated by a moderator and “exclusive” to people who live in Danville.  People are driven to trust based on our affinity for our little home-town of Danville.  One person even said, “I trust you, we’re both Danvilleians” That’s the secret code for people who live in Danville.

Kind of odd. But think about it, when you’re in the farthest reaches of Asia or Europe and you hear someone with an American accent, your ears perk up and you say, “Hey, buddy, American?”  When they answer back with “Hell Yes!”, this guy’s your new best buddy.  You’re drinki’n and talking Rogers vs. Brady vs. Brees with him. If you ran into the same schmo in Orange County, you probably
would have cut him off in the In-n-out Burger drive-thru and then flipped him off when he gives you the stink-eye, but now the power and affinity of the blue passport has you huggin and chuggin like old friends.

Mmmm, Affinity establishes community and community builds trust, trust lubricates commerce. (Said in your best Yoda voice)

Threading the Three Needles of a New Product

Have you seen the video of the guy in the Wingsuit threading the needle in a rock wall at around 160KM/Hour?  Watch it. It underscores the point that there is a subtle difference between wingsuit glory and being a stain on a rock wall in the Swiss Alps.

New product introduction at a start-up is kinda like that wingsuit maneuver.  Careful planning, in-flight adjustments followed by moments of self-doubt and shear panic. 

As a fledgling company without established customers, channels and brand, you have the combination that makes start-ups so hard and yet so rewarding at the same time. You see being at an established company you can lean on your stabilizing ballast of revenue, customers, a ramped channel or direct field with established customer relationships.  Introducing a new product into an existing channel presents its own challenges but nothing is as daunting as a new product into a new channel.

This is why new product intro at a start-up is like threading three needles at once. The right message at the right time to the right person.

Subjects that are top of mind and selfishly make a person’s career prosper. In other words, technology that will make them successful individuals in their organization.

You’re asking them to change what they are currently doing, how they allocate their budget and how they train their people. 

Tech people brand themselves as “Certified Professionals” in vendor proprietary technology whether its VMWare, SAP, Microsoft or Oracle, this personal branding is strongly tied to technologists as a badge of achievement and often advancement.

But in introducing a new product, you’re mission is to disrupt this and replace it with branding people as innovators, forward thinkers, visionaries.  Sophisticated orgs are often bifurcated between run-time environments and next-gen platforms each with different groups managing them.  Bifurcated teams introduces politics and turf.

The smart start-up customer org must be highly aware of all of these dynamics, navigate the org, and tune the message and the audience.  Find the innovators and the run-rate teams.  Tailor your message on the fly.  Careful planning, slight in flight adjustments and guts to hit that hole-shot and avoid being a stain on a wall.

Local Clouds and The Coming Death of Legacy Stacks in the Cloud

Let me tell you a little secret about the “cloud.” It’s that right now in the enterprise, it’s a local game. 

A few options come to mind when we think of enterprise, like the predominate force of AWS in test and development environments. Then you have providers like, HP-ES, IBM/SL, RackSpace, Teremark, and Google that are all trying to play enterprise production load catch-up.

For the meantime, I think it’s safe to call the enterprise private cloud a local game.

Take KIO Networks in Mexico City, or LGCNS in South Korea, or T-Systems in Germany; the strength that these local service providers have is that they are entrenched into the local economy. They are tied in with the governmental entities either with contracts, investment tax incentives, or in some cases, board relationships.  Governments encourage these types of business, as they are clean, provide local jobs, and are a good high-tech face for the country.

The key characteristics of these service providers are that they have the local connections and the P&L margin expectations for the long-tail economics of a service provider business.

 In the case of KIO Networks, their margins are so tight that they build their data centers in cooler zones in Mexico City or bury them into the side of a mountain; because not running their chillers for several months out of the year is a competitive differentiator. Coming from a world of plump enterprise software/systems deployments, margins like this seemed like a foreign concept to me.

Just like our friends at AWS, service providers don’t write the books, they just sell them and are perfectly happy living with the retail economics. 

But in this never ending quest for margin, service providers need to standardize on control layers to manage each plane, like, computing, networking, storing, provisioning, and managing charge-back across these heterogeneous, at times customer dictated, and other times, commodity resource pools is vital. 

This long-term platform migration, which is primarily driven by the evolution of the service provider, is what’s posing a sea changing threat to the legacy of major profit pools from the likes of: IBM, HP, EMC, NetApp, etc.

In the early stages of a customer’s journey into the private/public cloud, they generally dictate the same legacy platforms they have run on for decades with these environments lifted and shifted into the services provider data center. In other words, let’s move my expensive proprietary boxes off of my data center floor and onto yours. 

This is precisely where the market is now.  But this phenomenon is just a hosting/colo way station on the way to the true public cloud. 

Going forward, the confluence of software driven reliability, fault tolerance, and compliance are being delivered on top of commodity infrastructure selected by the service providers will be a way of life.   

Service provider’s margins will never tolerate the proprietary stacks of today.  

These business models are too far out of synch. AWS’s cloud doesn’t run on proprietary gear, why should yours?