Glimpsing IBM Watson's High Tech Analytics In Silicon Valley

Silicon Valley types want me hanging out at their business events. One such event last week brought me down to one of the Valley's private venues for an IBM Watson presentation. I'm not the target clientele for this Big Data analytics solution but I had to check things out. There was no suitable on-location backdrop for my badge selfie, so I had to take the photo below at an undisclosed location.

I signed up to hear their two tracks on procurement intelligence and trade-off analytics after the main pitch. IBM people get the API economy. I heard them pitch their API developer ecosystem at Oracle OpenWorld 2015, and now it's good to see the Watson engine in action. The Alchemy Language API looks like an incredible business intelligence (BI) tool. The "news explorer" live link diagram showing connected news stories would be excellent for PR or marketing people, or for open-source intelligence (OSINT) practitioners.

The main pitch dude's recommended reading list included a book on machine learning, but I couldn't write down the author's name from where I sat. Amazon lists plenty of machine learning best-sellers, so my local library must have one. I did capture Pedro Domingos' The Master Algorithm and Provost/Fawcett's Data Science for Business from his list, unless I copied the titles incorrectly. I have so many books to read already that adding these will push the completion of my business reading list well into 2016. That's what it takes to demonstrate thought leadership, and that's why I get invited to these events.

One IBM guy introduced his "Cognitive Computing Index" describing multiple ways for human operators to educate maturing AI systems. IBM suggests Watson's clients iterate revisions every 90 days for whatever they have the system compute. Iterative approaches to refining BI output are supposed to maximize the BI's monetary value, and seat count users should see this value in their commission revenue.

The trade-off analytics session demonstrated Watson's Pareto optimization, graphical outputs, and social media stream matching. The recommended pathway records are a useful audit trail for some data miner to explore. I bet that data mining the faulty pathways will reveal how the top 20% of data scientists in an enterprise are making 80% of the correct decisions. That would be some useful Pareto optimization when performance bonus allocation time comes around.

The procurement intelligence session was all about making purchasing people into knowledge workers. I remember how I did purchasing as a junior supply officer in the US Army back in the late 1990s. I searched the Web for three different vendors and picked the one with the lowest price. It was too easy and probably sub-optimal. The difference today is that Watson is supposed to make research on prices, vendor choices, and spending history a Big Data effort. If AI truly integrates internal and external data feeds as advertised, then it's a bona fide ERP revolution. If users comprehend Watson's word clouds, heat maps, and visualizations, then it's also a knowledge management (KM) solution.

I keep hearing Silicon Valley people talk about how they increasingly prefer workflow ERP solutions over managing legacy files. I told several IBM reps at this event that they will have to integrate workflow data signatures into the internal feeds Watson ingests if they want to stay relevant. It will still be a challenge for developers to build APIs that handle unstructured data, especially if the enterprise has no data warehouse or data lake aggregating external data feeds. The best developers will figure it out. I would figure it out but I'd rather fiddle with financial applications. Watson and other AIs are supposed to be the "easy button" for data transformation once operators are comfortable educating the systems. The AI revolution means everyone becomes an amateur data scientist.

More Thoughts On Launching API Startups

My experience at Integrate 2015 got me thinking about how APIs and other elements of the data supply chain can be stand-alone enterprises. I will throw my thoughts from our VC perspectives panel out on the Interwebs to see if any of them stick to entrepreneurs.

The successful API supports an app ecosystem. Other businesses in related sectors code their own apps to share affiliate revenue with the API owner for every transaction their apps process. Airlines and hotels build apps around Uber's API because their customers see value in having a short-haul transportation link. Uber's app ecosystem becomes a durable competitive advantage because the app owners would have to build a whole new app if another virtual taxi company tried to displace Uber. Digital infrastructure like apps impose some switching costs on partners, but those costs are probably not insurmountable for competitors.

One thing making APIs a core business model is the continuing integration of cloud, mobile, and Big Data solutions. Some parts of this convergence are coming along fine, like the analytics suites now part of most middleware. Other parts are not working because venture capitalists have wasted lots of money on startups with lame mobile concepts. Venture funds can force their portfolio companies to pivot from a mobile or cloud solution when something isn't working out. That's how they try to save their investments. Targeting APIs for venture investment means the least successful among them will be candidates for forced pivots.

I like startups that understand Customer Development, the Business Model Canvas, and Cloudonomics. Founders should download those white papers, hit the books, and work the equations. Revising the API business plan after working those three areas shows investors that they take de-risking seriously. Corporate development groups in particular carry marching orders form their enterprise mothership to fund startups that improve their internal KPIs and match their product lines. Startups can use Cloudonomics calculations to prove their APIs are a better investment. Cloudonomics is as compelling for IT as modern portfolio theory is for finance, because it offers a disciplined methodology for allocating limited capital among a potentially unlimited number of investment options.

I do not like startups that do not understand the new opportunities and risks of raising capital under the JOBS Act. The SEC continues to publish new rules defining what private companies can and cannot do to attract investment. Anything I blogged about in the past regarding crowdfunding will likely be obsolete by the end of 2015 as the SEC finishes its unfinished business. Startups will have to engage competent legal counsel earlier in their development process to understand JOBS Act compliance. Founders must meet all of the SEC's compliance requirements before they appear in front of pitch fest panels or hang pitch decks on crowdfunding portals. No one wants to be shut out of raising capital because they did something noncompliant.

Data sector startups can tell their stories more effectively by having a founding CEO who knows sales. It really is that simple. Technical founders love solving technical problems, but they may not know how to solve problems with financial or emotional components if they have never worked in sales. It goes back to the classic split in startup teams between the scientific co-founder who becomes the CTO and the MBA-type serial entrepreneur who becomes the co-founding CEO.

If I only had so much money to commit to a venture investment, I would prefer Big Data and cloud concepts over APIs or IoT. The industry standards for data and cloud are firmer than those for APIs and IoT, so the durable competitive advantages of a given business model will be clearer where standards are firmer.

No enterprise can ever indemnify itself by outsourcing risk. Trusting an untrustworthy partner brings a boatload of risk assumptions. Outsourcing an API means trusting someone else's coding with no supervisory input. If the API passes dirty or fraudulent data into analytics, the enterprise's compliance regime becomes a target for regulators. Don't ask for trouble by handing off mission critical API management to a third party.

Chew on all that stuff, API people. There's enough genius here for several weeks of boardroom discussions. Barriers to entry in API development are pretty low, just like in gaming, because the only immediate limit is a developer's imagination. The API businesses will be the next big thing in the mobile Big Data cloud.

API Monetization And Distribution At Integrate 2015

I scored a seat as a panelist at Integrate 2015, so I had to check out other parts of the conference to see what's new in API World. There's more to app ecosystems than hackathons and gamification. Apps need information fed from the remainder of the data supply chain: data warehouses, SDKs, and APIs.

Deep linking into the app ecosystem is the brand new way of getting content to searchers. I think "discovery service" will be an emerging buzzword for content marketers. App distribution has been one-to-many so far, unlike Web searches matching many-to-many. New app store indexing with knowledge graphs and search will bring apps into many-to-many distribution. The deep linking experts on hand at Integrate 2015 claimed that cheap and free discovery drives app downloads, but they had no examples of success. Paid social media promotion still seems to be a key to driving app downloads. Aspiring startups building APIs and apps must still budget for marketing and raise capital with promotional milestones in mind.

One speaker shared some pretty good insights on the API value chain. Adding details after basic data implies adding value. The API and app have different price points, so the "spread" between them is the added value. The speaker identified different business models based on who pays for distribution. Each model had several different permutations of pricing structures. The cool thing about the broader distribution models is that they allowed for multiple price points. Developers can get paid with affiliate revenue sharing, like the way Uber's API gets revenue when partners in travel and tourism use it to make their own apps. Subscription-based fees for units or tokens tracking API calls resemble the pricing plans we recognize in our smartphone data plans. The acceptance of subscription plans for API calls comes when API providers have large numbers of APIs addressing many segments. The biggest revelation for me was the high cost of hosting APIs in the cloud if they generate high data transaction rates. Hybrid cloud solutions may be best for Big Data providers, with high-volume transactions hosted on premise and other less intense data hosted in the cloud.

The developer experience (DX) is another term that belongs with UX and UI. Cool incentive promotions can generate developer buzz. Displaying an API with pleasing aesthetics means app developers will find it attractive and give it social proof. One piece of conventional wisdom floating around Integrate 2015 is that it's okay to raise prices until some portion of your customers complain, provided you understand your API's sales cycle. I think raising prices until 20% of your customers complain validates the Pareto principle. The other 80% won't notice the price increase because the API relationship isn't significant enough to them.

Proprietary API certification means little if it does not reference some independent computer sector standard. Do a Google search of "API certification" and see the links related to petroleum engineering, not coding. The data sector covering SDKs, APIs, and apps is still so young that it does not have a standards body. The elegant solution would be OASIS standards for API call testing, installation, configuration, documentation, escalation processes, and versioning triggers for re-certification.

API developers must find distribution channels. One successful developer insisted on using a universally accepted protocol. I suppose JSON fits the bill because a lot of developers at Integrate 2015 were enthusiastic about it. Partner networks matter, and so does saying "no" to the wrong partners who want distribution for the wrong business reasons.

I'll conclude with a brief overview of my own participation on one of the panels. I shared the stage with Nicole Bryan from Tasktop and Salil Deshpande from Bain Capital Ventures to discuss venture capitalists' perspectives on investing in API-centric startups. We explored the relationship between monetization and distribution, some changes in the economic landscape that make APIs viable as a core business model, the stories an API startup can tell to make outsiders care, and some standout aspects of an API business model that we would notice in a format like Integrate's startup challenge. The audience of developers and tech aficionados needed to hear the back-and-forth of both genders on a panel representing tech practitioners, venture investors, and the analyst community. I thought our panel was more well-attended than a couple of the main stage headliner talks. Our audience asked sharp questions relevant to launching an API startup. This is the kind of high-level attention developers can get when they attend conferences like Integrate 2015.

The Haiku of Finance for 09/08/15

Data lake deep dive
Design governance up front
Stay out of the swamp

CIO Deep Dive In The Data Lake

The data lake concept has been around for a few years. It really came into its own in 2014 after PwC implied it can reduce information silos and Gartner warned of its limits. It is certainly a marketing boon for Hadoop architecture vendors and their integration specialists. A data lake that can scale as fast as cloud storage expands means more efficient IT spending, at least initially. It may also mean more IT spending at the back end of a large project if CIOs do not define the lake's governance up front.

The fourth stage of maturity in Edd Dumbill's data lake dream throws down a challenge to IT pros who design strong application clouds. Any CIO with an eye on the long game must design analytics, governance, and security into the data lake at the very beginning. The CIO's budget proposal to the CFO will then be a realistic estimate that won't come back to haunt the company in a year when the Chief Data Officer (or the CIO again, if they also wear the data hat) asks for more money to make the cloud apps work. The CFO should not need to explain a negative capex surprise to analysts in a future conference call with analysts if the CIO is realistic about the data lake's eventual requirements.

The emergence of XML as a strong industry standard means data lakes should be portable if an enterprise switches to another public cloud IaaS provider. An immature data lake will pose problems for data lifecycle management (DLM) if it ignores the midlife activities of processing and analytics just to save money on storage and retrieval. The Data Management Association (DAMA) Data Management Body of Knowledge (DMBOK) is a CIO's help file for optimizing a data lake's DLM. Call the process data administration or DLM, but the result in matching investment outlay to Big Data enterprise goals is the same.

Dumping dirty data into a data lake may seem like a cheap and easy way to assemble an integrated data base. Completing the dump without layering analytics into the Hadoop structure risks turning it into a data swamp. The swamp metaphor became a joke among IT pros soon after the Gartner release linked above hit the wires. Calculating the Cloudonomics risk/return tradeoff of a data lake approach is the CIO's key to staying out of a swamp.

CIOarena IT Security Inspiration 2015

I secured a last-minute invitation to CIOarena's San Francisco conference last week.  I had to skip the last day of Apps World North America but that turned out to be the right call.  The CIO types held forth on security policies that enterprises must address.  I did not see any signs worth photographing nest to my handwritten name badge, so forget that Alfidi Capital tradition this time.  Just imagine the InterContinental Mark Hopkins San Francisco in all its glory.  My thoughts below reflect what I learned from the speakers.

I get my normal fill of updates on advanced persistent threats (APTs) through military-related news.  The private sector tracks the same open sources.  IT gatekeepers should think hard about what they reveal on LinkedIn to avoid becoming social engineering targets.  The APT attack process is sufficiently well-defined that proactive IT people can monitor data exfiltration and shut down exposed portals that display abnormal usage spikes.  Machine learning means automated IT security audits should develop predictive abilities after some critical mass of iterations.

I love the term "managed services."  It ranks right up there with "paradigm shift" and "game changer" for scoring points in after-work drinking games.  Outsourcing routine IT ops means inexperienced contract managers can hand managed services over to high-cost outsiders.  Watch out when senior managers start using the term in strategic planning when they need to cut headcount.  Enterprises seem to have challenges maintaining a robust configuration management database (CMDB).  I don't see how any outsourcing makes that challenge easier to handle.

I noticed that no one at the Apps World talks I attended mentioned any preference for HTML 5 or Javascript.  They may be keeping some tactics close to the vest.  I did not discern a clear preference at CIOarena either.  The choice of one over the other is probably clearer after a Cloudonomics analysis.  Listen up, IT people.  Cloudonomics is to IT/cloud/mobile what modern portfolio theory is to finance.  It is the defining framework for making asset allocation decisions.  Cloud and mobile pros must prove they can do the math before settling on a favorite tech.  CIOs can earn credibility with CFOs by being more agnostic toward programming choices.

I have no elegant solution to identity management problems.  Managing identities with MS SharePoint was simple enough when I was a knowledge management officer several years ago.  I can only suggest a way forward.  Building a 2x2 matrix to optimize identity management for each business unit would be a start, with number of identities on one axis and number of devices on the other axis.  The SBUs in the quadrant with the most of each get the closest scrutiny.  I also have no elegant solution for data lifecycle management.  Industry standards for data lifecycles and analytics frameworks are widely available.  Lifecycles will compress as speed becomes the critical factor in processing huge Big Data volumes.  High performance computing (HPC) will be a growth industry, given the need for speed in more organizations handling Big Data.

CIOarena met its stated goal of furthering my educational needs.  I can't speak for the other attendees, who did not appear to be taking notes.  I'm usually the only person who takes notes at these things.  I have no idea why other humans have so little interest in documenting what they know for further reference.  Maybe some top corporate people think they can blow through their careers without ever applying what they are supposed to learn.  That is not my style.

Mobile Monday's Geospatial Big Data In Silicon Valley

I trekked down to Silicon Valley last week for my regular taste of Mobile Monday.  The Silicon Valley campus playing host to this particular event had one of these post-modern water sculptures out front.  I see these things so frequently now on such "campuses" that I'm pretty sure the big tech firms are trying to subtly outdo each other with understated water installations.  The drought is still on in California but these displays all claim to use recycled water.  Okay, whatever.  I did not take any photos of the water display or its sponsor.  You'll just have to believe me when I say I was there.

Anyway, geospatial is shaping up to be another next big thing now that all the other next big things - Web 2.0. cleantech, social/mobile/local - have run their course.  The leading geospatial player on my radar is DigitalGlobe.  You may have seen their work cited in open sources when US military officials with NATO used DigitalGlobe photos to bolster their argument that Russia was using military force in Ukraine.  The DigitalGlobe rep who spoke at Mobile Monday made a clear case for mining geo-linked data sets.  Making those data sets available to retail users in real time will take a lot of bandwidth.  Fortunately for DigitalGlobe, plenty of users love playing with high-resolution maps.

The experts on hand discussed geotagging as a user engagement strategy.  The good news for them is that incentivizing users to tag images is easy with some gamification experience.  Users who score can unlock "expert geoanalyst" badges and build their reputations in open-source imagery analysis.  Geodata startups should pay attention to exploiting all the free labor they can get in finding a mass audience for their analytical solutions.  The best freelance analysts will eventually demand to be paid premiums, much like programmers who become repeat hackathon winners.  You heard it here first at Alfidi Capital.

I am not aware of any accelerators specifically focused on geospatial startups.  I expect that to change as companies like DigitalGlobe succeed in monetizing crowdsourced geodata.  One of the expert panelists mentioned how years of map data add context to whatever users do with a download.  I would add that years of embedded links from news articles and social media feeds can add more searchable context if the download sets were amenable to enterprise knowledge management solutions.  The difference between layering and filtering data matters little to retail users but becomes more salient for knowledge managers farther up in a large enterprise.

It's time for some personal stories that add color to the geospatial sector.  My own experience with geotagged image data dates to 1996, when I was on active duty in the US Army.  In the '90s I worked with systems that used scanned 2D maps overlaid with crude geotags.  The geotags did not connect to embedded data and the maps were poor simulations of 3D terrain features like elevation changes.  The military systems I worked with since 2008 showed vast improvements in both 3D rendering and embedded links.  I know from experience how enterprise search offers a compelling way for geodata to add value.  In other words, I know what right looks like.

Here come my predictions for the geospatial sector.  I expect crowdsourced geotags will be worth more if they are segmented by user competence.  Data providers should ask taggers to initially self-identify their expertise in recognizing image anomalies or data elements.  It's worth investigating to see if gamifying mass involvement will truly identify skilled analysts.  I expect data purveyors to pursue bifurcated pricing models, with one payment track for enterprises and a much cheaper track for individuals.  It will look like software pricing strategies that chase seat counts, but the winning startups will know how to cover the variable costs of processing and storage.  Geodata startup founders must read Cloudonomics if they want to win.

I would not be surprised to see the emerging relationships between geospatial sector firms and Big Data firms to lead to mergers.  The VCs chasing geospatial startups are going to be disappointed once they discover the very high costs of putting satellites into orbit.  The only possible entrepreneurial disruption available there would be from some space launch technology that does not use a traditional multistage booster to escape earth orbit.  Rocket sled launch technology would be great if it relied upon a railgun for its initial propulsion.  I respect SpaceX for getting the conversation started but I don't understand why they still seem stuck on rocket boosters as their tech mainstay.

Geospatial enterprises will be fun to watch in the next few years.  Lots of startups will jump into it thinking they have some app that DigitialGlobe or Google would love to acquire.  If said app reduces the cost and speed of processing embedded map data, they just might have a chance.  A bunch of VCs will throw money at any startup with "geo-something" in the first line of their business plan because chasing fads is in Silicon Valley's DNA.  I'll be around to laugh at the VCs who fund the worst ideas first, and to congratulate the best ones that win.

Full disclosure:  No position in DigitalGlobe (ticker DGI) at this time.

The Haiku of Finance for 12/26/14

Locate a context
Total customer tracking
Beacon to smartphone

The Haiku of Finance for 11/21/14

Next cleantech pivot
Bring Big Data into mix
Venture cash will flow

First Experience with Larry Ellison's Opening Keynote at Oracle OpenWorld 2014

I am attending Oracle OpenWorld for the first time this year.  Tonight I heard the opening keynotes, where the cloud was everywhere.  The big show both inside and outside Moscone North was loud and bright.  Big Data, big video screens, big stunts, and big personalities are all over this big convention.  Go big or go home.

The Intel big cheese on stage was a familiar face from another conference I attended last year.  She knows her data strategies pretty well.  It is obvious that Big Data is taking over the known universe with its shear mass, though I'm not sure what metric Intel used to tabulate the number of data iterations our civilization creates.  The Intel people sometimes referred to Oracle as if their two companies were one and the same.  The love-fest aspect of these big conventions is always cute.  

The Intel talk reinforced for me once again that data centers are a pick-and-shovel play in the rush to create value in the data sector.  I want someone on a major stage to mention the following concepts:  Cloudonomics, data supply chain, and the DIKW pyramid.  Those should be factors in assembling an enterprise IT architecture.  The coming of software defined infrastructure (SDI) and the software-defined data center (SDDC) mean humans must design some very human considerations into these new IT architectures before they push the virtualization button.

Intel thinks hybrid clouds matter based on the combination of data centers built out for public clouds and the expectations for workload traffic in private clouds.  I've been watching the hybrid cloud evolution since 2011 and some data sets simply belong in private clouds for security reasons.  Public cloud providers have fallen down on their promises of security and this month's numerous high-profile data breaches are the data sector's indictment.

Intel's enthusiasm for new modalities in data transfer and virtualization gave me an insight.  Data centers that add virtualization into their cloud services should see higher ROIs as their costs come down.  Cloudonomics metrics should prove my theory.  The enhanced ROI relationship should also hold for the addition of remote resources and non-volatile memory.  

Larry Ellison pushed the cloud every time he mentioned Oracle's market position and new products.  He also pushed security as the fulfillment of his company's ethos.  It's no secret that Oracle battles it out for CRM and ERP market leadership with Salesforce and a few other contenders.  Cloud security is going to be in every vendor's sales pitch thanks to a year's worth of revelations and debacles.

Larry's Coke bottle on stage looked like it had an import sticker on the side.  I have only seen those square white stickers on bottles imported from Mexico.  Die-hard Coca-Cola aficionados prefer Mexican Coke because its use of cane sugar makes the taste more palatable compared to US-bottled coke, which uses beet sugar.  This has nothing to do with enterprise computing but I notice all kinds of little details wherever I go.  Knowing a key leader's biography down to minute personal preferences helps illuminate their leadership style.

Oracle is entering a price war with Apple, Amazon, and Google over cloud services at the same time it is moving its entire app family into its own cloud.  The other players either already had their cloud capability before they built their app product lines or built both capabilities out simultaneously.  They could build apps with no concern for legacy integration with on-premise products that had not yet migrated to their clouds.  Oracle has the more difficult road to pave as it must preserve support for on-premise legacy products while it builds out its cloud.  The competitor with the deepest pockets always wins a price war.  Let's compare the cash positions of these four giants to see who starts off ahead.

Cash positions, from Yahoo Finance . . .
Oracle (ORCL):  $24.2B (August 31, 2014)
Apple (AAPL):  $13B (June 28, 2014)
Amazon (AMZN):  $5B  (June 30, 2014)
Google (GOOG):  $19.6B (June 30, 2014)

Please note that these figures do not necessarily include the huge amount of cash Apple has sequestered in a separate entity named Braeburn Capital.  I don't have time tonight to dig through Apple's financials to see how they distinguish their various cash management programs.  Apple's massive cash hoard probably places it in the leading position to win a cloud service price war, assuming nothing else changes.

I noticed that I was the only audience member in sight who was taking notes.  I was nowhere near the section reserved for press, analysts, and bloggers so I can't tell which among them took notes or blogged live.  A few news outlets published their stories ahead of mine, but I caught a couple of interesting quotes.  I'm pretty sure Larry said "think different" (a Steve Jobs quote from his resurrection of Apple) and "at the speed of thought" (a Bill Gates quote).  Larry obviously admires his competitors.  I admit that I admire this guy too after seeing him on stage.

Data Supply Chains at DataWeek and API World 2014

I attended DataWeek and API World this year at the Hotel Kabuki in San Francisco.  The "data sector" is an emerging subset of the enterprise computing sector.  I first noticed it when I attended this conference last year.  The term "digital supply chain" already describes content creation in the media sector, so the data sector needs a special term for its own value-added process.  The best fit so far is "data supply chain."  Accenture defines the data supply chain and InfoWorld describes how to build one.  My best insights from this conference are in bold text.

Coding for apps, APIs, and SDKs is a new form of literacy that requires knowledge of interactive tools, formats, and standards.  It also requires knowledge of visualization concepts for the final presentation of data.  Poor use of visualization inhibits the progression of data up the DIKW pyramid to ultimate wisdom.  Mastering these skill sets is probably beyond the capabilities of those on the left-hand side of the IQ bell curve.  Data visualization is still relevant to less literate audiences as elites use it to channel their behavior.  One DataWeek speaker on visualization mentioned that "data trumps opinion" at Google and other highly literate enterprises.  Most humans don't think at that level.  Visualization does not have to enable critical thinking to be useful.  It is valuable as an elite tool for social management.  Control systems in a post-literate society will be very pretty to behold.

The keynote on graphing reiterated the importance of link density in assessing the value of data points and networks nodes.  Links form valuable patterns and fraud detection relies upon identifying links to outlier data.  Gartner's five graphs of the consumer web help visualize the value of linked data.  Some verticals are good early adopters of graph solutions.  SaaS providers should seek use cases in segments that must link several master data sets.  Pain points in visualization applications are connected to an enterprise's need to manage workflows that handle critical data volumes.

Money transfer could use more robust data supply chains.  Your local bank still has physical branches yet smartphones enable payment and money transfer independent of banks.  Your transaction history should be tied to your personal identity first and your banks' identity second.  I have attended enough fin-tech meetings in San Francisco to see the innovation coming out of unregulated startups, and regulated financial institutions are taking note.

Visualization best practices exist primarily in the works of Edward Tufte and Stephen Few.  Designers of enterprise dashboards, UIs, knowledge management pages, and data visualization products should read those geniuses.  They should also read my DataWeek 2013 write-up where I mentioned some really good sources for chart display factors.  There must be a market for cheap and simple business intelligence tools priced for SMBs.

John Musser from API Science presented ten reasons why developers hate your API.  Read the ten reasons yourself so I don't have to repeat them here.  These are the kinds of best practices that make attending the conference well worth my time.  Programmable Web is a huge API directory for developers.  Developers should pay attention to these factors because at some point their APIs will get traction if they get these things right.  Heavily-adopted APIs must then migrate from small freemium hosts to major cloud hosts and they will face all of the growing pains of an SMB that becomes a large enterprise.  Check out the developer pages of Facebook, GitHub, Google, Twitter, Apple, and Microsoft to see how industry leaders manage their API platforms.

My biggest discovery at DataWeek / API World is that multiple freemium platforms enable the creation of an entire data supply chain . . . free of charge.  That's right, folks.  Do some Google searches yourself to find the providers.  I have not seen a standard definition of an API life cycle but some common stages are emerging around development, deployment, management, and retirement.  All of this can be done at no cost to developers, at least until the data supply chain gains traction with other developers and users.  Converting a Google Doc or Microsoft Office file into an app or API establishes a minimum viable product.  Freemium translation platforms can also automatically build an API into an SDK.  They can even add speech recognition.  This opens up transformational possibilities for business domain experts who are not proficient in coding.  I am tempted to create a data supply chain for Alfidi Capital.  A valuable data supply chain reflects unique domain knowledge and deep master data sets.  Remember that Data.gov is a free source, ready for the taking.

Anya Stettler from Avalara had one of the best talks I've ever seen at a tech conference, hands down.  Her tips on documenting APIs walked through examples of technical references, code snippets, tutorials, and live interactive formats that keep developers excited about an API.  Check out her presentation on SlideShare, because it's too good to miss.  More speakers need to focus on action items with just enough of a soft sell to let us know their brand is a go-to source for expert services.  "Do this to be successful" is the kind of talk I like to hear.

Open data from governments is a free input to data supply chains.  Bay Area government agencies have held "datathons" encouraging citizens to construct visualization products from government data.  It's understandable that they have to farm out product development to the public if government agencies don't have the bench strength in data science to do it themselves.  The philosophy behind the Science Exchange's reproducibility initiative needs to make its way to government research.  Aspiring data analysts don't have to wait for the creation of a GitHub or Bitbucket for the analytics community; they can get right to work on DataSF.

I learned a new term during an excellent talk on API lifecycle management.  That term is "data scraping," the harvesting of proprietary data from a popular API.  Platform managers who implement protective measures against scraping will also deter legitimate developers from using an API.  There's always a tradeoff between usability and security.

The IoT talks were not as informative as I had expected because they were mostly disguised product pitches.  The platforms with the largest API ecosystems - Google and a few others - will be the defaults for IoT integration when devices are ready to connect to ERP systems.  That's why Google bought Nest.  Data scientist job descriptions in the future will include a lot more emphasis on machine learning and BRMS than they do today.  Mark my words.

Privacy thought leaders are on board with the Privacy by Design approach that allows tailoring for different regulatory regimes.  It won't be enough in an era of persistent surveillance but it's the thought that counts.  SaaS vendors know they need a new industry-wide seal of approval to reassure consumers that someone has their privacy in mind.  The new C-suite position of Chief Privacy Officer is the likely final resting place of a Peter Principle manager who can't perch anywhere else.  Any cloud-based strategy to protect privacy should start with strict internal controls on access to personal data, which would make profitability impossible because customer service reps would never get access.  Masking metadata is another technically feasible solution that will unravel when enterprises need to share opt-in data with third parties.  The US-EU Safe Harbor privacy principles predate any surveillance revelations, which is why they need strengthening.  Forget any illusions about monetizing an open source privacy policy generator; those are already free online.

IBM had a lot to say about analytics at this conference.  Their offerings at IBM Watson Analytics and IBM InfoSphere BigInsights look cool.  Big Data requires iterative and exploratory analytics in a whole new layer between the Hadoop back-ends and data processing front-ends.  This sounds like the old term "middleware" made new again to incorporate compilers and optimizers.  Analytic language must graduate from running on small systems because Big Data is so big.  Business domain experts can learn more about this at Big Data University because they need to close their language gaps with data scientists.

The potential end of Moore's Law has implications for data storage.  If data flow volume grows faster than data storage density, SaaS and PaaS cloud providers will have to spend major capex building out data centers.  I am doing some research on data center providers organized as REITs.  I first noticed them as potential hard assets in a hyperinflation-resistant portfolio, because they are really just inputs into a supply chain.  I now think they may be a growth opportunity by themselves as a pick-and-shovel play on the data sector.  Keep watching my blog for future discussions of data centers.

The final panel I attended was appropriately the venture investors' discussion of funding and acquisition for data and infrastructure startups.  If the emerging term "Infrastructure 2.0" for the data / API sector catches on, it must encompass apps, SDKs, and tools for visualization and analytics.  The VCs think they can make money at all levels of the tech stack but I have blogged many times that unaddressed needs in ERP links are probably the most lucrative markets.  I do not share their pessimism that open source business models are too hard to monetize.  After all, IBM seems to be doing just fine selling Hadoop-based solutions because they can address multiple vertical segments as a "horizontal" provider.  I did pay close attention to the mention of several parts of a data science pipeline:  data cleansing, feature engineering, collaboration, and modeling.  I now have some new buzzwords to throw around at the next data sector conference.

I had a blast exploring new developments in the data supply chain.  Rest assured that any Alfidi Capital effort to construct data manipulation products will not in any way collect any user information at all.  I never have to publish a privacy policy if I never ask anyone to hand over anything private.  Hot women are always welcome to send me their hot photos but those won't be going into any hypothetical data products either.  It is safe to say that any Alfidi Capital apps, APIs, or SDKs will be a big hit in the data sector thanks to my extreme genius.  I can't wait until next year's DataWeek / API World to see how the sector reacts to what I plan to launch.  

The Haiku of Finance for 09/08/14

Home Depot hacking
Someone broke through all those locks
With a handy tool

The Haiku of Finance for 09/03/14

Where is your data?
Did someone hack into it?
Better check it now

How APIs Make Money

I have attended enough conferences on the social / mobile / cloud / Big Data economic nexus to recognize the new buzzwords entrepreneurs throw around.  Venture investors listening to pitches from e-commerce entrepreneurs must be getting pretty jaded hearing about apps.  The one key driver of the mobile app revolution is the application programming interface (API).  Developers building apps for mobile platforms find they can more easily drive user adoption if a branded platform has an effective API.  I now wonder how and why developers choose to enter the API sector.

An API has to make money somehow for its sponsor.  A major e-commerce platform adopts an API that allows its developer ecosystem to build apps that enhance its user experience (UX) and reduce friction in transactions.  Check out eBay's developers program for examples of APIs that broaden their platform's appeal.  We publishing platforms deploy APIs so their channel distribution partners have additional means of sharing their content.  NPR's API is one example, and that API's creator reveals on ProgrammableWeb how it creates revenue opportunities among very focused target audiences.

The US federal government's extensive API collection at Data.gov don't bring in extra tax revenue.  They make it easier for businesses to manipulate the government's public data into formats their customers can use.   I suspect there's an untapped market among government IT contractors for services that clean up dirty data.  They can tap that market if they figure out the APIs that lead them there.

API monetization is probably a tough road to travel for stand-alone developers.  Most large enterprises seem to open their APIs to developers for free.  A free tech giveaway encourages adoption.  The apps employing the API can make money through in-line ads or e-commerce fees.  I think independent software developers who focus on creating APIs for enterprises can make money on a contractual basis.  This does not mean the API itself is a stand-alone money maker akin to a retail portal.  It means API development is a specialized niche.  API developers who insist on placing the most desirable features behind a pay wall risk being shut out of an ecosystem if users fail to adopt.  Freemium APIs that showcase a developer's quality may be the best way for IT contractors to build a portfolio of work.

Alfidi Capital does not have any APIs available for download.  This firm maintains no client data, performance data, or any other kind of data that developers could ever use.  The analysis you see here is data-driven but that data is all in the public domain, linked where appropriate in these blog articles.  I don't have the computing skills to throw APIs at the public.  If I did, I'd deploy an API that enables female tech developers to share photos of their hot bodies with each other and me.  That day may never come, so techies will just have to launch APIs at each other and hope some major platform adopts one as its standard.

FTC Report On Data Brokers Heralds End of Privacy

Everything we've heard about the US government's electronic surveillance of everyone is now irrelevant.  All of those programs, classified or otherwise, are a drop in the bucket compared to the private sector's Big Data collection.  The FTC's May 2014 report on data brokers shows how marketing companies have broken down every living American's personal habits into detailed profiles.  All of this data is available for a price.

I had speculated months ago about how to assign an accounting value to stored data.  Its marketability clearly makes it a tangible asset.  The formalization of data brokerage categories gives us hints as to how much some data categories are worth.  The most valuable data probably has the densest connectivity to other data.  In other words, consumers with extensive credit histories are very lucrative data nodes to track. If you spend a lot on food, clothes, entertainment and travel, your data profile is worth a lot.  The poorest of the poor in rural areas probably aren't worth much at all.  The funniest category in that FTC report has got to be "Rural Everlasting," a euphemism for the country bumpkins who think social media is when you yell over your neighbor's fence.

Forget about laws and regulations prohibiting personal identification links to these data brokers' stacks.  Regulatory capture is a fact of life for every federal agency and the FTC is no different.  Any FTC senior manager skilled in assembling knowledge taxonomies would make a prize recruit for a data brokerage.  There is no way they will implement a regulation that would materially harm a future employer.  It works the same way as SEC attorneys angling for a Wall Street career.  No prosecutions?  No problems.

The FTC has tons of privacy policy guidance for businesses.  They subject personal data to commercial controls provided enterprises take minimalist precautions.  Data is now too important to the economy to keep it completely private.  The highest bids always win.  The winning bid means privacy loses.  The developed world enters the third phase of the Industrial Revolution with the antiquated notion of personal privacy rapidly fading in its rear view mirror.  

International Trade Centre Scores Big With Big Data Maps

I am not easily impressed but multinational institutions continue to impress me with Big Data tools.  I noticed that the UN/WTO International Trade Centre maintains several search maps of data on trade and investment.  I don't believe I've ever seen them before.  They look too useful to ignore.

The ITC Trade Map covers the import/export side of business equations.  If I were analyzing a country whose economy I believe is export-driven (i.e., Australia's natural resource exports), I would examine its trade flows with major customers (i.e., China) to see periodic changes.  This would help me assess directional changes in the exporting country's GDP and currency.

The ITC Investment Map has FDI data that may prove useful to me as I try to understand why foreign investors target some sectors and regions within the US.

The ITC Market Access Map shows further data on tariffs and quotas.  I am not an expert on tariff systems but knowing where to find them will help me understand barriers to entry in the sectors I routinely follow.

The ITC Trade Competitiveness Map is an indexed comparison of how different countries compare in the competitiveness of their products.

The ITC Standards Map looks like it's the most boring of the available maps.  Maybe lobbyists and labor unions like to wade through reams of conduct rules.  I don't have much patience for that in my analysis but I would like to give it at least a cursory look.

The bad news is that access to these databases is password restricted.  Users from developed countries can only obtain limited access for free.  I'm based in the US and I'm sure as all get-out not paying a penny for something that should be free.  The US is the UN system's largest source of funding and we should insist that this data be free to the entire world as a global public good.  I haven't tried to register for an account yet.  Let's see how efficient this UN entity proves to be in processing my requests for access.  I can't wait to test drive as much data as they'll let me have.  

The Haiku of Finance for 01/10/14

Target data breach
Impact keeps getting bigger
Neiman Marcus too

XTL Accelerates Tech Transfer While LEAF Integrates Big Data

I watch news from the government labs to see if their scientists have things worth commercializing.  Two programs give me hope that not everything in government moves at a snail's pace.

Innovation magazine reports on the Y-12 complex's Xpress Terms Licensing (XTL).  I noticed that DOE's Technology Transfer Working Group official licensing guide doesn't mention this program, so that document needs an update.  Aspiring entrepreneurs would have to contact Y-12's Office of Technology Transfer to get their hands on cool tech.

The same issue of Innovation magazine describes the labs' alliance with USDA in the Landscape Environmental Assessment Framework (LEAF).  The program is designed to make integrated data sets on land use available to the private sector.  It's a great idea but I can't figure out how industry is supposed to license it for use.  There's a Google Code site on LEAF with some links and points of contact.

These things pop up on my radar and entrepreneurs should take them seriously.  I'm pretty sure that agribusiness and commercial timber harvesters would pay for LEAF's geospatial data and computational tools if they knew whom to pay.  It probably has a large market waiting for it among urban planners and energy utilities.  It looks like it meets at least TRL 7 according to DOE's EERE definitions.  Let's see if a big agribusiness or energy company takes the plunge and licenses it.  

Catching Up at Telx MarketplaceLIVE West 2013

I admire companies that cultivate their product ecosystems.  That's why I had to drop in at the Telx MarketplaceLIVE West 2013 in San Francisco last week.  I missed the morning sessions due to a prior scheduling commitment, but such is the life of a busy finance blogger.  There's always something grabbing my attention.  Telx showed off their marketplace portal in advance of opening its West Coast HQ in San Francisco.

The conference MC was none other than Joe Weinman, author of Cloudonomics.  I scored a free autographed copy of his book and he recognized me from when we briefly met earlier this year at one of the UBM conferences in Santa Clara.  What can I say, folks; I'm becoming extremely recognizable among the technorati.  I missed the talk from Kevin Slavin, a TED figure from the MIT Media Lab.

The rapid session from TW Telecom made me think about virtualization for the first time since the VTUG conference I attended this past August.  IMHO clients who rely heavily on virtualization are better off using a cloud solution instead of expanding their proprietary data centers.  It's so obvious there's even a Dummies article to point the way.

David Kidder's keynote didn't merely recap his lessons from The Startup Playbook, and (oh yes) I scored an autographed copy of that one too.  He filters a lot of entrepreneurial stories through a lens / instincts / impacts pedagogy.  His formula for startup success boils down to proprietary gifts, extreme focus, painkillers over vitamins, building 10x better solutions, and monopolistic customer capture aggressively designed in from day one.  David recommended the 2006 HBR study "Eager Sellers and Stony Buyers" for insights into why humans adopt new products.  This synopsis of that HBR study makes me think consumer resistance to change can be plotted in a 2x2 matrix.  David also said that the entrepreneurs he interviewed estimate 75% of their success is from luck!  That confirms what I've witnessed and experienced.  I should make a 2x2 matrix for that insight, with three whole quadrants plotted as not worth the effort.  I agree with David that the psychology of playing to win increases one's chances of success in iterative moves.  The key is staying in the market long enough to find an opportunity for enormous growth.  In simpler terms, don't run out of money and don't ever quit.

One of David's observations about risk led me to ask my only question of this conference.  He thinks extreme accountability and permission to empower progress must be as pervasive in an enterprise as permission to fail, and that's why larger companies have difficulty with innovation.  The preeminence of Six Sigma means outliers aren't tolerated.  I asked David if there's anything good about Six Sigma in smaller organizations.  He said it is useful in the right place, specifically performing QA/QC of complex things.  The problem he sees is that it forces de-risking across an enterprise in areas that have nothing to do with process control.  That's what kills innovation.  He wants C-suites to turn off those de-risking forces in their internal reviews of innovation and staff intrapreneurial projects with cross-functional teams.  I want to believe him when he says CEOs should champion change at big companies, but my perception of most CEOs is that they are too ego-driven to want to be anything other than celebrities within their industries.  Only a few who are outliers themselves will have the guts to unleash experiments unimpeded by Six Sigma.

The other afternoon panels tended towards technical specifics but I did get the impression that subsea cables are a very resilient way to deliver bandwidth.  In case anyone is really into subsea cables, feel free to attend the SubOptic conference, peruse TeleGeography's Submarine Cable Map, or join the International Cable Protection Committee.  One panelist mentioned that the finance sector used to pay premiums for microsecond latency advantages, but that business has dried up as brokers are becoming subject to more regulatory scrutiny.  Advertising networks are the new source of hot bidding for data speed.  Tolerance for latency is tied to some verticals more than others and the unit cost of data delivered matters to price-sensitive customers.  Guess what, folks.  The ones that aren't price sensitive, like the ad networks, will pass the price-inelastic premiums they pay along to you.

I learned a new phrase from another presenter:  "hypervisor-agnostic."  I did a Google search to find out what the heck that means.  A hypervisor is something that runs virtual machines, so an agnostic solution must be something that presents a paravirtualization technique to its host machine regardless of the host's hardware and software configurations.  That's my story and I'm sticking to it.  These cloud services need to have low latency to be competitive, virtualization or not.

The final panel tried to predict the future of the cloud.  The IEEE Intercloud Testbed Project is formulating standards for interoperability in multi-cloud, federated cloud, and intercloud environments.  If you haven't had enough of the cloud yet, watch those clouds on the horizon.  Fans of the Terminator movie series will recall that SkyNet became self-aware and nuked all of the humans it could find.  Cloud providers need to make sure that they don't infuse any of these cloud virtual machines with autoimmune responses.

I'm not a cloud technician but my blogging, web hosting, and email services all reside in clouds.  I've been using this tech without realizing it for as long as I've been self-employed.  It's enough for me to know the outlines of the cloud sector and its major players.  The finance community definitely tracks the cloud, partly thanks to the efforts of Yours Truly at Alfidi Capital.  

Angel Launch Launches Startup Venture Summit

I was privileged to attend Angel Launch's inaugural Startup Venture Summit last week in Silicon Valley.  It was the perfect counterpoint in many ways to my Dreamforce 2013 experience.  The Salesforce honchos told their entire ecosystem which markets they wanted to penetrate and laid out the details of their platform updates.  Entrepreneurs at the Startup Venture Summit met investors and partners who could help them gain traction with a big ERP ecosystem.

That layout is typical of what you'll find at eBay's Town Hall venue.  I can be a very aggressive networker when I have a mind to do so but I now find that people at these events seek me out.  I'm sure my listing as moderator of two panels helped attract people, along with my pinch hitting moderation of a third panel at the last minute!

The first VC panel shared their perspectives on top trends.  Stars from Khosla Ventures, Draper Nexus, and Garage Technology Ventures were on hand to let us know that startups need to show investors their "map of the world" on how they will realize their vision.  Paradoxically, VCs are more comfortable funding a business that doesn't immediately need the money if said business spends more time chasing customers than investors.  They like product demos that are installed with customers.  I must agree, because there are way too many shelf-ready products launching at venues like DEMO that never make it anywhere but the showroom.  VCs like it when startups figure out the minimal amount of capital they need to solve their biggest risks.  I just did a Google search of "overfunded startup" to see the frustrations investors have when startups waste money just because they can.  We hear the term "traction" a lot at startup events but the VCs know the term is a proxy for sales and other metrics tracking external acceptance.

The next panel on attracting angels and family funds featured some investors I've known for a while, and a new media outlet named BayLive.  These guys have been around the block enough to know that pitching them something they don't care about is a waste of their time.  This was not the first time I'd heard the startup investing "T's" cited all in one place:  Team, Tech, Traction, Timing, and Total addressable market.  I don't think the set of T's is standardized because different gurus tend to reword them based on their investing preferences.  These guys were big on networking because strong networks share visibility and investor referrals.  Many investors prefer referrals from their networks of attorneys, bankers, and consultants over cold contact from unknowns.  One panelist modestly admonished startups to take money from well-known investors first if they have the luxury of choosing their investors.  I should add that name recognition isn't the only thing prominent venture investors bring to the table and that some third-generation investors just do it because it's the "family business."  I should also add that some banks go out of their way to offer startups a beginner's guide to the early venture ecosystem.  I'm thinking Umpqua Bank, New Resource Bank, and Silicon Valley Bank would be good places for startups to keep their cash if they can't afford a pedigreed introduction at Goldman Sachs.

The moderator for the panel on valuation, acquisition, and expansion strategies was a no-show so I jumped up to offer the conference organizer my services.  I was already there as moderator for two other panels so I might as well save the morning.  "Luck is what happens when preparation meets opportunity" was Seneca's ancient wisdom.  Well, I was prepared when this opportunity presented itself.  Salil Pradhan and Bill Reichert reprised their roles from the VC panel and got more specific on how startups prove their worth as they grow.  They commented that early stage valuations are too high right now, and I was tempted to refer to the infamous Bin 38 "Angel Gate" meeting from a few years back that tried to hold valuations down.  Ron Conway had the correct reaction to that ill-conceived plan.  Anyway, I asked our panelists what they thought of valuation models like the market comparable method.  They thought it was more useful in later stages as the exit event looms.  Early stage valuation is different because it accounts for how much money it will take to build the company, and entrepreneurs need reasonable data points to show early investors.  They also thought expansion strategies must be sales-driven, with a sales capability ready on day one to do CustDev.  The point is not to lead investors into discussing valuation, but to get investors to commit to a business model that can move up a sliding scale of growth.  Experienced VCs have seen that customer acquisition costs are unique to each company and are typically costly in early stages.  Startups that don't need money have the luxury of saying no to investors.  I suspect those startups are rare.  Rarer still are those that can negotiate from positions of strength at an exit.  Our panel felt that taking a decent exit early may be preferable to facing more VC-backed startups later.  I got two things out of my hasty entry into this panel.  First, the ABC of "always be closing" recalls Guy Kawasaki's lesson from "The Art of Rainmaking" that sales fixes everything.  Two, your network is your net worth, and the ecosystem of partners a startup creates looks even better when a potential acquirer wants to merge it with their own ecosystem.

I moderated my next panel on Big Data, analytics, and business intelligence.  Our topic had a huge scope but the collection of VCs from Fung Capital USA, North Hill Ventures, and Sierra Ventures had it under control.  I'm pretty sure the entrepreneurs in the audience benefited from hearing that product-market fit, tenacity, and fast-scaling sales were some of the things VCs want to see when evaluating early startups.  Stuff like proof of concept and financial due diligence come into play in later rounds.  These VC think ERP pain points exist in Salesforce's mobile integration and in customer in-store behavior prior to the point of sale.  Take note of that, entrepreneurs.  If you can meet those data needs and bridge the gaps between technology platforms, you've got an audience.  They also think that focusing on unit economics early on is a key to scaling up later.  Your high customer churn rates will look bad to VCs so get sticky customers whose lifetime value is clearly measurable.  I asked the panel if any non-transaction ERP functions like HR and supply chain management allow for disruption.  They said that supply chain forecasting is often guesswork and could benefit from data that saves margins.  They key is assessing whether an organization's IT mindset is amenable to changing other non-transaction modules.  The VCs had very diverse opinions on the types of KPIs enterprises should use for Big Data; they mentioned revenue per employee (FTE) should be at least $400K, and that the customer lifetime value must compare favorably to the customer acquisition cost.  Payback period matters too, because VCs can't wait forever before they see a return on their investment.

Some more experienced angels from SF Angel and World Capital Market were up next to present strategies for funding pitches.  My biggest takeaway was the importance of an attention-grabbing story, because investors will want to know what's in it for them.  A lot of the lessons from Guy Kawasaki's 10/20/30 rule carry over as long as entrepreneurs remember that execution matters more than the idea.  I liked that they understood how a founder should be ready to surrender the CEO role or a majority stake if that is what will help the company make it.  Their list of speakers to study for good habits includes Steve Blank and Alexander Osterwalder.  I've heard Steve Blank talk and that guy knows his stuff.

My moderator duties continued with a third panel on enterprise platforms, cloud services, and infrastructure. This was another huge topic for serious investors from RWI Ventures, Second Century Ventures, Opus Capital, Scale Venture Partners, and IPV Capital.  Once again, product-market fit emerged as a key early investment criterion but the size of a startup's opportunity and its whether its revenue growth is sustainable also mattered.  One VC surprised me by mentioning LinkedIn as a due diligence tool.  See folks, your network and professional history don't escape notice.  I personally like to use the name query functions on superior court websites to see if someone I meet has a history of bankruptcies or lawsuits.  I've avoided several potential business partners who had sub-par legal histories.  Anyway, my panelists thought universal connectivity has validated Big Data and users want it accessible 24/7 anywhere.  The roadblock to full implementation is that enterprises don't yet fully trust the public cloud for sensitive data and still use hybrids.  The type of customer an enterprise faces determines whether security trumps performance in the inevitable tradeoff.  Cloud solutions offer value to SMBs that have few tools but startups need to make adoption easy if they want to disrupt big models.  The switching costs and key features of the big providers' walled gardens inhibit customer switches, so the pain points would have to be pretty high to induce a switch.  I asked our resident chip expert whether Moore's law still holds; he thought it was still valid because quantum computing and MEMS can extend chip life.  Ecosystems matter because a startup has to participate in a community of contributors to open source hardware (Arduino) and software (Hadoop) to make them all happy.  Companies can add valuable customized layers on top of open source tech.  The investors like freemium product distribution as a good way to qualify leads, and app marketplaces are a good way to evaluate the size of a platform's ecosystems.  I'll close this one by listing these investors' favorite accelerators:  Y Combinator, InnoSpring, and NAR REach.  There you go, folks.  I know my recollections of these panels can be jumbled in a stream of consciousness style, but buried in all that knowledge are hints you can use to move your startup forward.

I had time to listen to one more panel on investment opportunities in Europe after I had completed all of my moderating duties (three panels!) for the day.  Europort is the EU's vehicle for trade promotion in Silicon Valley.  The Bay Area Council Economic Institute is becoming ubiquitous at these types of events by noting that foreign R&D facilities are growing in the Bay Area.  This panel's observation that virtual trade missions are economical for SMBs mirrored what I heard the US Commercial Service say at a trade promotion event several months ago.  The Euro-folks cut the audience a break by noting that an entertainment budget for a trade mission to Europe wasn't nearly as important as one for a similar mission to Asia.  I guess those old legends about drinking Asian business executives under the table in some Tokyo karaoke bar are true.  I had to do some of that during my years in South Korea in the late 1990s for different reasons.  Perhaps I should have done a lot more.

I was impressed with this AngelLaunch event.  It attracted the right experts and hit the right themes.  Entrepreneurs need to hear this stuff as much as possible in many venues.  The endless pitchfest circuit is always a gauntlet but the addition of crowdfunding kicks fundraising into overdrive.  Tech startups need a 24/7 presence on multiple fundraising portals that leverages the feedback they get at these summits.  I am totally looking forward to similar events from iHollywood Forum.  I'm ready for my close-up.