Telruptive

Cloudify, from the scalability experts GigaSpaces, is still its early stages. Unlike Google App Engine, Azure, Heroku, etc. this PaaS is more focused on the application life cycle and not on being a “transparent” application server and database.  The main focus is automating application and services deployment, monitoring, autoscaling, etc. The closest competitor would be Scalr.

Unlike Scalr, Cloudify’s focus is on Cloud-neutrality. Cloudify is not focusing on using specific Amazon services for scalability but instead to make a neutral Cloud platform. The advantage is that every possible Cloud being it private or public can be used and scenarios like hybrid clouds with Cloud bursting from private to public cloud are possible. The deep understanding of large-scale architectures in a company like GigaSpaces is a guarantee that Cloudify will scale in the future.

Cloudify is still missing some important functionality like security, multi-tenancy, integrations with lower-level automation frameworks (e.g. Chef and Puppet), complex upgrade management [e.g. rolling upgrades, MySQL schema upgrades, A/B testing of new features, etc.], etc. However the roadmap is pointing towards most of these items.

Software architects should understand the possibilities Cloudify, Scalr, etc. bring. By having a reusable automation framework companies are able to spend more development and operations time on bringing new business features and less on reinventing the wheel.

A lot of people are talking about home automation, M2M in cars, etc. However there is a simpler solution than investing thousands of euros to automate everything. What if a new standard was developed that would combine UPnP, REST and WiFi and it would be embedded in most consumer appliances, cars, etc.? The idea is simple: allow devices to be discovered [UPnP] connected to your home network [WiFi] and allow them to expose their main functionality [REST].

What would be the big deal? 

At the moment you can connect your SmartTV to your home network and download a mobile app that will discover your television and allow it to be controlled remotely. This is all nice and well. However it keeps on limiting the consumer on using one app per device. The real difference would be if every device could be integrated with via a very easy API [REST]. Ideally there would be standard APIs with the minimum common functionality per type of device, for instance for cars, fridges, ovens, radios, etc.

What type of use cases are possible?

At home people could turn the oven on and get a notification on their mobile when it is warmed up or when your pie is ready even. Parents can get alerted when their children left the fridge open.

On the road your car could talk to your iPad and the entertainment system could be driven from your iPad. New apps could be downloaded and installed inside the car.

At work people could be voting about the temperature of the air-conditioning. The Coke machine could be linked to your Paypal and you would not have to carry coins any more.

A lot more use cases are possible. However easy integration [REST], auto-discovery [UPnP] and connectivity [WiFi] are the basics…

Twitter is having a Real-Time Analytics solution that could easily become as important as Hadoop. They talked about open sourcing it but so far have not done so.

This post is an open invitation to Twitter open source Rainbird and accelerate Real-Time Analytics adoption in the world. Hadoop has changed thousands if not millions of companies. Rainbird could do a similar thing.

In order to gather people around this subject, I am proposing that you include #TWOSRB in your tweets. #TWOSRB stands for Twitter please Open Source RainBird:
Tweet #TWOSRB
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Although the number of solutions Amazon AWS is offering has become very large, here are 5 ideas of what Amazon could be adding next.

API Marketplaces

There are thousands of APIs out there. However what is missing is an easy way for companies to control their costs. In line with other marketplaces Amazon runs, there could be an API marketplace. An API marketplace would allow third-party API providers to let Amazon do the charging. Companies would be able to pay one bill to Amazon AWS and use thousands of APIs. Also third-party API providers would be winning because they often can not charge small amounts to a large set of developers. Amazon already sends you a bill or charges your credit card, hence adding some dollar/euro cents for external API usage would be easy to do. The third-party API provider would avoid having to lock-in users in large monthly usage fees to offset credit card and management charges. Amazon of course would be the big winner because they could get a revenue share on these thousands of APIs. End-users would also be winning because they can easily compare different APIs and get community feedback from other developers and pick those APIs with the best reputation. The typical advantages of any online marketplace. Also cross-selling, advertisement, etc. and other areas can be reused by Amazon. A final advantage would even be to have Amazon be in the middle and offer a standard interface with third-parties offering competing implementations. This would allow developers to easily switch providers.

Language APIs

A lot of applications would be helped if they could use language APIs that are paid per request. Language APIs is a group name for text-to-speech, speech recognition, natural language processing, even mood analysis APIs. These are all APIs that are available individually but there is a clear economies of scale effect. The more speech you transcribe or text documents you process, the better your algorithms become. Also there is an over-supply of English language APIs but an under-supply of any other language in the world, except for Spanish, French and German perhaps. Another problem with existing APIs is that a high monthly volume is needed in the even the most basic subscription plan. Examples are Acapela VaaS pricing that costs a minimum of €1500. Very few applications will use this amount of voice.

M2M APIs and Services

Amazon is already working hard on Big Data solutions. M2M sensors can generate large volumes of data pretty quickly. S3 or DynamoDB would be ideal to store this data. However what is missing is an easy way to connect and manage large number of sensors and devices and their accompanying applications. There are few standards but with examples like Pachube, Amazon should be able to get inspired. Especially the end-to-end service management, provisioning, SLA management, etc. could use a big boost from a disruptive innovator like Amazon. Also M2M sensor intelligence could be offered from Amazon, see my other article about this subject.

Mobile APIs and Solutions

With billions of phones out there, mobilizing the Web will be the next challenge. Securely exposing company data, applications and processes towards mobile devices is a challenge today. BYOD, bring-your-own-device, is a headache for CIOs. We do not all have a MAC so we can not sign iPhone apps and launch them on the App Store. Ideally there would be a technical solution for enterprises to manage private app stores, deploy apps on different devices and be able to send notification to all or subsets of their employees. Also functionality like Usergrid in which developers would not have to focus on the backoffice logic would be of interest. Also tools to develop front-end for different devices would be appreciated, examples like Tiggzi come to mind. There are a lot of island solutions but few really integrated total solutions.

Support APIs and Services

Amazon is becoming more and more important in the global IT infrastructure business. This means that solutions will move more and more to the Cloud and sometimes be hybrid cloud. With these complex solution scenarios in which third-parties, Amazon and on-site enterprise services have to be combined, risks of things going wrong are high. Support services both from a technical point of view:

• detect failures and to automatically try to solve them
• manage support ticket distributions between different partners
• measure SLAs
• etc.

as well as from a functional point of view:

• dynamic call centers with temporary agents
• 3rd party certification programs in case small partners do not have local resources
• 3rd party support marketplace to offer more competition and compare reputations
• etc.

are all areas in which global solutions could disrupt local and island solutions that are currently in place.

If you are a VC and you are unclear where to invest then this post might be of interest to you.

Some Disruptive Technologies and ideas that startups might be working on or for which you might want to assemble a team:

Alternative networks

WiFi and 3/4/5G have their limitations. Any alternative networking technology that can change complete industries is probably a good pick. An example would be LiFi.
Networks as a Service – Software-Defined Networks – Openflow

This area is very hot at the moment. Today’s network are very hard to configure and manage, they are very tightly-coupled with hardware, they can not be extended easily.

Anything that makes Software-Defined Networks/Openflow easy for mass adoption is going to be a winner.

Anything that allows enterprises to buy a box once and get the network software later based on day-to-day business requirements, e.g. think about appstore for Openflow.

Anything that links Openflow to the Cloud.

M2M Disruptive Technologies
Printing electronics to make sensors cheaper.

Battery-free electronics to make sensors more mobile and less expensive to maintain.

Auto-discovery sensor mesh networks to avoid paying expensive 3/4G subscriptions.

M2M appstores to allow people to reuse the work others did.

Super-easy M2M APIs/PaaS. Look at Pachube as a model to beat.

Cloud Disruptive Technologies

Niche SaaSification in which applications that are only used in small niches can be offered as SaaS subscriptions in a global way.

Plug-and-Cloud Equipment for Hybrid Cloud & Exposure (Single Sign-on, Internal data sources, Internal integrations) – on-site equipment that allows enterprises in an easy and secure way to expose their internal assets to the Cloud e.g. employee single sign-on, secure exposure of company data, secure exposure and easy integration of company applications

Plug-and-Play SaaS integrations that allow multiple SaaS offerings to be easily integrated without programming.
Mobile

Mobile PaaS = mobile GUI drag-and-drop designer + no-programming back-end systems like Usergrid + plug-and-play integration with external and enterprise APIs + enterprise mobile app / SaaS stores + BYOD made easy solutions (some elements are optional)

Big Data / Data Analytics

Visual data miner as a service

Big Data PaaS (easy tools/APIs for complex big data operations like mood analysis, natural language processing, etc.)

Gamification/Crowdsourcing

Kaggle type of services but for other domains e.g. competition to create the easiest/best mobile interface or API

Kaggle + Kickstarter => competition together with crowdfunding. Who can build the best solution for this problem, gets their venture funded.

Nail-it-then-scale-it/Lean Startup type of crowdsourcing in which ideas get tested (e.g. paper prototypes, business model discovery, etc. before actual prototype) and funding is delivered bit by bit. Ideally with stock options of the funders in the new venture.
Enterprise/Consumer Telecom

Managed enterprise software-defined networks or BYOD – services that help enterprises to maintain their networks or devices that employees bring along in a managed way hence no experts need to be hired and the service is pay-as-you-go instead of CAPEX.

Cloud + Set-up Boxes – Appstores for ADSL/Cable Modem set-up boxes, SDKs to manage large sets of consumer’s set-up boxes, etc.

Conclusion

These are just a handful of ideas. If you want more or need more detail, let me know at maarten at telruptive dot com. Also if you are in need of an external adviser or executive in a new venture, let me now…

Every company is using Microsoft Office and especially Excel to do some sort of data analytics. However data volumes have grown exponentially and have outgrown Spreadsheets. You need experts in the business domain, in data analytics, in data migration/extraction/transformation/loading, in server management, etc. to get data analytics done on Big Data scale. This makes it expensive and only usable for the happy few.

Why? There must be easier ways to do it.

I think there are. For those unfamiliar with data analytics but eager to learn, you should take a look at a product called RapidMiner. It is close to amazing how a non-expert is able to use Neural Networks, Decision Trees, Support Vector Machines, Genetic Algorithms, etc. and get meaningful results in minutes. The amazing part is also that RapidMiner is open source hence for usage by 1 analyst it is free.

Rapid-i.com, the company behind RapidMiner, also offers server software to run data analytics remotely. It is here where big data opportunities meet easy data analytics. What if RapidMiner data analytics could be ran on hundreds of servers in parallel and you pay by usage just as you pay for any Cloud compute and storage instances?

RapidMiner as a Service

RapidMiner as a Service, RMaaS, would allow millions of business people to be able to analyse Big Data “without Big Investments”. This type of Data Analytics as a Service would provide any SME with the same data analytics tools as large corporations. Data could come from Amazon S3, Amazon’s DynamoDB, Hosted Hadoops, any webservices, any social network, etc.

Visual as a Service

RapidMiner as a Service is only one of the many domain specific tools that could be offered as a visual drag-and-drop Cloud service. VAS as a Service is another example in which complex telecom assets can be easily combined in a drag-and-drop manner. There are many more. These services will be the real revolution of Cloud Computing since they combine IaaS/PaaS/SaaS into a new generation of solutions that bring large savings for new users and potential large revenues for their providers…

LTE roll-outs are taking place in America and Europe. Over-the-top-players are likely to start offering large-scale and free HD mobile VoIP over the next 6-18 months. Steeply declining ARPU will be the result. The telecom industry needs new revenue: telecom revenue 2.0. How can they do it?

1. Become a Telecom Venture Capitalist

Buying the number 2 o 3 player in a new market or creating a copy-cat solution has not worked. Think about Terra/Lycos/Vivendi portals, Keteque, etc. So the better option is to make sure innovative startups get partly funded by telecom operators. This assures that operators will be able to launch innovative solutions in the future. Just being a VC will not be enough. Also investment in quickly launching the new startup services and incorporating them into the existing product catalog are necessary.

2. SaaSification & Monetization

SaaS monetization is not reselling SaaS and keeping a 30-50% revenue share. SaaS monetization means offering others the development/hosting tools, sales channels, support facilities, etc. to quickly launch new SaaS solutions that are targeted at new niche or long tail segments. SaaSification means that existing license-based on-site applications can be quickly converted into subscription-based SaaS offerings. The operator is a SaaS enabler and brings together SaaS creators with SaaS customers.

3. Enterprise Mobilization, BPaaS and BYOD

There are millions of small, medium and large enterprises that have employees which bring smartphones and tablets to work [a.k.a. BYOD - bring-your-own-device]. Managing these solutions (security, provisioning, etc.) as well as mobilizing applications and internal processes [a.k.a. BPaaS - business processes as a service] will be a big opportunity. Corporate mobile app and mobile SaaS stores will be an important starting point. Solutions to quickly mobilize existing solutions, ideally without programming should come next.

4. M2M Monetization Solutions

At the moment M2M is not having big industry standards yet. Operators are ideally positioned to bring standards to quickly connect millions of devices and sensors to value added services. Most of these solutions will not be SIM-based so a pure-SIM strategy is likely to fail. Operators should think about enabling others to take advantage of the M2M revolution instead of building services themselves. Be the restaurant, tool shop and clothing store and not the gold digger during a gold rush.

5. Big Data and Data Intelligence as a Service

Operators are used to manage peta-bytes of data. However converting this data into information and knowledge is the next step towards monetizing data. At the moment big data solutions focus on storing, manipulating and reporting large volume of data. However the Big Data revolution is only just starting. We need big data apps, big data app stores, “big datafication” tools, etc.

6. All-you-can-eat HD Video-on-Demand

Global content distribution can be better done with the help of operators then without. Exporting Netflix-like business models to Europe, Asia, Africa, Latin-America, etc. is urgently necessary if Hollywood wants to avoid the next generation believing “content = free”. All-you-can-eat movies, series and music for €15/month is what should be aimed for.

7. NFC, micro-subscriptions, nano-payments, anonymous digital cash, etc.

Payment solutions are hot. Look at Paypal, Square, Dwolla, etc. Operators could play it nice and ask Visa, Mastercard, etc. how they can assist. However going a more disruptive route and helping Square and Dwolla serve a global marketplace are probably more lucrative. Except for NFC solutions also micro-subscriptions (e.g. €0.05/month) or nano-payments (e.g. €0.001/transaction) should be looked at.

Don’t forget that people will still want to buy things in a digital world which they do not want others to know about or from people or companies they do not trust. Anonymous digital cash solutions are needed when physical cash is no longer available. Unless of course you expect people to buy books about getting a divorce with the family’s credit card…

8. Build your own VAS for consumers and enterprises – iVAS.

Conference calls, PBX, etc. were the most advanced communication solutions offered by operators until recently. However creating visual drag-and-drop environments in which non-technical users can combine telecom and web assets to create new value-added-services can result in a new generation of VAS: iVAS. The VAS in which personal solutions are resolved by the people who suffer them. Especially in emerging countries where wide-spread smartphones and LTE are still some years off, iVAS can still have some good 3-5 years ahead. Examples would be personalized numbering schemas for my family & friends, distorting voices when I call somebody, etc. Let consumers and small enterprises be the creators by offering them visual  do-it-yourself tools. Combine solutions like Invox, OpenVBX, Google’s App Inventor, etc.

9. Software-defined networking solutions & Network as a Service

Networks are changing from hardware to software. This means network virtualization, outsourcing of network solutions (e.g. virtualized firewalls), etc. Operators are in a good position to offer a new generation of complex network solutions that can be very easily managed via a browser. Enterprises could substitute expensive on-site hardware for cheap monthly subscriptions of virtualized network solutions.

10. Long-Tail Solutions

Operators could be offering a large catalog of long-tail solutions that are targeted at specific industries or problem domains. Thousands of companies are building multi-device solutions. Mobile &  SmartTV virtualization and automated testing solutions would be of interest to them. Low-latency solutions could be of interest to the financial sector, e.g. automated trading. Call center and customer support services on-demand and via a subscription model. Many possible services in the collective intelligence, crowd-sourcing, gamification, computer vision, natural language processing, etc. domains.

Basically operators should create new departments that are financially and structurally independent from the main business and that look at new disruptive technologies/business ideas and how either directly or via partners new revenue can be generated with them.

What not to do?

Waste any more time. Do not focus on small or late-to-market solutions, e.g. reselling Microsoft 365, RCS like Joyn, etc. Focus on industry-changers, disruptive innovations, etc.

Yes LTE roll-out is important but without any solutions for telecom revenue 2.0, LTE will just kill ARPU. So action is required now. Action needs to be quick [forget about RFQs], agile [forget about standards - the iPhone / AppStore is a proprietary solution], well subsidized [no supplier will invest big R&D budgets to get a 15% revenue share] and independent [of red tape and corporate control so risk taking is rewarded, unless of course you predicted 5 years ago that Facebook and Angry Bird would be changing industries]…

Enterprises no longer have a lack of data. Data can be obtained from everywhere. The hard part is to convert data into valuable information that can trigger positive actions. The problem is that you need currently four experts to get this process up and running:

1) Data ETL expert – is able to extract, transform and load data into a central system.

2) Data Mining expert – is able to suggest great statistical algorithms and able to interpret the results.

3) Big Data programmer – is an expert in Hadoop, Map-Reduce, Pig,  Hive, HBase, etc.

4) A business expert – that is able to guide all the experts into extracting the right information and taking the right actions based on the results.

A Big Data PaaS should focus on making sure that the first three are needed as little as possible. Ideally they are not needed at all.

How could a business expert be enabled in Big Data?

The answer is Big Data Apps and Big Data PaaS. What if a Big Data PaaS is available, ideally open source as well as hosted, that comes with a community marketplace for Big Data ETL connectors and Big Data Apps? You would have Big Data ETL connectors to all major databases, Excel, Access, Web server logs, Twitter, Facebook, Linkedin, etc. For a fee different data sources could be accessed in order to enhance the quality of data. Companies should be able to easily buy access to data of others on a Pay-as-you-use basis.

The next steps are Big Data Apps. Business experts often have very simple questions: “Which age group is buying my product?”, “Which products are also bought by my customers?”, etc. Small re-useable Big Data Apps could be built by experts and reused by business experts.

A Big Data App example

A medium sized company is selling household appliances. This company has a database with all the customers. Another database with all the product sales. What if a Big Data App could find which products tend to be sold together and if there are any specific customer features (age, gender, customer since, hobbies, income, number of children, etc.) and other features (e.g. time of the year) that are significant? Customer data in the company’s database could be enhanced with publicly available information (from Facebook, Twitter, Linkedin, etc.). Perhaps the Big Data App could find out that parents (number of children >0), whose children like football (Facebook), are 90% more likely to buy waffle makers, pancake makers, oil fryers, etc. three times a year. Local football clubs might organize events three times a year to gain extra funding. Sponsorship, direct mailing, special offers, etc. could all help to attract more parents, of football-loving-kids, to the shop.

The Big Data Apps would focus on solving a specific problem each: “Finding products that are sold together”, “Clustering customers based on social aspects”, etc. As long as a simple wizard can guide a non-technical expert in selecting the right data sources and understanding the results, it could be packaged up as a Big Data App. A marketplace could exist for the best Big Data Apps. External Big Data PaaS platforms could also allow data from different enterprises to be brought together and generate extra revenue as long as individual persons can not be identified.

Apigee bought Usergrid. Usergrid is the type of Mobile PaaS that you would expect mobile operators to be launching. Usergrid is open source as well as available as a hosted service. Usergrid allows mobile developers to focus on mobile apps and not on the server. Everything from storing users, groups, roles, single sign-on authentication, social aspects (e.g. likes), feeds, queries, connections between users and objects (e.g. which friends of user X like restaurant Y), etc. is dealt with via an incredibly easy REST API. Usergrid also comes with toolkits for easy iOS and Android development.

Usergrid is impressive both as an idea as well as in how easy it is to build complex mobile applications, e.g. collective voting during a conference, etc. without back-end developement.

What is next?

Combining Usergrid with one of the many visual drag-and-drop mobile app development tools would allow users to create complete mobile apps without coding.

Being able to integrate other API based services into the same visual drag-and-drop development tool would allow even more complex applications: e.g. look at programmableweb for a list of thousands of public APIs. However ideally also private APIs (e.g. towards enterprise back-office systems) could be incorporated.

Finally being able to monetize these new mobile apps via in-app advertisement, enterprise mobile app stores, etc. would motivate developers to build millions of useful mobile apps.

Mobile PaaS is a very exciting domain and operators should be very actively investing in it…

With Hadoop/Hbase/Hive, Cassandra, etc. you can store and manipulate peta-bytes of data. But what if you want to get nice looking reports or compare data held in a NoSQL solution with data held elsewhere? There have been two market leaders in the Open Source business intelligence space that are putting all their firepower onto Big Data now.

Pentaho Big Data seems to be a bit further ahead. They offer a graphical ETL tool, a report designer and a business intelligence server. These are existing tools but support for Hadoop HDFS, Map-Reduce, Hbase, Hive, Pig, Cassandra, etc. have been added.

Jaspersoft’s Open Source Big Data strategy is a little bit behind because connectors are not included yet into the main product and several are still in beta quality and with missing documentation.

Both companies will accelerate the adoption of big data since the main problem with Big Data is easy reporting. Unstructured data is harder to format into a very structured report than structured data. Any solutions that will make this possible and additionally are Open Source are very welcome in times of cost cutting…