Dec 30, 2017

Data Protection by Design

This is the new age and new way of doing business that relies on data more than wisdom at the same time make organisations more respectful of ownership of customer’s personal data. Any data that can identify the customer in real world is a personal data.

Recently smart TV maker “Vizio” was fined for spying on its users; Uber employee was caught tracking a journalist using Uber’s tool “God View”, which is only available to Uber employees to track drivers and customers; AT&T was fined $25m because few employees sold customers data; Netflix has been fined in past; Google may face $18b fine for breaking web privacy laws in Netherlands; EU is drawing the line (with GDPR) and if companies cross the line they will be fined € 10 m or 4 % of revenues. Companies build their business model and marketing strategy around data but it is really difficult for company to decide when it becomes unethical.  Second biggest problem companies might face is how to control their employees from misusing the data.
GDPR, a data protection by design framework, solves the data privacy issues to large extent and creates a win-win-win situation in which companies get data they want, customers privacy is protected, and government has law in place.
According to law, if an organisation fails to protect the privacy of customer it should be fined.  Organisations work data protection by design for coping with threats from hackers, social engineering, and compromised employee including CEO.


GDPR – General Data Protection Regulation – by EU is a framework, guideline, and applicable for any organisation that has day-to-day responsibility of data protection.  It is living document that is some changes are still being made.  The GDPR will fully apply in the UK from 25 May 2018, and UK government has confirmed Brexit will not affect the commencement of GDPR.  Under GDPR, data protection of customer’s personal data is main responsibility of organisations – Controllers and Processors.
Subject - Personals who possess personal information and submit information
Controller - Organisations that collect information, store information, and forward information
Processor - Organisations that do correlation between information, process information, do analysis.
GDPR places strong emphasis “consent” and how subjects have more control over the consent they give to controllers and processors.

Organisations need to do data and network mapping - Get to know every data they collecting, where they are storing, and were they are forwarding. Organisations need to know the processor for each and every data. Every time they bring a change - small or big software or infrastructure change - do impact assessment on how data will be handled, who will process, and where and how data leaks might happen. Review organisational policies and procedures and verify that they are GDPR compliant. Most important right now is how Organisations plan and implement the protection strategies.

Data Protection by Design

Two big question should and would come in users mind one day or always:
1. What is happening to my data? How data is managed.
2. Who can access my data? How data is accessed.
Above two questions are also core and motivation behind GDPR.
Organisations need to address both questions in GDPR context; the organisations need strategies to handle both questions, that is strategy for Data Management and strategy for access management.
In following sections, we will see in detail what is recommended strategy in GDPR for Data management and Data Access.

Data Management Strategy

Minimization - Information should be collected to bare minimum and also only minimal should be stored. For example, if company is doing survey and they don’t need to identity or name of user they should not collect it. If any extra information is collected (it is still allowed to collect more info) it must be encrypted; therefore, any extra data collected and/or stored by company should be hidden (encrypted). Data collected should be stored in chunks means not all information at one place so that whole information is not hacked.  Later chunks are aggregate in a secure database. 
One of the major source of information within organisations is Log file - a file that records events and every evet has some details for an example a fund transfer event will be logged in log file with details such as amount, from account, to account, payee details, date, time, IP address, etc. and log files are used for organisations for debugging or monitoring if any issue comes in future. GDPR applies to Log files and a log entry with card number will be invalid. Organisations now need to filter data that goes in log file; they need to encrypt data and store anonymized data (user can't be identified by data); they need to protect data during storage and transfer.
Break information (data collected for a user) into two parts: Process-able data and personal data (information that is not needed at this point but is important - It should not be necessarily personal data).  The two types of information are stored separately. The two information are related by a correlation key.  Aggregate all data in one centralized location and further from there distribute required data to tools doing analysis.

Access Management Strategy

GDPR recommends every organisation to hire a data protection officer. DPO will report to supervisory authority which is an entity in EU to updating them that GDPR is followed and DPO need to provide evidence that organisation is GDPR compliant. DPO will govern all data handling processes within organisation and monitor privileged people, who have access to major data, within organisation. Privileged people will include Database administrators, support people, analytics people, etc. who have access to personal information of end users. People with high profile account that have access to any server, data base, and application, they can alter data, and they function unsupervised. DPO will document every activity done for personal information. An access management device monitors the stores where personal information is stored. If unauthorized attempt is noticed, then DPO should be alerted and connection would be blocked. DPO will have to put in place real monitoring system between privileged users, clients, and machines that contain personal information; also, capability of revoke access at any time. DPO will receive an audit trail of all activities done by privilege users on personal data


Consent = Genuine Choice + Understanding the implications.
The subject (customer) gives consent based on understanding the implications, subject had choice, subject can manage consent, and subject can revoke consent. 
Individuals will be informed (in clear, plain, and concise language) whenever their data is used and Individuals have power to access, rectify/modify, erase/delete, restrict, extract and reuse for personal purpose, etc.
Organisations need to present a consent form, which won’t be similar to multiple pages long complex EULA, and individuals will fill form and consent receipt will be generated.  GDPR sets standards for consent form as being specific and unbundled (separate from terms and conditions), unticked opt-in boxes by default, granular (different check box for different type of use), named (organisation and any third party who can possibly use personal data), documented (history and track), easy to withdraw (anytime individuals can withdraw using form), and consent should not be pre-condition for a service unless required (customer can use Google search without providing personal data).
Consent Receipt is the record of the consent provided by PII (Personal Information Identifier) Principal (natural person to whom PII relates to) or Subject to Controller to collect, use, and disclose PII in accordance with an agreed set of terms.  
Consent Receipt will be a simple form number of fields and it will be available on customer’s mobile and customer will be able to view, modify, or revoke the consent given to any organisation.  They will be informed when their data is shared and they will be able to delete their data. Indeed, customers will have full and granular control of their personal data even though data is stored remotely in premises of companies.



Dec 29, 2017

Open Banking: An Innovation Ecosystem

In my blog post on innovation ecosystem I discussed how ecosystem fosters innovation and why governments and organisations should promote an ecosystem to encourage innovation.

Though Silicon Valley is widely known example of innovation ecosystem.  Open Banking is a government led initiative for innovation ecosystem.

Till few years back we can broadly classify companies into tech or non-tech.  But slowly classification will become invalid since every organisation will have to focus on technology led innovation and immerse technology into step of value chain.  Organisations such as GE and John Deere, Governments such as Estonian and UK have lot of focus on technology.  Tech and Non-tech – both are transforming and their businesses are overlapping.  The tech companies have cash, technology, customers, and resources to disrupt any industry.  The cash rich big tech companies such as Amazon, Alphabet (formerly Google), and Microsoft build their own ecosystem.  The non-tech companies are inspired by the success of the tech companies; they see disruptive technology both as threat and opportunity.
Today’s world is not linear and companies have to think more widely.  Companies (Industry Incumbents such as General Motors and Barclays) not just face competition from another company in the same industry but also from Digital Giants (Tech Giants) and Tech Entrepreneurs.  Digital Giants with technology edge at its core, transform the business, such as Amazon did in Retail, Apple did in communications, and Google did in Advertising.  Tech entrepreneurs (Start-ups) use digital technologies to disrupt the industry as Netflix did for movie rentals, Tesla did in automobiles, Uber in taxi service, and Kickstarter in business financing. 
Industry Incumbents, Tech Giants, and Start-ups can couple loosely to share complimentary resources, raise standards of industry, innovation, and customer services; for example, in the process of digital transformation a bank collaborates with other players in industry to create an innovative ecosystem.    
In the value chain, IT Infrastructure is huge cost for any bank and it is often termed as back office operations.  Banks consistently look for ways to optimise the cost and over time cut jobs or outsource to other IT companies.  IT infrastructure also require hiring technology resources and it is difficult because technologies keep on changing.  Upgradation in IT is complicated and learning is slow.  

Tech giants such as Amazon and Google are experts of IT and they manage huge data centres, they have highly skilled technical resources, they are either innovators of technology or they are very quick in adopting technologies, and provide cloud services (AWS, Azure, and so on) and channels (Apple AppStore and Google Play Store).

Banks face another challenge of technology led innovation because they considered IT as a supporting activity.  Tech Start-ups create a new value chain that is based on technology, the new value chain is a platform (Data -> Technology -> Service), because, they refrain from huge investments so they build services using technology.  A Digital only challenger bank will not invest in brick walls and human resources to sit in branches.  Start-ups have idea and passion but they still need data and access or reach to customers (held by Incumbent banks).
Cloud computing and Open banking are initiatives that change the traditional value system of banks, creating an innovation ecosystem.  Cloud computing enables bank to export data to cloud provider and use the advance technologies (as a service) to replace its legacy IT infrastructure.  Cloud providers not only provide data management but also latest technologies (Machine Learning, Analytics, Blockchain) as a service, making it easier for banks to focus more on business and be less worried about IT infrastructure and resources.  Open banking will require Banks to provide customer account information to third parties via APIs and allow third parties (such as Payment Initiation Service Providers) to initiate transactions ordered at customers request[1].  Combining Cloud computing and Open Banking, below is very high level of one value system.

Data Centre and IT Services are provided by the Digital Giants (such as Amazon), Banks write API’s (Application Programming Interface) that expose customer data and transaction services in secure way, and FinTech’s innovate and implement their ideas over the infrastructure. 
In past few years, companies have started accelerator programs and collaborating more and more with Start-ups.  Banks are now less reluctant to transfer their data to cloud managed by third party.  Cloud providers not only manage data but also, they provide high-tech services such as Google is providing services for image recognition and artificial intelligence API’s, which can be used by companies to build new business models.  Bank, Cloud provider, and Start-up’s combine to form a very efficient value chain and create innovations.   


Innovation Ecosystem: Where statups thrive

Image Source:

For long US has dominated in innovation for obvious reasons: best institutes, tech giants, government policies, start-ups, and easily available funding.  Presence of Silicon Valley in US has led the economy growth of country and attracted best talents from world.  Companies based in Silicon Valley benefit from the system coopetition.  Many global companies from Silicon Valley have centres in many countries, they have acquired companies, and they have partnership with many companies.  Following the success of Silicon Valley, many clusters of innovation, made of Policy-makers, established organisations, and start-ups, emerged that are similar to Silicon Valley but are unique in their own sense.  Ecosystems are not bound to technology or boundaries and are open, as agents and networks keeps on changing.  Innovation and collaborative ecosystem have become a need organisations, government, and industries.

Every year since 2005, Strategy& has conducted the Global Innovation 1000 study, which investigates the relationship between how much companies spend on R&D and what their overall financial performance is[1]; insights gathered from report suggest, how innovation ecosystem is centred in specific regions of world.  All top five Most Innovative Companies in the report are from same country, United States, and four out of top six Top R&D Spenders are from United States[2].  R&D is undergoing transformation, in past few years, as companies strengthen their software and service offerings.  The transformation is driven by improvement in software, growing use of embedded sensors, cloud based data storage, and rising customer expectations. The sectors in which R&D spending is growing rapidly – healthcare and internet – are heavily weighted to companies headquartered in North America (see Exhibit 1).  Companies based in most developed economy (in US) are retaining their significant lead in investment in R&D. For the past seven years, Apple and Alphabet (both based in US) are voted the most innovative companies, 3M and Tesla (also based in US) have been rising, and other US companies (Amazon, IBM, and Microsoft) maintain their position in top ten (see Exhibit 2). Traditional research and development approaches, such as stage-gate development for products and technology, remain valid, but more innovative approaches such as open innovation models, design thinking, agile methodologies, co-creation, and in-company incubators are becoming more important as portfolios change (Jaruzelski, Staack, & Shinozaki, 2016).
Most of the top innovative companies from US are based in Silicon Valley, southern portion of San Francisco Bay Area, and they are technology companies.  Few tech companies (Apple, Microsoft, and Alphabet) from Silicon Valley are among top in the list of Companies with the largest cash piles[3]. The cash rich tech companies and innovation culture along with talented workforce make Silicon Valley ecosystem, world’s centre or innovation and technology.
Organisations in Silicon Valley are imbricated in that system that they interact with, shape it, and are shaped by.  The industry, local context, and national legislative context combined create a contextual complexity of Silicon Valley. Contextual complexity once understood, it becomes competitive advantage because it is unique and can’t be copied (because of inherent complexities, uncertainties, ambiguous, and adaptive).  Silicon Valley can’t be copied[4].
Entrepreneurial mindset, access to skilled labour, proximity to customers and competitors, weather, and universities are top strengths of Silicon Valley, but success of the ecosystem such as Silicon Valley, depends not only on continuous innovation and stellar products, but also on a rich environment (Best schools, Affordable housing, Fluid transportation, and Government support) to support the businesses that provide them[5]
Following Silicon Valley, many technological hubs have developed in recent years such as FinTech’s in New York and London, Security in Tel Aviv, Health in Austin, and Start-ups around world from Cambridge to Bangalore.  These technological (or innovation) hubs are created after learning lessons from Silicon Valley and creating a cluster of innovation that is unique in its own sense.  All innovation hubs are different and they are tailored to the context (country, culture, society) in which they originate and exist.  
The geographic footprint for innovation is changing as R&D becomes global.  The companies are shifting their innovation spending to countries in which sales and manufacturing is increasing and right access to technical talent; therefore, innovation spending is increasing in India and China[6].  The innovators from all industries are shifting their focus of R&D from products to programming[7]. Digital start-ups are mushrooming in every industry and in every market and similarly Digital Giants.
In a slightly bigger context, Innovation ecosystem can expand to city or state in which many loosely coupled agents cooperate and compete.  Massachusetts is innovation ecosystem and an economy and a top destination for technology companies, entrepreneurs, incubators, scientists and others, to conduct business, innovate new ideas and launch products[8].
Dr. Deborah Jackson authored a white paper[9] that described an innovation ecosystem models the economy and is believed to be fundamental source of wealth generation. The actors in an innovation ecosystem are materials (funds, facilities, etc.), humans, and institutes (universities, Venture Capitalists, policy makers, etc.).  An important aspect of innovation ecosystem is balance of resources for research economy (R&D) and resources for commercial economy (marketplace), and efficient translation of innovations from research economy to commercial sector.  Entities within the ecosystem can be geographically localized (such as Silicon Valley) or strategically linked to develop a specific technology (such as link between Silicon Valley and Bangalore, India).  At one end of innovation spectrum is heavy government investment (institutes, policies, and support) for research and at the other end higher industry investments for product development and commercial success, but in between lies valley of death, in which many innovations die.  Surviving the valley of death depends on the robustness of collaboration among agents – consultancies, conferences, and so on (Jackson, 2015)
Focus of the report “Collaborative Innovation Transforming Business, Driving Growth”[10], part of World Economic Forum’s work on Enhancing European Competitiveness is to find new forms on innovation and overcome two challenges: first, young firms face wide range of challenges and barriers to scale their ideas, and second, tradition in-house R&D models of large firms is not so good at creating disruptive products and entirely new markets.  Based on extensive firm-level research and interviews with policy makers, report proposes a robust and promising approach for innovation: collaboration between young dynamic firms and established businesses to create value that spans from firms to customers to economies.  Report also emphasises the increased responsibilities of policy makers and political leaders in fostering collaborative innovation, supportive regulations, raising awareness and networking, and skills support.  
Collaborative innovation is the next big idea that needs to shape up with actionable items, allowing players across the value chains to participate in the emergence of new collaborative business models.” - Mark Esposito, Professor of Business and Economics, Harvard University Extension School, Grenoble Ecole de Management

Collaborative Innovation

Collaborative Innovation between young and established firms is sensitive and unique. The capabilities and challenges of young and established firms are different. 

The report “Collaborative Innovation Transforming Business, Driving Growth” from World Economic Forum proposes Collaborative Innovation as a 3-step process: Prepare, Partner, and Pioneer.  
Image Source: World Economic Forum (2015). "Collaborative Innovation Transforming Business, Driving Growth."

The role of Policy makers in Collaborative Innovation is very crucial in order to attract Venture Capitalists and Angel Investors from around world.  The Policy makers should give clear signal of government commitment, strategy to support collaboration, supportive legal and regulatory frameworks, infrastructure, and opportunities in innovation.  The report provides more details on three strategies taken by policy-makers: Empower, Educate, and Enable[11].   Joint Venture of Takeover’s are not only options for partnership, instead procurement (Incubator and Accelerator), project specific partnership (partnering with non-disclosure), and Equity investments are more popular approaches.  Young firms need security and freedom but established firms need more control and a balanced partnership that creates value for both is solution.  Partnership can be one of the four types: Smart procurement, Collaborative Innovation project, Smart direct investment, Strategic partnership, or Joint Venture.

Both young and established firms are embracing this form of collaboration.  Young firms (Start-ups) are able to scale and Established firms (Incumbents) get innovations.
 “More and more large organizations are learning to work with innovative start-ups, and it is clear that corporates are now much better connected in this regard than a decade ago. Those who create such links derive strategic value as they tap into an efficient and growing reservoir of ideas and technologies.” - Luis Alvarez, Chief Executive Officer, Global Services, BT


The success of Silicon Valley is inspiring to many governments and they have taken initiatives to set a creative environment by facilitating the venture capitalists (even from outside country) to invest in the potential start-ups. One of the barriers of growth for young businesses is funding that is solved by Angel investors and Venture Capitalists, who help start-ups cross the valley of death (between product development and commercial success).  Fragmented innovations lead to higher competitions and loss of businesses.  Some sectors, which require heavy initial investments, are dominated by established firms making it very hard for young firms to scale and making overall industry conservative, such as shipping.  Collaborative innovation is the key for growth and need of time for many industries that are facing challenges of increasing cost and reduced profits. The loosely coupled digital giants, established firms, young firms, and policy-makers fuel the growth of any industry at international scale.   Once the ecosystem is established, it will keep on expanding its horizons in terms of industries, boundaries, and technologies and benefit all: local communities by creating more jobs and international communities by providing access to innovation, products, and technology.

Final Remark

As a startup look for innovation ecosystem; where it is located and how to reach there.   


Jackson, D. D. (2015, Mar). What Is an Innovation Ecosystem? Retrieved from National Science Foundation:
Jaruzelski, B., Staack, V., & Shinozaki, A. (2016, Oct). Software-as-a-Catalyst. Retrieved from strategy+business:
Peng, M., & Meyer, K. (2016). International Business. Australia: Cengage Learning.
Snowden, D. J., & Kurttz, C. F. (2003). The new dynamics of strategy: Sense-making in a complex and complicated world. IBM Systems Journal.


Dec 28, 2017

Oracle Acquisition of Sun Micro Systems - How things could have been different


Global financial crisis of 2007-2008 hit “Sun Microsystems” really hard and company’s stock lost 80% of value from November 2007 to November 2008, reducing the market value of company to $3 billion – company posted revenue of $13.8 billion in 2007 and had $2 billion cash but revenue fell by 7 % in 2008 and company posted loss of $1.68 billion in 2008 first quarter.  Company planned 15% - 18% layoffs and were looking for white knight; and immediately sharks such as IBM, HP, Cisco, and Oracle started circling to acquire. Sun Microsystems had impressive technology stack including Servers, Hardware, Database, Operating System, Cloud computing, Data Centre, and Java, a programming language using which world was building applications; Oracle and IBM looked this an opportunity for world dominance.  Oracle announced a definitive agreement to acquire Sun Microsystems in April, 2009 at approximately $7.4 billion – Sun stocks surged by 40% and Oracle now owns Java and enters in to hardware market.
Founded in 1982, Sun was initially known as hardware company manufacturing workstations and servers, but also became open source software powerhouse with Solaris operating system, Java programming language, MySQL database, OpenOffice office suite, Network File System, and Virtual Machines.  "The history of the Valley is littered with the dried husks of companies that had great technology but didn't understand the dynamics of the commercial market they were trying to compete in," Charles King, IT Analyst.  Not only the inability to commercialise the software but also the dominant hardware culture in management failed Sun Microsystems and it was stated in a statement by CEO Scott McNealy - "The mistake we made was putting it on our own hardware. If we hadn't metal-wrapped it, it would have been more widely adopted. If we had put Solaris early on an Intel box, Linux never would have never happened."  In 80s, Sun Microsystems sold their SPARC based systems but industry was moving towards open architecture x86 based systems such as by Intel (x86 and SPARC are processors).  With best-in-class software’s such as operating system Solaris, Sun had opportunity to transition to a leading software firm of world but hardware emphasis surpassed the voices supporting Solaris on multiple machines including Intel.  Solaris faced competition from Linux, which was open source and running on Intel and AMD machines.  Indeed, Sun was doing mistake similar to Blackberry and Nokia, selling an integrated machine with hardware and software; Organizations sometimes have to segregate hardware and software and let them prosper separately, but it is not easy such organizations to forgot their core business – Hardware and they keep on trying to sell their hardware powering with their proprietary software’s.  Once low-end Intel chips surpassed Sun’s proprietary chips by 90s. Sun produced Solaris for x86 but it was late and Linux was adopted by industry.  In 1995, Sun released Java – open source programming language that runs on any operating system and on any hardware.  Mature Java came at the same time as dot com boom, Java was choice for serious website makers, and Sun promoted their hardware on the name of Java; but post bubble bust in 2001, sales of Sun’s hardware went into free fall. The famous slogan and advertising phrase during dot-com bubble by SUN – “We put the dot in dot com” really worked and at its peak SUN was rumoured valued approximately $200 billion, Stock prices were in 3 digits in 2000 but as sun sets and share prices of Sun came down to $8-$9 in 2001 post bubble burst.  Sun tried to compete in many hardware and software markets but was placed beyond top three and big acquisitions made by Sun failed such as $2 billion acquisition of Cobalt Networks (maker of Linux-based Server appliances) in 2000 which never produced any dividend, $4 billion acquisition of StorageTek (data storage technology company) in 2005 but Sun performed poorly in storage market, and $1 billion acquisition of MySQL in 2008 to challenge Oracle and IBM in database market but Oracle remained king of databases. Crucial factors for failure for Sun microsystems are summarized as: Unable to monetize Java, Unable to react to open source Linux on time, and not building products for x86.  This is where Oracle comes in with expertise in monetizing things.
Oracle buying Sun was like any other tech M&A: One tech company with cash acquires another tech company with knowledge, capabilities, and passion.  A database company swallows company with servers, storage, cloud, chip, language, operating system, and few more – probably it hurts and so many visionaries and founders within Sun left after Oracle’s acquisition.  Oracle had multiple reasons for acquiring and major ones included ownership of Java – most popular programming language of world, and entry into hardware business.  Oracle got ownership of Java, though Java generated negligible revenue for Sun but Oracle will find new ways to monetize Java such as filing lawsuit against Google for use of Java in Android. Oracle entered into hardware business, which contributed to most of the revenue of Sun, but revenue kept on declining:  $7 billion, $6 billion, and $5 billion in year 2011, 2012, and 2013 respectively, though prior acquisition, Sun posted $12 billion from the sale and service of its hardware products in 2008 and $9 billion in 2009.  Java still remains the top programming language but multiple extensions (frameworks build over open source Java libraries) came and enterprises adopted them instead of directly using Java. Though Oracle got capabilities and vision of cloud computing from Sun but Oracle was unable to utilize it.  Oracle also closed many reputed projects of Sun.
The acquisition of Sun Microsystems by Oracle lacked vision and planning on how to grow and how to maximize the potential of Sun’s hardware and software capabilities and offerings. Oracle has been blamed for slow pace of Java development and disinterest in Java Enterprise Edition.  Oracle stopped selling many Sun boxes and instead focussed on melding software and hardware to sell integrated server machine for its major product Oracle database.


Unlike most of the acquisitions done in technology industry in which acquired company has one major product such as WhatsApp, LinkedIn, BEA, and NXP, Sun Microsystems had an array of products and Oracle could have better used them to shape the future of technology and could have become Apple of Enterprise software’s.  

Most of the IT analysts agree that it was right time for Sun to be sold because anyhow Sun was going to lose hardware battle to IBM, HP, and EMC; CEO “Jonathan Schwartz” appointed in 2006 faced series of loses and also got featured the list of worst CEO’s of America; and Sun would have never sued Google as Oracle did to make money.  Few claimed Sun could have become biggest enterprise technology company of world but Jonathan killed that option, he never had a business model for generating revenue from Java, increase hardware market share; layoffs were brutal, and finally he left in 2010 post Oracle acquisition, which few considered “brutality continued”. 

Companies do Strategic Due Diligence before executing the M&A’s.  Strategic Due Diligence is framework with many variants and it is a forward-looking process that helps understand how to create value from acquisition. We will use a very simple version of this framework and analyse the market, the company, and strategy post acquisition to make the investment decision.  We believe Oracle must have done a detailed M&A analysis and detailed Strategic due diligence before acquisition.  Our simpler version will give rough idea of why acquisition was an attractive growth path for Oracle.

Oracle and Sun was a perfect marriage and both needed each other.  Oracle had multiple reasons to buy Sun:
  • Oracle Fusion Middleware, Oracle fastest growing business and product as that time, was built over Java
  • Ownership of two key assets of Sun software: Java and Solaris and also Oracle didn’t wanted Java to go in hands of IBM or Cisco
  • Oracle wanted hardware capabilities and end dependency on HP for Server machines to run Oracle database
  • Ownership of rival open source database MySQL
  • Sun’s huge customer base

IBM and others had similar reasons to acquire SUN. 

Larry Ellison, CEO of Oracle, had a roadmap, post SUN acquisition:
  • Not bother about declining sales of SUN hardware – commodity servers.  Time was for either for very powerful servers or Cloud, and Oracle preferred building high end machines using Sun engineering and hardware. 
  • Abandon non-performing products. 
  • Find new opportunities to generate revenue – probably sue Google or IBM when time is right. 
  • Make potential open source products more successful and introduce commercial version of them. 
  • And few more. 
  • The plan worked in the similar way but Sun’s hardware sales declined, many products and projects started by Sun were abandoned, Key visionaries and founders within Sun left, and Oracle still trying to find ways to monetize open source software’s.  Though Oracle executives claim that Sun acquisition was a success and amount paid was back in 3 years, Oracle could have done lot better with Sun.  

How things could have been different

Oracle could have done much better with Sun than what they did. Oracle had options to become Apple or Amazon along with being Oracle but they were not in mind of Larry Ellison. We will analyse the two options which Oracle could have tried and proved the Sun’s acquisition worth more than multi billion dollars.

Option 1 – Sun under Oracle could have been Apple

Let’s go back in history: In 1979, Xerox denoted few Alto computers to Stanford University, university involved in developing the early internet. Alto inspired the project SUN (Stanford University Network) to develop a relatively low cost modular personal workstation. Ten workstations were built in 1981 and 1982 and University decided not to build anymore but few Stanford research and MBA candidates partnered to form SUN Microsystems and develop SUN-1: first generation of UNIX computer workstations. Important is to know Xerox Alto was also inspiration behind Apple Lisa (first personal computer to offer Graphical User Interface) and Macintosh systems. Steve Jobs was impressed by Alto, made multiple Xerox visit, excited by the mouse driven GUI of Alto, made deal with Xerox for designs.  Though SUN developed workstations (a subset of personal computers but targeted at engineers and professionals) not PC’s but their journey had same start as Apple – Same time and same inspiration. Not to ignore both Sun and Apple were making fine hardware, had charismatic founders, and strong anti-Microsoft beliefs; rumours also suggest there were attempts of mergers and acquisitions between two.  SUN and Apple followed different path and late 2000’s was critical for both: SUN was struggling to regain market share and Apple launched new generation of MacBook’s.  Apple’s strategy always included integrating hardware and software: Machine, Operating System, Applications, and Cloud. SUN under Oracle had similar capabilities: Machines (not PC’s but Servers), Operating System, Applications, Databases, and Cloud Computing.  SUN under Oracle could have been the Apple of enterprise and also entered into new markets such as selling powerful commodity machines. Though later Oracle made a similar move by combining Oracle database and Sun hardware but it was not enough.  Google provided an alternative to consumers with Android OS, software’s, and partnering with hardware manufacturers.   Oracle could have aggressive plans to gain market share in commodity systems with introduction of new integrated machines – powerful personal computers targeted at gamers and professionals with strong software and application support. Open source Java was behind Android, Gaming applications, business applications, dot com applications, and enterprises.  Oracle could have better leveraged potential of Java and SUN’s hardware and software to build a Macintosh like system. 

Option 2 – Sun under Oracle could have been Amazon 

Probably no one saw AWS coming and conquering world till late 2000’s. Amazon launched AWS Cloud computing (Amazon Cloud Storage was available for past few years by that time) in 2006 but it was lot after the SUN’s vision – “the network is the computer” – famous SUN slogan in 1984.  SUN employee knew the network and cloud will be computers one day and predicted cloud computing as early as 1984. Sun was early entrant in Cloud offerings with Sun Cloud and later Sun Grid in 2006 supported by Sun open source technologies like Solaris, Sun Grid Engine, and Java, but Oracle discontinued the project in 2010 and made site inaccessible.  Oracle didn’t saw AWS coming and made very late entry in Infrastructure as a Service market and now lagging lot behind Amazon, Microsoft, and Google. AWS mostly ranked first in Gartner’s magic quadrant in all quarters and AWS is expected to contribute more than 30% in Amazon’s EBITA.  Worldwide public cloud market was expected to be $208.6 billion by Gartner and Oracle could have got major piece of it.  In the same line as Amazon offerings Cloud Storage and Amazon Web Services (AWS or Cloud computing), SUN planned offering first Cloud services: Sun Cloud Storage and Sun Cloud Compute.  On March 18, 2009 SUN announced grand entry in cloud market; SUN also announced API’s (Application programming Interface) for enterprises to build private cloud.  SUN’s cloud computing business unit was formed in July 2008 and SUN announced that Cloud computing will run in parallel with in which will focus more on high-performance computing and research whereas new Cloud offerings will be targeted at developers and enterprises. But SUN Cloud came in turbulent times of SUN and it never saw growth with Oracle management.  Oracle kept on struggling with pricing model.

More alternate options were suggested in the past and will come in future because Java still remains number one programming language in world, Oracle’s integrated server machines are success, Lawsuit against Google for usage of Java is still in court, Sun’s MySQL database is hugely used worldwide now, and Oracle has entered cloud market.  


IT System Architecture Complexity

System architecture are like Networks in which elements are nodes and edges.  Systems evolve over time and patterns represent the underlying system.  System can be simple, complicated, complex, or chaos - System can be in order or un-order domain.  Understanding the domain in which system lies determines the way to manage it.  A common implementation of the theory of understanding the system complexity and managing it is - Microservices architecture is complex and event-sourcing is a way to manage it.

“A system is a set of elements or parts that is coherently organised and interconnected in a pattern or structure that produces a characteristic set of behaviours, often classified as its function or purpose” (Meadows, 2008)

Some systems are natural such as living, colonies, and galaxies; some are social such as family and caste but IT systems are Activity systems – ‘purpose wholes’ and ‘strictly goal oriented’. (Checkland, 1981).  If looking at system, try to identify
  • What are the parts of system?
  • How parts affect each other?
  • Do parts together produce an effect that is different from the effect of each part on its own?
  • Does the effect, the behaviour over time persist, either in its original state or in evolved state, under a variety of circumstances?   

System can be defined by constraints
  • High – Ordered, Structured, Repeatable, clear causality
  • Selective - Complexity adaptive, partial constraints, flexible, co-evolution. They show contextual complexity and path dependency
  • Low – may be chaos, absence of constraints.  It might show utility such as open source software 
Core Disciplines
  • Evolution: the study of how systems adapt to constantly changing environments
  • Dynamics: the study of continually changing structure and behaviour of systems
  • Information: the study of representation, symbols, and communication
  • Computation: the study of how systems process information and act on the results
Features of a Complex System
  1. Complex system consists of a large number of elements
  2. These elements interact dynamically
  3. Interactions are rich; any element in the system can influence or be influenced by any other
  4. Interactions are non-linear
  5. Interactions are short-range
  6. There are positive & negative feedback loops of interactions
  7. Open Systems – boundaries will keep on changing
  8. System operate under conditions far from equilibrium
  9. System has history
  10. Individual elements are typically ignorant of the behaviour of the whole system in which they are embedded. 
Complexity is contextual - IT systems reality is it is network of networks:
  • Diversity of Stakeholders
  • Diversity of Interests
  • Multilevel structures
  • Processes

Any enterprise architecture, including the greenfield microservices, can be complex adaptive system:
  • ·       Nonlinear, dynamic, and do not inherently reach fixed equilibrium point
  • ·       Composed of diverse independent agents
  • ·       Intelligent agents’ goals and behaviours are less likely to conflict, leading agents to adapt to one another thus changing the overall systems behaviour over time.
  • ·       Adaptation and learning tends to result in self-organizing and patterns of behaviour that emerge rather than being designed into the system.  The nature of such emergent behaviours may range from valuable outcomes to unfortunate accidents.
  • ·       There is no single point of control – system behaviours are often unpredictable and uncontrollable, and no one is ‘in charge’; therefore, behaviours of complex adaptive systems usually can be influenced more than they can be controlled.  

Why understanding the system complexity is important?

Because it tells the right way to manage it.  Theoretically Brain-swarming, Naturalistic way, Simulation, Prototyping, and Intuition are ways to manage the complexity of IT Systems; Practically, Enterprise Service Bus, Modular Design, Event driven architecture, Swarm-Intelligence, and  Platforms are ways to manage the complexity

Read more
Checkland. 1981. Systems Thinking, Systems Practice. 1981.
Kurtz, Cynthia F. and Snowden, David J. 2006. Bramble Bushes in a Thicket. [book auth.] Michel Gibbert and Durand & Thomas. Strategic Networks: Learning to Compete. 2006.
Meadows, Donella H. 2008. Thinking in Systems. s.l. : Earthscan, 2008. 978-1-84407-726.


Dec 27, 2017

Micro services - Boundaries

When designing Micro services, one of the question that one will face is the size of micro service? how big? how small?

From a purely technical perspective, the smaller the microservice the easier it can be developed quicker (Agile), iterated on quicker (Lean), and deployed more frequently (Continuous Delivery). But on the modelling side, it is important to avoid creating services that are “too small.”

Domain driven design: 
Any sophisticated business domain consists of one or more bounded contexts, each responsible for one part of the domain. A bounded context contains models describing the domain and is also a boundary for the meaning of these models.
A context map shows all bounded contexts and their relation to each other and also describes the contract between them.
The core domain is the part of the domain most closely associated with the strategy of the company.  A support domain is a part of the domain that indirectly supports the core domain without actually belonging to it.  A generic domain is one that is universally well-known, without any need for specialization in the core domain.

Our technical need for the size of a service can sometimes be different (smaller) from what DDD modeling can facilitate. Bounded context analysis is an “excellent start,” but not the sole prescription for how to size microservices.

Options for boundaries of MicroServices
  • Bounded Context – smallest business process or function  
  • Business Capability – Exchange Rates
  • CRUD – User
  • Service - Login
If starting from scratch, then bounded context is the best place.  


Dec 26, 2017

Getting Started with Spring Boot

This tutorial is for someone doing the development first time and that too with Spring boot.  The tutorial does not require any prior knowledge of any technology.

Getting started with development requires a development environment - a tool for development
Refer - IDE in Wikipedia
Therefore, download STS (
It should be a zip file on windows -> unzip it and go to location:
Click the green STS. 

Choose a location of work space. A good location that you wont delete accidentally.  Everything you will do in the IDE (STS) will be saved in this location.  

Click File, New, Spring Starter Project
Don't change anything on next screen

 Click Next.  On next screen check box against Web

Click Finish.  The first micro service is ready.

Check the code
That's all code (and all is generated).  Right click the class and run
You can see everything is fine in console
Hit the URL (http://localhost:8080/) in browser:

All is done. The micro service is up and running. 


Dec 15, 2017

Business Plan Template

You’ve got a business idea. You’ve decided to start a business. You want to get going.  But there’s a lot more to a good business than a good idea.  You need to think things through to maximize your chances of success.
  • Are you the right person to run the business?
  • Will customers like your product?
A business plan will help you turn an idea into a business.
It needs you to think through all the parts of your business to plan how everything will work. It will take a few weeks to write if you’re going to do it properly. Some parts will be easier to complete than others.
Stick at it because it’s not the final document that’s important, it’s the process. Although you want to have a good plan when you’re done, an OK plan is better than no plan.

I have been looking for a business plan template and working on to prepare my own business plan and it is tough exercise to prepare the business plan.  After researching a lot I found really good business plan template on prince trust website.

I have prepared the visio files similar to the business plan template from prince trust website and please contact me -
if you wish see demo or purchase it.
Below are few slides of the template:


Aug 1, 2017

The most powerful visualization of data from history showing horrors of war

Image source:

Probably the best statistical graphic ever drawn, this map by Charles Joseph Minard (in 1868) portrays the losses suffered by Napoleon's army in the Russian campaign of 1812.  The map he draw to remind France of horrors of war when they felt very proud of Napoleon. The thick band shows the size of the army at each position (as army moves towards Moscow, its width decreases) and after reaching Moscow they return. The path of Napoleon's retreat from Moscow in the bitterly cold winter is depicted by the dark lower band, which is tied to temperature and time scales. It is expected 98% of soldiers died in this return journey.

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