The Fun-damental Laws of Enterprise Architecture

Compiled by: Shak Kathirvel  Linus' Law - Given enough eyeballs, all bugs are shallow. Linus' Law - Simple programs never work the first time. Complex programs never work. Ryan Singer - So much complexity in software comes from trying to make one thing do two things. Sturgeon's Law - Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code as cleverly as possible, you are, by definition, not smart enough to debug it. Gall's Law - A complex system that works is invariably found to have evolved from a simple system that worked. Conway's Law - Any piece of software reflects the organizational structure that produced it. Brooks' Law - Adding manpower to a late software project makes it later. AND The bearing of a child takes nine months, no matter how many women are assigned. Wirth's Law - Software gets slower faster than hardware gets faster. Gate's Law - The probability of a bug manifesting itself in software quadruples when said software is being demonstrated. Amara's Law - We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run. Kranzberg's First Law of Technology - Technology is neither good nor bad; nor is it neutral. Classen's Law - In order to achieve a linear improvement in usefulness over time, it's necessary to have an exponential increase in technology over time. Gustafson's Law - In computer engineering, any sufficiently large problem can be efficiently parallelized. Koomey's Law - The energy of computation is halved every year and a half. Moore's Law - An empirical observation stating that the complexity of integrated circuits doubles every 24 months. Malik's Laws of Service Oriented Architecture - Click here for full list. Hofstadter's Law - Estimates are called estimates for the same reason that fishing isn't called catching. Hofstadter's Law - Double your estimate and replace with next unit of time. For example: original estimate: 6 weeks. Double: 12 weeks. Next unit of time: 12 months. Niven's Law - Any sufficiently and rigorously defined magic is indistinguishable from technology. Niven's Law - Ethics change with technology. Sayre's Law - In any dispute, the intensity of feeling is inversely proportional to the value of the stakes at issue. Douglas Adams - A common mistake people make when trying to design something completely foolproof is to underestimate the ingenuity of complete fools. Bill Gates - If the car industry behaved like the computer industry over the last 30 years, a Rolls-Royce would cost $5 and get 300 miles per gallon. General Laws Campbell's Law - The more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor. Sowa's Law of Standards - Whenever a major organization develops a new system as an official standard for X, the primary result is the widespread adoption of some simpler system as a de facto standard for X. (e.g. Gold!!) _________________________________________________

Data is Hyper liquid.

Data, information, and knowledge – The Trinity of enterprise value

Based on the fundamental premise that data, information, and knowledge, are three intricate, indispensable and interdependent elements, all modern HyperScale systems will be built to unearth its untapped business values and business models with unprecedented processing power, speed and accuracy. We view data as simple discrete facts that become information when they are combined into meaningful structures, which subsequently becomes domain knowledge when put together in context. For strategic and growth needs, invariably all enterprises explore new avenues to harness the value buried deep inside this domain knowledge base by generating metrics, categorizing results, assessing values, making forecasting and predictions and finally making informed decisions.

Data has been there for a long time. What is different now? The 3Vs. The vast amount of dataset (Volume) that are generated now a days are distributed, diverse, disparate and heterogeneous in nature. They are structured, unstructured and semi- structured (Variety). Besides, these data set are growing at exponential pace (Velocity). For example, a single blade in a GE gas or wind turbine, generates 500 gigabytes of data each day and that’s just one blade. So that means in 30 days you're generating as much content as the print collection of the Library of Congress. There are about 4,500 GE gas turbines around the world, each with dozens of blades. There are around 22,000 GE wind turbines all outfitted with multiple sensors.

We recognize and convinced that the 3Vs of the modern data characteristics - Volume, Velocity and Variety - generates enormous challenges in areas of Storage, Retrieval, Security, Sharing, Analysis and Reporting. On the flip side they also open up massive business opportunities throughout different phases of data life cycle. Besides the 3Vs nature of the data we also recognize the density of information embedded in them. That is where true enterprise value lies. Without it any enterprise will be flying blind.

Data is Liquid and it is everywhere

Nature of today’s HyperScale data has striking similarities with water – very precious resource but abundant on earth. Needless to say it is one of the essential element for survival of all living forms.  Remarkably, HyperScale data shares the basic characteristics of water – precious, abundant, storable, transportable, transformable, vulnerable and susceptible to contamination and process able for consumption. 

Much like the way massive resources and infrastructures – dams/reservoirs, electric power stations, security and backup systems, purification and filtration systems, distribution-channels, maintenance, monitoring systems, billing/accounting/monetization systems – are built to harness the power of water, we believe that HyperScale data needs massive storage structures, security systems, high speed networks, HyperScale processing machines, monitoring and regulatory structure to extract and harness the vast power embedded in them.

Data Security – Multilayered approach

With big data comes big responsibility. Its security. A multilayered data security strategy – Prevention, Detection, and Policy/Administration - could be implemented as it is proven efficient to achieve all facets of data security. It maximizes the security controls at each layer as its thwarts the intruder’s malicious intensions and efforts at multiple levels.

Preventive controls stop intruders from gaining unauthorized access to our data.  Detective controls centralize auditing and reporting across the organization so that either security breach or compromised system can be swiftly detected and necessary actions be taken. Using policies / administrative controls, unlimited and ad-hoc access to application data can be prevented but the same time allow legitimate administrative activity.

HyperScale Data Security

HyperScale data security layer has been designed to include a comprehensive data security management and governance model. It is implemented as 3 pronged approach.

·       2 Level Identification,

·       Data Encryption and

·       Data de-identification.

Two Level authentication: It provides secure access to our data by authenticating more than just the user’s password. The 2nd level is something in addition to the password. It delivers strong authentication with a range of easy 2nd level verification options—phone call, text message, or mobile app notification—allowing users to choose the method they prefer.

Data Encryption: We ensure that the data we handle is encrypted when it is at rest and as well in transit. We recognize that Data level encryption is more efficient than application level encryption since the same data is shared by wide array of applications. Rather than coding encryption and decryption algorithm into each application, we chose to handle encryption and decryption steps at the data level. When each application needs to process the data, it is decrypted at the data level and securely transmitted using our SSL channels. 

Data de-identification. Besides encryption, we secure our data by de- identification which redacts sensitive data out of the application layer. Users looking at an application may see asterisks instead of actual information. For example, social security numbers might reveal only the last four digits for reference purposes. The data in the database is encrypted, but redacted when viewed.

Shak Kathirvel