Digital India before Digital World

Past one year Indians have been riding the Digital Wave, from the prime minister to the local MP everybody wants to do something to be part of the Digital India wave. Funny thing is when I came across our local Member of Parliament he told me his idea of going Digital was to launch his long pending website ! I could not resist and I ended giving him a 5 mins Booster Lecture on Digital. I will share my view of Digital India and how India is implementing Digital at various level at a later date. As 2016 begins to settle in, now is the ideal time to look at how technology will be driving digital transformation in businesses.
The digital transformation wave

While new technologies continue to provide the ability to transform business models, effectively engage customers and improve efficiency of business operations, the majority of organisations were still struggling with the basics in 2015, trying to keep up with the application backlog and managing IT infrastructure and user devices. At the same time,  forward-looking organisations are putting user or customer engagement at the top of their technology agendas. Led by the need to think about the entire customer engagement journey, across all digital platforms (mobile, web sites and so on) and in-person interaction, more and more companies will focus their efforts on their own digital transformation in 2016. They will extend traditional systems or systems of record that house core data assets, by delivering applications that engage customers and employees more effectively and provide analytical insight. Organisations that don’t make this transition will be left behind.

From leveraging Big Data to the modernization of core business applications, the to-do list for everyone from the CTO to the CIO to your developer team has never been greater. So what are the key factors driving digital transformation? 

1) The modernization of core business applications

To compete in this increasingly mobile, social world, companies must find ways to engage customers and prospects in a more digital way. Modernising apps to play well in the digital space will be a must. The websites built by sophisticated market players who realize digitizing the enterprise is a critical component of future success will proliferate; no longer is the website a simple billboard for the company, it is an interactive, dynamic resource that encompasses the next generation of application development. 

2) Digital interactions merge channels and break down silos

In 2016, biggest realization for organizations should be 'There is no web & mobile strategy : only a customer-centric digital strategy, regardless of channel'. There is no marketing data, sales data and support data : only the customer life-cycle data. Companies will endeavor to provide the best experience based on the combination of individuals and where they are in the lifespan of their relationship with the organisation, from new prospect to long-term buyer. In 2016, digital strategy will mature as companies get serious about bringing together all customer and prospect information and goals, and how best to serve them with a single, continuous digital strategy. Recently Airtel has decided to share the location of their mobile towers with their customers to bring in more transparency, a month back the mobile companies refused to share the data with customers! The past five years were about bringing commerce, marketing, sales and support online. The next five will be about bringing them together by understanding the journey and making it better, cheaper and faster. 

3) Big data insights will be extended to the enterprise including mobile devices 

today the choice of applications that leverage big data, machine learning and so on is where the advantage lies. This first wave of big data focused on the infrastructure stack–storage, scale and integration. It’s actually the next wave of technology that is more exciting because it will make big data mainstream and consumable by everyone. Companies will stop thinking about big data as a big data warehouse to be managed and scaled. Instead, they’ll think about the marketing analytics application that automatically provides the next best piece of content to users and drives higher conversion levels. True big data value will emerge from this next wave of applications and services.

From 2016 to mid 2017 we should see 'Watchers' evolve from reading about Big Data transformation to actively implementing it themselves. The success of the competitors is going to drive the late starters to evolve to survive if not to succeed.

Should you learn Phyton or R ? - For Aspiring Data Science Students

Why Python is preferred for data science

• Guido van Rossum created Python
• Python was released in 1989. It has been around for a long time, and it is object-oriented
• IPython / Jupyter’s notebook IDE is excellent.
• There’s a large ecosystem. For example, Scikit-Learn’s page receives 150,000 – 160,000 unique visitors per month.
• There’s Anaconda from Continuum Analytics, making package management very easy.
• The Pandas library makes it simple to work with data frames and time series data.

Why R is preferred for data science

• John Chambers created R and prior to that he created S
• R was created in 1992, after Python, and was therefore able to learn from Python’s lessons.
• Rcpp makes it very easy to extend R with C++.
• RStudio is a mature and excellent IDE.
• CRAN has many machine learning algorithms and statistical tools.
• The Caret package makes it easy to use different algorithms from 1 single interface, much like what Scikit-Learn in Python

I started by learning R and then picked up Phyton. I personally think Phyton is much more versatile than R but it is good to learn both the languages.

Value creation from Big Data & Analytic in the Insurance Industry

Insurance company have to imbibe a culture where business leaders trust Data Analytics and act on the insights provided to get maximum value from the potential value of Big Data. Insurers should take steps to create that culture today if it doesn’t already exist in their companies.
The key is to start small with a PoC. Following is an example of how insurers can leverage a Big Data platform and some key considerations to keep in mind. In this example, IT is interested in using a Big Data environment to speed up long-running ETL processes in a traditional data warehouse environment, because the traditional processing is leading them to miss reporting SLAs for business.

Big Data Challenges: Insurers are faced with a number of factors that combine to make Big Data a big challenge:

1. Proliferation of channels and the explosion of data
2. Increasingly competitive landscape, especially in the P&C and life sectors
3. The financial tsunami of the past several years, as well as the resulting increasingly demanding regulatory requirements in both North America and Europe
4. 
   
5. An unusually high number of catastrophic losses caused by natural disasters like brush fires, hurricanes, earthquakes in recent years;
6. Siloed data environments. 

Having said that, it is important for insurers to develop a good business use case for meeting the strategic objectives of that line of business. In addition, solid backing from top level executive is extremely important not only for funding, but to evangelize and communicate the objectives and need for adoption of Big Data to the entire organization, including partners and vendors. Although the scope and investment in terms of people (a dozen employee big data team), tools (for example, open source Hadoop ecosystem), technologies and infrastructure might be small, the architecture should keep the long term view in mind. For the effective harnessing and harvesting of Big Data, close collaboration between IT and business is imperative to iteratively experiment and drive actionable insights by building proof of concepts. Insurers can then use this incremental success to get increased funding for next phases and/or use cases.

Insurers who aren’t exploring and embracing Big Data, and developing a Big Data strategy will find that they are losing their competitive advantage. They will be unable to get actionable insights from the mountains of data flooding into their organizations. Some of the key findings of the market research with respect to Big Data adoption and opportunity in Insurance vertical were: 

• A vast majority of insurers are using analytics for actuarial  & pricing  processes. Very few insurers are using analytics to improve operational areas like sales, marketing or optimized work assignment for underwriters & claims adjusters. 
• Relatively few insurers have got a comprehensive Big Data strategy and are reaping its benefits However most insurers are planning their Big Data approach.
• Even fewer insurers capture, persist, and analyze Big Data within their computing environment today, but those that do typically leverage traditional computing, storage, database and analytics, in addition to newer platforms such as the Hadoop ecosystem. 
• Larger insurance players plan to embrace Big Data and analytics across all financial and risk management areas while less than 50% of the smaller insurers are planning the same actions.

US Elections : The one who leverages Big Data will win the elections !

It is said that in 2012, "Obama's campaign began the election campaign knowing the name of each one of the 69,456,000 Americans votes who had voted him to White House."  So Obama knows each American who voted for him! Don't worry, it's not just Obama, by now Donald Trump, Hillary Clinton & all other presidential candidates know a lot about each American voter.

Scary? Well don't blame the candidates, the digital foot print that each one of us leaves every minute of the day creates enough data so that even the dumbest computer with 512mb RAM knows more about you then you can imagine! What you buy, what you read, what you say on social network, what you like and what you dislike, who you associate with, what your mood is morning/noon/night, where you work, what you do, what is your health situation, where you've donated, what clothing styles you like, what car models you buy, your favorite Cola brand, your favorite phone brand, the medicine you buy-- all of that information is available to those with the budget to buy the relevant data & have the algorithms to aggregate and analyze the data. One Super market chain was once rumored to have predicted 'pregnancy of its woman consumers based on their digital footprint and they did not even use the social media data' (think about it when you post personal opinion on social media)

So how do political analyst use this data? Assume you were a soccer mom, a broke- & angry, a comfortable middle-aged guy, etc. Each of those demographic and psycho graphic categories will help decide a campaign target resources. All the candidates need is a team that can use some data crunching and parallel processing software like Haddop along with analytic dashboard to fit you into a category, overlay that to a region, and they have their campaign plan along with 'draft' for the candidate speech that mentions what to cover in the speech and what not to cover.
Using Big Data & Analytics one can identify favorable localities for the candidate, identify the top demand and concerns that matter to the voters of each locality, identify active voters by age group in each locality and that helps define 1/more profiles of the voters of a region which can be used to plan strategy for the campaign. The campaign teams knows what ads to put, what issues to raise in local ads, how to collect funds and what should be the content of the speech.  If they have smart Predictive Analytic software I am sure the presidential candidates know who you are going to vote for even before you have thought about it. Scary but true.
            We used a set of social media data available on the web to find if it comes to Hillary vs Trump showdown who would win. The results were interesting as Donald Trump seemed to have an edge in  Jan 2016 but in 3 months public sentiments seem to have changed and social media favours Hillary Clinton and she could be a clear winner with 20% higher votes if she becomes the democratic nominee!  So if Ms Hillary Clinton' team is using Big Data Predictive Analytics & her team is giving him smart insights to strategize her campaign, which I am sure they are, then Hillary Clinton would be the next US President.  Don't forget you read it here first!

Data Strategy - Hadoop cannot replace DWH

I was discussing Hadoop architecture with a team and the meeting ended in agreeing to disagree on the architecture! There seems to be a confusion among new generation data experts about Data Warehouse, Data Marts & Hadoop Data Lakes.
                                                          Data Warehouses were designed way back in 1980s and the idea was to design a data reflection of the business to be used for analytics. I do not think the concept of DWH changes with advent of Big Data and yet we keep hearing of Hadoop will get rid of DWH. there could be cases where Hadoop Data Lake would serve the business purpose but to say that it is a replacement of Data Marts & Data Warehouse is incorrect.The integration in Data Warehouse is not just to arrange and store data for business but it also takes care of 'cleansing data to solve various data quality & validity issues' that affect business.
                                                       

How insurance Industry Can Leverage Big Data

I have written about various industries using Big Data and today we will go through some pointers for Insurance Industry. Having worked for implementing 2 solutions for insurance industry I am aware of the challenges faced by the industry.  Insurance Industry data today comes from disparate sources that include customer interactions across channels such as call centers, telematics devices, social media like Facebook & twitter, agent conversations, smart phones, emails, faxes,  day-to-day business activities and others sources.
                                                       Most of the data processed by organisation today is structured data and it is hardly 10% of the data available. Insurance company can reap real benefits like
1) Increased productivity
2) Improved competitive advantage and
3) Enhanced customer experience
4) Derive business insights and business value from Data Analysis
by capturing, storing, aggregating, and eventually analyzing the data from new age sources. The value comes from harnessing the actionable insights from this data.  The strategic objectives of the Insurance Business can be achieved by  having a well defines Business Objectives & KPI, clearly defined Business Intelligence requirements and Analytical requirements that help the Data Science team to define the Data Processing for Big Data thus leveraging the 100% data rather than 10 to 20% data that is leveraged today by the industry and achieve actionable insights to achieve the Business Objectives. Clearly just having a Big Data strategy is not enough and we need a well defined custom Analytic Strategy that extracts the true value of the data for the business. In other words the business should have a 2 independent approaches & 2 set of experts to process big Data and to perform analytic on Big Data, these are unique streams of IT , their goals are unique & one should not expert expertise across these 2 technologies ( I must admit there are a handful of people who do have expertise across technologies but the technology is still very new).
                                                        Investing in Big Data, like any other technology should be a phased process starting with Business Vision, Strategic Objectives, Technology Vision,Priority Business Cases and Prototyping and finally refinement to the Vision and Strategy. Real value is derived when actionable insights can make a positive difference in achieving the Insurance organization’s strategic objectives. (Is this is too technical for some industry readers I will be happy to simplify it).

Once prototype is successful it is easier to convince the business to invest in Big Data & Analytic Strategy.
Key points for the business to consider
1) By tapping into more than 80% of untapped data business will discover new insights
2) Processing of entire data gives better transparency and accurate perspective to the the business
3) Big Data & Analytic require complete digitization thus enabling 360 degree insight
4) We are also enabling Real time or near real time processing of data that will enable insurer to experiment with products to identify needs of customer which helps in deliver new products and retaining existing customers

Next we will discuss the value creation from Big Data & Analytic & enabling a Real Time Insurance Enterprise.

By-product of Digital Revolution Part-1 - Increasing Mobile Devices & Health Concerns Due To Mobile Radiations

For many months now I have been doing research on Mobile Towers, Radiations & potential health hazards due to concentration of mobile radiations and I want to share some information to create awareness.

          Increasing concentration of mobile phones and mobile towers is a concern that very few people are aware of. Mobile tower radiations in particular are harmful beyond a certain limit. Unfortunately in India government and mobile companies do not have strict policies and they don't test radiations. It is important for people to read about mobile radiations particularly if you stay or work in the vicinity of mobile towers. I know people who have lived near a Mobile tower and are suffering from Cancer through their is no definite scientific evidence to support the case. 

     We have to understand that our generation (those in 40s) was not affected by mobile radiations till the age of 20 but today's children get exposed to radiations from day-1 and by the age of 12 they start using a mobile so the effect of radiations could be more severe . Mobile industry is growing and it is a cash cow for the government and neither government nor mobile company care about the heath issues of mobile radiations.  There are many scientist who have been doing research on mobile hazards & I recently started interacting with Prof Girish Kumar (Dept of Electrical Engineering at IIT Mumbai). Dr Kumar has been doing research on mobile radiations & their effect on health and he has been creating awareness on this issue for more than 7 years now. I am going to share some of the research docs with my friends circle. Please share it with your friends to create awareness. If you need data collected by Dr Girish Kumar you can write to him or to me and I will share the data with you. 

What is Technical Architecture ? Common Technology Architecture Terms

Technical Architecture is
1.    A formal description of a system, or a detailed plan of the system at a component level to guide its implementation.

2.    The structure of components, their inter-relationships, and the principles and guidelines governing their design and evolution over time.


Key Terms

1. Activity: A task or collection of tasks that support the functions of an organization; for example, a user entering data into an IT system or traveling to visit customers.
2. Application :A deployed and operational IT system that supports business functions and services; for example, a payroll. Applications use data and are supported by multiple technology components but are distinct from the technology components that support the application.
3. Application Architecture : A description of the major logical grouping of capabilities that manage the data objects necessary to process the data and support the business.
4. Building Block : Represents a (potentially re-usable) component of business, IT, or architectural capability that can be combined with other building blocks to deliver architectures and solutions.
5. Architecture Building Block (ABB) : A constituent of the architecture model that describes a single aspect of the overall model.
6. Business Architecture : The business strategy, governance, organization, and key business processes information, as well as the interaction between these concepts.
7. Architecture Principles : A qualitative statement of intent that should be met by the architecture. Has at least a supporting rationale and a measure of importance.
8. Architecture Continuum : A part of the Enterprise Continuum. A repository of architectural elements with increasing detail and specialization. This Continuum begins with foundational definitions such as reference models, core strategies, and basic building blocks. From there it spans to Industry Architectures and all the way to an organization’s specific architecture.
9. Architecture Development Method (ADM) : The core of TOGAF. A step-by-step approach to develop and use an enterprise architecture.
10. Architecture Domain : The architectural area being considered. There are four architecture domains within TOGAF: Business, Data, Application, and Technology.
11. Architecture Framework : A foundational structure, or set of structures, which can be used for developing a broad range of different architectures. It should contain a method for designing an information system in terms of a set of building blocks, and for showing how the building blocks fit together. It should contain a set of tools and provide a common vocabulary. It should also include a list of recommended standards and compliant products that can be used to implement the building blocks.
12. Architecture View : A view is a representation of a system from the perspective of a related set of concerns. A view is what you see (or what a stakeholder sees). Views are specific.
13. Architecture Viewpoint : where you are looking from; the vantage point or perspective. Viewpoints are generic. A model (or description) of the information contained in a view.
14. Architecture Vision : A high-level, aspirational view of the Target Architecture. / A phase in the ADM which delivers understanding and definition of the Architecture Vision /Level of granularity of work to be done.
15. Baseline : A specification that has been formally reviewed and agreed upon, that thereafter serves as the basis for further development or change and that can be changed only through formal change control procedures or a type of procedure such as configuration management.
16. Baseline Architecture : The existing defined system architecture before entering a cycle of architecture review and redesign.
17. Business Governance : Concerned with ensuring that the business processes and policies (and their operation) deliver the business outcomes and adhere to relevant business regulation.
18. Capability : An ability that an organization, person, or system possesses. Capabilities are typically expressed in general and high-level terms and typically require a combination of organization, people, processes, and technology to achieve; or example, marketing, customer contact, or outbound telemarketing.
19. Concerns : The key interests that are crucially important to the stakeholders in a system, and determine the acceptability of the system. Concerns may pertain to any aspect of the system’s functioning, development, or operation, including considerations such as performance, reliability, security, distribution, and evolvability. Longer lasting than problem (eg. state of the economy), not a requirement, which is short term.
20. Enterprise : The highest level (typically) of description of an organization and typically covers all missions and functions. An enterprise will often span multiple organizations.
21. A "pattern" has been defined as: "an idea that has been useful in one practical context and will probably be useful in others" [Analysis Patterns - Re-usable Object Models].

Mobile Phone are contributing more Big Data then you can imagine

According to IDC by 2016 60% of internet traffic will come from wireless devices as opposed to desktops. Couple of years back one good-for-nothing senior at work asked me if I think programmers can use Ipad to replace laptops/desktops and I thought he had one drink too many, but now I am not so sure...

Mobile apps are constantly producing a tons of information like user behavior data (session starts, events, transactions) and machine generated data (crashes, apps logs, location data, network logs).  The volume, value & velocity in this constant stream of mobile data qualifies it as “Big Data”.

Mobile applications are necessity and Mobile Big Data is reality. To capitalize on the wealth of mobile data from smartphones, the challenge of collecting, analyzing and acting on data while it was still relevant had to be met. Mobile developers have the competitive business edge because they can identify factors that impact user behavior as they happen, they can be more reactive, prioritize more effectively and meet customer needs more effectively.

What is different about Mobile Application data is that the data 'has to be processed at high speed' to give user experience. The technology that helps high speed processing in real-time is in-memory databases. In-memory databases provide the “in-motion” part of Big Data – that is processing the data at an exponential pace and providing results while they still matter.  In-memory databases provide in-motion, real-time, in-memory data processing from mobile devices. The other area of application of in-memory databases is  collecting, analyzing and trending data from sources like cars and home systems, all at the speed of business.

Predictive analytics - one dimension to tap max potential of your Big Data

Predictive analytics is the process of using data analytics to make predictions based on data. This process uses data along with analysis, statistics, and machine learning techniques to create a predictive model for forecasting future events. When we say “predictive analytics” we mean   application of a statistical or machine learning technique to create a quantitative prediction about the future.
For example False Alarms from electrocardiographs and other patient monitoring devices are a serious problem in intensive care units (ICUs). Noise from false alarms disturbs patients’ sleep, and frequent false alarms desensitize clinical staff to genuine warnings. Metlab has developed an algorithms that can detect QRS complexes, distinguish between normal and ventricular heartbeats, and filter out false QRS complexes caused by cardiac pacemaker stimuli. The algorithms produced a true positive rate (TPR) and true negative rate (TNR) of 92% and 88%, respectively. What it means is data of ECG, ABP & PPG devices are now being processed to identify a false alarm and it is improving life of patients as well as hospital staff.


Predictive analytic's has become the key to helping businesses create differentiated, personalized customer experiences and help customer make better decisions. As we know predictive analytics strategy and architecture are very custom to the client landscape & requirements.

Key considerations while architecting for Predictive Analytics-

1. Predictive analytics must cover the full customer life cycle                                                       . Organizations require predictable insights into customer behaviors and business operations. Design idea should be to deliver value to customers throughout their life cycle to differentiate their customer experience and sustain business growth. The business stakeholders input is key and it will help identify effective mechanisms for translating the business knowledge to predictive algorithm inputs thereby optimizing predictive models faster and realizing deeper customer insights.
2. Build your Big Data architecture around predictive analytic's                                                    There is no dependency of predictive analytic's on big data, Only thing that has changed is with Big Data processing e have access to larger data sets that can be used for predictive analytic's.  By putting predictive analytics solutions at the core of your platform we create synergies between the analytic's layer and big data processing.
3. Learn from leading companies embracing digital transformation.                                         A large Australian telco improved customer discovery for precise targeting of ads; Another US telco optimized its marketing mix for effective channel interaction; A UK telco major deepened its insights into the voice of the customer to improve customer satisfaction; and many companies are providing personalized digital experiences to maximize customer loyalty.

Career choice - What has better future Android Development or Big Data Development?

I was at a Big Data seminar and one of the participants asked me this questions about choosing career between Android & Big Data. Incidentally I have experience of working on both technologies as an architect/developer/designer and I found the dilemma interesting for someone who has to choose a career track and I decided to post my thoughts.

Choosing a career track is a difficult decision when you are a fresher and don't have any development experience. My suggestion is to first spend some time& effort in learning about both these technologies. If possible you should attend a basic training and do some hands on development on both technologies to get an idea of what is it all about.

Both technologies 'Android' and 'Big Data' (Big Data is actually a set of technologies) have tremendous potential and while Android is already accepted and adapted by industries, Big Data is still in the stage where more than 80% of the industry is still planning their move. Architecturally Big Data is more complex as it is about restructuring the approach to process and consume your data and requires experienced data architects to plan the Big data journey where as android /mobile architecture is kind of 'add-on' that can sit on your existing architecture and does not involve huge invests. So investment is also a decision factor for companies to adopt Big Data thought you can always start small with Big Data. Big Data is actually a set of technologies from Haddop, HBase, Hive, MAP-R  to Data Analytics and more. In the long run there is an opportunity to become a data scientist and we are already short of data scientist and the demand for data scientist is expected to grow exponentially. So is Big Data a good career track ? Definitely it is a interesting and challenging career track and this is a good time to get started on Big Data set of technologies. Do

The reason Android (or mobile architecture in particular) is a hot technology (and has been so for last few years) is because the exponential rise is use of mobile devices and improved & cheaper mobile internet connectivity across the world. Today the companies do not have an option but to make their services available over mobiles for their consumers as well as their work force. Android market share is increasingly growing as compared to iOS so it is a definitely good career track to aspire for. The other reason why one can choose mobile technology as a career track because there is big potential innovate in this area and you can see many startups leveraging mobile technology and it does not require huge investment. One good idea & an innovative mobile application can change the game for you. I am fascinated by the potential of these technologies and have alternated between the two (fortunately). So get trained on both the technologies and then make your move. Best wishes!
   

Blogmint If your blog is influencing people, why not let it influence your bank account?

I recently went through the Blogmint.com. Blogmint.com has a unique model that serves brands and bloggers.Bloggers have reach in the social media and their own nice followers based on the content of their blogs. The brands want to reach-out to the consumers using trusted channels and Blogmint offers the platform. Various brands register and launch campaign programs and registered bloggers can show interest in a brand campaign they are familiar with and if the brand accepts their blogger credentials the blogger can blog/write about their experience with the brand and get paid for their writing. Interesting idea because the blogger can 'Choose a  brand he is familiar with'to blog and he is not forced to write for 'any brand'
Here is the link to Blogmint.com in case you are interested in knowing more.
Link : http://blogmint.ref-r.com/c/i/7230/3008573

Downgrading from Windows 10 to Windows 8 when your laptop manufacturer does not support Windows 10

Downgrading from Windows 10 to Windows 8  ( when your laptop manufacturer does not support Windows 10 or if your system becomes sluggish)

Recently I started facing issue with my Dell 14Z Inspiron Ultrabook. I had upgraded OS from Windows 8 to Windows 10 around 2 months back. After upgrading to Windows 10 I felt the system became slow and started giving various random errors. When I checked with Dell Support they advised me to revert to the OS factory version which was Windows 8 in my case. Thankfully as soon as I bought the laptop I had created DVD backup media to restore the system to factory image in case of failure ( I had created a 4 DVD set using Dell Backup & Recovery software).

I took backup of all my data and software and then I tried to use the DVD media to restore to factory image but there was some issue and the backup would not run, it started giving me Boot error (Error Code- 0xc00000d ).

 After lots of troubleshooting I could not fix this error and then reached out to Dell Support and they told me they can send me a USB with Factory Restore media for my laptop ( Fantastic Support! If nothing else one should buy dell laptops for their after sales support. IN my case I did not even have a extended warranty yet they provided dedicated support and even send the USB media without charging me anything) .
Once I received the USB with Dell Backup and Restore media I changed the setting in laptop bios to boot from USB instead of the default boot from disk. The restore failed for the first time because the default setting on my laptop was 'Secured Boot' as pointed by very helpful Dell support. Once I disabled the Secured Boot in my bios I was able to complete the factory restore to Windows 8

After the install I checked the driver and I found couple of drivers needed update (See image below)

Once the drivers were updated the next step was to check for Windows updates. There were around 190+ Windows updates that needed to be installed and once the task was completed the system was completely restored. It has been 2 weeks since I have rolled back my Dell 14Z Ultrabook from Windows 10 to Windows 8 and the laptop is faster and stable. Yesterday I started getting messages to update my OS to Windows 8.1. I did not want to upgrade to 8.1 as Dell has not tested Windows 8.1 on my laptop model ( Frankly there is no harm in upgrading from Windows 8 to Windows 8.1 as there are few changes and all these changes are to resolve some cosmetic issues present in Windows 8 but my recommendation is check with laptop manufacturer before upgrading Windows OS).

How to stop Windows from forcing you to upgrade to Windows 8.1

Check this useful link to disable automatic upgrade to Windows 8.1
: http://www.askvg.com/how-to-stop-automatic-forced-upgrade-from-windows-8-to-windows-8-1/

I hope this post helps those wop want to rollback from Windows 10 to Windows 8 after the 1 month rollback period is over. I struggled a lot initially but with help of Dell support it was easy to roll back to Windows 8. If you are a Windows user, one thing to note is that if laptop manufacturer does not support Windows 10 then you may think of changing your laptop in near future because Windows will soon stop support for Windows 8 and then you will be stuck with your old laptop. You can of course put the old laptop to some good use. I have a Samsung X05 Pentium Windows XP laptop with 30GB HDD. There is not much you can do with a laptop that has 30 GB HDD but if the screen resolution is good then you can use it for surfing and watching videos. I have put the XP laptop in my kitchen to view cookery videos while I am experimenting with cooking. Good luck!

How to use Gapminder DatatSet - R Language

Here are some quick points I picked while experimenting with gapminder dataset
Before you can work with the gapminder dataset, you'll need to load two R packages that contain the tools for working with it, then display the gapminder dataset so that you can see what it contains.

Note - Install  gapminder & dplyr packages before trying following commands. You can search this blog for instructions on how to install R packages.

##### Code Sample #######

##### Load packages gapminder & dplyr

library(gapminder)
library(dplyr)


# Filter the gapminder dataset for the year 1957
gapminder %>% filter(year == 1957) 



# Filter for India in 2002
gapminder %>% filter(country == "India" , year == 2002)


---

Flooding in metro cities of Mumbai & Chennai. Act now to avoid future flooding or will we drown in the next rains

The administration in Mumbai, Chennai and other major cities is ignoring the basic issues that are leading to flooding of the cities. The civic officials are too busy to divert attention rather than highlighting the efforts to prevent flooding.

1) Use of plastic bags - On an average every citizen shops twice a week which means every citizen contributes 8 plastic bags to the city waste every month. So 22 million population of Mumbai adds at least 176 million plastic bags every month! Apart from plastic carry bags there are other types of plastic that gets thrown around the streets by people for example plastic packet of wafers, chocolates, biscuits etc. So my estimate is Mumbai is contributing close to 250 million plastic bags/packets every month. People do not realize that 250 million bags are being added every month - they are accumulating and the soil is loosing the ability to absorb water. So there is no time left but to act right now or we will have worse flooding next year that will kill hundreds of people.

Solution options - There should be absolute ban on plastic bags and anyone carrying a plastic bag should be fined on spot. Unfortunately there is no other option but to be harsh and strict to ensure no more bags are getting added to existing problem that we have.

2) Not Recycling plastic bags - Are we recycling 250 million plastic bags every month? To add to the problem Mumbai city does not have space to dispose the garbage. Civic administration digs pits and dump garbage in the pit for most of the garbage except for the few modern garbage disposal plants, that do not have capacity to handle the entire garbage produced by the city. So most of the  plastic bags are not recycled and end up in drainage systems that clog the drainage pipelines.

Solution options - We need private companies to help setup garbage treatment plants particularly to treat plastic and non biodegradable garbage. Government should also setup 'Plastic collection points where citizen should be encouraged to deposit their plastic bags and plastic items so that they are recycled and do not end up blocking our drains and blocking oil from absorbing water.

3) Concretization & Tar-ification of soil - Modern Smart Cities have reduced the soil cover. If you compare the satellite picture of Mumbai over 5 years we will see that the soil that used to absorb rain water has now been covered by cement an tar. What this means is the rain water is not getting absorbed by the soil and the only way for the water to escape is by drainage system which were not designed to carry huge volume of water.

Solution options - The builders and residents should realize that thy have to play an important role in maintaining the environment. If we cover the entire building premise with cement and roads with tar then we are blocking the rain water from seeping in and inviting floods. Do not use concrete to cover land inside your buildings and parking lots.