All posts by Neil Harwani

Interested in movies, music, history, computer science, software, engineering, management and technology
StartUp

List of things needed to create a startup in India in IT products / services area – Part 2

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Many students in management and other advanced courses struggle with details on how to start their entrepreneurial journey. Questions around cost, process, steps, office, digital / software items needed, etc. keep them confused and away from taking the first step. In that series, I had posted a blog / article in the past: https://www.linkedin.com/pulse/how-build-lean-startup-tier-2-3-cities-india-part-1-neil-harwani/

Further to above, find below a detailed list for what is needed for a mid sized company / startup of 20 to 100 employees to start working:

  1. Professional email & website
  2. Target market, target customer, target offerings, target geography, target method of sales details among other things
  3. Details of idea & product / services, pitch and marketing information decks
  4. Udyog Aadhaar registration
  5. PAN, TAN & GST registration depending on your business size
  6. Lawyer and CA for company formation, tax filing and yearly audit certification
  7. Startup registration with Government of India
  8. Bank account to disburse salary and maintain operations
  9. EPF registration in specific cases
  10. Medical, life & travel insurance subscription depending on need and compliance
  11. Online storage from Google, Microsoft OR Apple
  12. Software for accounting, managing docs, intranet, etc.
  13. Membership of meetups, incubators, etc. that help startups
  14. LinkedIn premium, good smartphone, Facebook / Twitter / LinkedIn pages
  15. Desktops & laptops which could be on Ubuntu Linux or Windows or Mac
  16. Co-Founders and focus / advisory groups, mentors
  17. Trademark filing and intellectual property rights management
  18. Funding clarity: boot strapped or funded or mixed
  19. List of angel investors, incubators, venture capitalists, government agencies, banks, crowd funding platforms who could fund the business
  20. Operating space: Digital or physical or mixed location business
  21. Network of related entrepreneurs & customers who could potentially support your journey
  22. Sample contracts for employees, contractors, consultants, customers, vendors, suppliers, sales, etc.
  23. Templates for internal documents
  24. Leave management, employee management portal & cyber security software
  25. Target quality and other certifications like ISO / CMMI, etc.
  26. Admin, HR & Finance staff

Hope this helps the students community in understanding what to expect in terms of starting the journey of business.

Email me: neil@TechAndTrain.com

Education

Required reforms in Indian Education System — 1

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Having an interest in life long learning, teaching and overall education ecosystem and based on my experience with going through various diplomas and degrees, below is what I would say should be the future of education in India. If India needs to have more well educated and better / productive citizens, then education & health have to be in primary focus.

  1. Online education courses should be promoted widely. Degree & diploma granting via online modes is a critical step for learning in people who cannot be involved in full time education in colleges / universities. Higher education related regulatory bodies have taken steps in this direction last year by approving a framework for online education. NPTEL, Swayam have already existed since few years and this framework for regulation is the logical next step
  2. Innovative courses around areas like cyber security, pharma management, analytics, bio-technology, quantum computing, satellite technology, geo-sciences, bioinformatics linked to innovative and new upcoming areas, space sciences, etc. should be readily promoted
  3. Work integrated learning programs where learning is integrated with work via exercises, self study on top of online modules should be promoted
  4. Bachelors & Masters degrees should be made a lot more flexible in terms of what students can study, how they get entry into it and what majors they specialize in. A Physics / engineering student should be allowed to take credits from arts, economics, other sciences, medicine, pharmacy, etc. as long as s/he meets the pre-requisites. Admissions should be based on standardized tests rather than long theoretical / domain mapped exercises / tests. Change of full major in Bachelors and Masters should be allowed mid way through the course as long as credit and requirements are met
  5. Complete change in areas of specialization between Bachelors and Masters should be allowed based on student interest and background
  6. Executive education in terms of work integrated, online, mixed mode should be encouraged and institutionalized
  7. Industry internships / linkages should be increased in realistic terms not just as an academic exercise by allowing 1-2 semesters in full degree to be done at a company with industry outcomes mapped to them
  8. Focus of regulation for courses in educational institutions should be on accreditation rather than approvals
  9. Part time, distance, online and executive education which in the recent years has faced major setbacks in terms of course closures, less or no approvals, inter state jurisdiction issues, etc. should be resolved at the earliest
  10. Single regulator for overall education system should be formalized and created
  11. School education should also be built on a lot more modular system where taking economics with biology or physics and literature or history should be considered normal not frowned upon
  12. Top universities from around the world should be allowed to form joint ventures in India for education, granting degrees, course development, up-gradation, consulting, etc.
  13. Research process, patent filing, trademarks, entrepreneurship, intellectual property rights and related laws, company formation, business incubation assistance, cyber security, etc. should be discussed and taught right from school level with compulsory modules / subjects in higher education. The full ecosystem of paper publishing, research, journals, conferences, research methods, statistics, etc. should be available as a module in schools & colleges for every student
  14. Rigid norms for PhD in terms of how, where, when, with what background and who can research should be relaxed so that industry professionals can jointly undertake research with universities at their work place easily
  15. Linkages with industry in terms of visiting faculty, adjunct faculty, part time professors from industry, joint research should be promoted in big way and institutionalized

Many of these things are already fully or partially enabled at some of the top universities and institutions in India like IIMs, IITs, BITS Pilani, NITs, IIITs, etc. but this now needs to percolate to the larger ecosystem

Email me at neil@TechAndTrain.com

Internet, Social Media

My journey on Social Media & Internet

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Internet is the greatest enabler of work and education in human history as per me and the opportunities it opens up in terms of what we can study, learn and work on are amazing. My suggestion to all youngsters in school, college and who recently have started their career in terms of job / startups is to use this enabler in a positive sense to collaborate, learn, absorb knowledge, fuel creativity and build their own ecosystem especially with professional networks like LinkedIn.

I got introduced to computers in standard 4th, 5th, 6th at Air-force School, Gwalior, MP, India. After that stint in LOGO programming building basic shapes & creating rudimentary BASIC language programs in computer labs there was a slow down in terms of access to computers when I moved to my home state. Then again in 1999 I got a HP desktop computer with dial-up modem and good features for that time with an internet connection (ICENET was the first private ISP in Gujarat at that time). Dial up modem would support me for accessing internet at slow speeds and I got introduced to the online world. I never looked back or logged off since then.

Things I have had access to over time:

  1. ICQ
  2. Yahoo Messenger & Portal
  3. Yahoo Search followed Google Search
  4. Many tutorials, free websites to build web pages which got me freelance contracts for building product pages for few people followed by building my own basic websites and learning CPanel, Web programming & Databases along with DNS and basics of computer networks
  5. Experts-Exchange.com, ASP, upcoming PHP, VB – I got exposure to all of these in early days of 1999/2000/01/02
  6. Local portals of Gujarat and so on
  7. Exploring Linux (Red Hat Fedora) and trying to configure Conexant modem on it (I failed at it)
  8. Exploring IETF, IEEE, CSI, AIMA, Wikipedia, Internet Archive, News Portals, MSN, etc. as they came onto the scene
  9. Then came onto the scene – Gmail and I was among the first few to use Gmail in India via invitation followed by YouTube, Google News, Facebook, Twitter, LinkedIn and so on all the while going through Dot Com Bubble and 2008/9 recession
  10. Post this Coursera, NPTEL, MIT OCW, AWS and so many more sources / portals / websites / apps / universities / products

All the while, my focus was on learning around software, management & engineering. Based on that passion and unending desire to learn, I could succeed in teaching as well as software industry with help of internet and my network. Big thank you to all who have helped / interacted with me over the years directly or indirectly via their work / websites and artifacts over internet.

To me LinkedIn has been one of the biggest sources of learning around Machine Learning, Artificial Intelligence and many other topics other than for networking with people from across the globe in the last few years.

If you have access to internet, you have no excuse to stay behind. Be thankful to those who created all these amazing things on internet. That’s my message to all in my network. 🙂

Entrepreneurship, StartUp

How I scaled my startup ?

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  • As a starting point, I never wanted my startup to scale with the philosophy of people increasing linearly with revenue. What I wanted to build right from beginning was a startup focused on one of my passions which is education with right philosophy and associated people who have similar goals
  • My idea was clear: Focus on education & academics at Gujarat level and provide services & products around it. Maybe we will scale beyond Gujarat, maybe not – we can decide at the appropriate time in future
  • Build a small consulting type firm with under 5-6 contractors / employees / partners / consultants rather than outsourcing type large firm
  • Keep the company organization, offerings, relations, taxation and other things simple
  • Allow all to work digitally from wherever they are, they only move to customer locations to fulfill their work for them, rest of the time they are at home. On top of this there is no restriction for a person associated with my startup around what they can do in off time with other customers that they build on their own
  • Focus on relationships and good deliverable to customers over billing rates and revenue
  • Narrow down focus on what you can and cannot do. Learn to say no when things are beyond you
  • Bootstrap through and through on own money for all parts: digital, physical, taxation, legal, etc. Avoid investor money till as long as possible to continue on the initial direction that the startup has
  • Have a professional website, blog, email, number, references and other details to build a good digital presence
  • Build a community of supporters and network around your startup built via genuine help & mentor-ship to them. If you are not authentic, don’t expect others to support you. Provide good services at high quality and you are very likely to have a good network
  • Be generous in sharing revenue and credit with people who are associated with you. Put them on your website, promote them, endorse and recommend them, share revenue no matter how small or big – you can’t win alone.
  • Raise the bar of your ecosystem and network rather than only yourself
  • Don’t give up on people who helped you in your journey
  • Find clear revenue sources for yourself outside of your startup domain as well to sustain the venture for long term
  • Have good reading habits, devote time to reading daily around your industry and domain
  • As of now we have 4+ universities & colleges around Gujarat as our customers
  • It’s not easy doing multiple things around startup, consulting and balancing work / life / family / network. Be ready to sacrifice lot of time, effort, money and other things on the way to achieving what you want. If you are not ready for all this, it will be difficult to scale or sustain. Be ready for at-least 12 to 24+ months of real hard work to make your brand known in your market
  • You will have bad days and you will have good days. Learn to live with them, both will come and go. Keep the count of good days larger than bad days
  • Reach out to me at neil@TechAndTrain.com if you want to discuss Data Science / R / Java / Python / etc. or want to conduct a training for MBA / BE / MCA / MSc students or are interested in having a workshop for on Data Science / R / Java / AWS / Excel / etc.
Automation

Automation problems in Boeing 737 Max in times of machine learning & artificial intelligence

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Quote – French air accident investigation agency BEA said on Tuesday the flight data recorder in the Ethiopian crash that killed 157 people showed “clear similarities” to the Lion Air disaster. Since the Lion Air crash, Boeing has been pursuing a software upgrade to change how much authority is given to the Manoeuvring Characteristics Augmentation System, or MCAS, a new anti-stall system developed for the 737 MAX. – Unqoute.

Quote – The captain fought to climb, but the computer, still incorrectly sensing a stall, continued to push the nose down using the plane’s trim system. Normally, trim adjusts an aircraft’s control surfaces to ensure it flies straight and level – Unquote 

Analysis from publicly available news & articles, sources and references at the end of article.

Sequence of events:

  1. 737 Max has heavier engines which are mounted higher than other planes
  2. This may cause stall at lower speeds by pushing nose up
  3. To correct this a mechanical sensor is kept at the front of the plane to detect air flowing in parallel or at an angle known as angle of attack
  4. This sensor is connected to MCAS automation software based system which controls TRIM electro-mechanical system which keeps aircraft level and straight
  5. MCAS typically pushes nose down due to stall created by higher nose level as above
  6. In this case the sensor seemed to be faulty so MCAS wrongly took over
  7. Pilots didn’t know how to disengage the MCAS. They had to cut power to the motor that pushes nose down
  8. Another pilot who was in one of the same flights earlier could disengage MCAS by stopping the motor for a similar problem by cutting power to motor pushing nose down

Sources & references below:

https://www.ndtv.com/india-news/all-boeing-737-max-8-aircraft-in-india-will-be-grounded-by-4-pm-today-says-dgca-official-2006734

https://timesofindia.indiatimes.com/world/rest-of-world/pilot-who-hitched-a-ride-saved-lion-air-boeing-737-max-day-before-deadly-crash/articleshow/68499359.cms

https://www.ndtv.com/world-news/lion-air-plane-cockpit-voice-recorder-reveals-pilots-frantic-search-for-fix-report-2010292

https://en.wikipedia.org/wiki/Automation

https://airlinerwatch.com/boeing-737-max-8-design-or-software-problem/

Analytics, Data Science

What should be the subjects & course structure for teaching Data Analytics / Data Science in MBA?

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Data Science & Analytics including Operations / Decision Science are evolving fields which are in demand currently for various reasons. Most companies are experimenting and creating projects / products around analytics / data science. I am listing the subjects & courses that an MBA student should take to cover Data Science / Analytics:

  1. Mathematics — Intermediate level statistics, linear algebra, discreet mathematics & basic calculus
  2. Introduction to Business Analytics & Data Science — covering basics of the subjects like what is machine learning, artificial intelligence, major software / products, data science / analytics basics including various types of data, sentiment analysis, basics of algorithms and contemporary topics
  3. BigData ecosystem — Concepts of Hadoop, Spark, MapReduce, NoSQL and the ecosystem around it
  4. Business Intelligence — covering reporting, dash-boarding, visualization and contemporary topics around it
  5. Business Analysis — covering concepts of how to collect requirements, build a project plan / statement of work, proposals, proof of concepts, concepts of AGILE / DevOps, data analysis, business process re-engineering and similar
  6. Programming in R & Python for managers — Intermediate level topics including data manipulation / cleaning, charting / visualization, running major machine learning algorithms, mathematics functions and libraries
  7. Data warehousing — covering the introduction of it and multi-dimensional cubes, business dimensions, star / snowflake schema, process of ETL and similar
  8. Data mining — covering major algorithms in supervised / unsupervised / semi-supervised areas and their implementation
  9. Cloud computing — covering cloud architecture, offerings & major product companies
  10. Operations subjects — which should include Operations management, Operations research, Project Management, Logistics & Supply Chain management, Total Quality Management
  11. Case study, use case and industry driven internships and projects which give exposure to students using proprietary / open source tools & products mapped to domains like Digital marketing, Financial analytics, HR analytics, Web / Mobile analytics, Advertising, Operational Analytics, eCommerce, Manufacturing, Banking, etc. used in industry to join all of the above together into implementation
  12. Above goes with an assumption that students already have intermediate level skills in productivity tools like MS-Office / Google Docs/Sheets, Linux, Year 1 general management subjects like Finance, HR, Marketing, etc.

Reach out to me at neil@TechAndTrain.com if you want to discuss Data Science / R / Java / Python / etc. or want to conduct a training for MBA / BE / MCA / MSc students or are interested in having a workshop for on Data Science / R / Java / AWS / Excel / etc.

Sales

Sales – From Showing up to Consulting and Design thinking

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Over the years Indian IT industry has had success in getting and executing projects from the western world. Indian IT industry is now US$ 160+ billion a year in exports and domestic market combined. With India largely importing electrical & industrial machinery, oil and few other items like precious stones, it stays as an economy that needs large exports to cover it’s imports especially oil & electrical / industrial machinery. Overall it stays a net importer as of 2018 with a current account deficit which needs FDI / FII / External debt / Foreign exchange reserves to cover the same directly / indirectly.

You can get more details about economy, export and import of India in below links:

https://globaledge.msu.edu/countries/india/tradestats

https://en.wikipedia.org/wiki/Economy_of_India

Services & IT industry have been a large net exporter and medium to earn precious foreign currency. It has also created a large middle class of employees who have uplifted themselves out of lower income groups moving to upper middle class and taking up further education to move up the value chain of IT industry.

Till few years back IT industry sales was relatively normal process compared to what it’s now.

  1. Show up at the right company in the western world
  2. Showcase good past projects or capability around IT and domain if required
  3. Consider doing a Proof of concept / Small project / Consulting assignment
  4. Finalize the contract and your project would start
  5. Expand it into a larger back office extended development center for maintenance and support. You now have steady revenue
  6. People & Technology / Delivery issues in terms learning, salaries, churn, attrition, renewals of contracts, compliance and similar were the main problems
  7. Price and skills around project delivery were the main criteria for customers

In came Agile, Cloud, DevOps, Scaled agile, Digital transformation, Bigdata, Automation, Analytics & recession. The paradigm from customer’s side now has changed to:

  1. What is the value you bring to my program ?
  2. Why should I outsource to you ? How many jobs are you creating in my country ?
  3. How well do you know my domain / industry ?
  4. How can you help me in my company’s success via increased sales or operational efficiency ?
  5. Will you deliver quality output for code, domain, knowledge transfer and maintenance ?
  6. Will you follow best global practices ?
  7. Can you provide help in my local timezone in my geographical area ?
  8. Instead of sales pitches do you have a consulting and design thinking mindset ?
  9. Are your employees skilled in digital, analytics & cloud to begin with and are they willing to pick up more skills as we go ahead and evolve ?
  10. What products / intellectual property do you bring to the table ?
  11. How will you align to my business model or upgrade it ?
  12. Cost is no longer one to the top 3 criteria, it’s the above questions that matter.

In response to this existential change in IT industry many firms and employees are going through reskilling and updating their knowledge / way of working.

Result will be in terms of two groups:

  1. The ones who accept the change and upgrade themselves whether it’s a company or set of employees
  2. The ones who can’t accept this change, they will be sidelined and move to other industries or roles that don’t need to cater to this evolution

Indian IT industry is changing and there is no looking back !!! Rather than thinking cost and basic skills think of the newer questions above. That is the only way to collaborate with clients and take the industry forward.

Reach out to me at neil@TechAndTrain.com if you want to discuss Data Science / R / Java / Python / etc. or want to conduct a training for MBA / BE / MCA / MSc students or are interested in having a workshop for on Data Science / R / Java / AWS / Excel / etc.


Analytics

How to solve a machine learning problem ? – 1

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  1. Select a language like for example either of R or Python
  2. Select a machine learning package to use and associated data manipulation, charting, output, etc. packages
  3. Get and explore the data using techniques like Exploratory Data Analysis for an initial understanding of data and some inferences
  4. Break your original data set into training set and testing set. Clarify what you want to predict in testing set – for example do you want to give loan to customer based on his profile OR what services to offer based on their past recorded behavior in data sets. Typically testing set is smaller than training set and testing set would not have the prediction output (result) column in data set. That would be available in training set
  5. Find out dependent / independent variables, skewness, outliers in data, check if any values need to be converted into categorical values from numeric if they have only few states – typical examples: levels like 1, 2, 3 or YES/NO type fields / columns
  6. Plot histograms, box plots, etc. in above step 4 for help
  7. Add missing values using various techniques: Either simpler options like add mean, median, mode depending on type of data OR you can use machine learning algorithm for the same for replacing missing value or creating dummy fields / columns
  8. Move onto feature engineering by creating completely new variables from available data OR / AND transform by adding thresholds, etc. to remove outliers. Find out the important feature/s and check the relevance of the newly created features. If the new features have high co-relation to earlier features / variables you may not get many new inferences (mostly) so it would be good to do some more manipulation to get create new variables which have new inferences / results / observations
  9. Select your statistical model and create the tasks for machine learning (ML)
  10. Train your ML tasks with training data using the selected algorithm like decision tree, regression, random forest, etc. based on the fitment and suitability
  11. Predict using prediction task based on your testing data set from the trained model in step 10
  12. Check your accuracy by observing the result in real situation versus your result from step 11

This is part 1 of the series on Machine Learning. Treat this as a generic guideline. Many times we will be required to tailor this to various situations and data sets in which case the steps will get enhanced / substituted / refined as per requirement.

Reach out to me at neil@techandtrain.com if you want to discuss Data Science / R / Java / Python / etc. or want to conduct a training for MBA / BE / MCA / MSc students or are interested in having a workshop for your managers / executives on Data Science / R / Java / AWS / Excel / etc.

Analytics, Data Science

How to explore and learn “Analytics & Data Science” ?

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One of my students asked me as to how can someone explore and learn Analytics / Data Science domain with an intention to build their career in it ?

There are three types of roles available in Data Science / Analytics:

  1. Functional consultant like a Business / Data Analyst
  2. Technical Consultant like a Data Engineer.
  3. Mixed profiles like a Data Scientist where you need to know the business domain and technology both

Here are some suggestions to start your journey in Analytics:

1. Learn either of R or Python to start with. Its good if you know Java & AWS as well.

2. Explore concepts of Machine Learning, Artificial Intelligence, BigData, BlockChain, NoSQL & IoT

3. Check the free or cheap courses on Coursera, edX, Udemy, Khan Academy, NPTEL, MIT OCW, etc. for above topics

4. If you have LinkedIn premium account, good courses are available in LinkedIn Learning as well

5. Regularly check job descriptions for Data Scientist, Data Analyst & Data Engineer – This tells you what’s happening in the market and where to align your skills

6. Follow people on LinkedIn / Twitter / Medium / etc. who are into Data Science / Analytics. They post really good information there

7. Regularly read EconomicTimes, LiveMint, Business Standard, CNN Money, BBC Business, Bloomberg, similar sites and update yourself in at-least one functional domain like Digital Marketing, Finance, HR, Operations, Banking, Insurance, etc. via NPTEL, MIT OCW, DataScienceCentral.com, Quora, etc. Explore certifications like Google Analytics.

8. You especially may want to follow people like Andriy Burkov, Andrew NG, Liz Ryan, etc. and sites like Harvard Business Review, Inc., Forbes, Technology Review, ZDNet.com, Kaggle & Sloan Management Review. Here is an example list.

9. Make a list of blogs to follow around these topics too. Here is an example list. 

10. Meet like minded professionals and students in your area using Meetup app. Build your own blog / website / small startup on what you are learning, write articles on LinkedIn / Medium, etc. which will help you to network. Offer some consulting to startups in and around your area. You can get a target list here for Gujarat (some are proper established companies, some are small / young): https://www.techandtrain.com/gujjobs.html – I update this once a month

11. Revise / study concepts of statistics, calculus, linear algebra, operations research and discrete mathematics

12. Explore the tools used by Data Scientists. Here is an example list. 

Many jobs in Analytics / Data Science are available. You can go light on technical topics if you intend to be a functional consultant. This is an evolving field and one website or one book won’t give you full information. Get into a habit of surfing from across the net and buy few good books around above topics. Things change / update / evolve in Analytics every few months.

Reach out to me at neil@techandtrain.com if you want to discuss Data Science / R / Java / etc. or want to conduct a training for MBA / BE / MCA / MSc students or are interested in having a workshop for your managers / executives on Data Science / R / Java / AWS / Excel / etc.

Internet, Software, Trends

History & Future of Information Technology

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Late 1970s – Ethernet

Late 1980s & Early 1990s – Internet

Late 1990s – Y2K was the trend and revolution of easy operating systems like Windows with word processing and spreadsheets happened. Also, LINUX. ERP, CRM, BPM, HRM and similar software pick up in market.

Early 2000s – Internet boom, Email, Browsers, Dot Com, Start of E-Commerce, Java, boom in Portals, Scanning solutions, Enterprise Content Management, Search and associated technologies

Post 2011- Shift to Mobiles, GPS, Location tracking, cameras, virtual / augmented reality on mobiles and so on. Lot of growth for companies like Google / Apple.

Post Mobiles – Cloud, Machine Learning, Artificial Intelligence, Analytics, IoT, Beacons, Business Intelligence & BigData

2010/11 onwards – RPA, SAAS/PAAS/IAAS, Facebook, Social Media, Agile, DevOps, Crypto currency, Focus on Internet, Network & Cloud security

Time is now right to have an AI / ML based System in Enterprise IT ecosystem that can create and manage systems on it’s own or at least most of it at Enterprise / Technical & Solution Architecture level along with automated creation, deployment and maintenance of software systems using Cloud

Reach out to me at neil@techandtrain.com if you want to discuss Data Science / R / Java / etc. or want to conduct a training for MBA / BE / MCA / MSc students or are interested in having a workshop for your managers / executives on Data Science / R / Java / AWS / Excel / etc.