Monday, November 27, 2017

Netiquette for Writing Emails: The 7 Elements of a Good Email

Email Access: Clip Art
Clip Art: Hotel Icon Email Access
Credit: Hotel Icon Email Access Clip Art - Red/white clip art

Max Haroon, a social entrepreneur, a speaker and an author is a retired IT and e-Marketing specialist. He is the founder of the Society of Internet Professionals, established in 1997. He has hosted numerous events and conducted workshops in Entrepreneurship and Leveraging the Technology over the last twenty years. He can be reached at

"There are four ways, and only four ways, in which we have contact with the world. We are evaluated and classified by these four contacts: what we do, how we look, what we say, and how we say it."
~  Dale Carnegie

Email is still the core of business communication. I spend a great deal of my day dealing with emails. I get tons of emails (one dozen of email addresses - I know, yikes). It is appalling for me to see some emails with typos, embarrassing errors and poorly formatted. Such emails have the potential to sabotage your reputation both personally and professionally. I am writing this article to help SIP members write “good” emails.

Just like any other task email communication has rules and etiquettes. By not following them you are risking your professionalism image and the reputation of organisation you representing.

Below are seven elements of email for each we discuss some issues and some pragmatic solutions you should be mindful in writing or replying to an email:
  1. Read Mindfully before Replying
  2. Typos and Grammatical Errors
  3. Subject and Salutation
  4. Closings
  5. Signature
  6. Down-Edited Reply
  7. Formatting and Style

1. Read Mindfully before Replying:
Your inner impulse may make you respond to an email without reading or understanding the intent fully. I have seen three replies from someone in the space of 1 minute (written around the midnight) which demonstrates that the reader was reading it bit by bit and replying to it bit by bit or didn’t review it before hitting “SEND” and then realizing more to add on!

2. Typos and Grammatical Mistakes:
Spelling and Grammatical mistakes show that you are lazy, inconsiderate or uneducated. Would you like to read newspaper with typos and mistakes? An example of a real email received   “I am deleted to introduce our ...” I think the sender meant ”I am delighted to introduce our ... ".

Use the Word for Windows to compose your reply or draft a new email. This has the added advantage of backup copy of your communication. The Word also provides Spelling and Grammar checker.

3. Subject and Salutation:
Subject Line: Make it the indicative of the content or to raise curiosity. People are more interested in the subject if it relates to them, such as their personal or professional issues. A good size of the subject line is 8 words.

Salutation: A highly overlooked area, this is where you are going to hurt someone‘s ego or relationship if you misspell their name or have not addressed properly. Some salutations like “Hey you guys or Hi folk” are laid-back and no-no for business or professional emails. In order of formality use “Dear .... , Hello . ., and Hi . .

4. Closings:
This comes just before signature. You have many choices, such as complimentary close or call to action (CTA). If you want response then say so in the closing. Some examples are: I’m looking forward to hearing from you; Kind thoughts; With many thanks.

5. Signature:
A signature consists of your name (full name), contact info, phone, email address, and website with a link.
If you sending the email on behalf of the organisation then make sure that your signature reflects the contact info, email address and the website of the organisation. If you wish to add any personal touch you can draw line underneath the signature and write follow me with icon and link to a LinkedIn and Facebook account. Do not go beyond six lines, it may be egocentric.

6. Down-Edited Reply:
Generally, people hit the reply button to an email and start the response from the top of the page then the sender do the same thing. If this cycle gets repeated the conversation becomes very lengthy. This approach is called Top-Edited Reply.

Use a more efficient approach called “Down-Edited Reply", explained below:
  • Begin on the top with a salutation and introduction, indicating the response is given below.
  • Eliminate lines and paragraphs of the sender’s email which have nothing to do with your response.
  • Write your response below the point you are responding.

7. Formatting and Style:
Presentation is equally, if not more, important then the content. Follow the rules of formatting and styles, such as:
  • Do not mix multiple sizes and multiple font types, use one size and the font easy to read on the computer/mobile.
  • Avoid multiple colours of text, one dark colour throughout is better.
  • Avoid a lot of exclamation marks. You can be excited about something once and not throughout the email.
  • All UPPER CASE means you are shouting and all lower case means you are too lazy.

In conclusion:
Please review and proof read your email; better still read it aloud to sense the tone of your message. That is why I have recommended earlier compose your email in Word document to give you extra time and space. If you are still composing your email in the email program then follow my safety point, write the email (new or reply) leaving the TO: temporarily blank, until you are ready to send. In this way you will not hit “SEND” without being mindful.

So, keep your email short, simple, clear, mind your manner, proof read it and check your tone.

Max Haroon | President
Society of Internet Professionals (SIP)

Your comments are welcomed

New Certificate Workshops "Leveraging Technology for Entrepreneurs" 
Max Haroon is planning to conduct three workshops on Writing Tools and Management of Emails. This is part of a series of Certificate Workshops “Leveraging Technology for Entrepreneurs” for members of the Society of Internet Professionals - one of many benefits of becoming a member of the SIP.

Please visit SIP’s Resources website page for the details.

Friday, November 24, 2017

What are the Industries that Most Use Internet of Things?

This article, authored by Cory Popescu, SIP Writers Forum, is for the IT / Internet professional, but it is equally applicable to anyone, as we use computer and mobile devices in all aspects of our lives regardless of our profession. 

Internet of Things (IoT) using machine learning as a powerful tool has spread in various industries to observe and predict behaviour patterns. This helps organizations to increase their revenues, improve utilizing their resources including their workforce, help them become more proactive to market dynamism.

IoT applications are used in a variety of industries and they started being developed and implemented over 15 years ago and earlier. Some of these applications are mentioned below.

The power of artificial intelligence (AI) and machine learning is harnessed in a field of paramount importance for humans: healthcare. Devices which incorporate new technology remind the patients to get important diagnostic tests to take their medication at prescribed time, to eat properly, and to keep them in reasonable health by not needing hospital services. Other devices can be used to predict infection and provide health analysis.  Predicting the probability of contracting a disease through sequencing of genomes and making comparisons with elements of large databases represent other important applications in medical services. This allows doctors to use optimum treatments for those diseases. 

In the early days of AI, industrial robots have been introduced to assemble and ship products in manufacturing. Nowadays, new versions of robots perform more complex tasks in the electronics, automotive and home industry. Preventative maintenance in operations management is increasing the number devices which use machine learning. These devices are used to predict which parts become faulty so that they can be replaced with minimum downtime and costs.

Customers are already used for quite some time to receiving reports and alerts about “what else” they might like after their online purchases. To understand customers better, organizations use IoT to scan customers preferences based on their transactions and offer them the “next best” product or service.  Monitoring the historical purchases for segments of customers, the machines learn how to respond to customers' demands in order to up-sell or cross-sell. Customers can make informed purchase decisions based on the IoT devices recommendations.

Customer Service
Developments in IoT devices help personalize machine and human interaction, making AI more efficient in customer service. Through digital devices, IoT applications generate questions and text response messages to accommodate chats with customers. The automation of the personalized interactions with customers and prospects significantly contributes to reducing administration costs and improves the net efficiency of the organizations.

Essential IoT machine learning developments manifest among others, in the fields of: transportation, network security, financials. Readers are welcome to provide comments of additional IoT applications that they know about, with proven capabilities as the industry of developing intelligent devices based on machine learning is continuously evolving.

The FOW Community blog: Future Of Work: 5 Industries Being Most Affected By Artificial Intelligence by Connie Chan

Forbes: Tech: 3 Industries That Will Be Transformed By AI, Machine Learning And Big Data In The Next Decade by Bernard Marr

EngineersGarage: Top 10 Industrial Applications of Artificial Intelligence by Neha Rastogi

MIT Technology Review: Business Report: AI Hits the Mainstream by Nanette Byrnes

Cory Popescu

Your comments are welcomed

Click on the links below to read the other articles by Cory Popescu:
The New Thought Process of Machine Learning 
How Internet Helps Machine Learning
Can IT Professionals Become Savvy Networkers?

Wednesday, November 22, 2017

The New Thought Process of Machine Learning

Maker Extravaganza at Toronto Reference Library #MakerFestivalTO

This article, authored by Cory Popescu, SIP Writers Forum, is for the IT / Internet professional, but it is equally applicable to anyone, as we use computer and mobile devices in all aspects of our lives regardless of our profession. 

For some time, businesses have been looking to build tools to understand their customers better, to improve operational processes, to provide higher quality deliveries. To accomplish this, businesses start using the services of data scientists mining into large databases, and seek upgrades to insight analysts' skills.

The labour dedicated to building business intelligence (BI) mechanisms to incorporate in current businesses is involved at higher degree while creating those tools and to a much lower extent when these devices become functional as expected. When employing the newly built devices embedding machine learning, the organizations gain awareness and powerful observations while decreasing time to respond to change.

Developing intelligent devices require an unconventional thought process to obtain enhanced results since the outcomes of data processing are not known or may only be vaguely imagined at the time of starting the application software. Some of the thought process aspects described below show significant differences between approaching development for traditional versus predictive BI.

A crucial aspect of the thought processes used to create applications involving machine learning is represented by building models with a proactive mindset. The modeller asks questions based on predictive thinking which relate to various business facets, for instance: what benefits emerge from using this data, how to quantify them, how to resolve frequent business issues, such as manufacturing faulty parts.

A predictive approach to modelling also formulates questions regarding the requirement for and the degree of technical skills involved when using the applications based on machine learning, how to present outcome in clear format, how to create groups of users who participate in building the models and what flexibility the predictive models have to generate and validate new models on demand.

Another aspect related to the thought process involved in machine learning refers to calculating for example the number of product offerings versus deriving a solution which predicts the payback on each product offering. Supplying a better view of the customer base, the predicted dollar payback along with tracking key financial variables provide opportunity for increasing company's efficiency and improving their bottom line.

Predicting events and trends become a necessity of the economy with large, expanded datasets which contain relationships and patterns extremely difficult to categorize and make assumptions based on the data. The use of hypothesis and predefined routes represent the foundation of conventional application software development and requires excessive efforts to apply to existing elaborated and complex databases. Leaving this data untapped by the machine learning applications built on the new thought processes would be a total oversight. 

University of California, Berkeley: Department of Statistics: Statistical Science: To Explain or to Predict? by Galit Shmueli

Smart Vision Europe: Top ten predictive analytics questions by Rachel Clinton

Cory Popescu

Your comments are welcomed

Click on the links below to read the other articles by Cory Popescu:
Health Concerns of IT Professionals
How Internet Helps Machine Learning
Can IT Professionals Become Savvy Networkers?