Jul. 25, 2018 "Non-financial metrics are vital to today's valuations": Today I found this article by Hugh Latif in the Globe and Mail:
Evaluating a company’s business performance is usually done through financial analysis and metrics that concentrate on such things as sales growth, profitability, return on equity, working capital and debt-to-worth ratio.
I grew up in business believing the key ingredient for success is profitability through healthy sales and revenue growth. At the end of the day, profitability separates the A team from the B team. While this is still true, in today’s business environment success or failure isn’t always owing to those traditional bottom-line metrics.
Take Amazon for example, which has a whopping market value of US$874-billion and a price-to-earnings ratio of 284. The price-to-earnings ratio is how much an investor can expect to invest in a company to make $1 of that company’s earnings, so in Amazon’s case that would be $284 to make a dollar, which on the surface doesn’t sound very good at all.
What’s more, Amazon’s profit performance is mediocre and the company pays no dividend.
What’s more, Amazon’s profit performance is mediocre and the company pays no dividend.
Tesla Inc., a company in California involved in electric vehicles, energy storage and the manufacturing of solar panels, is another one. It has a market value of US$51.6-billion, and a price-to-earnings ratio of zero, as it has never made a profit or paid a dividend.
Then there is Tim Hortons. Long a mainstay as a top Canadian brand, it now just makes it into the top 50, according to a recent Leger survey of consumers ranking company reputations. Other big Canadian companies that have dropped in reputation are Bombardier, Weston and Sears.
Shareholders generally assume a higher risk investing in companies that don’t show profitability, but the means of evaluating companies is changing. Today, managers and entrepreneurs should also pay attention to key, non-financial metrics and criteria that can affect success and valuation. What are they? At the top of the list is employee engagement and retention.
High turnover almost always translates into long-term problems. If people keep leaving the company, there must be a reason. It’s like a car that continues to run, but has that persistent noise under the hood.
People are still the No. 1 ingredient for business success, and that is true for those who work in sales, marketing, operations, finance and human resources. People make the difference.
Executives and department heads can only get troop engagement through strong leadership and team play. That means constantly developing your people, challenging them and celebrating their success.
Executives and department heads can only get troop engagement through strong leadership and team play. That means constantly developing your people, challenging them and celebrating their success.
Merely promoting attractive jobs is no longer sufficient. In today’s competitive workplace and dynamic business landscape, you need to promote rewarding careers to engage and retain high-calibre employees. How?
Performance reviews should include career planning and development criteria for your people. This helps build a road map for employees to learn new skills, reinforce their talents and develop their strengths.
Performance reviews should include career planning and development criteria for your people. This helps build a road map for employees to learn new skills, reinforce their talents and develop their strengths.
Promoting from within the organization is another way for leaders to get the best out of people. But measuring employee engagement can be tricky. While many companies do annual employee surveys, big corporations recognize that employee engagement is also related to the introduction of new products and services, and they have specific measuring tools for this.
U.S. multinational 3M is known to constantly introduce new products. It has annual sales of more than US$30-billion and employs 91,000 people in 65 countries, measures how new products contribute to sales and has a target of earning 25 per cent of its revenues from products developed in the past five years. Procter & Gamble Co., a large global consumer product manufacturer, targets 20 per cent to 30 per cent of its revenue from sales of products less than three years old.
Many think employee engagement depends principally on compensation levels. While compensation is a factor, employee turnover is also influenced by recognition, company culture and values, and how leaders demonstrate care for the troops.
Companies that promote a good work-life balance and supplement the benefit package with health and wellness programs help keep people fit and contribute to greater employee engagement. In other words, they live their core values and don’t just talk about them.
A good example is the Royal Bank of Canada. RBC has an in-house wellness program for employees and invests in continuing career development through tuition subsidies for courses for its people. It is no surprise the company is consistently ranked as one of the top employers in Canada.
A good example is the Royal Bank of Canada. RBC has an in-house wellness program for employees and invests in continuing career development through tuition subsidies for courses for its people. It is no surprise the company is consistently ranked as one of the top employers in Canada.
https://www.theglobeandmail.com/business/careers/leadership/article-key-non-financial-metrics-are-core-to-todays-valuations/
Aug. 1, 2018 Data engineer: Today I found this article by Jared Lindzon in the Globe and Mail:
Job: Data engineer
The role: To take advantage of new technologies such as machine learning, predictive analytics and artificial intelligence, organizations must first sort and organize data that are often stored on multiple, incompatible platforms. The role of a data engineer is to make data from different sources available for data scientists to utilize.
“You have all this disparate data that’s not mutually compatible with each other, and it’s very hard for a data scientist to work with,” explained Colin Fraser, a data scientist for Vancouver-based online charitable giving platform CHIMP. “A data engineer is someone who is fluent with all of those database types and languages, and can access the data out of all of them and integrate it into something that is usable for a data scientist.”
Mr. Fraser explains that companies often mistakenly hire data scientists to build new capabilities using the data they’ve gathered, only to discover that they first need to organize that data into a usable format, something most data scientists aren’t trained to do.
“Companies will jump to hiring a data scientist before they have a data engineer, and then the data scientist ends up sitting around for four, five, six months not really doing anything, because they're not skilled in data engineering,” he said.
Salary: According to online career resource Neuvoo.ca, the average data-engineer salary in Canada is about $100,000, while PayScale found the average salary to be a little over $81,500 annually.
“I would think in an entry-level position in data engineering, you would be looking at about $50,000 to $60,000 per year, and then mid-level would be $60,000 to $80,000 [annually] and upper would be $80,000 to over $100,000 [per year],” said Mr. Fraser, adding that the more platforms data scientists have proficiency in, the more they stand to earn.
Education: While there are no educational or licensing requirements, a vast majority of data engineers have at least an undergraduate-level degree in computer science, engineering, statistics or a related field.
The most important educational requirement for many employers, however, is certification or proven proficiency with popular database platforms, such as Amazon Web Services, Microsoft Azure and Google Cloud.
“If you’re someone that already knows three or four systems, you’re a lot more valuable than someone who has to learn it on the job,” Mr. Fraser said.
Those seeking certification can find training programs and exam preparation materials through the major database platforms, including Microsoft Certified Professional certification, Google Cloud Certified Professional Data Engineer and Amazon Web Services Certified Big Data Specialty. Exams are offered in major cities across Canada multiple times each year, and typically cost between $200 and $500 to write.
Job prospects: The demand for data scientists in Canada is skyrocketing, and organizations are gradually realizing the need for pairing them with data engineers. As a result, demand is growing, but not at the same rate as the more commonly understood data-scientist role.
“You might not find as many postings, but I think the need is just about the same,” Mr. Fraser said. “Big financial firms, banks, insurance companies, companies that have a long history of knowing that they need to protect and cherish their data will have bigger data-engineering teams, but it’s something that’s becoming more and more apparent and important for smaller companies, as well.”
Mr. Fraser adds that most employers are based in major cities, but data engineers are typically able to work remotely.
Challenges: Mr. Fraser says the biggest challenge most data engineers contend with is simply keeping up with the technology in a quickly evolving field.
“The way that you manage data today is very different even from how it was three years ago,” he said. “That means that whatever you learned in school five years ago is not used any more, so you constantly have to read about new technologies, try new technologies, go to conferences and see what other companies are doing.”
Why they do it: While keeping on top of advances in technology is the greatest challenge, Mr. Fraser says that, for many, it’s also the greatest perk.
“You’re really an architect when you’re a data engineer, in the sense that you’re building some kind of system with lots of moving parts that all have to talk to each other,” he said. “For the right personality, that’s a really exciting challenge.”
Misconceptions: The biggest misconception about data engineers, according to Mr. Fraser, is that companies don’t need to hire one if they already have a data scientist on staff. “The reality is that your data is probably very messy, your data is in lots of different places, and it takes a specialized, dedicated role to really process it and get it into a state where the data scientist can work with it,” he said.
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