Archive for April, 2010

Explaining the ‘lack of’ Venture Capital in Toronto

I figured it would be appropriate to write about the lack of a growing and robust venture capital community in Toronto since it cropped up in three places over the last 2 days  – once with several folks at Startup Drinks last night, today over coffee with Jeremy Laurin of OCE’s Investment Accelerator Fund and on Quora (the new social network launched by the ex-CTO of Facebook). On a side note, Quora is actually pretty snazzy with super-high-quality people.

Back to the main point of this thread — I’ve been talking about this situation for roughly 3.5 years now — first in the biotech/life science VC community in Toronto and now with the ICT community. I believe there is one problem at the root of both sectors — we need a kick-start in Canada.

What does that mean, a kick-start? Well, most people believe that there is a fundamental funding gap in Toronto’s venture community between pioneering research (in universities, by startups, etc…) and venture capital finance-able deals. That may be the case, but that is a different argument for a different day. I believe there is a more substantial funding gap that exists once a ’successful Canadian company’ reaches the point of raising a round of capital greater than $15 million. The existing VCs in the community (generally) just can’t get those kinds of deals done. It’s not in our Canadian cards (given the average fund size, risk thresholds, etc…). Canadians need later-stage financing options (or Government money) to back those deals and to create a better later-stage ecosystem.

So, what happens instead? Great Canadian companies knock on the doors of VCs South of the border who are flushed with cash and willing to invest larger amounts in later rounds. For the record, I love US VCs. However, for the purpose of this discussion, or monologue rather, they have tended to bring companies close to home to minimize their geographical risk with the investment. Now, as companies continue to grow and are eventually sold, the successful founders and key employees of those companies often (not always) stay South of the border to further progress their careers — joining US companies, or launching other companies in those locales. Worse for Canada, those successful folks often reinvest in US VC funds or Angel invest in other local US companies rather than Canadian startups.

Envision that cycle reoccurring over and over for the last 30 years. The trend becomes large enough that a substantial amount of capital, and human capital for that matter, gets lost from the Canadian startup ecosystem.

Some say that there is a lack of venture capital in Toronto because there just aren’t great deals. I disagree. I think that there is a lot of talent in Toronto and in the surrounding areas, like Waterloo for example.

Now, the scenario I’ve described may not be the only reason for the lack of capital in Toronto (or Canada), but I feel that it is a significant part of the problem. What are your thoughts?

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Choosing Product Features

Today I came across the question of how to best choose product features throughout the course of development of a product.

Of course, there are several approaches that you can take to figure this out. In fact, I’d love to hear feedback from others below. At the onset of determining your feature set, it helps to have a good understanding of what your users want. However, please keep in-mind that the features your business chooses to develop must also fit the long-term vision for your product. If you stay short-sighted for too long (i.e. fulfill immediate needs of your customers), you may fall into a habit of being reactive as opposed to proactive in developing new and innovative feature sets.

One method that I like to use is taking a holistic view of each feature that would be under consideration for development and figure out its net business value ROI, where [Return = (measured) Business Value] and [Investment = Development Time spent (on a given feature)]:

Step 1. Approximate how long it would take to develop/integrate each feature into your product.

Step 2. Measure the Business Value that each feature would add. Business Value could be things like increase user retention, increase monetization, increase viral or other distribution, increase engagement or any other metric that you find adds value to your business. You may need to approximate a business value here. Choose a scale that works for your metrics and try to stick to it.

Step 3. Work our your ROI = (Business Value / Development Time) for each feature. You will begin to see which features are going to be big payoffs in the long-run.

Most recently, I have been using SCRUM processes to manage products. Do you use SCRUM? If so, what tweaks have you made to the SCRUM process that you’ve found improved teamwork, decreased iteration time and led to better product-wide planning?

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Effectively Communicate Mission-Critical and Relevant Data

Whether you are a CEO, VP Product or a Web Developer, you are constantly evaluating your progress through a series of metrics or analytics, and generally against pre-defined goals. But, are you using the right dashboard to track your metrics day-to-day, and does your dashboard contain rich enough data to allow you to act proactively to ensure you reach your goals?

I recently read the book called Information Dashboard Design: The Effective Visual Communication of Data by Stephen Few. Although it took some digging to find real value, there were great nuggets of wisdom scattered throughout.

Getting back to the sole premise of a dashboard – its purpose is to act as a visual display of the most important information (note: not ALL information) needed to track progress toward your stated goals. In designing your dashboard, it is extremely important that the information is consolidated onto a single screen so that each metric being tracked can be viewed together – at a glance – so that the user can pick up on any trends and relationships between metrics and overall goals.

The book cites two great quotes that drives this concept home and explores a new facet of this equation, our visual perception:

“Dashboards and visualization are cognitive tools that improve your ‘span of control’ over a lot of business data. These tools help people visually identify trends, patterns and anomalies, reason about what they see and help guide them toward effective decisions. As such, these tools need to leverage people’s visual capabilities.” (Source: Richard Brath and Michael Peters, Dashboard Design: Why Design is Important)

“[We should be interested in visualization because the human visual system in a pattern seeker of enormous power and subtlety. The eye and the visual cortex of the brain form a massively parallel processor that provides the highest-bandwidth channel into human cognitive centers. At higher levels of processing, perception and cognition are closely interrelated. However, the visual system has its own rules. We can easily see patterns presented in certain ways, but if they are presented in other ways, they become invisible… If we can understand how perception works, our knowledge can be translated into rules for displaying information. Following perception-based rules, we can present our data in such a way that the important and informative patterns stand out. If we disobey the rules, our data will be incomprehensible or misleading.” (Source: Colin Ware, Information Visualization: Perception for Design, Second Edition)

Limitations of Man

Unfortunately, even the best of us are limited by our biological construct. Humans have limited short-term memory capacities and this is the main reason why information that belongs together shouldn’t be fragmented across multiple pages or dashboards. Once one set of information is no longer visible, it loses its position in short-term memory (unless you have a photographic memory, I suppose); on the flip-side, if all information is within one field of view, your brain can rapidly process information to and from your short-term memory. [HR Moment: If you’re hiring, consider those with photographic memories for data analyst positions!]

Effective Design

To design a most-effective dashboard, ensure to only include information that you absolutely need, in its most summarized form (without the loss of crucial value). This is where the KISS principle (”Keep It Simple, Stupid”) shines bright. The information should be well organized, condensed, specific to its audience/objectives and displayed concisely and clearly with emphasis on the data itself and not on background graphics or fancy designs (which tend to lose meaning and value quickly).

In reading the book, there were a few graphs that I found used space in a very effective way, whilst also allowing the dashboard reader to effectively take in a number of data points in a glance. The first of those graphs is called the “Bullet Graph” (details of how it works in diagram):

The second graph is called a “Pareto Chart” and below is demonstrated in a sales capacity, which quickly shows off what proportion of overall sales is contributed to by each product. Think about measuring the sales quota of each sales employee as a percentage of total sales (this will help sales managers know who to keep and who to let go using a clear and measurable decision-making tool:

My favourite graph, however, is the Sparkline. Its simplicity is unmatched and its span can be adjusted according to the scale you which to present. It is also great for summarizing large datasets into a small graphics that can be displayed alongside each other in dashboards. Sparklines are particularly good for analyzing trending data. In fact, this is used by Google in its Google Analytics product (below, Sparklines on the left of data and represent 3-month trending curves).

At the end of the book, Few shows a few sample designs of dashboards. I think these are among the best dashboards I have ever seen from a design, usability, trending and notifications standpoint. I have included images of dashboards for both sales managers and CIOs below, as designed by Stephen Few and presented in the book.

Sales Dashboard

(Source: Information Dashboard Design, by Stephen Few)

CIO Dashboard

(Source: Information Dashboard Design, by Stephen Few)

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Twitter Update from Chirp

Twitter held their annual developer conference called Chirp on April 14-15th, and it gathered quite a crowd. I recently came across a great summary of Twitter’s latest stats, collected and published by Ben Lorica, a Senior Analyst in the Research Group at O’Reilly Media. Thanks Ben!

Here are some of the key take-aways:

1. Number of registered users: 105,779,710 (1,500% growth over the last three years.)

2. Number of new sign-ups per day: ~ 300,000 (More recently, 60% of new accounts were from outside the U.S.)

3. Number of new tweets per day: 55 million

4. Number of unique daily visitors to the site twitter.com: ~ 180 million. (That’s actually dwarfed by the traffic that flows through twitter’s API – 75% of traffic is through the API.)

5. Number of API requests per day: 3 billion

6. Number of registered apps: 100,000 (from 50,000 in Dec/2009)

7. Number of search queries per day: 600 milion

8. Twitter’s instance, of their recently open-sourced graph database (FlockDB), has 13 billion edges and handles 100,000 reads per second.

9. Number of servers: “… in the hundreds”

10. BlackBerry’s just released twitter app accounted for 7% of new sign-ups over the last few days

11. A NY Times story gets tweeted every 4 seconds.

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