Archive for the ‘Contributed Article’ Category

How Small Data Helps Understand Individuals in Real Time

Wednesday, November 14th, 2018

This article originally appeared in Inside Big Data


In this special guest feature, Jeff McDowell, COO of Primal, discusses the importance of small data and big data analysis when providing timely and accurate AI recommendations based on interest compared to the stale irrelevant recommendations most online channels provide today. Primal is a leading AI research company that builds knowledge graphs in real time, allowing companies to better understand meaning even in small data environments. Jeff has a proven track record of driving strategic growth through a combination of non-linear insight and practical execution. Prior to joining Primal, Jeff is best known for leading the global alliances and enterprise marketing teams at BlackBerry, and the marketing and business development team at Desire2Learn.

There’s no question that companies have benefited from the rise of big data AI solutions, through an exponentially increased understanding of their customers’ behaviors. But is big data always the right approach for gleaning insights about individual customers interests?

It turns out big data approaches have some constraints, not the least of which is that they tend to have a limited ability to understand the fluidity of interests at an individual level. If someone was interested in running a year ago, and playing poker a few months ago, but is now no longer interested in either topic, a statistical approach using big data won’t be able to pick up on these nuanced changes.

As customers begin to demand more individualization of services, companies need to be able to understand them at this specific level and also keep up with how these interests may change over time. The answer lies in small data AI.

Does Your AI Really Understand Your Customers?

Most AI solutions today don’t take small data into account. Let’s take Twitter for example. Twitter attempts to understand its users by analyzing all the data they’ve collected to infer user’s interests and categorize them into static buckets. A year ago someone may have tweeted a few times about knitting (a passing interest of theirs) and Twitter put them in the broad interest category of “knitting”. Now a year later, they’re still included in the knitting interest category and advertisers are sending promotions about yarn. This is annoying for the user and a waste of money for the advertiser.

Try this for yourself by looking at your Twitter settings and click on “Your Twitter Data”. Scroll to the bottom to see a listing of the interest categories that Twitter has placed you in. If you’ve been on Twitter for awhile, this list will certainly include some outdated topics that no longer apply to you. You might also notice some “interests” that are way off. Twitter gives users the ability to remove themselves from any irrelevant or outdated categories, but how many people regularly review this, if ever?

Small Data AI to the Rescue

A real-time small data AI approach would solve Twitter’s ad targeting problem by looking at what individuals are interested in now rather than the categories they may have been placed into months or years ago. Small data analysis can read a very small piece of text (like a tweet) and use AI to understand the meaning and context of the words. Then it can build a knowledge graph around the post to expand upon the understanding of the interest. This provides a richer insight than keyword targeting, which only searches for matches to a very specific keyword or phrase making it much more limited than an AI approach.

As an example, a vendor of fly fishing rods might want to target people who tweet things like: “Catching lots of trout in the Saugeen River!” This tweet doesn’t use the keyword “fishing” but the expanded knowledge graph built by AI would understand that trout is fish, and a method for catching trout in a river is fly fishing.

All of this happens in real-time, so a small data AI solution is able to understand interests as they evolve. In our Twitter example, this approach would mean a user’s current tweets drive the type of advertisements recommended to them, rather than the approach of targeting using static interest categories. This ensures ads are highly relevant to what users are interested in right now – a win for both users and advertisers.

Could Small Data Work For You?

Of course, not every company is Twitter, but there are many other examples of small data – chat sessions with customer service representatives, comments to a blog post, or product reviews – that can be analyzed with AI to gain deeper insight into customers.

Small data isn’t a replacement for big data. An AI solution needs to analyze both if companies want to have a complete view of their customers at both an aggregate and an individual level.

How your employees can – and must – protect intellectual property

Tuesday, July 31st, 2018

This article originally appeared in the Globe & Mail Leadership Lab

by: Jeff McDowell, COO at Primal

The current economic uncertainty is nothing compared to the financial crisis of 10 years ago. Then, not many companies or consumers were considering the potential of artificial intelligence. In fact, AI was still somewhat seen as science fiction. As an AI company that endured that economic uncertainty, Primal has a unique perspective on how to motivate your work force to innovate to strengthen your business and the Canadian economy, independent of the global environment.


While no business can claim immunity from the challenges economic uncertainty brings, we believe there are a few strategies that can enable a business to innovate during these periods. The most important is establishing a compelling vision for what unique business problems your company is attempting to solve. The intellectual challenge of accomplishing something no one else has done before will motivate employees.

Businesses must have conviction about achieving your mission, no matter the external factors that may threaten to take you off-course, be it trade headwinds or the latest buzz-worthy AI tool.

We didn’t begin 12 years ago with a vision of being an AI company. We were focused on a specific problem – how to use technology to find meaning contained within small data. This involved complex data science and research that had never been focused on before. We had a strong nucleus of innovators that stayed true to this vision, and several years ago, it became clear that the emerging AI market was shifting toward us. That validated our mission and made us more focused.

Solving a major AI problem is like landing on the moon. There’s lots of risk, but incredible payoff in being the first one to do it. While some larger companies may provide financial incentives for patent discovery to encourage the discipline of protecting intellectual property (IP), the unique problem that our company was focused on was more than enough to keep our work force passionate about finding the solution.


To stay true to your mission, you can’t be a trend-chaser; you need to be thoughtful about integrating new technologies into your business. At a recent conference I attended, a panelist made reference to companies “sprinkling some AI on that” as a strategy − and although it was said in jest, sadly too often this is true (and not just with AI).

As with all things in business, the advantage comes not from AI technology itself, but from deeply understanding the mechanics of your company and how you will benefit from the deployment of new technologies.

For some companies, AI may fit best within their customer-service function − but that doesn’t mean you should just add chatbots to your website. We’re working on a case where AI acts as a virtual assistant, monitoring a chat between a customer and a service agent and suggesting highly relevant resources in real-time to the service agent as new questions arise. This allows the agent to remain empathic and engaged in the conversation, rather than furiously searching a knowledge base for prescriptive answers that may be hit or miss.


Unlike revenue, IP is an asset that is less impacted by short-term economic fluctuations and can help buffer your business to withstand times of economic uncertainty. Primal has 148 international patents, but as we built our company, we didn’t motivate our employees by setting a patent goal to achieve. We established a mindset of evaluating each new aspect of a solution to determine if the idea was new, core to our technology and worth protecting. If so, we patented it, and this led to an extensive portfolio. By protecting IP, it bolstered employee conviction that we were onto something noteworthy and solidified their commitment to the vision.

One of the strengths of Canada’s economy is the fact that we have so many innovative homegrown AI companies. As the global AI race speeds up in parallel to increasing economic uncertainty, the need to keep this valuable IP protected is imperative.

Unfortunately, protecting IP is an area where Canada lags globally. Only 10 per cent of small and medium-sized businesses in Canada have IP, and only 9 per cent have IP strategies.

The Canadian government is helping reverse this trend with its new national IP strategy, which supports local innovators through increased resources and legislation. But it’s Canadian companies ourselves who need to see the value in protecting IP − to keep our employees motivated and validate their innovations, to protect our businesses’ hard-won knowledge and to keep strong companies growing and thriving in the Canadian economy.