Toronto startup TrendSpottr uses big data to help brands stay ahead of the curve

Anna Starasts

May 15, 2012

This post appears as part of our Leaders Series, conversations with tech and media leaders about influential trends and topics in their field. 

Big data is a popular theme in tech headlines this spring. In light of this, we asked ourselves: what’s new and interesting in Toronto’s big data scene?

In the headlines, big data search and monitoring company Splunk Inc. was the first company of its kind to release an IPO, and it did so with a bang – the stock surged 109% in the first 24 hours of its release. Google also dived deeper into the big data game, opening up its new SaaS-based data analysis service Bigquery to the general public.

Big Data Week, a global platform for discussion about big data, also recently wrapped up, with speaking events and hackathons across the globe. In connection to this event, the Wall Street Journal highlighted the lack of talent catering to this new data-saturated world. The piece points out the need for more “data scientists” – imagine a math whiz with infrastructure and storage savvy, and the acumen to ask the right questions connected to business goals.

So, what is new and interesting on Toronto’s big data scene? We started by talking to Alain Chesnais, industry veteran and co-founder of TrendSpottr. The web service is a good example of a business tackling some of the new issues that have emerged around big data: how to make all this information accessible, understandable and actionable

What is big data, anyway?

The term big data refers to data sets that become so large and complex, that they exceed the ability of common database management tools to capture, manage and process within an acceptable time period.

There are certain fields where the availability and analysis of big data can provide significant benefits.

Companies with large transactional systems, like telecommunications companies, have a lot to gain. For example, big data analysis can help troubleshoot network problems and prevent revenue leakage due to data inaccuracies. This is a huge asset, especially for an industry that regularly pulls data from hundreds of interconnected systems to deliver an accurate monthly bill to each of its customers.

Advertisers, like Internet ad servers, can benefit from the availability of big data to better analyze click-throughs and gain a more accurate picture of visitors in order to achieve more relevant ad placement. And the revenue potential of using big data in this sector is, well, equally big. For example, McKinsey estimates that the worth of geo-targeted mobile advertising for advertising platform providers is USD $100 billion (something worth noting if you are working in Digital Ad Trafficking today).

For social networks or social media analytics platforms, big data analysis can build an accurate and detailed picture about trends in user behavior and conversation topics, for instance. The social sphere is positively swimming in a vast ocean of data points. On Facebook alone, for example, four billion pieces of content are shared every single day. When it comes to using the social web for business, sorting relevant information from background noise is the big challenge.

TrendSpottr: predicting trends through big data

TrendSpottr is a Toronto-based startup that directly tackles the issue of filtering and interpreting big data so that companies can act on it.

Most social media analytics evaluate past performance, which clearly is important for evaluating and learning from past activities. But TrendSpottr saw an opportunity to identify real-time trending information from Twitter and Facebook, and to predict the topics that will be trending in the near future.

The goal is to allow companies to sort through the huge data streams coming from social media and filter out only topics of interest – allowing them to identify impending PR issues, for example, or breaking news connected to their industry – so that they can take appropriate action on the items that the platform predicts will trend.

“What we’re doing is actually playing the role of an automated data scientist for you,” says Chesnais. “Now you can consume big data without needing a Ph.D in statistics.”

TrendSpottr recently partnered with HootSuite to offer a layer of real-time and predictive trend analysis to the publishing and analytics platform, creating a complete trifecta (past, present and future) for social media analysis.

Alain has found that Brand Managers and PR departments have been especially receptive to TrendSpottr. As guardians of a brand, these predictive capabilities mean an increased ability to act fast in an area where damage can be done quickly – your brand’s reputation. TrendSpottr can help these brand guardians amplify the good, like the positive reception of a new product or the virality of an ad campaign, or to proactively address problems, like customer service issues or product defects that are being talked about on social media.

TrendSpottr is a good example of a Toronto startup that is making big data analysis more understandable and actionable, giving brands an opportunity to anticipate, minimize or capitalize on trends.

A new place for math-minded developers?

Big data clearly carves out a business niche for entrepreneurs who are brilliant with numbers, like Chesnais.

But we think it also gives developers a chance to flex their left brained-capacities more often. Many developers have a math background that they rarely get to put to use in the workforce. Unless you’re working in a complex, algorithm-driven environment it’s hard to find organizations that have the complexities that truly leverage their skill and passion for math-related challenges.

Aligning themselves with a company where data is a core focus can give developers an opportunity to use those strong math skills in their work. Big data number crunching might be a very satisfying new space for math-deprived developers to thrive.

Do you know of any new and innovative big data startups in Toronto? Or are you a developer who has felt the impact of big data on your work, positively or negatively? 

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