How IR teams can adapt for a changing world: Big data, AI, and machine learning
25 April 2017
By Tiffany Regaudie
Big data, artificial intelligence, and machine learning are shaping all industries outside of investor relations. IR teams are now faced with the need to adapt for the speed with which investors within all sectors expect information — with resources being cut, however, many IR professionals are asking themselves how to change the way they work to fit the new pace of the world.
Last week, we hosted a discussion panel called “Behind the Hype: How to thrive in a data-driven world”. Darrell Heaps, CEO of Q4, moderated a discussion on new technologies and how they are shaping capital markets. Joined by Greg Secord, VP of IR at OpenText, Abraham Thomas co-founder and chief data scientist at Quandl, Adam Frederick, VP of intelligence at Q4, and Stephen Gibbs, senior data scientist at TMX, the panel provided some context on changing technologies and how both the buy-side and sell-side can keep up with the rate of change.
Greg Secord offered some valuable advice “from one IRO to another” during the panel discussion; we’ve pulled together his insight to provide tips on how IR teams can start to implement adaptation techniques into their workflow. Here’s how IR teams can adapt for a world that includes big data, AI, and machine learning.
How IR is changing
“Investors are going to have many more avenues to get information about our companies. We need to prepare for that dialogue with investors.” — Greg Secord, VP of IR at OpenText
According to Greg Secord, IR teams traditionally thought about reporting numbers to their management teams as set touchpoints throughout the year. Today, models are not static — they are updated in real-time, which means IR teams have the ability to report fluctuating numbers to their board and management teams.
Secord envisions a world where analysts are going to arrive at work in the morning and turn on models that have been updated overnight. These models will include alternative data such as weather patterns, satellite images, etc. IR teams will also need to operate within this world to keep up with the speed of research, from the way they communicate with their board and management teams to how they speak to investors about the current status of their company.
Secord also sees IR teams needing to become experts on their sector, as more datasets will compare their companies to more than just their competitors. He sees IROs needing broad knowledge of industry-related change, in order to understand data that was once more narrowly focused on peer comparison.
Can companies tell their story through data?
“You may find yourself as an IRO, in two to three years from now, sitting with your management team and saying, we have this dataset available to us every day — we could make it available to investors, too.” Greg Secord, VP of IR at OpenText
The same engine that is being used to inform short-term decisions — machine learning — will also soon analyze 10,000 conference call transcripts and be able to tell whether there was a change in tone among investors. This capability will open up the way in which IR teams shape their message and provide information to investors, including perhaps what kinds of data they share with investors at key points throughout the year.
Secord acknowledges that there may be new challenges in terms of compliance and disclosure, with the availability of data tracked in real-time. Alternative data — non-traditional data sources — could also be a significant tool IR teams use to describe their company, and IROs will need to review the availability of new datasets for investors against compliance policies.
You can watch the full panel discussion online here and read the conversation at #Q4IntelTO to see highlights from the April 20 event.