Are Computers the New Boss in HR?

Image courtesy of Flickr. By quinn.anya

Too many resumes, too few job openings. What’s an employer in today’s job market to do? Turn to computers of course! Sophisticated algorithms and personality tests are the new rage in HR circles as a method to separate the “wheat from the chaff” in terms of finding the right employee. However, there is danger in relying on machines to pick the best employees for your company, especially because hiring is such a complex process full of nuance and hundreds of variables and multiple predictors of success.

The article “The New Boss: Big Data”  in Macleans – a Canadian publication – discusses the challenges for human capital professionals in using machines for the hiring process– and coincidentally has a quote or two from me.

Net, net, with hundreds if not thousands of resumes to sort through and score for one to two open positions, it does appear this is an ideal task for machines.  However, I believe a careful balance is in order between relying on machines to solve the problem and also using intuition or “gut decision making” especially to determine cultural fit.  This is a complex problem to solve where the answer isn’t machine or HR professional –but in fact, both are necessary.

What’s Next – Predictive “Scores” for Health?

In the United States health information privacy is protected by the Health Information Portability and Accountability (HIPAA) act.  However, new gene sequencing technologies are now available making it feasible to read an individual’s DNA for as little as $1,000 USD.  If there is predictive value in reading a person’s gene sequence, what are implications of this advancement? And will healthcare data privacy laws be enough to protect employees from discrimination?

The Financial Times reports a breakthrough in technology for gene sequencing, where a person’s chemical building blocks can be catalogued—according to one website—for scientific purposes such as exploration of human biology and other complex phenomena. And whereas DNA sequencing was formerly a costly endeavor, the price has dropped from $100 million to just under $1,000 per genome.

These advances are built on the back of Moore’s Law where computation power doubles every 12-18 months paired with plummeting data storage costs and very sophisticated software for data analysis.  And from a predictive analytics perspective, there is quite a bit of power in discovering which medications might work best for a certain patient’s condition based on their genetic profile.

However, as Stan Lee’s Spiderman reminds us, with great power comes great responsibility.

The Financial Times article mentions; “Some fear scientific enthusiasm for mass coding of personal genomes could lead to an ethical minefield, raising problems such as access to DNA data by insurers.”  After all, if indeed there is predictive value via analyzing a patient’s genome, it might be possible to either offer or deny that patient health insurance—or employment—based  on potential risks of developing a debilitating disease.

In fact, it may become possible in the near future to assign a certain patient or group of patients something akin to a credit score based on their propensity to develop a particular disease.

And something like a predictive “score” for diseases isn’t too outlandish a thought, especially when futurists such as Aaron Saenz forecast; “One day soon we should have an understanding of our genomes such that getting everyone sequenced will make medical sense.”

Perhaps in the near future, getting everyone sequenced may make medical sense (for both patient and societal benefit) but there will likely need to be newer and more stringent laws—and associated penalties for misuse) to ensure such information is protected and not used for unethical purposes.


  • With costs for DNA sequencing now around $1000 per patient, it’s conceivable universities, research firms and other companies will pursue genetic information and analysis. Are we opening Pandora’s Box in terms of harvesting this data?

Implications of Computers Reading the News (News Analytics)

Wall Street analysts and traders have a new weapon at their disposal: news analytics. In an effort to keep up with the deluge of news, events and alerts, some investment firms are turning to machines to read and score news for sentiment and word counts. This information is then inserted into trading models, which may be responsible for a huge buy or sell in your company’s stock. Marketers, with machines reading news and making buy/sell decisions in near real time, what are the implications for your PR, communications, and social media strategies?

Wall Street analysts and traders have long believed that stock prices jump on the release of positive and/or negative news. But the sheer number of news sources and volume makes comprehension a daunting task for individual traders. Adding insult to injury, most of the data in the world is unstructured, meaning that it is not in a database and may consist of text, JPEG images, flash videos, etc. So, interpreting the “meaning” of unstructured data often takes too much time.

Enter analytics. With the assumption that news flow is a good indicator of trading volume and stock price volatility, traders are using real-time data feeds, advanced algorithms, and computer power to digest and execute trades on “news” in sub-seconds.Machines are reading press releases, news stories, analyst reports, stock alerts, and more to gauge the sentiment, relevance, novelty, and volume of news. And trading firms are busy designing models to forecast stock prices based on historical news volumes.

Machines reading the news are scanning for two key criteriasentiment and counts.

Let’s tackle sentiment first. Reading for sentiment, algorithms are scanning news looking for key phrases such as “better than expected” or other verbiage. They score news on how relevant a news item is to your particular company, whether the news is unique, the source of news (key analyst vs. small time shop), and what the specific headline says.

When examining counts, news algorithms seek how many times a key phrase shows up in the news, how often that key phrase is used over a time period (e.g., last 24 hours, past three days, and even how many articles were placed over a specified time frame to discern news volume.

This trend has significant implications for marketing and PR professionals. While we may not know the “weighting” system of what is most important to these algorithms (e.g., word counts might be more important than uniqueness), we should definitely bear in mind that in addition to human readers, we’ll now have to contend with machines.

At some point, most marketers have solely written, edited or approved a corporate press release. However, with machines starting to “read” the news items, your communication strategies might need more than a simple tweak. Ultimately, this means that press releases may need to be optimized for machine scanning. In addition, as these algorithms monitor news feeds from analysts, commentators, and other news professionals, one strategy might be doubling down on press and analyst relations to help shape content before the computers read it.

Machines are now reading the news and trading on what they discern. And news analytics isn’t just for large cap stocks! In fact, any company that trades on an exchange is fair game. Knowing this, your company’s stock price might go significantly up or down depending on your future marketing, social media and PR strategies. Fortunately news analytics is in the early adopter phase, but if there’s money to be made then surely this will be a growing trend.


  • Other than those listed above, what are the implications of machines reading the news?
  • What might be some PR, marketing, and social media strategies to take advantage of this trend?