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Diversity and inclusion

Predicting Behaviour with the IoT and Data Analytics

Mat Wollerman

Predicting Behaviour with the IoT and Data Analytics

Apple watchThe Internet of Things will help us make better decisions about everything, from the way we ship products and treat diseases, to how we power our cities and run our businesses.

Like no technology before it, the internet has connected people across the globe, often in ways that could never have been anticipated. The countless interactions between humans and machines that occur over these networks are producing data about almost everything that we do.


However, it’s not only people that are being connected: a vast, global network of electronic sensors are constantly transmitting a huge amount of data concerning nearly everything we do, down to the most minute details – they’re in our cars and kitchens, on our wrists, perched atop telephone poles and even inside our bodies, as Gigoam describes.

This network is called the Internet-of-Things (IoT), and like the traditional web, it’s churning out data of an unprecedented scale. Combining data from both networks, we can uncover some miraculous insights.

Already, this data is being used in a number of compelling, forward-thinking ways: for example, students and researchers at MIT have built an algorithm capable of predicting behavior faster and much more accurately than a human can – and by a large margin, according to QZ.

The implications of such technological developments are wide-ranging, and will likely prove transformative in the not-so-distant future. In fact, we’re already seeing many revolutionary changes taking place today.

Data-Driven Insights of the Modern Age

As CIO observes, the IoT has already had a tangible impact on many industries and fields of study. For example, wearable medical devices – like FitBit, BodyGuardian and other similar products – have revolutionized medical care, enabling doctors and clinicians to track patient health information from any location. Other firms are developing “smart pills,” which help patients keep to their regimen and ensure prescription medicine isn’t abused or stolen.

Transformative products, such as Apple Health, are enabling us to track our daily activity and habits, painting a comprehensive picture of our lifestyle choices and how they correlate to our overall health. As patterns emerge, we can actually start predicting how we will age, identifying our our risk factors for serious illnesses like heart disease, diabetes and alzheimer’s.

On a macro scale, this massive pool of data can help determine what kinds of health care certain segments of the population might need. Moreover, it enables public health officials to better allocate resources and anticipate spikes in demand well in advance.

For example, search and social media platforms can now identify outbreaks of bugs in specific regions before or as they actually occur, according to Emergency Management – in fact, Google engineers are helping healthcare specialists fight back against the current Zika crisis by combining regional search queries with other data (weather patterns, travel trends, etc.) This enables experts to map the virus’ spread across the globe, and even predict where the next outbreak might occur, according to Fortune.

Obviously, healthcare isn’t the only space being transformed by the IoT. Big data is revolutionising nearly every aspect of our day-to-day lives. In areas like transportation and infrastructure, IoT technology has helped major cities reduce traffic holdups, improve emergency response times, monitor the flow of consumer goods at port and create smarter energy grids, according to InfoBright.

We can now track how much time we spend at the office (and how much of our day is spent sitting at our desks), the nature of the environment we are in (e.g., how warm/cold/humid your house is) and even our eating habits and what’s in the fridge. While each of these data points.

Current Business Applications

Companies are now making active use of this data with powerful analytic tools and algorithms, enabling them to make informed, data-driven business decisions. This is a smart development – organisations all too frequently view their employees in terms of cost, but then rely on their gut when it comes time for high-impact decisions like promotions and letting people go. How can companies be sure if those costs are worth it?

The problem with existing approaches is they are vulnerable to biases, both conscious and unconscious, which can skew decision making processes and ultimately hurt the business. As Harvard Business Review puts it, “Most traditional HR metrics – such as employee turnover rate, average time to fill open positions and total hours of training provided – don’t predict organisational performance.”

Current “predictive” methods drive organisations to hire new candidates who are exactly like their existing top performers, who came into employment via historically biased hiring practices in the first place. Companies that perpetuate these biases suffer from a lack of cultural and intellectual diversity, which has been clearly linked to overall performance and success.

However, HBR observes that companies can get a clearer sense of who and what is valuable by developing models and benchmarks to score employees and business areas. Companies can then use this data to drive insight and predict which workers will thrive.

Google has probably the most mature analytics system of this kind – according to the Atlantic, their performance management system, “People Analytics,” is built from a set of constantly-evolving rubrics that define what employee success looks like.

As Lazlo Bock, Google’s Senior Vice President of People Operations (HR) told the magazine, “We need to be able to measure, to find out what does and doesn’t work at Google rather than just adopt best practices.” Always challenging their thinking about what is possible or desirable in the workplace, Google has developed an unworldly ability to spot and develop top talent within their organisation.

Data in the Hiring Process

Unsurprisingly, companies have started to incorporate analytics into their hiring processes. Hard data points typically trump our intuitive thinking, enabling HR professionals to make better hiring decisions while simultaneously reducing the overall strain on department resources.  

For example, a recent article in the Economist explained how Evolv, a workplace data-monitoring organisation, found that applicants tend to perform better in a role if they applied online using a non built-in internet browser – i.e., one that had to be deliberately downloaded and installed, such as Chrome or Firefox. The thinking is that these recruits take initiative about being tech-savvy and engaged – a detail that any recruiter would be unable detect without the help of data analytics.

Similarly, Evolv, working with Xerox, found that customer-service workers tended to stay longer in a role if they were members of one or two social networks, but not more than four – and that previous “job hopping” didn’t accurately predict whether workers were more likely to quit or not. These and many other data points helped Xerox to improve their hiring process to bolster employee retention rates, cutting drop-off by 20%.

Sears, amid a brand transformation and hiring push, cleverly collected data through a virtual, video-game-like hiring interface. According to the Korn Ferry Institute, Sears found that by weighting questions towards digital competence and selling ability, they were able to screen out 30% more applicants and speed up their hiring process by almost two months.

There are many different hiring criteria that can fall under the data analytics umbrella, but they all serve to help recruiters hire more objectively, help companies build stronger teams and reduce costs associated with turnovers.

A Problem of Bias

Again, the key issue surrounding traditional predictive methods for making important business decisions is they’re highly susceptible to unconscious bias. These biases can lead to a raft of issues around gender balance, a lack of diversity and reinforcing a view of top performers based on a historically flawed process.

Research shows that such tendencies lead to a lack of intellectual diversity in the workplace, stifling innovation and lower performance versus diverse organisations.

Employers who make decisions based on organisational fit or pre-defined attributes alone are at a significant disadvantage. While these techniques can certainly expedite the hiring process, they often unintentionally weed out exceptional candidates (for hire or promotion) who might otherwise turn out to a huge value add for the team.

There’s a strong business case for eliminating these hiring tendencies and fostering diversity. A PwC study found that 85% of CEOs whose company supports a diversity and inclusion strategy say that it’s increased performance overall. And according to the Center for Talent Innovation research, companies with diverse leadership are 70% more likely to expand their market share within a given year.

But when it comes to promoting diversity, many HR departments are faced with something of a paradox: while the company would benefit from expanding an emphasis on diversity, HR departments have limited budgets and are measured against a set of targets that don’t align with strategic diversity goals.

Building-in Big Data with Algorithms

It’s now becoming commonplace for organisations to incorporate rich data and analytics into decision-making and provide insights for the development of business processes from hiring to internal mobility to workforce planning.

New products built around machine learning algorithms are able to sort complex data into clear patterns and trends, allowing companies to understand which data points are significant and will yield better decision making, which, in turn will yield better business outcomes.

In the HR space in particular, the data being collected allows recruiters to “tell the story” of a candidate or employee relative to others, enabling companies to make fair and unbiased decisions in order to build a diverse and engaged workforce that will drive innovation.

A dramatic example of this idea is Deloitte UK’s recent announcement that their applications will now be “school-blind,” as reported by the Washington Post. Unless candidates choose to disclose their alma mater, they will be judged based on data metrics and scores relative to their peers, letting the strength of the numbers drive hiring decisions.

To augment this initial selection process, many companies are combining other digital tools to paint a more complete picture of candidates. Video recruiting technology allows hiring professionals to humanise this process – gauging candidates’ soft skills alongside the hard data – as well as to double down on bias-elimination efforts.

This allows recruiters to quickly segment their talent and identify different groups of candidates, from top performers to those that might be on the cusp and be bought through as they offer more than just good scores.

The mindset of not just HR but all senior business leaders must shift to encompass how this continuing explosion of data can be harnessed to provide business insights that lead to competitive advantages – these advantages will fundamentally change how the organisation operates by having an immensely positive impact on company culture, innovation and the bottom line.

Want to learn how technology can help your company become more diverse? Contact LaunchPad Recruits to find out how you can create a more equal and productive work environment for everyone.

(Main image credit: Pexels)

Mat Wollerman

Mat is Launchpad's Marketing Manager, driving the marketing strategy with a focus on digital and content.