It's Not Clairvoyance, It's Data Science

Marc Rind, VP, Product Development & Chief Data Scientist, ADP
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We’re in a fierce era of ever-increasing competition for great, reliable data. Around a decade ago, at the start of the Big Data boom, businesses were just beginning to devise ways to capture and record data. Today, we’re asking, “How do we make our data stronger to deliver insights that are more meaningful?”

For some companies, the answer has been to hire data scientists to help interpret the huge amount of data that companies are collecting, internally and online. In fact, according to Glassdoor, “Data Scientist” ranked as the best job in America in 2017 for the second year in a row. The growing demand for data analysts and scientists really goes to show how more companies are relying on data to help guide their workforce decisions and optimize their biggest asset: Their people.

But before you go out and hire a data scientist or analyst, it’s important to have clearly defined goals and ask, “What are the core metrics I want to measure to better inform specific business outcomes?” You should also know where the data you’re collecting originates and whether there’s proper standardization when entering data into your systems to improve reliability.

  If your organization truly wants to adopt a data-driven culture, it needs to be comfortable sharing data across functions and business units 

It’s also crucial to train your entire workforce to be more data-savvy. While finance and marketing roles have historically been driven by data, more companies are realizing that HR leaders also need to be data analysts. Where HR teams used to rely solely on “gut feel” to make decisions, they now increasingly use data to help identify top leaders and determine whether employees are being compensated and rewarded competitively.

How can you help your organization embrace Big Data to gain workforce insights?

You Need Big Data to Reveal the Big Picture

Plain and simple, people are busy. In the pace of today’s workplace, it can be easy to miss the forest for the trees. Therefore, more and more, companies are trusting Machine Learning and Artificial Intelligence (AI) to help provide higher-level organizational direction and business insight. AI can analyze company data, market trends, and consumer information to help businesses better understand their biggest pain points as well as those of their clients. But to get the most assistance from AI, you need to have a large amount of data so that you can train these systems to analyze various data streams and identify complex patterns.

By analyzing a wide spectrum of data, AI can provide the context needed to help businesses make better decisions. According to a 2017 report by Infosys, 76 percent of senior business decision makers cite AI as fundamental to the success of their organization’s strategy, and 64 percent believe their organization’s future growth is dependent on large-scale AI adoption. While the artificial brain can raise problem areas, it’s not intended to out-manage humans. You want to use AI to figure out how to guide your human perception and expertise.

The Power of Predictive Analytics and Benchmarking

More than ever, companies are fighting tooth and nail to recruit and retain skilled talent. By benchmarking internal data against competitors, your company can determine whether people have been in their current roles for too long, what they require (pay, benefits, and more) to stay, and what type of leadership works best for certain jobs. You may find that your organization has 30 employees reporting to one manager versus 10-to-one at your top competitors. The result may be that your new hires aren’t getting enough managerial direction and end up leaving. By using benchmarking and then applying predictive analytics, you can dig even deeper into how to keep talent engaged and determine the likelihood certain employees will leave the organization.

Once you identify these hot spots, it’s then up to managers to decide which people might require more interactions, career development, or other leadership initiatives. Analytics are meant to give managers a tickler. They alert them to when a team member is at high risk for leaving because they’ve been in their role for too long or they may dislike their team members. Once the employee is on their radar, leaders can take action and improve the overall situation.

Empowering the Manager 2.0

Data analytics have revealed a whole new way of measuring how to manage people. The once-a-year performance review can be a good benchmark, but doesn’t help managers stay engaged with their employees the other 364 days of the year. Data can reveal to managers how they need to change their behaviors. But managers are busy. That’s why it’s best to show them the headline: What areas does the data say they need to improve, and how can they take steps to improve in those areas? Rather than asking managers to interpret data on their own, this helps bring the proverbial fish to the manager in a much smaller pond. Predictive analytics can also help build better teams. For example, if a project requires 10 Python developers, and four of the developers have been identified as high risk for turnover, this is a signal to the manager that she needs to be particularly engaged in that assignment, focusing on the individuals who may need more attention or ensuring the pipeline for replacements is healthy.

Opening Up Your Data

There’s nothing worse than collecting data, and not sharing the patterns and insights you spot because you fear others in the organization will misinterpret the findings. If your organization truly wants to adopt a data-driven culture, it needs to be comfortable sharing data across functions and business units so that the combined data can help identify trends that will truly move the needle.

While data analytics is not intended to replace human instinct and insight, it should act as a guide to where people should consider focusing their attention for maximum results. Predictive analytics and benchmarking will never “out-human” humans. I like to use the analogy of your smartphone providing you with the best route home and how long it will take once you the leave the office. While four out of five days you may head straight home, the phone may not know that today you’re heading to your child’s softball game after work. Data may not always provide absolute answers, but it’s better to have some guidance into where to focus your energies than going it alone. Those companies that truly embrace predictive analytics will be the ones with the competitive edge down the road.

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