How Data Analysis Can Be Used in Workforce Planning

  Brian Richmond  is a former senior data scientist at WeWork where he was responsible for leading and providing a vision for the people analytics team of the company. In 2018, Brian Richmond joined Aura Health as a senior data scientist to apply data to drive product improvements in the health and wellness space.

One of the many fields where data and statistical analysis are starting to shine is in providing a reasonable expectation of workplace evolution. Based on the rate of innovation and changes in the workplace, most future jobs haven’t been invented yet. In that kind of environment, it is difficult to know for upcoming members of the workforce where to focus their efforts. Predictive data analysis can help in this area.

Using large data sets across long periods of time, predictive analysis can provide some insight into gaps that tend to crop up in a technologically changing marketplace. When this type of analysis is combined with reliable qualitative input, it can generate very reliable data that can help shape the workforce of the future.

Global Big Data Analytics Market to Keep Growing

Big data coding image: pexels.com

A PhD graduate in anthropological sciences from Stony Brook University, Brian Richmond serves as a senior data scientist at Aura Health in San Francisco. In addition to establishing the company’s product intelligence team, Brian Richmond built its first data infrastructure and applied machine learning models to optimize the product.

Every year, more and more companies recognize the importance of integrating big data analytics into their business strategies. In December 2018, Research and Markets, the world’s largest collection of market research information, released the Global Big Data Analytics Market Forecast to 2023, which showed technology trends, competitive analysis, market drivers and constraints, and a five-year revenue forecast for the global big data analytics market.

According to the report, the big data analytics market will increase from $8.5 billion in 2017 to $40.6 billion by 2023. This massive growth is attributed to the positive impact of big data analytics in operational management, vendor management, customer engagement, and customer and enterprise data security.

Using Data Analytics to Improve the Hiring Process

Brian Richmond

Prior to leading the product intelligence team for the startup health and wellness company Aura Health, Brian Richmond founded the innovative people analytics department at WeWork. As an experienced data scientist with a background in statistics and anthropology , Brian Richmond advocates for data-driven decision making to inform the hiring process.

By collecting and analyzing the appropriate data, employers can gain insight into their hiring process and achieve a number of benefits, including:

Attracting Diverse Hires – Data analytics can identify how current recruiting practices might inadvertently introduce bias into the hiring process, and how to create a more diverse and inclusive workplace. This data can also reveal any strategies that have successfully appealed to a wider range of candidates and the factors that improve retention.

Budgeting Effectively – Advertising on job platforms, offering sign-on bonuses, and other recruiting tactics are costly. Employers can use data analytics to determine which recruiting strategies are most effective at attracting the most talented candidates and reallocate the budget accordingly.

Tracking Hiring and On-boarding Efficiency – Employers can use data analytics to understand how much time and money it takes to fill vacancies depending on position type and how many applicants need to be sourced before a suitable candidate is found. This data can also identify points in the recruiting process that could be shortened or even eliminated, and how to improve the efficiency of getting new employees up to full speed.

Quantifying Employee Engagement

 

Brian Richmond

Leveraging his experience as the head of people analytics for WeWork, data scientist Brian Richmond co-organized the NY HR Analytics meetup. Brian Richmond has contributed extensively to the use of data, machine learning, and model building in common human resource topics, including employee retention and engagement.

Since employee engagement is directly tied to productivity, turnover, and customer satisfaction levels, businesses need to understand the factors that increase or reduce employee engagement. One of the most reliable ways to gather this data is to solicit information from employees through frequent, agile surveys.

To avoid overloading workers with lengthy questionnaires, employers can issue three to five item surveys through email or platforms like Slack. Questions should help measure indicators associated with high engagement, including a sense of purpose, day to day energy levels at work, feeling included , and perceived opportunities for professional growth.

Another highly predictive factor of employee engagement is known as the employee net promoter score, which measures how likely a worker would recommend working at the company to a friend. A higher score is correlated with higher rates of employee engagement.