Interview with President & CEO of EzDataMunch, Anupama Desai.

Q: How is a specific line of business / business unit using your EzDataMunch predictive Workforce Analytics Software and Apps? How is your product deployed into operations?

A: EzDataMunch is being used to evaluate Sales, Marketing, operational, HR and financial impact by all the different departments within an organization. Specifically, EzDataMunch Workforce Analytics Software is being used by finance and HR to evaluate compensation policy costs based on operational issues of unique units in their business. They are looking at the necessity of pay programs based on what drives labor demand and employee supply. Based on the analysis, even high cost pay programs may be acceptable also based on operational requirements, low-cost programs may stop being overlooked because operationally they are unnecessary. Decision makers are leveraging the EzDataMunch Labor Cost Analysis App to evaluate when and what to change in job assignments to reduce cost and maintain necessary front-line activities at an acceptable level given the specific situation. Employers of all types will need to predict the potential cost of converting employees to hourly and the capacity to provide adequate labor hours under more constrained conditions to run their business. The businesses would use the EzDataMunch Labor Cost Analysis App by loading data through our Cloud platform to identify units/teams with higher-than-expected overtime, understand the drivers of overtime, and make use of this data to inform the actions they would like to take to address this issue before it occurs. The EzDataMunch Workforce Analytics Software and App itself does not provide recommendations, rather it provides additional clarity into the causes of overtime and predicts – based on history – which units are most likely to be prone to overtime and why. For EzDataMunch Workforce Analytics App to be used most effectively it needs to be paired with strong domain knowledge into how to best mitigate the drivers that cause the issue and with the business knowledge to determine what areas need attention. Some units may naturally be more prone to overtime – though this does not necessarily mean the functioning should be changed.

Q: If HR were 100% ready and the data were available, what would your Workforce Analytics tool can do for them?

A: HR would be able to forecast how pay policies and schedules are driving labor cost, productivity and revenue. People drive the business and the bottom line and having a clearer picture of how compensation motivates workers and how schedules drive attendance, recruitment and retention would enable them to position workers based on schedule fit, worker skills and cost. They would also help managers influence the daily situations where pay policy and work opportunity converge and potentially inflate labor spending or upset employees. The right data would mean finding the best approach to manage a person based on their skillset and place in their career to match them with the best opportunities within an organization. I think it’s safe to say that if people enjoy what they do, they will be more productive, motivated and generate better long-term results for their employers. The right data would help define what employees do enjoy about their work situation – how many hours they work, when they work, the predictability and stability of the hours they work, work that includes the activities they want to perform and skills they want to build, all go into job satisfaction. So, a comprehensive approach to aligning people to what they most enjoy and are suited for, would be the boldest data-science creation.

Q: When do you think businesses will be ready for “black box” workforce predictive methods such as EzDataMunch Workforce Analytics Software and Apps?

A: When businesses have understood the implications of using black-box models and determine where it is appropriate to use and where it is not. It’s going to take time and practice. Organizations are going to have to lay out a plan that includes incrementally changing the way organizations operate. It could take many rounds of change to build up to these methods. Readiness will come when they have developed the confidence and ability to incorporate predictive workforce analytics into their decision making, not use it to replace human decision making. They also need to be skilled in modeling and testing to validate weather these intelligent systems are leading them to the proper conclusions. Readiness is not just about conversion but about using tools such as EzDataMunch workforce analytics to strengthen business processes and decision making. The decision-making processes and the data must be sound before these tools are deployed. Readiness may involve developing or hiring for the right skill sets which include knowing how the business will be impacted by these predictive tools and managing the transformation to a data driven model. There are no major technical hurdles to creating black-box models however, human resource challenges are not easily ‘optimized’ in the manner that engineering challenges are. Furthermore, it must be appreciated by end users that models are not infallible or necessarily fair – they tend to reflect pre-existing biases – and there is risk involved in this, doubly-so with black-box models where the mechanics are not well understood.

Q: Do you have suggestions for data scientists trying to explain the complexity of their work, to those solving workforce challenges without an easy tool like EzDataMunch?

A: If you can’t explain it simply, you don’t understand it well enough – Einstein’s maxim is extremely applicable in this case. A good starting point is to always try and establish a clear link between what you are doing and the problem at hand. Usually, it is advisable not to dive into the technicalities of the work you are doing but rather to explain – in jargon-free terms – why a particular activity is necessary to arrive to the solution. Take the time to show them how using the wrong data or not asking the right questions can give a false answer; map out how visualizations are produced so that they are not fooled by charts and graphs and understand what must go on behind the scenes with the mathematical modelling.

Q: What is one specific way in which predictive workforce analytics is actively driving decisions in HR and Labor Cost analysis?

A: Making decisions based on data isn’t new. What is new is that predictive analytics gives leaders more confidence to make not only bold moves, but measured moves that are based on solid data and give leaders greater confidence in sustainability once a decision is made. Making a change to an outdated pay policy is easy, getting the outcome you desire and having that “stick” are where predictive analytics can refine and bolster decision making. Workforce retention models are actively used by analytics-savvy firms to improve on their ability to retain their at-risk talent. As a result of being able to identify which segments of the workforce are most at risk of departing, they can make informed decisions on how to pursue those individuals deemed critical to the workforce.

Q: How does business culture, including HR, need to evolve to accept the full promise of predictive workforce Analytics tools like EzDataMunch?

A: A few things need to happen. First, data generating systems need to be designed with analytics in mind – without good data no insights can be uncovered. Second, business culture needs to understand how to make use of the insights driven by analytics – this means both determining where analytics fits in their day-to-day decision process as well as understanding the nuances and limitations of analytics. Finally, businesses need to develop an imagination of the types of questions that can be addressed by analytics – this will mean going beyond known problems and starting to try to uncover unknown-unknowns. To do that, HR leadership needs to be willing to go beyond superficial changes and have the courage to fundamentally change how their organization operates. Systems, processes and policies may need to be completely abandoned in the new world of work. HR needs to accept that behavior will be changed most effectively when predictive workforce analytics are used to their full potential. Resist the temptation to do what you know, embrace disruption and recognize where you do not have sufficient skills.

FAQ’s

What is predictive workforce analytics, and why is it important?

Predictive workforce analytics involves using data and statistical algorithms to forecast future trends in areas such as employee turnover, performance, and productivity. It’s crucial because it helps organizations make informed decisions about workforce planning, talent acquisition, and employee development, ultimately improving overall business performance.

What kind of data is typically used in predictive workforce analytics?

Predictive workforce analytics relies on various types of data, including employee demographics, performance evaluations, training records, engagement surveys, and even external factors like market trends and economic indicators. By analyzing these data sources, organizations can identify patterns and make predictions about future workforce behavior.

How accurate are predictions made through workforce analytics?

The accuracy of predictions in workforce analytics can vary depending on factors such as the quality and quantity of data available, the sophistication of the analytical models used, and the complexity of the workforce dynamics. While no prediction can be 100% accurate, advanced analytics techniques can significantly improve the reliability of forecasts.

What are some common challenges organizations face when implementing predictive workforce analytics?

Implementing predictive workforce analytics can be challenging due to factors such as data quality issues, privacy concerns, resistance from employees or managers, and the need for specialized analytical skills. Additionally, integrating predictive analytics into existing HR processes and systems may require significant organizational change and buy-in from stakeholders.

How can organizations get started with predictive workforce analytics?

To begin with predictive workforce analytics, organizations should first define clear objectives and identify the key metrics they want to predict or improve. They should then assess their data infrastructure and quality, invest in appropriate analytical tools and expertise, and develop a strategy for collecting, analyzing, and acting on workforce data. It’s also essential to continuously evaluate and refine predictive models based on feedback and real-world outcomes.