Assessing employee engagement once or twice a year is not insightful. It is a snapshot in time. Applying the principles of operational data and analytics to the employee experience data process can deliver the “why” and “what next” of employee engagement.
— By Sharon Ross
X’s and O’s signify affection when used as a signature on a birthday or holiday card. For organizations, they mean something very different. X-data is employee experience data, and O-data is operational data. Collecting and analyzing each is frequently viewed as entirely separate processes.
One of the biggest differences between these sets of data is the frequency and thoroughness of data collection. Operational data is almost continuously monitored today, while employee experience data continues to be collected on an infrequent basis and often offers very limited information.
Imagine applying operational data collection and analyzation to employee experience data collection and analyzation. Instead of once-a-year employee surveys, the data would be regularly collected, assessed, and analyzed within the larger context of organizational culture and business strategies. It is a process for continual improvement and reinforcement of employee engagement.
Adding the Human Factor
Operational data was originally collected and reported to provide a snapshot of a point in time. It includes a wide range of data on things like the workforce, customers, competitors, inputs and outputs, sales, CRM, supply chain, creditors, accounting data, projected resource needs, customers, productivity, and so much more.
The data provides information, but it is the analyzation of the data that enables insights for better decision-making. To discover information like inefficiencies in workflows or to streamline business processes, data analytics that incorporate large amounts of data from various sources is needed.
For example, workflows are evaluated by incorporating and analyzing data from production outputs, industry standards, seasonal patterns, number of errors, and other sources that have a bearing on workflow processes. From the aggregation and analyzation of the redefined O-data, insights are gained. The data is collected continuously, and today analytics are always available via management dashboards that guide decision-making. Missing is the human sentiment.
Experience data adds a human factor because it reflects emotions, beliefs and sentiments. So operational data is used to measure the past. X-data is concerned with why something is happening. Employee experience data then is used to identify how people feel and believe, and data analytics provide insights as to why it is happening and guide decision-makers in what needs to happen next. Should there be more leadership training, revisions to the recruiting and hiring process to improve diversity, or better communication between employees and management?
X-data has traditionally been collected by asking employees questions via an infrequent survey, often annual in many organizations, about workplace satisfaction. It is point-in-time information that is limited and provides few insights as to why employees answer the way they do and provides no information about the relationship of employee sentiments to operations and the bottom line. The managers getting the information must do their own contextualization and analyzing to eke real information out of the statistics.
Richer Data Produces Richer Results
Applying the principles of operational data and analytics to employee experience data and analytics can transform the process for assessing and improving the employee experience. Collect data from multiple sources on a regular basis, and create analytics that provide forward-thinking holistic information to improve the employee engagement from beginning to end.
For example, an organization discovers its diversity statistics are below the industry averages. The question is “Why?” A survey of employees leads to data analytics that deliver insights like women of colour are more likely to lack a sense of belonging in the workplace and do not believe their supervisors support their professional development. It should not stop there. Ask the question again: “Why?” Pulling richer data reveals it is not just particular supervisors in identified departments who are impeding the women’s engagement. It may be the organizational culture or a management style that does not work well for women of colour.
Combining O-data with X-data can produce deep insights about talent and operations. For example, collect customer operational data and combine it with employee experience data. The operational data can come from systems like the Human Resources information system and the customer relationship management system. By bringing the operational and experience data together, managers learn that areas or groups of people with low employee engagement are negatively impacting the customer experience. This information can flip how an organization approaches improving customer experience – start with the employee experience evaluation first.
X-data can inform as to how employees feel or believe about their careers, bias in the workforce, customer services, management, mental health, and numerous other factors. Add O-data like employee tenure, promotions by demographics, and performance ratings, and decision-makers can determine the level of influence the O-factors have on employee satisfaction. From there, the data is analyzed to identify the segments of employees who need additional development or attention in some other way. Resources are focused on the employees who need their employee experience improved.
Experience data adds a human factor because it reflects emotions, beliefs and sentiments. So operational data is used to measure the past. X-data is concerned with why something is happening.
Johnson & Johnson’s Global Head of Workforce Analytics Piyush Mathur spoke with Qualtrics, a leader in combining X-data and O-data, about using people and operational analytics to draw insights that lead to desired outcomes which include a direct impact on the bottom line.
For example, identifying the characteristics of successful salespeople leads to better recruitment and development of talent with similar traits which in turns drives business impact. The O-data and X-data must be connected to glean these insights.
In fact, Mathur said the biggest insights come from combining experience and operational data because it allows to look at insights holistically. At one point, the people analytics team wanted to determine if there was a way to more strategically direct health and well-being resources around the hundreds of global sites to better meet employee needs. X-data survey results were combined with leave absence data, site calls to the employee assistance program, and people calling out employee relations/labour relations issues (O-data). The results were analyzed at an aggregated level for each site, leading to more strategic allocation of resources.
Start With Desired Insights
SAP experts recommend starting at the end when designing employee engagement surveys.
First identify the insights desired in order to commit to actions. The purpose of survey X-data and O-data is to identify the actions needed to improve employee engagement, so starting at the end helps employers determine the data to include in employee surveys.
Connecting operational data and employee experience data brings organizations new opportunities for gaining a deep understanding of the business dynamics.