Creating Organizational Change Resilience with Data Analytics
Identifying and anticipating the unknown creates a competitive advantage when aligned with organizational change capability. Growing revenue amid uncertainty amplifies the need for organizations to make data-driven decisions. This past year every organization's ability to respond to significant disruption was tested. A recent study on the economic impacts of COVID-19 found that small businesses were among the hardest hit. Over a third of the small businesses in the United States in the leisure and hospitality sector reported no financial transaction data between January to September 2020.
Organizational Change Resilience is "the ability to respond productively to significant disruptive change and transform challenges into opportunities" (Witmer et al., 2016).
A scientific study of 101 companies revealed that big data holds a key for helping organizations detect and respond to disruption. Descriptive data analytics improve sensing, and predictive data analytics enhance a company's ability to change and seize new opportunities.
Descriptive Data Analytics: The interpretation of historical data to understand better changes in a business. Examples include social media usage and engagement, organizing survey results, and operational efficiency data trends.
Predictive Data Analytics: uses historical data, statistical algorithms, and machine learning to identify the likelihood of future outcomes. Examples include predicting customer preferences based on past purchasing behaviors, predicting employee retention flight risk based on assessment data, and predicting workforce staffing levels based on seasonal trends.
One theme with organizations that remain resilient amid change is sharing data with decision-makers openly. According to the International Organization for Standardization (ISO) on the principles and attributes of organizational resilience (ISO 22316:2017), knowledge and information need to be:
accessible, understandable, and adequate to support the organization's objectives;
effectively shared to enable decision-making;
recognized as a critical resource of the organization;
created, retained, and applied through established systems and processes;
shared in a timely manner with all relevant interested parties;
applied in organizational learning.
Although not easy to implement, data analytics investments provide competitive advantages by using data to foster growth and improve decision making.
An organization's ability to improve its organizational change resilience and data analytic decision making is connected to its culture.
Organizational culture is "the basic tacit assumptions about how the world is and ought to be that a group of people share and that determines their perceptions, thoughts, feelings, and their overt behavior" (Schein, 2017).
Organizations need to consider the role of people, processes, and company culture adoption and utilization when making investments into data analytics. Culture mediates analytical knowledge and big data analytics capability on firm performance.
"Culture is more powerful than anything else in the organization," and often why good management ideas fail (Schneider, 2000).
Often two cultural myths get in the way of companies advancing data analytics:
Myth #1: Data is unique within a given company
Myth #2: The application of statistical tools is unique to each company
These myths contribute to analytical solutions in one company being ignored by another. In reality, cross-industry experience creates an advantage when it comes to data analytics. In reality, companies usually have more than enough industry-specific knowledge and underestimate the value of organizational talent analytic capability.
We can partner with you to develop a customized solution to transform your organizational culture and build a more change resilient organization. Walk with us and build increased organizational resilience and a culture that fits the future of the business you need. Contact us to get started today.
Bartlett, R. (2013). A practitioner's guide to data analytics: Using data analysis to improve your organization's decision making and strategy. McGraw-Hill. New York.
Chetty, R., Friedman, J Hendren, N., Stepner, M., & Opportunity Insights Team (2020). The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data [PDF].
Ji-fan Ren, S., Fosso Wamba, S., Akter, S., Dubey, R., & Childe, S. J. (2017). Modeling quality dynamics, business value, and firm performance in a big data analytics environment. International Journal of Production Research, 55(17), 5011-5026. doi:10.1080/00207543.2016.1154209
Schein, E. H. (2017). Organizational culture and leadership (5th ed.). Hoboken, New Jersey: John Wiley & Sons, Inc.
Schneider, W. E. (2000). Why good management ideas fail. Strategy & Leadership, 28(1), 24-29. doi:10.1108/10878570010336001
Upadhyay, P., & Kumar, A. (2020). The intermediating role of organizational culture and internal analytical knowledge between the capability of big data analytics and a firm’s performance. International Journal of Information Management, 52, 102100. doi:10.1016/j.ijinfomgt.2020.102100
van Rijmenam, M., Erekhinskaya, T., Schweitzer, J., & Williams, M. (2019). Avoid being the turkey: How big data analytics changes the game of strategy in times of ambiguity and uncertainty. Long Range Planning, 52(5), 101841. doi:10.1016/j.lrp.2018.05.007
Witmer, H., Mellinger, M. S., Faculty of Culture and Society, Urban Studies (US), Malmö University, & Centre for Work-Life and Evaluation Studies (CTA). (2016). Organizational resilience: Nonprofit organizations' response to change. Work (Reading, Mass.), 54(2), 255-265. doi:10.3233/WOR-162303
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