Business Analytics as a Catalyst for Change Management

Abstract

This paper argues the importance of applying business analytics for businesses to make data-driven decisions.  The application of business analytics data focuses on a predictive model as a means to review the performance of business processes, systems, and job roles as well as implement change and track employee engagement within a collaborative environment.  A review of the present literature is conducted to identify the importance and appropriate use of analytical data and data-driven decisions to effect change management.

Business Analytics as a Catalyst for Change Management

Business analytics data focuses on performance trends in correlation with market fluctuation, supply and demand, and the effect of organizational change.  In the recent past, more businesses have begun to implement the same data-driven decision making to employee engagement in change management.  Both uses of analytical data can be used to review a business’s overall performance and return on investment within specific internal stimuli as personnel resources and external stimuli of manufacturing and cost of goods sold (Dumas & Weidlich, 2018).  Business analytics (BA) consists of the repetitious exploration of a firm’s accumulated data to make profitable decisions based on objective statistical analysis.  Management uses BA to gain insight into business performance and inform business decisions by the application of predictive analytics to change management by mechanizing and enhancing business processes.

Literature Review and Applied Analytics to Change Management

With the rapid advancement of technology and globalization of markets in the last thirty years, data-driven companies leverage their data for a competitive advantage in the industry. In their book, Basu and Basu (2016) attribute business analytics to the increased frequency of change management within well-established companies.  They argued that change management had been the recurring theme in established companies that have shifted focus to data-driven results, thereby streamlining processes, reorganizing personnel resources, and maintaining viability within an ever-changing market (p 210-212).  Through business analytics, management is connected with the information needed to streamline processes and workload to create an innovative system of engagement with consumers and within the market (Basu & Basu, 2016, p 247-252).

Companies devise timelines to review business analytics for efficiency.  The use of a practical approach to a company’s analytics division has a cause and effect relationship with a variety of departments, processes, and operational activities.  Basu and Basu (2016) go on to explain that the development of a capable team of analysts to adequately track, gather, and explain accumulated data, is a crucial indicator of a company’s acknowledgment of the role in which business analytics plays in aligning a company’s business plan with its operational model (p 313).

Four types of business analytics are available to businesses to use to make data-driven decisions.  The four types of business analytics include descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.  The use of these four types of analytics aids companies in developing and portraying the intentions of their strategies, according to Marlon Dumas and Matthias Weidlich (2018), and addresses relevant change management actions for the entire company (p 2).

The use of business analytics assures companies of a system of decision making that considers the functional areas of a company and how they interconnect to produce effective business plans.  Dumas and Weidlich (2018) explained that BA data allows decision makers to identify larger areas of concern within an organization, objectively (p 4-6).  The steps taken to correct such areas often calls for change management that will impact one or more of the following: organization structure, job roles, technical systems, and operational processes.

While change management is implemented to improve the likelihood of a business’s success and return on investment, it typically results as a response to specific issues or opportunities the firm is experiencing based on various stimuli.  For example, market data may depict the need for a firm to be more competitive or more customer service oriented to maintain its place within the industry.  While this data can be the motivation to initiate change, these larger goals ultimately advise adjustments that impact the firm’s operations, technical systems, organization structures, and job roles. Duan, Cao, and Edwards (2018) noted this as the process of defining the change in change management and attributed business analytics as the determining factor for firms to undertake change management.

Data analytics is directly tied to change management.  The key to constructing predictive models is perceiving what you want to foresee and assembling big various data sets that may aid companies in doing so.  Although predictive models for change management are rare, organizations can begin to capture the right data. Baesens, Bapna, Marsden, Vanthienen, and Zhao (2016) outlined how organizations can begin to capture the data necessary to apply a predictive model for change management (p 808).

One of the most prevalent tools companies are utilizing for change management is the use of employee opinion tools that gather information in real-time, rather than an emailed survey sent once or twice a year.  In their article, Baesans et al. (2016) viewed this modern tradition of data collected once a year limits a company to make changes after data collection only once a year (p 811).  Depending on the resources involved in necessary change, companies are limited on the time, personnel, and financial investment that changes may need when data is collected this way.  Alternatively, the authors favor the approach of gathering employee data routinely to address and make improvements regularly, rather than all at once.  This approach has been adopted by startups and smaller corporate entities in an effort to help answer questions like: Is a particular change being well received, equally, across locations/departments? and Are some managers better than others at delivering messages to employees? in response to change management.

Erevelles, Fukawa, and Swayne (2016) continue this outline to emphasize the importance of data-driven decisions for change management and developing change leaders within project teams or functional departments (p 899).  Despite businesses’ fixation on calculating small changes in operational performance by capturing data on sales, inventory turns, and industrial efficiency, when it comes to change, few companies track performance from project to project beyond knowing which projects or departments met their deadlines.  Although projects have distinctive characteristics, there are many similarities amongst process enhancement, system change, and restructuring projects.

Laursen and Thorlund (2016) agreed that there are opportunities to capture information about the team involved, the population involved in the transformation, the time it takes to implement, what strategies were used, and so on (p 115).  Constructing a reference data set like this may not yield an instant advantage, but as the overall data set grows, gathering this data will make it easier to shape precise predictive models of change management.

In both their arguments, Erevelles et al. and Laursen and Thorlund agreed that companies typically choose candidates for change leadership positions by using data-driven methods.  Most of us know this and see it take place in any job setting.  However, the difference is that BA data is not just being used to identify high performers for promotion, but is also being applied to the hiring process of retail frontline staff.  In the application of predictive analytics when building a team improves the project performance and aids in constructing another new data set (Laursen and Thorlund, 2016, p 187).

According to Otondo (2019), “If every change leader and team member underwent psychometric testing and evaluation before the project, this data would become variables to include as one searches for a fundamental model on what leads to successful change projects” (p 21).  The model can be extended to include more informal roles such as change agents, allowing companies to improve election of candidates based on what they know about successful personalities for change leadership and change agent roles.  Change management engagement depends on the total interruption created in separate employees’ day-to-day work.  Engagement in change management also depends on the business’s qualities, such as philosophy, value system, and history with prior changes.

Findings and Results of my Research

Through my research, I learned that real-time employee feedback gives companies the prospect to experiment with diverse change strategies within chosen populations of the firm.  The real-time feedback allows us to become knowledgeable very quickly on how communications or engagement tactics have been received, thus optimizing our actions more rapidly that is true with conventional perspectives.  This data can then feed into a predictive model, helping us know with precision the actions that are going to quicken acceptance of a new practice, process, or behavior by a given employee group.

My research further supported my pre-existing belief that data analysis is not only important to a business’s performance in the market but is also important in applying to change management through the application of predictive analytics to the workforce.  Decision makers review report data to identify return on investment.  This comes from financial audits, reports of product and service sales, manufacturing costs, and so on.  Predictive analytics allows decision makers to make data-driven decisions with regard to its industry.  So too can they apply predictive analytics to internal performance reports on employee productivity and use real-time data to gauge employee sentiment toward change management.

The results of my research supported my experience and belief that analytics is amongst the most powerful tools at any business’s fingertips, if understood and applied correctly.  My current career could benefit from the development of data sets during employee hire, real-time feedback, and performance evaluations to make sure that the investment of hiring and training personnel resources is applied more efficiently.  Concurrently, I would like to propose the use of predictive analytics to my employer as a tool to assist us in gauging employee sentiment toward change as many processes are being altered and streamlined simultaneously.  The lack of employee feedback as the company continues to grow, expand, and make changes rapidly has slowed productivity and threatened the harmonious workplace environment.

Recommendations for Future Research

Future research on the impacts of analytics on and in change management would benefit from three distinct avenues.  The first avenue involves research into the effectiveness data has on decisions and change management by the importance those at the top ranks of a business place on the application of business analytics in the workplace.  Support for data and analytics initiatives must have the support of high-level decision makers to be a useful tool.  Analytical data can often be difficult to understand and can dissuade employees from accepting and participating in such initiatives.  Additional research into the close collaboration between analytics and business teams based on analytical data should be conducted for larger consideration of implementation by businesses of any size, location, or industry.

The second avenue of future research should be in the availability and use of tools to gauge employee sentiment and buy-in of change management with real-time requests for feedback from sample groups of employees.  While there exists a small market for these commercially available products, at present, the demand is low.  One product, in particular, Waggl.com, is a web-based system that creates an environment for ongoing dialogue amongst employees about a change effort.  This platform allows change leaders to link this discourse to the progress of projects they and their teams are undertaking.  As it stands, this type of tool can have a meaningful impact on change programs, but the data stream it creates could be modified to help businesses build predictive models of change.

The third and final avenue of future research recommended would be on the expansion of the current analytical framework in which data is driven to hire and build effective teams.  Recent developments in linguistic analysis of script would allow firms to identify signs about an individual’s behavior from their own or other people’s word choice.  Further research into this analytical data and potential development of commercial tools to neutralize tone or anonymize dialogue in company email might prove beneficial on employees’ providing real-time insight into change enthusiasm and willingness as well as the reactions of employees to different change initiatives.

Conclusion

In conclusion, predictive analytics is used to inform leaders of data-driven decisions toward and in review of employee engagement of change management.  This business analytics approach to internal and external performance review places a higher probability of success by utilizing predictive analytics to hire employees, create teams or departments, and develop change leaders or change agents based on data.  The literature on these topics emphasizes the importance of companies’ use of business analytics to develop change leaders through the use of real-time feedback of change management who will lead firms into the future through data-driven decision making.  Each of these factors helps any company to evolve in the direction of growth and engage their business analytics schemes for the benefit of every stakeholder.

References

• Baesens, B., Bapna, R., Marsden, J. R., Vanthienen, J., & Zhao, J. L. (December 2016). Transformational Issues of Big Data and Analytics in Networked Business. MIS Quarterly, 40(4), 807-818.
• Basu, A., & Basu, S. (2016). A user’s guide to business analytics. New York: Chapman and Hall/CRC.
• Duan, Y., Cao, G., & Edwards, J. S. (2018). Understanding the impact of business analytics on innovation. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2018.06.021
• Dumas, M., & Weidlich, M. (2018). Business process analytics. Encyclopedia of Big Data Technologies, 1-8.
• Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897-904.
• Laursen, G. H., & Thorlund, J. (2016). Business analytics for managers: Taking business intelligence beyond reporting. New Jersey: John Wiley & Sons.
• Otondo, R. F. (2019). How long can this party last? What the rise and fall of OR/MS can teach us about the future of business analytics. European Journal of Information Systems, 1-23.