Overcoming resistance to change in a big data analytics implementation case study

Abstract

BigBank is a large commercial bank in Malaysia. The bank aims to implement big data analytics to enhance decision-making and improve customer services. Integrating big data analytics into the bank’s existing information systems not only requires specialized hardware, software, and data processing techniques but also a restructuring of the organization structure and processes. It is currently confronting challenges, particularly in terms of employee resistance. To tackle this issue, BigBank has introduced a new role known as “Translators,” who serve as intermediaries between the analytics IT team and operating business departments. In this case study, students will take on the role of Translators and analyze these obstacles while proposing strategic solutions.

Introduction

Change is hard because people overestimate the value of what they have and underestimate the value of what they may gain by giving that up (Belasco and Stayer, 1994).

In today’s demanding and ever-changing environment, change is occurring at an accelerating rate. Resistance to change is considered “natural,” and people often have normal emotional reactions to change (Craine, 2007). Resistance to change can be considered rational and logical because the employees might voice that the costs of the change outweigh the benefits (Darmawan and Azizah, 2020). Resistance to change can also be helpful for management, as employees provide feedback to management that the implementation plan is incorrect (Warrick, 2023). However, this resistance can have significant negative consequences. Problems arise when individuals who resist change also refuse to participate in or use the new technology system, or even worse, sabotage the new IT implementation efforts (Craine, 2007).

To stay current in today’s rapidly changing global landscape, businesses prioritize investing in cutting-edge technologies, with a particular focus on big data analytics (BDA). BDA encompasses a comprehensive approach to handle, process, and analyze data across five dimensions (volume, velocity, variety, veracity, and value). It aims to generate useful and actionable insights and obtain a competitive edge. In the banking industry, for example, developing and integrating new technologies such as BDA infrastructure have significant and varying impacts on the institutions. BDA implementation is not straightforward in installing hardware and software where the users can expect a “plug and play” situation. Previous implementation of BDA by BigBank has resulted in not meeting the management’s expectations. For example, BigBank’s business departments did not act upon the insights gained from BDA.

Implementing BDA involves a dynamic process that demands a significant transformation in both the way people work and how they organize themselves. The complex transformation process impacts various dimensions at the organizational level, including employees’ experiences of change. Changes in systems and processes can introduce greater uncertainty and contribute to a shift in organizational culture among its members (Chew and Choo, 2008).

This case study is designed to help undergraduate/postgraduate students enhance their critical thinking and problem-solving abilities through a real-world situation. In this scenario, students will be tasked with addressing and solving problems related to dealing with employee resistance to change.

The objectives of this case study are:

1. To understand the concept of managing resistance to change in the context of implementing big data analytics in the banking sector;

2. To understand the concept and processes of system transformation within an organization when implementing big data analytics;

3. To identify issues related to implementing system changes from the perspective of resistance to change;

4. To provide solutions and strategies to encourage employees to embrace the changes brought about by big data analytics within the organization.

Understanding BigBank

BigBank was established in the early 20th century and is recognized as one of the largest banks in Malaysia and the ASEAN region. It maintains a presence with branches across the ASEAN (Association of Southeast Asian Nations) region, including countries such as Singapore, Indonesia, Thailand, Vietnam, and the Philippines. BigBank also takes pride in its status as one of the largest Islamic banks on a global scale.

However, with the current emergence of Fintech (financial technology), the banking industry is being disrupted. Fintech refers to using innovative technology that enhances the efficiency, user-friendliness, and accessibility of financial services. Although Fintech is helping consumers, it is affecting traditional banks like BigBank by bringing more competition and innovation to the Malaysian financial services market (IMF, 2020).

BigBank is committed to proactively positioning itself as a disruptor rather than succumbing to disruption. To seize emerging opportunities, bolster customer loyalty, and ensure enduring relevance, BigBank has embarked on an organization-wide, data-driven transformation. This transformation focuses on making BigBank more customer-centric and digitally oriented. It represents a fundamental shift from human judgment to automated consumer data analysis. The data-driven transformation involves the utilization of big data in decision-making processes, enabling a differentiated approach to delivering financial services:

Data utilization will soon be a necessity and not an option. Creating a niche by studying the data will enable institutions to formulate a healthier, more sustainable, and ultimately profitable set of products and services (Senior Management, Corporate Affairs, BigBank).

Utilizing big data analytics, BigBank hopes to access and analyze real-time information that enables the company to make decisions that promptly meet the needs and preferences of its customers. Furthermore, by leveraging advanced analytical tools, BigBank’s managers seek to gain insights from extensive data sets, thereby fostering a deeper understanding of their business. This, in turn, enhances decision-making and BigBank’s performance.

Big data analytics implementation in BigBank

BigBank allocated resources for investments in acquiring various big data-related technology and tools such as the Hadoop software, data warehouse software, and hardware platforms as they embarked on implementing a new data-driven system. To increase data-driven transformation efforts, BigBank made several structural changes by changing the organizational structure and introducing new roles, and teams to mobilize data and new system activities.

BigBank introduced a new stream within its organization called the analytics stream that worked on web-analytic activities. New roles that had not been in existence before were created to manage the new data-driven landscape of banks. Senior management personnel at BigBank said, “The transformation started, slowly we started having new roles like the CDO, chief data officer, we didn’t have that before, no CDO roles, but now it’s vital” (Senior Management, Corporate Affairs, BigBank).

Another critical structural change was when the analytics team (also called the enabler unit by BigBank) was placed in a prominent position within the structure, giving it more power:

And I think the most important thing I have seen is where the analytics shop is set. So, for me, for example, I report to the CEO. So, I sit with the peers running the P and L [Profit and Loss] side of the business and then the distribution. So, at that level, if anything that I say needs to be done is not implemented, the CEO is there to step in and question them. And because of that structure, it has worked for us in terms of implementing a new way of doing things. (Managing Director, BigBank)

The enabler unit is a separate department responsible for performing analytics and supporting BigBank’s other business departments. Previously, the enabler unit worked at the back end with limited communication with the business departments. It became evident that cooperation between the business departments and the enabler unit is essential for leveraging big data analytics technology effectively. The enabler unit worked in isolation and as a result, they couldn’t engage the business departments to implement the necessary data-driven changes. This resulted in missed data collection opportunities by the business departments for big data analytics, and the enabler unit failed to perform the necessary analytics. This is mainly due to the business departments’ lack of awareness regarding analytics requirements and capabilities.

To rectify the previous failures, a company-wide transformation was implemented. As a result, the enabler unit gained more prominence, and steps were taken to change the existing practices. Formerly, the business departments held the highest authority as they were responsible for generating the business. By aligning the enabler unit at an equivalent structural level to the business departments, it facilitated integration with the rest of the organization. This organization structure change would encourage collaboration between the business departments and the enabler unit as it allowed the recognition of the importance of functions performed by the enabler unit.

Finally, translators represent one of the key new roles created at BigBank during the transformation process. They are strategically placed within business departments to bridge the gap between these departments and the enabler unit, fostering greater integration among teams. Translators are individuals with strong analytical skills situated in business departments to ensure that someone who “speaks the language of data” collaborates with business professionals, translating big data insights into products and services.

This “analytics insider” plays a crucial role in improving communication between business departments and the enabler unit. Instead of being concerned about an “outsider” from another department, having an analytics expert in the role of a translator facilitates the exchange of information, ensuring that the business department’s needs are effectively communicated to the analytics department, and vice versa. This proactive approach prevents the risk of the analytics team operating in isolation. In addition, the translators act as big data analytics champions in their respective business departments. As big data analytics champions, the translators would promote the benefits of data-driven insights from big data analytics. The translators were staff previously trained in analytics and then placed in the business department to understand the business better and apply analytics appropriately:

The translator role is someone who has an appreciation of data science and analytics but sticks with the business. He is your bridge. Translator is a new role, and they are people with an analytical background, but we place them in business departments. (Managing Director, BigBank)

In this case study, you take on the role of the translator of BigBank. You are part of the change committee that oversees the organization’s transformation process.

The issues

As the company-wide transformation process begins, your team encounters various problems related to organizational issues, particularly employee’s resistance to change. The resistance is especially significant when employees have been in the organization for a long time and have become used to the “non-technology” culture. Their resistance to accepting the “new work-way” hinders the transformation activities. The bank is large, with diverse employees with various demographics and experience levels. As noted by BigBank’s analytics service provider, the ability and willingness to accept change brought by the use of data vary among the employees, “Bank has a diverse group of people, and people can range from very, very receptive to change to people who are not receptive at all because they’ve been with the banks for so many years…. the change is not easy” (Anthony, Director- Analytics Service Provider).

There is also a large population of more experienced workers in the bank who still struggle with adopting new ways of doing things because they are comfortable with “doing things as they have always been done.” These experienced workers have been in BigBank’s business environment for a long time and may prefer to rely on their “gut feeling” rather than being data-driven. The transformation activities require some level of changing from old routines to new ways of doing things and this takes people away from their usual duties and entails added work. Transformative activities such as data integration efforts are a significant endeavor and require the cooperation of people from many different departments. While this new initiative requires attention, the usual business is ongoing, and the added workload and time required in incorporating new initiatives is not very much welcomed by employees who have been used to their work routines. A senior executive said, “The bank’s BAU [Business As Usual] continues. So, any of these new initiatives, data related, especially it is additional work for most people” (Parthiban, Assistant Vice President, Modelling & Portfolio Analytics).

The new initiatives may also cause temporary disruption to their routine work and cause delays. For example, the Data Governance senior manager said, “This new style of doing things, which they are not keen to experiment with because it will cause a temporary disruption in the efficiency of how they operate, you know. So, of course, there’s no motivation to pick things up there as well” (Leo Dass, Senior Manager- Data Governance). A new system and new way of doing things also take learning and a period of adjustment. However, the existing workload and performance requirements may not give room for any form of learning or adjustment. The added work and responsibility are also more prominent at the middle-management level as they must deal with top-level management and ground-level operational work. The direction comes from the top management, and change itself may take time, causing middle management to look inefficient:

I think it’s the middle [management] is more resistant to change than everything else. The top-level, which is your C-suite is on board with the idea of doing data [analytics] and wanting to move things to the floor, [introduce] a new way of doing things. But the middle is very resistant because the work is heavy. It shows the disconnect between senior and middle management. (Parthiban, Assistant Vice President, Modelling & Portfolio Analytics)

Apart from that, your team also realized that bank employees felt threatened by the transformation activities as they perceived that some of their job functions could be automated for cost-saving purposes. With many job functions being highly specialized and monotonous, some staff at BigBank feared the impact of technology on their job security. The fear of being made redundant or job loss among bank employees increases the resistance to change. There was an interesting story related by one of the data scientists in BigBank:

Faiz is 48 years old, and his job is to pull up the daily reports and compile them together into a single report. For 13 years his main job activity was to compile data from multiple spreadsheets into a single spreadsheet. This job will take him the whole day because he has to copy-paste and pivot, copy-paste and pivot, from so many spreadsheets. Then the transformation team was set up, and one of the things they did was to automate this entire [thing] into a one-click thing where you dump all the Excel files into a single folder, add a script, and create the output wanted. Faiz was very unhappy because he felt that he had been de-skilled.

As these technologies take precedence, employees feel de-skilled and unable to contribute to the company they have been working for many years. They must be retrained to do something else or need to exit the workplace. However, not many people may be interested in trying new things or climbing the corporate ladder by expanding their job scope.

In addition to operational-level employee resistance, managers in the decision-making capacity are also generally a problem as they are conservative in their activity and investment. Any project needs to be justified and bring returns in the short to medium term. Accordingly, every potential investment needs to show reasonable returns before commitment. When pitching data integration and data cleaning activities, the head of the department said, “What am I going to get out of this? What’s the ROI [Return on Investment]? Show me the manifestos.”

This conservative “act with caution,” risk-averse norm hampers the transformation process as any use case for data analytics projects must be mapped out with the potential returns associated with the investment. The higher management is required to see viable project returns as this is the prescribed value for their role. The enabler unit has been having a challenging time convincing the business department manager of the viability of data projects and moving to a new system.

The banking industry is not like any other retail practice where practitioners can play around with data as a simple mistake can cause a major breakdown. As such, generally, the industry is risk-averse; as mentioned by a senior executive, “We don’t have the luxury of mistakenly pushing a bad model into production, a single portfolio could be losses with millions” (Parthiban, Assistant Vice President, Modelling & Portfolio Analytics). Banks are not willing to take risks as the fear of failure is high, and any form of mistake or misjudgment may cause a great deal of financial and reputational loss. The director of analytics architecture at BigBank mentioned, “The problem with banks is that they don’t want to take risks” (Joshi, Director- Lead Analytics Architect). However, he stressed the need to have a strategy but to be ready for failures and treat them as a learning experience.

What’s next?

Your team of translators and the change committee discuss possible ways to overcome these issues to ensure a positive outcome of the transformation process. One of the solutions suggested by the committee is to establish a company-wide data and technology learning program. “We should increase awareness of employees and make available learning programs through our learning portal. Let’s make this portal accessible even to our tea lady”, said Sara from the change management committee. However, the committee chair had some reservations about this idea as employees are already feeling burdened and demoralized and adding on learning or training programs may backfire.

As a translator overseeing the “patching” between the enabler unit and business departments, you have proposed an idea to align Key Performance Indicators (KPIs) with individual, business departments, and organizational strategies. The committee chair is keen to learn more about this suggestion, and it could become a reality. She is particularly concerned about managers in decision-making roles who may not fully support the transformation process and frequently request the ROI for projects like data cleaning. Data projects are long-term endeavors, and their returns are not easily quantified in the short term. While you understand the manager’s concerns, the system transformation process needs to involve everyone.

The CEO called for a meeting with you, the other translators, and the change committee. He directed a pertinent question to you and the other translators, “So, as translators, how are you going to solve the resistance to change issues?”

Suggested assignment/discussion questions

1. In the context of BigBank’s case, what are the key challenges associated with employee resistance to technological change?

2. Can resistance to change be disregarded? What would happen if BigBank ignores this resistance and pressures its employees to adopt big data analytics? What are the risks associated with this approach?

3. Does the establishment of a “company-wide data and technology learning program” proposal in this case study have the potential to be effective? Provide justification for your answer.

4. Analyze the distinctive concerns of BigBank’s more experienced employees regarding technology adoption. What strategies can be implemented to overcome this issue and facilitate a smoother transition to a technology-driven culture?

5. The case also mentioned concerns about a heavy existing workload and the fear of de-skilling considering the big data analytics transformation. Recommend strategies for BigBank to address these issues.

6. One of the reasons cited for resistance to change is that business department managers do not perceive the benefits of big data analytics, as their focus is primarily on short-term ROI. What strategies can be employed to secure buy-in from these managers?

7. Another factor contributing to resistance to change is the reluctance of business department managers and some senior executives to take risks. Is this concern valid, and what strategies can be put in place to address this challenge?