How can Data Analytics be used to Plan and Control Change Management?   Abstract Change within an organization has been identified to be a continuous process. Through this, the primary goal of this study was to perform a critical evaluation of the role of data analytics in planning and controlling change management. The results reveal that changes within an organization are primarily initiated by the constant technological inventions and innovations witnessed within a business environment coupled with demographic changes and intense competition within an individual business’s environment. Similarly, data analytics significantly contributes to the effective management and implementation of a proposed change. Specifically, bid data analytics ensures reliability, accountability, and flexibility, which are all essential in effective change management and implementation. The study concludes that the use of data analytics is essential in ensuring effective change management, which consequently translates to positive performances through enhanced employee motivation and higher consumer retention rates.     Table of Contents Chapter One: Introduction. 5 1.1         Research Background. 5 1.2         Statement of the Problem.. 5 1.3.Aim and Objectives. 6 1.3.1. Aim for Research Study. 6 1.3.2. Objectives of Research Study. 7 Chapter Two. Literature review.. 8 2.1 Introduction. 8 2.2.       Change management. 8 2.2.1 Lewin’s three-step change management model 9 2.2.2 Kotter’s 8-step change management model 10 2.3.       Data Analytics. 13 2.4.       Importance of Big Data in Data Analytics. 16 2.5.       Change Management and Data Analytics. 18 2.6.       Resistance to change and data analytics. 20

3. Chapter Three: Research Methodology. 23

3.1.       Introduction. 23 3.2.       Research Design. 23 3.3.       The population of the Study/Target Population. 24 3.4.       Sampling Design. 24 3.5.       Data Collection Methods. 25 3.6.       Data Analysis. 25 3.7.       Privacy and Ethics. 26 3.8.       Validity and Reliability. 26

4. Chapter Four: Findings. 28

4.1.       Findings. 28 Chapter Five: Discussion. 30

1. Chapter Six: Conclusion and Recommendations. 33

1.1        Practical Implications. 33 1.2         Limitations and Suggestions for Future Research. 33

2. Conclusion. 34

References. 36  

Chapter One: Introduction

1.1  Research Background

The application for data analytics in change management is a novel research sector that examines the effect of intelligent data on organizational change management. Effective competition and sustainability of a firm depend on its aptitude to plan and adapt to new situations, especially in unstable and complex markets (Valverde et al, 2006). Due to this, Vance (2006) articulates that researchers on organizational change are starting to agree that change analytics is becoming a key part of long term success and can inspire positive change among the members of an organization. In regard to change analytics, the research project examines a gap pertaining to the effect of using intelligence systems to manage change (Kanter, 2008). In particular, it investigates how a data-driven approach to change management can be used for managing and controlling change. Determining the individual approaches of data analytics between experts of both data analytics and change management fields, as well as comprehending the interest of companies and/or project managers is important in determining the success of change management. It acts as a guide to firms to determine the particular moves and interventions of data-driven approaches to change management to enable positive outcomes.

1.2  Statement of the Problem

Modern organizations are starting to use data analytics systems to enable decision making, planning, and controlling for change management. In this regard, quality analytics has a key role to play in minimizing the chances of change management failure, therefore, promote acceptance of data analytics among change management experts and project managers is a key role (Parry, 2015). As such, it is critical to apply the use of information systems to collect and observe data. In any sector, change management is comprised includes the operational as well as administrative systems, procedures, and systems applied to observe, highlight, analyze, mitigate, and prevent threats to operations. The amalgamation of change management and data analytics is referred to as change analytics (Bleifuß et al, 2018). Through the application of change analytics, organizations can effectively adapt to change and engage decision making actively and systematically (Allen et al., 2007). Change analytics also protects other aspects of the organization that are key to its business, such as reputation, assets, and its overall value. The deployment of data analytics in an organization will promote a comprehensive system for managing change decisions that optimize value, safeguarding, and development by managing uncertainty and risk as well as their association with overall value. Change analytics is centered on the application of innovation to interweave efforts geared towards prevention across the entire organization and mitigate risk associated with fragmented departments and enterprise units (Bleifuß et al, 2018). In addition, data analytics are combined to support decision making, the collaboration of workers, the delegation of resources, and the ranking of risk (Domingos, 2015). Analytics are important for monitoring standards as a way of showing value for change management projects. These features of data analytics are developed along with a governance framework that embeds the change management program to an organization’s operations.

1.3.Aim and Objectives

1.3.1. Aim for the Research Study

The aim of the study is to establish how organizations can use data analytics in managing and controlling change management to enable effective and efficient operations.

1.3.2. Objectives of the Research Study

• To critically explore the concept of change management within an organization in consideration of the most utilized change management models.
• To evaluate the perceptions of stakeholders within organizations to change through data analytics.
• To evaluate to what extent information provided by data analytics enables organizations to manage change.
• To discern the significance of using data analytics to inform change management
• To discern the significance of big data in data analytics in ensuring effective implementation of change
• To explore the various ways of navigating around the challenge of change resistance

The research will examine the advantages and disadvantages of using data analytics in change management. The insight is important in evaluating the limits of data analytics in change management.  

Chapter Two. Literature review

2.1 Introduction

In the technological world today, no organization or company would want to remain uncompetitive. Technology has transformed the way people work. For instance, technology has transformed marketing since the field is currently focused on social media platforms. The introduction of data analytics into the organization is expected to bolster the performance of organizations and the overall decision-making processes, as management pattern gains go from empiric to strategic (Eastman and McCarthy, 2010). According to Domingos (2015), the purpose of using data analytics is to add value to the performances of the organization and bring about transformation into the organization. At the same time, organizational employees are thrilled to acquire new skills, meet the minimum threshold, in terms of, performance required of them, and form part of the organizational success (Hallencreutz and Turner, 2011). It can only happen when there is a consensus within the organizational leadership to implement change analytics (Domingos, 2015).

2.2.Change management

Organizations in the modern world are working hard to attain efficiency in their operations. Due to the trends of globalization, companies have been attempting to deploy data analytics in their operations. Moreover, due to the demand and the competitive nature for business, and the desire for excellence, organizations are forced to seek ways they can enhance their productivity in the new world where data is the prime recourse (Bleifuß et al, 2018). These demands have made companies rethink how to gain a competitive advantage. Eby and colleagues (2000) highlight that any slightest attempt to create organizational change should call for a proper adjustment in the existing systems so that resources can be created to accommodate the change. Some of these are geared towards achieving customer satisfaction, change in a business environment, knowledge development as well as technological advancements (Yeung, et al, 1994; Robertson, 2000). Eastman and McCarthy (2010) stipulated that the elites who carry out effective technology implementation adjust themselves with tailored solutions that influence behavioral change and bring about substantial outcomes. Such attempts create the necessary capacity that facilitates faster responses to new demanding situations brought about by change. There are several change management models that individual organizations can utilize to foster effective and efficient changes within their systems and procedures. Change is always necessary to ensure effective and efficient competition. The two models that are heavily utilized by organizations in the contemporary world to accomplish this mission include Lewin’s three-step change model and Kotter’s 8-step change model (Ali, 2012).

2.2.1 Lewin’s three-step change management model

As observed by Ansoff, Kipley, Lewis, Helm-Stevens, and Ansoff (2018), Lewin's three-step change model has phases that are to be used in ensuring effective change. The first phase is the unfreezing stage in which an organization makes the stakeholders realize that there is a need for change within it's the systems and procedures. This is a crucial phase considering that change can face opposition from stakeholders given that it a proposed change always make them depart their comfort zones in relation to a high level of expertise and skills acquired as a result of repeating the initial procedures (Ansoff et al., 2018). The second phase involves the implementation of the actual change. In this individual organization involved in the proposed change abandons the systems and procedure that it had been utilizing initially to effect the necessary changes needed within the organization. As long as all the stakeholders within the organization had recognized the need for change, this process will always be smooth. However, there are some other challenges apart from the resistance that organizations face at this stage, such include lack of enough resources that can be used in effecting the change and incompatibility of the skills and level of expertise among the current human resource to the required standards in the proposed change (Argenti, 2018). The third step in this process is the refreezing phase. At this stage, the organization anchors the implemented change to its culture that all stakeholders are required to adhere to. Engraving a change on to an organization's culture ensures sustainability and long term utilization of the implemented change.

2.2.2 Kotter’s 8-step change management model

The first step in this change management model is to create a sense of urgency through a bold and informative statement. Identifying the possible threats that are inherent in policies should prepare the company in case the proposed change is delayed. Consequently, this phase entails describing the possible advantages that can be enjoyed by a company as a result of incorporating data analytics in change management (Clemmer, 2018). The second step in the scheme is to create a coalition with stakeholders who routinely interact with the organization. Foremost, this phase will begin by identifying potential leaders from the company's large workforce. Afterward, it will be possible to request for the emotional commitment of the team with the aim of discerning any weaknesses in their performance. The proposed change will be championed by a team of skilled experts in data analytics and change management. Next, the corporation needs to develop a strategic vision and strategy for the desired goals of change analytics. Focusing on the company's desirable values should ensure that change is sustainable during and after its implementation. Consequently, this step entails creating a simple summary of the change, developing an appropriate execution strategy, and formulating an evaluation strategy for benchmarking changes in management policies (Clemmer, 2018). The primary root-cause of this phase is to ensure that proposed changes are fair for all stakeholders related to change analytics. From the results of the interviews, we can identify that the group that gives more importance to this step is the change management exerts group. The first three steps demonstrate one of the objectives of the research, that managers can plan change management using data analytics, by enabling managers to make more informed decisions in regard to change management. The fourth step in the plan is to communicate the vision with the objective of rallying the support of people who embrace change analytics. Informing others of the existence of a developed strategy is important for determining the level of success of management transformations (Burnes, 2011). Communicating the corporate vision of a change should clarify the significance of policy amendments. In addition, the team should soothe any concerns and anxieties possessed by stakeholders over the change. With that done, it will be possible to stress the significance of policy change by emphasizing the vision through routine company operations. Managers are more concerned about this step as they are the ones that need to deal with the resistance to change. It is becoming clear that communication, with all stakeholders, of the change process, enhances the chances for success, and that data analytics allows a fast and accurate communication (Clemmer, 2018). Both managers and change experts consider that after communicating the corporate vision, the organization needs to empower broad-based actions. Removing possible obstacles, which would hinder the success of an undertaking is crucial for change analytics. To achieve this, a company needs to designate responsibilities to employees who are charged with delivering change (Argenti, 2018). Creating a chain of command for issuing instructions and feedback should also contribute to this objective. Finally, introducing a reward system for high performing employees should be a good motivation factor for boosting employees’ morale. The sixth step in the scheme is to generate short term wins thereby enabling a company to monitor the achievement of the company's goals using data analytics tools. Chief among the ways of producing results is to set attainable goals that are aligned to a structured time schedule. Consequently, offering support for efficient employees and rewards to short-term winners contributes to the achievement of the change plan (Burnes, 2011). In a nutshell, managers and change management experts conclude that wins must be recognized if a company desires to track progress and sensitize support for the undertaking. The next step in the change plan is to consolidate gains and to produce change. Patience is important for orienting short-term wins to long-term goals because the success of change analytics is compromised when victory is prematurely announced. The phase entails evaluating the short-wins as well as resetting goals when they are achieved to advance change. When the company sets higher ambitions, it will be able to accelerate change analytics. The final phase of the scheme is to anchor new approaches into the organizational culture. In the workplace, it is often said that organization culture is what determines what gets gone and what is neglected. Talking about the progress of management changes and also publicly recognizing the contributions of key leaders should augment business policies (Argenti, 2018). With that achieved, the company can replace critical leaders with fresh and innovative recruits who support the change

2.3. Data Analytics

The term data analytics encompasses tools that use algorithms to enable professionals to understand the relationship between different variables. Application of data analytics has the potential to change the way organizations approach change management due to the insights it reveals about a firm’s process (Stenius and Vuori, 2017). Data analytics is an emergent form of data analysis used to examine complex relationships. It reveals shifting points of data and relationships between these data points into an easy to use format. When examining the multifaceted associations, or the wide connections between data, change analytics facilitates a solution that codes analysis more efficiently to enable easy interpretation of results (Yan et al., 2017).  By offering such insight, graph analytics are increasingly becoming important in strategic management. Another type of emerging technology is the digital ecosystem. A digital ecosystem is a set of interconnected IT resources than can work as a single unit (Nguyen, Lenharthet al, 2013). The ecosystem is comprised of customers, suppliers, third-party service providers, applications, and trading partners, and all associated technologies. The digital ecosystem shows managers the effect changes will have on the external stakeholders (Nguyen et al, 2013). The amalgamation of these technologies creates synergies that increase efficiency and productivity. The implementation of the network for a data analytics company requires planning based on the requirements and user roles. Connecting the staff is the main idea in a network architecture. The requirements for a data analytics company shall need data analysts for decision making and identification of opportunities in the market sector (Kumar and Kirthika, 2017). A good network workflow shall guarantee an improvement in service offering for the company in context. A highly skilled team shall be required for the achievement of the objectives with the use of the network workflow. The composition of a data analytics company is made of the lead director of analytics, data science manager, and analytics manager. The director is in charge of management of the analytics and data science manager. They shall oversee the activities of the lower position leaders. The exploratory and description of the analyses shall be conducted by the data engineers in the lower departments. All data scientists shall be controlled by the data science manager in their respective roles. The company shall also have professionals in different fields and qualifications in other areas who shall collaborate in the achievement of objectives and goals of the institution. The skillset shall include; data architects, statisticians, software engineers, business analysts, and data visualizers. The major component of the network in the data analytics company is the software applied for data analysis. The software is created by software engineers and comprises of components that can collect and process data (Cao et al., 2009). The role of the software engineers, in this case, is to guide the company with their skills and expertise for the best technology and implementation procedures. The advice and education on how to use the software by data engineers are acquired from the software engineers. Also, the training should include every member of the staff in order to allow even the high-rank managers in being in a position to use the software. Some will use the software for direct hands-on data analysis while others will use the software for managerial purposes. The creation of the network and workflow in the software is the role of the software engineers. Collection of data, analysis, and interpretation shall be the role of the Statisticians. The analysis of data for specific requirements shall be conducted by the Statisticians on the network with the data analysis software implemented. Therefore, this group relies on software engineers for the production of the software that they utilize for their daily activities. The connectivity between these two groups will be achieved via a VLAN to help implement data privacy and integrity. The group of software engineers will not access any information about raw data used by the Statisticians as well as the expected out. No modifications to data shall be done by any other group other than the data scientists. Connectivity among the groups shall require intelligent switches to achieve maximum data security. The network of the company shall also have a data repository database server. The role of the database shall connect all the team members who need to perform operations on the data, or view information. Cleaning and refinement of data shall include a group of data analysts and scientists in their respective fields from the network developed in a coherent manner. The company can involve data hygienists in their network in order to achieve the highest level of accuracy. The hygienists shall perform adjustments on the data analyzed to make a rightful presentation. The protection of data from errors will involve the hygienists in preventing any problems related to wrong data representation in the company (Mertler and Reinhart, 2016). After cleansing is achieved, the next level of users on the network includes data architects. The role of this group is to ensure that the data presented is in the right structure and is meaningful to the users. The group shall also have access to the data repository database. The procedures at this level will make data readable to different members of the company. The team makes sure that data is efficient and quality to the users. The data architects then forward the information to the next team of data scientists to create complex models of analytics in the company. All analytics and modeling frameworks are implemented at this level in the network. Value addition is achieved at this level of the workflow. The teams have their individual separate user accounts joined on the same workgroup for collaboration. The director of analytics, data scientists and analytics manager must be part of this sub-network for collaboration and interactions (Mertler and Reinhart, 2016). Visualization of data to customers and users is the final step that shall be achieved by the data visualizers after the previous group.

2.4.Importance of Big Data in Data Analytics

With the invention of new technology data management has been viewed as one aspect where technology should be implemented. Despite the existence of traditional approaches and methodologies in data storage and management the way data is handled differs from one organization to the other. Big data analysis and storage remains a challenge to many, however, much has been achieved and individuals are reaping from the changes, which come with new technology techniques such as cloud computing and many other data management technologies (Curry, 2016). With individuals having data sources such as the Amazon websites and Yahoo among others proper data storage and accessing tools are important. It is important to appreciate the fact that for one to attain high integrity levels in big data management and analysis there are several factors to put into consideration. These factors include but are not limited to the flexibility the method of analyzing and storing individual’s data should meet the ever-changing technological changes and demands. Maintaining the mechanism should be maintainable by the user's accountability and reliability users should trust the chosen system and rely on it without fear of losing their data and any other critical information. The term big data has brought a lot of influences in the data storage, while it is used to refer to a collection of several data from different sources majority are yet to accept the fact that this has been there for ancients with traditional methods applied to access and store the relevant data. Today due to raise in technology security threats have diversified something which calls for users to be alert and practice ethical secure methods when using big data (Gandomi and Haider, 2015). Likewise, users need to put into consideration the necessity of the data and ensure they observe the set policies so as to ensure that they do not go against the laid procedures and regulations while using the data. Additionally, there are several big data approaches all of which have certain capabilities in managing data. To succeed in big data approach individuals ought to put into consideration the security concerns and the responses each model offers. Data access, management, and storage is a critical aspect; therefore, necessary frameworks should be considered before anything. It is important to establish the programming languages used in the data storage systems; the language extensions and query language since these are key determinants of how successful the chosen approach will appear. A clear programming language that accommodates the technological demands remains crucial; the extension languages should also be reliable and manageable (Lampitt, 2013). Any storage approach which is not flexible cannot adapt to changes this demands for a new data storage system whenever there is a change in technology and data type. Good and reliable big data architecture should support all the three major components of a generic architecture, which are, extraction, and transforming. The chosen design should allow users to extract data for use following the necessary procedures and observing all the necessary security precautions. Also, the design should be able to transform the data in a certain way such that it can be integrated into the system and finally enable users to load the information (Guirguis et al, 2016). To establish effective change analytics, it is important for managers and companies to consider the above in constructing resources for successful implementation.

2.5.Change Management and Data Analytics

Change management is a set of tools, techniques, and processes designed to help individuals embrace change in an institution.  In recent times, one of the key tools that have been used to inform change management is data analytics. According to Bleifu and colleagues (2018), the amalgamation of these concepts is referred to as change analytics. Change analytics is characterized by the use of data analytics by stakeholders to guarantee that the process remains under control. Further than that, data analytics is used to ensure that the activities conducted in this process are maintained at a certain budget of resources. Bleifu et al., (2018) believe that change analytics encompasses the concept of change leadership and data analytics in articulating a vision for the future of an organization. It also involves the mobilization of data resources needed for the project and driving the entire change process to guarantee that it runs smoothly. Vidgen, Shaw, and Grant (2017) state that for the successful implementation of change analytics, there are various competencies that a change leader has to possess. First, one has to have a clear vision for the future of the organization and a suitable form of change analytics to be implemented to help in achieving this vision. Change leaders see beyond the scope of acceptance in the long-run, where the company's ideologies are transformed. In addition to that, change leaders show their competence in how they incorporate data analytics in the changes to be made to meet the company’s goals (Vidgen et al, 2017). They present the changes in a visionary way that is informed by data analytics. A change leader should also be flexible and prompt in implementing data analytic ideas and varying concepts. It is because change analytics involves the incorporation of abrupt solutions to address an urgent matter. Morin and colleagues (2016) assert that for effective change analytics, it is important to create a coalition that guides the implementation of changes through data-based decisions. It can be a group of individuals with experience in data analytics and enough will and power to initiate the effort of change while encouraging others to work together as a team, demonstrating that data analytics and change management are easier to be introduced if they are linked, and used together. It is also necessary to authorize broad-based action by eliminating any possible obstacles along the way, minimizing resistance to change using change management techniques, backed by information using data analytics. (Morin et al, 2016). It also involves removing any systems and structures that can destabilize the amalgamation of data analytics in the change process. For effective change analytics, Morin et al., (2016) assert that it is beneficial to encourage non-traditional activities, ideas, or actions that would transform the company positively and encourage team members to take risks in ideas that they perceive to be beneficial. Stenius and Vuori (2018) suggest that successful change analytics focuses on how the change will be implemented in the company and its overall impact on the stakeholders. Therefore, one of the main roles of change analytics is the ability to influence stakeholders to accept and promote change. Stakeholders play a significant role in the implementation of any change processes and as such, it is important to get their support (Stenius and Vuori, 2018).  Additionally, Stenius and Vuori (2018) state that effective change analytics control the processes involved to see that the change is successfully implemented. Change managers are also concerned with the financial and resource expenditure of the projects, ensuring that they are standardized according to their budgets. Güven et al., (2017) take the view that a change manager needs competencies that require further development for better results through the use of data analytics. Firstly, it is important to create an appropriate program for change whereby the goals and means of the project are aligned through data analytics. Güven et al., (2017) believe that effective change analytics must be able to mobilize the organization through an organized process engaging the leadership and all involved individuals, raising the importance of data analytics competencies to be developed within the companies. It will help in suitably addressing any upcoming issues that may prevent the company from changing positively, by monitoring the outcomes using data tools for faster responses, and informed decision making. Using the above, data analytics should have the power to enable positive change in an organization.

2.6.Resistance to change and data analytics

In the business and commerce sector, it is common for changes in corporate policies to be opposed by various factions. Lack of information about change analytics can trigger profound opposition in employees who feel that they have lost control of the company (Kanter, 2012). In line with the loss of engagement, it will be evident that decisions affecting specific employees can induce dissatisfaction. Even if the previous initiative failed, the change management team needs to take into account the current morale of employees, otherwise, it might build resistance (Kanter, 2012). Another possible cause of resistance is fear of the ripple effects of proposed ideas. When departments that were not featured in the management change are affected by issues such as budget cuts, resistance can arise. Such reactions are common when required adjustments are proposed and implemented in organizations. An organization’s readiness and resistance to change can be measured through the use of diagnostic tools. There are numerous diagnostic tools for policies including the Seven-S models, Culture, Operations, People, and Systems model (COPS), and the Burke Litwin model. Among the mentioned techniques, the Seven-S model and the Burke-Litwin models are the most effective diagnostic tools for assessing the impact of data analytics on change management. The Burke-Litwin model offers benefits like projecting the outcome of proposed changes. Furthermore, it also allows an organization to distinguish the types of transformation that can occur due to change. In contrast, the Seven-S model is suitable when the objective of an assessment is to ascertain a corporation’s readiness and resistance to change (Parry, 2015) As aforementioned, the Seven-S model is suitable for determining whether a company is ready for change. The strategic technique measures the integration of the company's variables: strategy, systems, skills, style, staff, and shared values (Hamilton, McLaren, and Mulhall, 2007). Assessing strategy and style revealed that a strong business culture should allow employees to respond well to change. Evaluating staff and shared values indicated that involved human resource management systems guide innovation, development, and socialization of staff. Examining skills and organization structure disclosed that a company's high competence and specialization of employees enhance the diversity of ideas (Burnes, 2011). Lastly, assessing the company's systems divulged that both formal and informal procedures of management support change. One of the most important ways of mitigating resistance is to utilize an effective communication plan. Employees need to be informed that change is coming so as to minimize the emergence of harmful rumors while also keeping them up-to-date with trending events (Kanter, 2012). On top of that, a company should actively involve employees in the decision-making process to compel the collaboration of senior and subordinate staff. Moreover, informing employees of the benefits of change to a group, a department or an entire organization can minimize opposition. Lastly, listening to the opinions and sentiments of employees through a feedback loop can keep them from opposing change(Clemmer, 2018). Effective communication can curb resistance to change analytics since it allows the leadership and the employees to coordinate on the change process. There are three types of communication strategies that can be introduced by a company: consistent and frequent communication, precision communication, and two-way communication. Consistent and frequent communication occurs through multiple channels and ensures that staff is aware of the change as soon as it occurs (Clemmer, 2018). Contrarily, precision communication presents information in an authentic manner so that it is easy to trust by the stakeholders. Among the three communication strategies, two-way communication is applicable for change analytics since it streamlines the endeavors of subordinate staff and the company's leadership in the change process.  

3. Chapter Three: Research Methodology

3.1.Introduction

The research aims to determine the interaction between data analytics and organizational change. This chapter will evaluate the research objectives stated in the first chapter, elaborate the research design, the study's methodology, illustrate the scope of the sampling design by using detailed description, highlighting the study's target population, relevant sampling methods and techniques, collection of data and the analysis of the same using the relevant methods. It will also discuss briefly the significance of the study and research resources available for this research (Eriksson and Kovalainen, 2008).

3.2.Research Design

            The research will be a qualitative study that will use semi-structured interviews to collect data from the respondents. The design is suitable since it allows the researcher to collect the evidence needed to gain responses from the interviews. The study chose to utilize this type of interview since it enables the capturing of the emotional aspect of respondents, which is essential in performing an effective analysis. This cannot be achieved by other data collection techniques like the questionnaire. As observed by Eriksson and Kovalainen (2008), emotion among respondents is essential in the qualitative study since it can be used in ensuring validity and reliability of data collected. Additionally, it is selected since it leverages the fact that the data to be gathered will include the complexities that would come up in the research process (Gioia et al, 2013). The design will also enable the researcher to ascertain the interaction between data analytics and change management.

3.3.The population of the Study/Target Population

The study will target a sample population of 15. The respondents will be experts drawn from the fields of data analytics, change management, and the business community. Table 1:Target population Target Population                                         Frequency Change Management Experts                                     3 Data Analytics Experts                                               3 Managers responsible for implementing change         9 Total                                                                           15 The target population has been identified from the professional network of the researcher and it consists of business consulting experts in change management, forms a reputable management consulting company, data analytics experts from a company that develops data analytics tools for the retail industry using artificial intelligence, and the third group is consistent of managers responsible for implementing change in their companies using the data analytics tool sold by the data analytics company.

3.4.Sampling Design

A total of 15 experts have been identified to participate in the study. The study has utilized stratified sampling in identifying suitable respondents for the study. Stratified sampling allows for the division of the population into specific groups called strata. Through this, the study constructed three strata, which include data analytic experts, change management experts, and managers responsible for implementing change within their respective organizations. Stratified sampling is essential for this particular study considering that it allows for the selection of suitable respondents based on their groups, which ensures complete coverage of all the variables involved (Golafshani, 2003).  Through this, 15 experts have been identified to participate in this study from the three strata constructed.

3.5.Data Collection Methods

            The data collection tools to be used in this study will involve semi-structured interviews. The interviews have been identified as the useful tool for gathering essential information from a given population. The individuals selected to participate in the interview process were first contacted and upon their acceptance of the exercise, the actual interviews were arranged. The interviews were conducted within the premises of the individual respondent's organization and in their own free time. Digital recording devices were used in recording the interview process, which allowed for effective analysis at a later date. A total of 15 semi-structured interviews were conducted to the respondent’s courtesy of the respondents’ professional network.

3.6.Data Analysis

            The data collected using the interviews will be first sorted out and edited for the purposes of detecting any inconsistencies observed during the collection of data. Besides, the data will be coded through the creation of dummy variables (Janesick, 1994). Collected data will be analyzed to determine the themes pertaining to the interaction between data analytics and change management. The target population has been grouped into three groups of experts for better segmentation of answers and to enable interpretation of data collected. The three groups are:

• Change Management Experts
• Data Analytics Experts
• Managers responsible for implementing change

3.7.Privacy and Ethics

The research is largely concerned about the privacy of other people; therefore, the researcher will undertake the study while at the same time observing the utmost integrity to prevent any element of unethical behavior. The researcher will observe ethical principles that guide research work to avoid cases of data collection breaches and anonymity of information. For records, the researcher will maintain personal, original, and first-hand data gathered and make a very honest presentation during analysis. Throughout the study, the researcher will maintain objectivity to obtain accurate and reliable information (Murthy, 2008).

3.8.Validity and Reliability

The study ensured the data obtain were valid and reliable, which allowed for the generation of effective conclusion. Validity can be described to mean how well a given test measures what it is supposed to measure. In this regard, the approaches used yielded information that was relevant to the study. Therefore, the validity of this study was established through the triangulation of the data obtained to ascertain consistency. Errors were corrected upon identification. Reliability, on the other hand, relates to the extent to which an evaluation tool yields consistent results (Eriksson and Kovalainen, 2008). In this regard, the study employed a ‘test-retest’ method to develop reliability. It was done repeatedly to ensure that consistency and stability are achieved throughout the study.  

4.      Chapter Four: Findings

4.1.Findings

The interviews revealed the success factors that can be combined by organizations for indoctrinating new policies into typical business processes to enable the use of data analytics in change management, and that most of the respondents agree on. In regards to change management, fourteen of the fifteen respondents were certain that change within an organization is a continuous process; hence all organizations are ever-changing their systems and procedures. One of the managers responsible for change within an organization stated that it is only through change that an organization can ensure it effectively competes within its environment. She further observed that she cannot work in an organization that is adamant of change as that will not help her personal development as an individual. Another respondent who is change management expert also asserted that an organization that is adamant to change is like a ship that is steered into an unwavering storm with its occupants turning a blind eye to such actions. The two primary factors that the respondents observed to instigate the necessity for change in an organization is the highly volatile technological space and the stiff competition organizations face within the environment. The three data analytics experts asserted that the constant technological inventions and innovations witnessed within the technological environment have resulted in the need for individual organizations to incorporate new systems and procedures within their ranks to ensure effectiveness. One of the managers responsible for change within an organization observed that consumers have been swept with constant technological innovations and inventions, which has resulted in changes in consumer tastes and preferences. Through this, individual organizations are required to ensure they incorporate new systems and procedures within their ranks to foster productivity and high customer retention rates through enhanced service provision. Similarly, close to three-quarters of the respondents were in agreement that the stiff competition witnessed within the contemporary world's business environment requires organizations to ensure effective competition through strategic management. Change is a core element within strategic management. All the respondents were in agreement that the use of a well-recognized change management model in implementing a proposed change within an organization is essential in ensuring the effectiveness of the proposed change. Additionally, ten respondents observed that the use of these models helps in overcoming the challenge of resistance among stakeholders. Two change management experts asserted that other challenges likely to be experienced while implementing a proposed change include the availability of scarce resources and incompatibility of skills and level of expertise possessed by the existing human resource in the organization. However, through the use of a well-structured communication framework and proper planning organizations can overcome these obstacles. In regards to the significance of data analytics in implementing change, thirteen respondents observed that the use of data analytics in implementing a proposed change is essential in ensuring the effectiveness and efficiency of the change. These respondents further observed that data analytics can be used in identifying the need for change as well as in identifying the best change to be implemented among the available alternatives. Additionally, they asserted that data analytics can be used to support the implementation process for the proposed change as well as in the comparison of the milestones achieved after the implementing change to the organization's performance before the change. Eleven respondents acknowledged the significance of big data analysis in implementing change within an organization. These respondents asserted that big data analysis allows for the collection of a huge amount of data and condensing them to viable information that can be used in proper decision-making. This is largely facilitated by the emerging trends in the technological environment in regards to the internet of things (IoT) and cloud computing. Through all these, big data analysis ensures reliability, accountability, and flexibility in the implementation of change.  

Chapter Five: Discussion

Originations have to ensure they anchor appropriate mechanisms that allow incorporation and use of any emerging systems and procedures within their respective industries. The increased competitions they face within these industries require the formulation of viable strategies to be used in ensuring effective competition (Argenti, 2018). Some of these strategies require the initiation of change within the organization's systems and procedures. Similarly, the volatile technological environment has resulted in constant innovation and invention. Consequently, these have resulted in the emergence of new systems and procedures primarily channeled to ensure effectiveness and efficiency by enhancing the quality of services rendered to individual consumers as well as those of the goods available to them (Clemmer, 2018). Through this, organizations are required to incorporate these new developments to ensure effective competition. Further, technological advancements have resulted in new tastes and preferences among individual consumers, which require the invention of new products and services to fill these emerging consumption gaps (Burnes, 2011; Clemmer, 2018). Though not observed by the respondents studies have revealed that changes in demographic structures within the society also results in the need for change in the organization's systems and procedures; changes in demographic structure results in changes in consumer tastes and preferences On the same note, as revealed by the respondents, there exists a connection between employee job satisfaction to an organization’s need for change. Organizations implementing the necessary changes in their environment are quite appealing to their employees since the majority of employees view such as a chance to enhance their skills and level of expertise (Burnes, 2011; Erkmen, Hancer and Leong, 2017). Consequently, these organizations enjoy a highly motivated workforce, which is reflected in better performances through higher customer retention rates and increased market share. Similarly, there is a need to ensure total employee involvement in all the stages of a proposed change within an organization. This is essential in ensuring they own every step of the plan, which is crucial in motivating them. Challenges in all systems and procedures are always a common phenomenon including in change management (Kuch, 2018). However, the strategies utilized in navigating around such challenges have an essential significance in ensuring the effectiveness of the proposed change. Apart from the utilization of the frameworks within change management models in navigating around proposed change, there is the need for ensuring a well-structured communication system and proper planning that takes into account the unique characteristics exhibited by the stakeholders. The technological advancements witnessed within the contemporary world has resulted in well-enhanced systems and procedures. One of these is the use of data analytics in effective and efficient knowledge management (Hiatt and Creasey, 2012; Clemmer, 2018). Better utilization of data analytics in knowledge management within an organization ensure enhanced conversion of tacit knowledge into viable information that can be used in decision making. The emergence of cloud computing and IoT has resulted in better techniques for handling big data analysis. Big data analysis inspires flexibility, accountability, and reliability given that it allows for the collection and analysis of a considerably huge amount of data. These are essential in ensuring well-structured change management within an organization. Flexibility ensures the organization is ready to implement any necessary modifications that will result in the effective realization of the set objectives (Clemmer, 2018). On the same note, accountability ensures individuals take full responsibility of their decisions and acknowledge the consequences of their actions while reliability ensures that all decisions made from the information received from data analysis is viable and can be used in the realization of an organization's objectives (Lourenço et al., 2015). Change is inevitable; therefore, and a company should use data analytics to analyze how it can remain relevant in a competitive market while also achieving its goals. The incorporation of change analytics will provide career development opportunities for employees through consistent strategies. The career development strategies are crucial in assisting personnel as well as recruits to improve their performance. Lastly, it will also provide an opportunity for global expansion in light of recent success in the marketplace (Nesterkin, 2013). The use of data analytics should further enumerate why an organization needs change.  

 

1.      Chapter Six: Conclusion and Recommendations

1.1  Practical Implications

The results from the evaluation show that most workers would embrace the use of data analytics at the workplace. Data analytics is bound to transform the way organizations approach change management since it highlights the complex relationship between different variables in the firm’s environment. No single business organization can flourish and gain global dominance without a clear strategic plan which incorporates strategic goals. The calculated goals determine the bearing the organization should take in the realization of strategic plans. Data analytics helps with the development of strategic goals. The emerging technology is bound to become a key part of change management (Lampitt, 2013). The sooner companies adopt it, the more advantage they will gain in the industry.

1.2  Limitations and Suggestions for Future Research

One of the biggest drawbacks of data analytical tools is the inflexible models. They act upon the guidance of machine intuition and do give users an opportunity to apply their input. Inflexibility can make them hard to use in certain situations (Morin et al, 2016). Future research should look into how this limitation can be overcome to enable effective integration of data analytics.    

2.      Conclusion

A change management process is an approach of postulating an organization's objectives, plans, ideas, programs, and policies to achieve the goals of an organization and delegating resources to deploy the strategies, policies, ideas, and programs. In other terms, change management can be viewed as the management of combined features of the three states of the strategy process; these three stages are strategy development, strategy deployment, and strategy examination. The significant influence that technological advancement and stiff competition, as well as demographic changes, have within an organization cannot be overlooked. Technological advancements initiate new procedures and systems that individual organizations must incorporate to ensure effective competition. Similarly, both technological advancement and demographic changes significantly influence consumers' tastes and preferences, which requires organizations to fill the consumption gaps created by such. Stiff competition always ensures organizations fill such gaps to gain competitive advantage within their environment (sErkmen, Hancer, and Leong, 2017). The integration of data analytics has a direct and positive influence on organizational functions and has improved the administrative duties including managing necessary changes. Data analytics is crucial in identifying the need for a change and the viable change strategy among the available options. It also supports the implementation of the proposed change and allows for comparing the organization's performance after the implementation of the change and before the enactment of the change. The use of big data analytics enabled by emerging trends in technology like IoT and cloud computing is also essential in ensuring reliability, accountability, and flexibility in decision making during the implementation of a proposed change. The use of data analytics in implementing a proposed change ensures value creation to all stakeholders involved, which is essential in generating effective and efficient organization performances.  

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How should change information be communicated to employees and which channels should be employed? Section 2: Data Analytics Q1. What is your understanding on the application of data analytics in business? Q2. How can data analytics be amalgamated with other operations of the business?  Also explain its advantages and limitations. Q3. How do you understand big data, its benefits over traditional analytics, and challenges? Q4. How can data analytics be integrated into making of business decisions? Section 3: Integrating Change Management and Data Analytics Q1. What is the application of data that you collect and what departments in your organization take part? Q2. What is your take on this integration of data analytics to manage change management, and can it be used at an organizational level? Q3. How is the application of data analytics on change management communicated within the organization? Q4. How do you perceive change management in the future, particularly regarding data analytics?