Faculty Of Engineering, Built Environment And Information Technology

In a recent study done by the leading global professional services company, Accenture, 79% of enterprise executives agree that companies that do not start implementing Big Data, are at risk of losing their competitive position and may face possible extinction. According to Columbus, “big data is expected to grow exponentially by 2020” (2015). It is therefore crucial that organizations start implementing Big Data for them to uphold their current competitive position and avoid the risk of extermination. E. M Roger’s Diffusion of Innovation Theory developed in 1962, applied to the adoption of Big Data, plays a big role in assisting organisations in adopting big data. In this essay, I will give a brief overview of what big data is, the implementation challenges faced, attributes of the innovation model and the stages of the adoption model applied to Big Data, followed by recommendations on how organizations can successfully, and effectively adopt Big Data. Big data has been used to reveal trends or patterns relating to human behaviour that allows organisations to do forecasts on this behaviour to improve the following areas: their forecasts of product demand and production, the services provided to customers and, the relevant support provided to customers. This ensures customer satisfaction and improves manufacturing performance and customer retention, which is what places an organisation at a competitive advantage.

According to a McKinsey Analytics survey, nearly 50% responded saying “big data has fundamentally changed business practices in their sales and marketing functions”. Davenport and Bean have also stated that “84% of enterprises have launched advanced analytics and big data initiatives to bring greater accuracy and accelerate their decision making” (2012). This shows what a major impact Big Data has on the way organizations operate and on their manufacturing procedures. Organisations are currently facing a situation where it is required of them to be “data smart” to not only have a competitive advantage over another organisation, but to surpass them as well. It is therefore of vital importance that organisations consider implementing Big Data into their day-to-day operations, if they have not done so already, to no longer place them at risk for possible extinction. Implementing change in an organisation is not always an easy procedure. The Diffusion of Innovation theory is therefore used to assist organisations to understand the process of change in an organisation. Big data adoption comes with many implementation challenges. To mention a few: it is a long-term and extensive process that has many legal, privacy and security concerns as well as technology and infrastructural requirements which create funding challenges. According to authors Mikalef, Pappas, Krogstie and Giannakos, many organizations also have a lack of understanding how to use big data to make better business decisions (2017). An organization, therefore, needs to be skilled in big data and should know what they want to achieve and make sure that they have all the required resources and legal rights, before adopting big data. Another general problem area in the practice of information systems, is the lack of diffusion on IT innovations. Rogers’ theory helps organisations understand how innovative ideas spread within an organisation.

According to Rogers, “not everyone has the same motivation for adopting a new idea”. He has identified the following five types of adopters: Innovators, Early Adopters, Early Majority, Late Majority and Laggards. Innovators are the ones willing to be the first to try the innovation. They are usually willing to take risks and very open to new ideas. Then there are laggards who are sceptical and will only adopt an innovation once it has been tested by the majority and good feedback has been given. In an organisation, all employees fall into one of the above-mentioned categories. It is therefore important to know how to work with these employees in their different categories, to ensure implementation will take place effortlessly. Roger also introduced the innovation-decision process which involves the time take from when potential adopters first become aware of the innovation to the time where the adopter chooses to adopt the innovation or reject it. When this innovation-decision process occurs within an organization, it becomes more complex and comprises of the following five stages: Agenda-setting, matching, restructuring, clarifying and routinizing.

The first two stages contain the initiation phase where information is gathered, and planning occurs. After these two stages, the innovation is either adopted or rejected. If the innovation is adopted, the last three stages will become the implementation phase where decisions are made, and the innovation is put into practice within the organization. These five stages have a significant influence on the rate of adoption and will briefly be explained, according to Roger’s theory (1995) and is applied to the adoption of big data. Agenda-setting is where the organization will prioritize its activities and tend to any issues (based on priority) that arise, with potential remedies. Matching is where the organization assess the possible consequences based on this, matches the issue with a potential remedy and determines how suitable it is. The third stage, restructuring/redefining, is when the innovation is redefined and changed to better suit the organization. The clarifying stage takes place after implementation has started. This is the period where any modification takes place between the innovation and the organization. The last stage, routinizing, is where the innovation merges with the routine of the organization. During this stage, the innovation becomes institutionalized.

Along with these 5 categories, the following factors influence innovation diffusion: relative advantage, compatibility, complexity, trialability and observability. Relative advantage is the improvement that will be made once the innovation has been implemented. With regards to big data, the organization will have a great relative advantage as implementing big data will enhance the way in which the organization’s data is being captured and in return, the organization will have a lot more valuable data that can be used when making a business decision. Compatibility is how well the innovation is in line with the experiences or value required of whomever is adopting it. Most organizations collect data and readily ensure customer satisfaction. With big data, this process can be enhanced. Complexity is how easy it is to understand and use this innovation. With big data, there are plenty tools to use and online help that will assist the implementing of big data and ensure simplicity. Trialability is the level at which the user can assess the innovation before implementing it in their organization.

As mentioned before, there are many tools online that can be used to assess the adoption of big data and the benefits it will provided. Observability is how obvious the benefits of the innovation is. In this case, big data will assist business decisions made, and will assist organizations in improving customer satisfaction, which will in return, improve customer retention. When adopting big data, the above-mentioned characteristics of the innovation model, as well as the stages of the adoption model, should be considered. Organizations should keep in mind that not all employees will readily adopt the innovation and that the process of adopting big data may not be as simple. It requires extensive research and implementation procedures to ensure that the innovation is adopted effectively with as little conflict and as much ease as possible. It is a process that will have to be implemented over a long term to ensure all employees are satisfied and able to work with big data, and that it is in line with the organization’s goals and operations.

To ensure an easy and effective implementation procedure, I recommend that the organization adopting big data, focus on the stages of adoption model and how each of these stages can be managed when adopting big data. I recommend that the innovation process is carefully followed and applied when adopting big data. The characteristics of the innovation model and process should also be studied to see how big data will change the organization’s current day-to-day operations. Once these factors have been assessed and taken into consideration, the organization can continue to adopt big data. Along with these factors, the available resources and the organization’s goals should also be assessed when deciding on adopting big data. With Big Data growing exponentially, it has become crucial that organizations start implementing Big Data in order for them to uphold their current competitive position and avoid the risk of extermination.

The stages of the adoption model, the innovation-process as well as the characteristics of the innovation model are all factors that may influence the adoption of big data and should therefore, be assessed, extensively, before the implementation procedure takes place. The easier the implementation procedure is, the easier it will be to adopt big data. If the gap between innovators and laggards can be decreased, the adoption of big data will be much more effective and will also take place at a faster rate. If the innovation process is followed and assessed carefully, the adoption will take place effortlessly. Also, the greater the relative advantage, compatibility, trialability, observability, and the less complex big data adoption is, the greater the chances of adoption in an organization. The acquiring organization should therefore take all the above-mentioned factors as well as resources and implementation challenges, into consideration when and before implementing big data to ensure satisfaction from all the organization’s employees and clients, and a successful innovation adoption process.