The Effectiveness of using Big Data as a Strategy to Improve Organizational Performance. A Case Study of Technological Start-Up Companies Kenya.

Chapter 1

Introduction and Background

Introduction.

Big data exists in almost every organization in Kenya, and globally. Structured and unstructured, big data presages huge data volumes generated in a business daily. Despite the amount of data available at an organizational level, the focus is on what companies do with it — for instance, making better business decisions.Mayer- Schonberger, and Cuker (2013, p.19) defines big data as the burgeoning ability to assess and analyze extensive collections of information, and draw a significant conclusion from it. Mayer-Schonberger and Cuker further note that big data is more of understanding and seeing the connections between pieces of information that has been difficult to grasp in recent decades. There has been an increase in data technology caused by the growth of smartphones use, multimedia files, social networks and the cloud (Oberhofer et al. 2014, p.6). As a result, the digital age has given rise to unprecedented amounts of data organizations has to deal with, to mine useful and actionable information. Effective use of big data in an organization requires management strategies. Thus businesses are adopting Big Data Management solutions with the aim of obtaining actionable information for business planning and customer support among other organizational management.

In the past managing big data was more straightforward compared to the modern complex big data management in an organization? While most organizations use database technology, the challenge is that the technique cannot handle multiple continuous streams of data. However, organizations used in the past non- complex techniques to retrieve, transform and load data from vast data stores. The systems used in the past centuries had Business Intelligence inbuilt, useful in obtaining reportsfor organizations (Ankam, 2016, p.1). Effective organizational decision-making depends on Big Data solutions. Appropriate techniques provide more accurate analyzes based on highly indexed and optimized data structures, besides extraction capabilities and reporting interfaces. Useful decisions assists in cost reduction, increase in sales, marketing, and customer satisfaction, primarily by providing quality and competitive products and services. Effective organizational strategies of big data management also help the business increase the client’s commitment to the company’s services and products, which ultimately produce a profitable and long relationship with the client. Arguably, organizations that use proper strategies in the management of Big Data can turn raw data into meaningful objectives based on predictions, projections, and trends in performance.

Background of study.

Several studies have been conducted to determine the benefits of big data in organizational performance. Through the examination of large amounts of data, organizations can discover hidden patterns, insights, and correlations between the several pieces of information at their disposal.Currently, most organizations understand the importance of capturing all the data that streams into their businesses and apply analytical tools to get value from the information gathered (Marr, 2017, p.76). Since organizations face an uphill task of harnessing their data appropriately to identify new opportunities, make decisions and improve performance, there is, need to examine the effectiveness of using big data as a strategy to enhance performance in an organization. Marr notes that data is revolutionizing how people work, and it is organizations that consider data as a strategic asset that are likely to thrive and survive in a competitive world of business.Organizations in Kenya, just like in other countries depend on data for faster and better decision making, reduce management and production cost, as well as introduce new products and services.

Aiken and Harbour (2017, p.23) define big data strategy as an approach used by organizations to store, analyze and categorize data for careful planning. Aiken and Harbour provide insights into improving big data strategy by focusing on data governance practices including the identification and removal of data constraints. Other areas of concern entail an emphasis on how an organization uses data to achieve business goals.The use of big data by most organization in Kenya supported by advances in technology is laudable in decision-making. In their findings, Mayer- Schonberger, and Cuker (2013, p.117) state that Kenya is among the countries in the world that have implemented open- data strategies thus datasets of social and economic indicators made readily available. However, if not properly analyzed, big data can become impractical and costly in organizational decision-making processes.

The study aims to focus on the effectiveness of using big data as a strategy to improve organizational performance with a focus on the Kenyan- based organizations. The continued growth and development in the use of big data in modern private and public organizationsdepend ontechnology, which most Kenyans have embraced. Therefore, the study is interested in determining whether the use of big data contributes to the success, and meeting of business objectives amongst Kenyan organizations.

Statement of the problem.

Despite the continued growth in data volumes in Kenya, organizations face challenges of what to do with the raw data available.Effective use of big data will improve performance, customer and employee satisfaction and offer an organization a competitive advantage over the rival companies. To achieve the overall goals, Kenyan organizations require useful analytics tools, and new generations of ETL- extract, transform and load data.Vaghela (2018) statistically shows that out of the 85% of companies that use big data, only 37% of them succeeds in data-driven insights.[1]He further postulates that an increase in data accessibility by 10% can lead to a $65 million company net income. The suggestion shows how important big data is in organizational performance. The use of significant information is still at the nascent stages of evolution and development, thus faced with a set of issues. To begin with, data security is a significant challenge for most organizations in Kenya. The data set available to an enterprise originates from a broader range of sources, some of which do not conform with the organizational standards, while others cannot be trusted. Organizations, therefore, must use appropriate tools, and effective strategies to meet the data needs. Sometimes inconsistencies in data collection lead to inaccurate analysis, which may lead to ineffective decision making in the organization. In a 2016 survey, IDG Enterprise reports challenges faced by the respondents regarding their big data projects. 90% of the survey participants confirmed having difficulties with their big data and organizational level (IDG, 2016). The interview also revealed that the data security used by most organizations is insufficient, with additional security measures including data segregation at 42%, data encryption at 52%, and access and identity control at 59% put to use.

Other challenges include dealing with data growth and expansion. Storing and analyzing all the available information puts pressure on organizations. ICD (2014) estimates that by 2020, the amount of information stored in world’s IT systems will be adequate to fill a stack of tablets from the earth to the moon, more than six times. The estimation shows how large data is and continuing to expand. Insights generation promptly is another challenge associated with big data. Organizations are not only interested in storing big data, but also use it to achieve business goals (New Vantage Partners, 2017). The commonly known goals include operational cost efficiencies, instituting a data-driven organizational value, accelerating the deployment of services, and launching new products. The third challenge is recruiting and retaining professionals with big data skills. Since dealing with big data at the organizational level is on demand, the need for big data talents has increased dramatically, leading to a deficit in big data talents.Harvey (2017) presents findings from Robert Half Technology, which shows huge salaries earned by top big data talents. Big data engineers earned $130,000 to $200,000 on average, business intelligence analysts made $119 to $140,000 and data scientists pocketed a salary averaging from $117,000 to $165,000 per year. Since the use of big data in an organization is expensive, and come with a series of challenges, there is a need to determine whether its use adds value to the business performance in Kenyan organizations.

Purpose of the study.

The purpose of this study was to determine the relationship between big data and the achievement of organizational goals, through assessing the effectiveness of using big data to improve the performance of Technological Startup Companies in Kenya.

Objectives of the study.

To achieve the aim of the study, the following objectives form the pillars of the research:

1. To assess the implications of the use of big data by the Technological Startup Companies in Kenya.
2. To classify organizations by performance concerning the effective use of big data, and finally;
3. To make recommendations on how to improve the effectiveness of using big data to uplift the poorly performing Technological Startup Companies in Kenya.

Research questions.

The questions form the basis of the methodology applied in the entire study. The questions, therefore, give the direction and the expected outcome of the research (Bickman and Rog, 1998). The study is set to answer several questions concerning the use of big data in Technological Startup Companies in Kenya, which include:a) how does the effective use of big data contribute to the improvement of performance of Technological Startup Companies in Kenya?b) To what extent does the use of big data influence decision making in Technological Startup Companies in Kenya? c) Do significant data challenges hinder the operations and performance of Technological Startup Companies in Kenya? d) What are the possible measures for addressing challenges faced by Technological Startup Companies in Kenya concerning the use of big data? Finally, how would solving significant data challenges benefit Technological Startup Companies in Kenya?

Hypotheses.

Organizational decisions based on well-analyzed big data leads to high performance in Technological Startup Companies in Kenya.

Limitations delimitations of the study.

Limitations are inevitable in most studies, and their early identification helps find timely solutions necessary for the realization of research goals (Shipman, 2014, p.56). The limitations may emerge from research methodology or design, factors which affect the outcome of the study. As with the majority of the reviews, issues with sampling will significantly affect the result of the findings of this study. Sampling error may occur during the survey thus leading to sample bias. To address the challenge, purposive sampling may be used. Besides, the insufficient sample size is likely to influence the outcome of the study. Since there are, few skilled individuals in the field of big data use, finding them to participate in the study may be difficult, hence limiting the recommended number of participants. Other limitations may arise from limited access to data. The use of big data in organizations, especially in Kenya is a new field that has not received much attention hence lack of adequate data. Time constrain as well may disadvantage the outcome of the study as most organizations may guarantee limited time for the review. Some of the organizations could be operating under tight schedules that would not allow their respondents to take part in the study.

The justification for the Study.

While a plethora of previous research exists on prominent data use, the study on the relationship between effective use of big data and organizational performance is far from being exhausted. Specifically, further studies can be conducted to determine the extent to which Technological Startup Companies in Kenya use big data in decision-making. Alternatively, new reviews can be done to help understand how effective use of big data improves organizational performance. This is important for enhancing the productivity and performance of Technological Startup Companies in Kenya. The practical implications of this study relate to the use of big data in organizations, challenges faced by the use of big data at large, and the impacts of big data in improving organizational performance.

The significance of the study.

The study is to enable the researcher to understand how the effective use of big data impacts the performance of Technological Startup Companies in Kenya, and how to improve the use of big data in decision making. The study will also enable the researcher to contributes to the organizational improvements in general performance by recommending the appropriate use of big data analysis in decision-making.

Reference.

Aiken, P., and Harbour, T. (2017). Data Strategy and the Enterprise Data Executive: Ensuring that Business and IT are in Synch in the Post-Big Data Era. Technics Publications.

Ankam, V. (2016). Big Data Analytics. Packt Publishing Ltd.

Bickman, L., and Rog, D. J. (1998). Handbook of Applied Social Research Methods. SAGE.

Harvey, C. (2017). Big Data Challenges. Datamation. Available at: https://www.datamation.com/big-data/big-data-challenges.html

ICD. (2014).The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things. Available at: https://www.emc.com/leadership/digital-universe/2014iview/index.htm.

IDG. (2016). 2016 Data & Analytics Research. Tech Research. Available at: (https://www.idg.com/tools-for-marketers/tech-2016-data-analytics-research/ [Accessed 16 February 2019).

Mayer- Schonberger, V., and Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work, and Think. Houghton Mifflin Harcourt. P.19, 117.

Marr, B. (2017). Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things. Kogan Page. P.76.

Oberhofer et al. (2014). Beyond Big Data: Using Social MDM to Drive Deep Customer Insight. Pearson Education. P.6.

New Vantage Partners. (2017). Big Data Executive Survey 2017. Available at: http://newvantage.com/wp-content/uploads/2017/01/Big-Data-Executive-Survey-2017-Executive-Summary.pdf.

Shipman, M. D. (2014). The Limitations of Social Research. Routledge. P.56.

Vaghela, Y. (2018). Four Common Big Data Challenges. Dataversity. Available at: (https://www.dataversity.net/four-common-big-data-challenges/ [Accessed 15 February 2019).

[1] Big Data Executive Survey, 2017.

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