Building an Analytics Culture to Boost a Data-Driven Entrepreneur's Business Model

Building an Analytics Culture to Boost a Data-Driven Entrepreneur's Business Model

Soraya Sedkaoui (KM University, Algeria & SRY Consulting Montpellier, France) and Mounia Khelfaoui (Khemis Miliana University, Algeria)
Copyright: © 2019 |Pages: 32
DOI: 10.4018/978-1-5225-7277-0.ch014


This chapter treats the movement that marks, affects, and transforms any part of business and society. It is about big data that is creating, and the value generating that companies, startups, and entrepreneurs have to derive through sophisticated methods and advanced tools. This chapter suggests that analytics can be of crucial importance for business and entrepreneurial practices if correctly aligned with business process needs and can also lead to significant improvement of their performance and quality of the decisions they make. So, the main purpose of this chapter are exploring why small business, entrepreneur, and startups have to use data analytics and how they can integrate, operationally, analytics methods to extract value and create new opportunities.
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When we consider the opportunities offered by big data universe, the power of analytics, algorithm relevance and of what may seem to be revealed by each byte of data, and then the effort involved seems to be doubled to start down into how one can develop the new business model through joining big data analytics arena. In another way, every data byte tells a story and data analytics, in particular, the statistical methods coupled with the development of IT tools (Walwei, 2016), piece together that story’s reveal the underlying message (Sedkaoui, 2018a).

Many successful entrepreneurs’ experiences support that, analytics as a core capability of their startups. These include Sergey Brin and Larry Page of Google, Jeff Bezos of, Michael Bloomberg of Bloomberg LP, Travis Kalanick and Garrett Camp of Uber, Reed Hastings of Netflix and more. At this stage, one must wonder ‘how they do what they do?’ Somehow, the answer lies in the fact that these experiences have understood the underlying message revealed by the amount volume of data byte available today. They have seen the potential in using analytics not only to differentiate their business models but also to innovate.

With small budgets, limited staff and inexperience, entrepreneurs somehow have to find a way to boost their data-driven project orientation by realizing the potential of big data beyond a promising buzzword. They must pair a vision with a clear profit model if they want to join this arena. Nevertheless, there is a school of thought, which says that “Being entrepreneurial means that an opportunity must be pursued despite the lack of resources, and the ability to leverage external resources is one of the hallmarks of the entrepreneur” (Stevenson & Jarillo, 1990; Stokes & Wilson, 2010).

The challenge, therefore, lies in the ability to extract value from the amount volume of data produced in real-time continuous streams with multiple forms and from multiple sources. In another word, to explore data and uncover secrets from it, we need to find and develop applicable to generate knowledge that can conduct any business project strategies. Therefore, understand the leadership’s cognitive is necessary. It helps to determine the factors that can encourage the adoption of new methods as suggested by McAfee and Brynjolfsson (2012) and Ross et al (2013).

Of course, there are multiple ways an entrepreneur can become more data-driven.

By using big data technologies, by exploring the new methods to detect correlations between the quantities of available data, by developing algorithms and tools that can address the variety of data, by optimizing the Business Intelligence process, etc. This provides insights on how they can develop the new business model through the use of IT tools and by providing the ability to analyze data.

That’s what this chapter will explore by highlighting the contents and focusing on how to conduct an analytical approach to help entrepreneurs in their business model creation process. Therefore, in this study the following research question will be answered: How can small businesses drive an analytical approach to get more value out of the available data and optimize their business model in such a way that it will be more frequently used for better conduct their project?

Through this question, we recall the context of big data, its importance in conducting decision-making, its challenges and the role it plays as a complement to create new opportunities for small enterprise in order to address the different issues.

It is the question posed above that is discussed in the remainder of this chapter, by highlighting it through three sections, a discussion, and a conclusion. The first section discusses the general theoretical background necessary to understand the importance of big data analytics.

Key Terms in this Chapter

Entrepreneurial Activity: Entrepreneurial activity, as an output of the entrepreneurial ecosystem, is considered the process by which individuals create opportunities for innovation. This innovation will eventually lead to a new value in society and this is, therefore, the ultimate outcome of an entrepreneurial ecosystem while entrepreneurial activity is a more intermediary output of the system. This entrepreneurial activity has many manifestations, such as innovative start-ups, high-growth start-ups, and entrepreneurial employees.

Big Data: A generic term that designates the massive volume of data that is generated by the increasing use of digital tools and information systems. The term big data is used when the amount of data that an organization has to manage reaches a critical volume that requires new technological approaches in terms of storage, processing, and usage. Volume, velocity, and variety are usually the three criteria used to qualify a database as “big data.”

Entrepreneurial: A process, in which opportunities for creating new goods and services are explored, evaluated, and exploited.

Artificial Intelligence: The theory and development of computer systems able to perform tasks that traditionally have required human intelligence.

Analytics: Has emerged as a catch-all term for a variety of different business intelligence (BI) and application-related initiatives. For some, it is the process of analyzing information from a particular domain, such as Website analytics. For others, it is applying the breadth of BI capabilities to a specific content area (for example, sales, service, supply chain, and so on). In particular, BI vendors use the “analytics” moniker to differentiate their products from the competition. Increasingly, “analytics” is used to describe statistical and mathematical data analysis that clusters, segments, scores and predicts what scenarios are most likely to happen. Whatever the use cases, “analytics” has moved deeper into the business vernacular. Analytics has garnered a burgeoning interest from business and IT professionals looking to exploit huge mounds of internally generated and externally available data.

Knowledge: It is a type of know-how that makes it possible to transform information into instructions. Knowledge can either be obtained through transmission from those who possess it, or by extraction from experience.

Machine Learning: A method of designing a sequence of actions to solve a problem that optimizes automatically through experience and with limited or no human intervention.

Startups: The first thing that is associated with entrepreneurship is startups. It is necessary to establish its definition in order for it to be used later on in this study. A startup is a human institution designed to create a new product or service under conditions of extreme uncertainty. A startup is also considered as an organization formed to search for a repeatable and scalable business model. The term scalable suggests that the aim of every startup is to grow (and, consequently, to stop being a startup) and into mature to a fully functional company: to an SME.

Data Analysis: This is a class of statistical methods that make it possible to process a very large volume of data and identify the most interesting aspects of its structure. Some methods help to extract relations between different sets of data, and thus, draw statistical information that makes it possible to describe the most important information contained in the data in the most succinct manner possible. Other techniques make it possible to group data in order to identify its common denominators clearly, and thereby understand them better.

Algorithm: A set of computational rules to be followed to solve a mathematical problem. More recently, the term has been adopted to refer to a process to be followed, often by a computer.

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