Identifying Market Niches in the Age of Big Data

Abstract

This report aims to describe how information systems support management decisions at different levels within organisations and what are the theories, methods and techniques which relate to the design and development of IT solutions.

This paperwork is based on a case to be analyzed and it is structured on the answers to four questions, answers that want to identify market niches in the age of big data.

Introduction

Nowadays, in the evolution of the economy and society, the importance of database systems is constantly growing. Developments in information systems technology have led to an increase in the degree of computerization of many applications in various business areas. Data has become a resource in many organizations and thus, accessing it efficiently, distributing it, extracting information and creating a system capable of managing it effectively, have become more than urgent needs.

For a long time, database systems were exclusively the responsibility of IT specialists. Currently, the use of databases has become accessible to all computer users.

Analyse the Data

As a simple definition, Data are ”information, especially facts or numbers, collected to be examined and considered and used to help decision-making, or information in an electronic form that can be stored and used by a computer”. (Anon., 2020).

In other words, data is raw material divided in different forms (numbers, characters, symbols, images, sounds, electromagnetic waves, bits) that are being recorded and stored, and so knowledge are created. (Kitchin, 2014).

As Kitchin (2014) sustains, some data (numbers) can be easily organised, stored and analysed using calculus and algorithms, but some data are irregular, with an undefined structure (narrative text, photos, videos) and these can be stored and analysed using advanced databases and machine learning techniques.

Data can be generated either by capturing them directly from sources (surveys, observations, cameras, scanners, laboratory experiments, recordings), or are inherently produced by devices and systems.

According to Cuesta (2013), data analysis is a process in which organized data will be used to explain what happened and predict the future, so is about making questions and developing explanations. To do that, data analysis combines computer science, artificial intelligence and machine learning, statistics and mathematics, and knowledge domain.