•Exponential data growth fueled by connected devices and end points that make up the Internet of Things has compelled organizations to revisit their ability to use Big Data to make more intelligent, real-time decisions. Considering the hyper-competitive business environment, this critical need has given rise to a new breed of analytics solutions focused on prediction, data visualization, and dynamic decision making. •This study is based on an in-depth Latin America analysis of Big Data analytics solutions focused in three countries: Brazil, Mexico and Colombia. •Revenue of these three markets together were $ x million in 2014. Revenue is expected to reach $ x billion by 2019 at a x % compound annual growth rate (CAGR). •Though still nascent, these markets already have Enterprise companies investing in Big Data, where most of the projects are still on the planning and consulting phase. Starting 2015, investments in infrastructure and software are likely to increase in order to create an environment that can manage and analyze all data. •Use cases for analytics fall under four major categories: customer insight; risk, security, and intelligence; resource optimization; process/productivity improvement. Churn analytics, targeted promotions for better customer retention, and credit card fraud detection are some of the most profitable applications. •Absence of appropriate infrastructure, lack of relevant skills and knowledge, and the need for high investments, are the key challenges in the market. •Other challenges stem from the lack of market education, the difficulties associated with quantifying the benefits of additional information, and the lack of consensus of what is Big Data and how providers offer it. •Many solutions offered by the vendors require extensive IT expertise from customers. Because of the low concentration of these experts in the Latin America market, some companies are hiring these experts from United States to speed up the project. •Financial services and Telecom companies are early adopters. Retail and government are smart followers. •Most projects start inside business departments, especially marketing, followed by sales and procurement. IT also has some demands but are more related to data security against hackers.
Frost & Sullivan defines Big Data as volumes of data so large and moving at such a high velocity that they are difficult or impossible to work with using traditional database-management tools. Big Data represents the collective and exponential growth of both structured and unstructured data from every imaginable digital source, including data logs, pictures, audio, and video. To structure what Big Data entails, our study has defined the following criteria:
Volume Big Data is always large in volume. This Frost & Sullivan study considers projects that store at least x terabytes as Big Data projects.
Variety Big Data does not so much define the size of a database or individual file but rather the vast diversity of data types. Now organizations must deal with x types of data: structured and unstructured. Frost & Sullivan has identified x different data sources and considers projects that integrate at least x different types of data sources as Big Data projects.
Velocity With the increase of data volume and its variety, companies have a need to more rapidly capture and analyze this data. Frost & Sullivan considers projects that deliver analysis in less than x hour as Big Data projects.
The Big in Big Data
Big Data is a common catchphrase in the business world today. In reality, it is far from a buzzword, however, as it is radically changing the traditional perception of data. Big Data started transforming the decision-making process for entire organizations. In order to understand the Big Data concept, discussion must be had regarding the underlying factors that have led to the creation of Big Data, as well as the sources that are feeding the Big Data paradigm.
Internet of Things (IoT): The IoT has transformed the world into a state of hyper-connectivity. Frost & Sullivan defines the IoT as a network of interconnected objects uniquely addressable based on standard communication protocols, thereby creating smart networks composed of devices that communicate with one another. This seamless interactivity between devices and end points, such as tablets and smartphones, sensors, smart systems, and robots, is generating an overwhelming amount of data every second. The IoT has enabled isolated systems to interact with one another and transmit information that empowers humans. Connected homes, sensors on raw food products, and mail tracking systems are all applications of the IoT.
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