Business Intelligence Tools in the Big Data Era
At the dawn of the fourth industrial revolution, data is considered one of the most valuable assets of modern enterprises. Companies are increasingly becoming data driven, both in their business operations and in their managerial decision making. Nevertheless, data alone cannot boost optimal operations and effective decisions. Rather, it’s the processing of the data and its conversion into useful and actionable information that drive data driven business transformation and optimization. This is where Business Intelligence (BI) comes into play: BI refers to the business strategy and technological tools used for analyzing business information, including analysis of historical data, analysis of current data, as well as future predictions. Hence, BI is a business discipline, much as it is also a technology discipline. As part of the technological part of BI, companies use various databases and data analytics tools, which comprise their enterprise BI infrastructure. BI tools have been around for decades. However, in recent years, the advent of Big Data and Artificial Intelligence technologies have increased the number and broadened the functionalities of BI technologies.
Data management is a key prerequisite for the deployment and operation of BI tools. Business Intelligence relies in the collection, consolidation and analysis of large volumes of data, which are typically stored and managed in different types of databases. A relational (SQL) database in the most basic and widespread data management infrastructure, yet it is not sufficient for BI analytics and applications. In most cases BI analytics operate over historical data, which are kept within Data Warehousing systems. The latter keep track οf the full history of data entities, which is a foundation for data mining processes such as trends analysis, discovery of associations between business entities and predictive analytics. Data warehouses consolidate data from many different sources and operational databases, including data sources external to an organization. For example, a customer data warehouse consolidates data from many different sources of customer information such as e-banking systems, wealth management systems and more. The process of populating a data warehouse from some other data source, involves conversion of data between different structures and formats and is commonly known as ETL (Extract Transform Load).
Databases and data warehouses are two of the main pillars of any enterprise data management infrastructure. They are extremely effective in handling structured data and as such enable high quality and sophisticated reporting capabilities. Nevertheless, in the Big Data era, enterprises are offered with opportunities of handling large amounts of structured and semi-structured data, such as data from sensors, connected devices and social media. Such unstructured and semi-structured data are usually stored in the so-called noSQL databases, which provide better scalability for large amounts of unstructured data, yet they offer looser consistency in their transactions. Furthermore, in the era of Big Data organizations are also deploying data lakes, which persist and manage large amounts of data stored in raw format, such as object blobs or files. Nowadays, an organization’s enterprise management infrastructure consists not only of data warehouses, but also of data lakes as well.
BI tools are deployed on top of the enterprise data management infrastructure. From a functional perspective, they can be categorized as follows:
The above list of BI tools comprises utilities that are general purpose and sector agnostic. However, there is a host of sector specific tools such as tools for the business analysis of production planning and supply chain interactions, or even tools for gaming analytics and learning analytics. The field of Business Intelligent is growing rapidly and enterprises are provided with unprecedented opportunities for implementing data-driven business strategies and for improving their business performance. It’s therefore important to understand the BI infrastructures and tools landscape, in order to select the right tools and take proper decisions as part of your BI strategy.
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