Recently, there has been a big buzz by companies on big data and analytics. In this report, I discuss business intelligence tools and how they can be used to solve big data challenges. I also discuss the growth of BI over the past decade and some of its common features used for decision making.
Gone are the days of big data challenges. With advancements in technology, companies can now transform their data into useful information using Business Intelligence tools and applications. Business Intelligence (BI) tools, also referred to as Decision Support Systems, are defined as a set of systems, software, applications and tools used to analyze data with an aim to produce useful information that will assist decision makers to work more efficiently and productively (Castellanos, Dayal, Simitsis, & Wilkinson, 2009).
Business Intelligence tools first came into the limelight in 1865. However, it is not until the late 1990s, that the word became a popular phrase in the business world. The initial Business Intelligence tools were considered an extra investment for companies to gain a competitive advantage, therefore not many businesses had discovered its benefits. They were designed with an aim to produce and organize data into reports. However, time and complexity held back the development of these tools. For example, this generation of tools could only be used and accessed by IT staff or experts knowledgeable in the programming field (Boateng, 2016).
In the early 2000s, there was a shift in technology to address the issues above of complexity and time. The newer generation of Business Intelligence tools went through improvements in the following areas including self-service applications, automated reports, visualization and real time data processing. Furthermore, the visualization function evolved to include the needs of end users with little to no training making it simpler and more convenient to everyone. At this point, business intelligence tools were no longer a competitive edge but a necessity for most businesses to run profitably.
Another shift in Business Intelligence technology has been the introduction of cloud computing services to provide storage solutions and server infrastructure. Cloud computing has allowed companies to efficiently reuse their company’s IT resources while having immediate access to real time data processing. Though still in its infant stages, we expect cloud computing uptake to increase in the next years to come (Accenture, 2010).
The use of Business Intelligence tools has become a common feature in industries that rely on data analytics such as healthcare, insurance, finance, law and even SMEs like restaurants. These tools have been designed to meet the specific needs of these industries. For example, within the insurance industry, a business intelligence tool based on historical data may be used to detect and prevent fraud thereby reducing insurance costs.
In the future, we expect a further enhancement in the current Business Intelligence tools into simpler tools designed that features in all business process while meeting the needs of its end users.
The Common Features of Business Intelligence Tools that support Decision making.
Business Intelligence tools have evolved over the last decade from basic collection and organization of data to providing services such as cloud computing, reporting, and visualization. Some typical features of Business Intelligence applications and software include a data warehouse, data discovery tools such as data mining tools, OLAP techniques, cloud computing and reporting tools which are discussed briefly below.
A data warehouse is the core of a Business Intelligence tool. Data warehouses are used for storage and organization of historical data. Some data warehouses also include data management tools that ensure only quality and reliable data is stored and organized. Data warehouses are critical for industries that do a lot of data analytics such as insurance companies.
ETL and OLAP Tools
ETL tools extract, transform and restructure data into a useful form. Furthermore, data discovery applications like data mining can be used on the data to create custom reports such that a company can use this information to make decisions. The process involves discovering patterns relationships and patterns in the data. This additional feature of Business Intelligence tools make ETL tools more expensive and should be used in conjunction with a data warehouse for optimum results. OLAP tools on other hand provide multi-dimensional analysis of data (Olszak & Ziemba , 2006).
Reporting and Visualization
When it comes to advancements in Business Intelligence, the focus has been on user experience. Reporting tools provide users with simple easy to read reports and visualizations of data. Some examples include quick dashboards, score cards, and report writers. These additional tools help decision makers and end users to generate reports immediately while focusing on key metrics via the use of scorecards.
Cloud Computing Services
With the introduction of the internet, there was no need for companies to invest in expensive data warehouses and servers. Cloud computing services have allowed companies to efficiently reuse their IT resources while having quick access to real time data (Accenture, 2010).
Cloud computing was initially developed for storage solutions and servers infrastructure. However, with time, their functions have expanded to include businesses’ support functions such as office and email, customer service, web conferencing facilities, enterprise applications, project planning and other functions (Edara & Kandagatla, 2012).
We are now at a point where data has become a critical component of a company’s decision making process. Consequently, business intelligence tools are now a common feature in many businesses due to their capabilities to analyze data into useful information.
Business Intelligence tools have evolved from the complex tools that most people knew them as in the 90s, to simpler user friendly tools. As technology continues to advance, we expect Business Intelligence tools to become simpler and more collaborative in the process.
Furthermore, we expect the take up of Business Intelligence tools to increase in the coming years with most companies implementing these applications in the next few years.
Accenture. (2010). How Cloud Computing will Transform Insurance. Retrieved from Accenture:
Boateng, C. (2016, September 6). Informative Stats: The Growth and Value of Business Intelligence. Retrieved from Christian Seven:
Castellanos, M., Dayal, U., Simitsis, A., & Wilkinson, K. (2009). Proceedings of the 12th International Conference on Extending Database Technology: Advaces in Database Technology. Retrieved from
Edara, S., & Kandagatla, R. (2012). Cloud Computing in the Property &. Retrieved from Capgemini:
Olszak, C. M., & Ziemba , E. (2006). Business intelligence systems in the holistic infrastructure development supporting decision-making in organizations. nterdisciplinary Journal of Information, Knowledge and Management, 47-58.