Business Intelligence is defined “as a system comprised of both technical and organizational elements that present its users with historical information for analysis to enable effective decision-making and management support, with the overall purpose of increasing organizational performance.” Corporations invest millions of dollars in business intelligence tools to give business analysts the ability to make data-driven decisions, monitor key performance indicators, and gain insights on how to improve their results.
What is Business Intelligence (BI)?
Top Business Applications
Measurement: Enterprises use BI to create a hierarchy of performance metrics and bench-marking. This information gives executives and managers insights about their progress towards their business objectives.
Analytics: Companies use analytics to form quantitative processes so that businesses can create data-driven decisions. This allows businesses to uncover insights on the data they collected. This process includes data mining, process mining, statistical analysis, predictive analytics, predictive modelling, business process modelling, data lineage, complex event processing, and prescriptive analytics.
Reporting and enterprise reporting: Businesses use reporting to serve as an infrastructure for companies to communicate their progress with their strategic goals. This process includes data visualization, executive information systems, and Online Analytical Processing (OLAP).
Collaboration platform: This component gets different areas outside and within the business to work together with data-sharing and electronic data interchange. For example, companies use programs like Slack or Yammer to collaborate.
Knowledge management: Companies remain competitive and data-driven by using strategies and practices to identify, create, represent, distribute, and enable adoption of insights and experiences that are true business knowledge. Knowledge management is crucial for learning management to ensure their skills are up to date, knowledge transfer within teams, and regulatory compliance.
Many organizations fail to execute their strategy due to misaligned resources, thereby creating a need for intelligent planning and analysis. Financial planning is understanding a business’ current and future financial state in order to predict the effect of known variables on future cash flows and outcomes. This can create an overall structure for a business and help determine the allocation of resources in order to achieve operational and strategic goals.
The basic tools used for financial planning include balance sheets, income statements, and cash flows. Financial planning can also determine potential risks, allowing users to data-driven decisions, achieved by running different financial simulations.
Simulations help manage risk and cash flows by predicting different outputs as inputs change. This is done by creating relationships between known inputs, assigning mathematical equations to describe the relationships, and generate predicted future outcomes.
In order to test the quality of a financial simulation model, programs such as KNIME allow the user to partition data into a “learning data set” and testing data set. Once the program has been run, quality scores are given to the financial model allowing the user to determine how reliable the outcomes of the financial model are. It is important to note that the quality of input data in simulations determine the quality of its output.
Value Driver Trees
Value Driver Trees (VDT) are a simulation tool that builds connections with different functional areas of a business in order to run simulations and showcase how a change in one area can impact other areas. Examples of questions that VDTs can answer include: What if I fired half my employees? What if I gave everyone a raise? What if I discontinued my products?
VDTs are able to do this by assigning numeric data to different nodes, such as data source nodes, union nodes, and calculation nodes. VDTs typically start with data source nodes that represent raw data which are then built upon. The outcome is a financial model that is able to understand relationships between different data points.
Data visualization is the graphical representation of information and data. It can range from a simple bar chart depicting the most popular pizza toppings, a subway map colourfully representing an entire city’s transit routes, to a complex matrix linking the connections made between people on social media. By using these visual elements, data visualization is an accessible way to see and understand trends, outliers, and patterns in data.
The development of data visualization is often linked to the recent developments in modern statistics. However, the practice of conveying quantitative information in a visual manner has existed for centuries.
The early history of data visualization encompasses cartography, statistics and statistical graphs, and several applications in science and medicine. Its rising adoption can also be attributed to the rise of statistical thinking and the vast amounts of data collected for planning and commerce throughout the 19th century. Since then, the sophistication of data visualization has vastly improved to include better tools for reproducing images, data collection and observation, mapping, and more.