The term Business Intelligence (BI) was defined as ‘the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal.’ in a 1958 article by an IBM researcher, Hans Peter Luhn. It has evolved from the Decision Support Systems (DSS) which began in the 1960s. DSS originated in the computer-aided models created to assist with decision making and planning. From DSS, data warehouses, Executive Information Systems, OLAP and business intelligence came into focus beginning in the late 80s.
BI applications usually use the company repository of data, supplemented by public data, and transform it into information that gives insight into business trends that would otherwise not be visible. BI applications enable the company to take well informed strategic decisions. A good suite of BI applications, backed by high quality raw data, can give a company a significant edge over its competitors.
The Wikipedia entry on the topic states that Business Intelligence can be applied to five distinct purposes (MARCKM), in order to drive business value:
- Measurement – program that creates a hierarchy of Performance metrics (see also Metrics Reference Model) and Benchmarking that informs business leaders about progress towards business goals (AKA Business process management).
- Analytics – program that builds quantitative processes for a business to arrive at optimal decisions and to perform Business Knowledge Discovery. Frequently involves: data mining, process mining, statistical analysis, Predictive analytics, Predictive modeling, Business process modeling
- Reporting/Enterprise Reporting – program that builds infrastructure for Strategic Reporting to serve the Strategic management of a business, NOT Operational Reporting. Frequently involves: Data visualization, Executive information system, OLAP
- Collaboration/Collaboration platform – program that gets different areas (both inside and outside the business) to work together through Data sharing and Electronic Data Interchange.
- Knowledge Management – program to make the company data driven through strategies and practices to identify, create, represent, distribute, and enable adoption of insights and experiences that are true business knowledge. Knowledge Management leads to Learning Management and Regulatory compliance/Compliance.
The above five areas can be broadly grouped into two categories – the first three are Quantitative, and the other two are Qualitative. Good knowledge management and collaboration practices are essential to ensure that the data used for BI is of high quality (we all know ‘Garbage In Garbage Out’). The data then needs to be analysed to produce quantitative information, usually presented pictorially so that it is easy to understand.
The quantity of data that is available within an organisation is increasing exponentially. External data that needs to be taken into account is exploding even faster. Storing and processing this data to produce meaningful insights is becoming a challenge for the practitioners of BI because of increasing costs and the current economic climate.
Cloud Computing presents a simple answer to these challenges. A company, instead of purchasing its own hardware and building a data centre whose average utilisation is in the single digits or low teens at the best, can simply use the Cloud to rent processing power that is needed only for a day or two to run those monthly reports! Confidential data can be kept on-premises, and even proprietary algorithms can be executed on the company’s own infrastructure…build a hybrid solution that just offloads the non-confidential bits to a public cloud.
This can lead to significant savings, and empower a company to use algorithms and process data that is simply not affordable with traditional on-premises solutions.
There are extreme examples from Scientific Computing and Data Visualization where the Cloud has already delivered cost reductions from millions to thousands of dollars. BI in the Cloud certainly has the potential to reduce costs by an order of magnitude and simultaneously make it possible to run complex analytical algorithms.