Business Intelligence and analytics at a glance. As over-the-top as they may sound, their applications touch close to home.
The earliest known reference of the term ‘Business Intelligence emerged in 1865. “Throughout Holland, Flanders, France, and Germany, he maintained a complete and perfect train of business intelligence. The news of the many battles fought was thus received first by him, and the fall of Namur added to his profits, owing to his early receipt of the news.”, Cyclopædia of Commercial and Business Anecdotes[(1865), p. 210].
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IBM researcher Hans Peter Luhn, in a 1958 article described Business Intelligence quoting Webster’s dictionary of definition – “the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal.”
Howard Dresner (later a Gartner analyst), described Business Intelligence as an umbrella term, “concepts and methods to improve business decision making by using fact-based support systems”, in 1989.
However, Business Intelligence as we understand it today has its roots in Decision Support System (DSS) that started in the 1960s and was developed through the mid-1980s.
Business Intelligence (BI)
Business Intelligence can be broadly described as the strategic planning that goes into a business of any sort. From a technological point of view, it may be described as “a set of techniques and tools for the acquisition and transformation of raw data into meaningful and useful information for business analysis purposes”.
BI technologies throw light on the historical, current, and predictive views of business operations. Business intelligence technologies report, process online analysis, perform analytics data mining, process mining, process complex events, manage business performance, perform benchmarking, text mining, predictive analytics, and prescriptive analytics.
Applications of BI
Business Intelligence may be applied for the following business purposes in order to drive business value:
- Measurement – program that creates a hierarchy of performance metrics and benchmarking that informs business leaders about progress towards business goals (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, data lineage, complex event processing, and prescriptive analytics.
- 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, and OLAP. - Collaboration/collaboration platform – a 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.
Aside from the above Business Intelligence also serves to notify or alert people or systems when certain conditions are not met. For example, if any particular business metric exceeds a pre-defined threshold, the metric will be highlighted in standard reports and the business analyst may be alerted via e-mail or another monitoring service.
Business Analytics
Analytics follows from the understanding, detection, and communication of significant patterns of data. Analytics applied in businesses improve their performances.
Business analytics (BA) is drawn from the skills, technologies, practices for continuous iterative exploration and investigation of data from past business performance to gain insight and drive business planning. It focuses on developing new insights and understanding of business performance based on data and statistical methods. This comes as a stark contrast with business intelligence that traditionally focuses on the use of a consistent set of metrics to both measure past performance and to guide business planning, which is also based on data and statistical methods.
Predictive Analytics
Predictive analytics, a subset of Business Analytics, borders around a multitude of statistical techniques such as predictive modeling, machine learning, and data mining to analyze current and historical facts to predict future outcomes or otherwise unknown events.
In business, predictive models derive facts from patterns found in historical events and transactional data to identify potential risks and opportunities. Models capture relationships among many factors to base their assessment of risk or potential associated with a particular set of criteria, to guide the decision-making for candidate transactions.
Applications of Predictive Analytics
Here are some of the applications of predictive analysis:
1. Analytical Customer Relationship Management (CRM)
Analytical customer relationship management (CRM) is a popular and frequent commercial application of predictive analytics. Predictive analysis techniques are applied to customer data to pursue CRM objectives. This involves constructing a holistic view of the customer irrespective of where their information resides.
CRM uses predictive analysis in applications for purposes of marketing campaigns, driving sales, and customer services to name a few. These tools are required for a company to position and focus its efforts effectively across the breadth of its customer base. Companies must analyze to gain market insights of the products in demand or have the potential for high demand (in the recent future), foresee customers’ purchase habits in order to promote relevant products at multiple touchpoints. Companies must also proactively identify and mitigate issues that have the potential to lose customers or reduce their ability to gain new ones. Analytical customer relationship management can be applied throughout the customer’s lifecycle (acquisition, relationship growth, retention, and win-back).
2. Child Protection
Over the past few years, some child welfare agencies have initiated the practice of using predictive analytics to flag high-risk cases of child abuse. The approach has been lauded and tagged “innovative” by the Commission to Eliminate Child Abuse and Neglect Fatalities (CECANF), and in Hillsborough County, Florida. The lead child welfare agency uses a predictive modeling tool. There have been no abuse-related child deaths recorded since its inception.
3. Clinical Decision Support System
Experts in the domain utilize predictive analysis in health care chiefly to determine which patients stand at risk of developing certain conditions, like diabetes, asthma, heart disease, and other lifetime ailments.
Apart from that, sophisticated clinical decision support systems incorporate predictive analytics to bolster medical decision-making at the point of care.
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Winding-Up
The selectively focussed concept serves to provide an understanding of the derivation of predictions and foresight through analysis of data in the range of a timeline. The scope of this article serves to shed some light on the practical working of the technologies and how they touch ground in various departments.
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