CAIIB ABFM Module D Unit 5 : Business Analytics As Management Tool

CAIIB Paper 3 ABFM Module D Unit 5 : Business Analytics As Management Tool (New Syllabus) 

IIBF has released the New Syllabus Exam Pattern for CAIIB Exam 2023. Following the format of the current exam, CAIIB 2023 will have now four papers. The CAIIB Paper 3 (ADVANCED CONCEPTS OF FINANCIAL MANAGEMENT) includes an important topic called “Business Analytics As Management Tool”. Every candidate who are appearing for the CAIIB Certification Examination 2023 must understand each unit included in the syllabus.

In this article, we are going to cover all the necessary details of CAIIB Paper 3 (ABFM) Module D (EMERGING BUSINESS SOLUTIONS) Unit 5 : Business Analytics As Management Tool, Aspirants must go through this article to better understand the topic, Business Analytics As Management Tool and practice using our Online Mock Test Series to strengthen their knowledge of Business Analytics As Management Tool. Unit 5 : Business Analytics As Management Tool


  • Business analytics (BA) refers to the combination of skills, technologies, and practices that are used to analyse the data and performance of an organisation in order to gain insights and make decisions in the future, that are driven by data.
  • Statistical analysis is one of the most common methods used in business analytics.
  • Objective of business analysis: To determine which datasets are valuable and which have the potential to boost revenue, productivity, and efficiency.
  • Used to make accurate predictions of future events that are related to the activities of consumers, and trends in the market.
  • Also help create more efficient operations, which could contribute to an increase in revenue, if it is used to its full potential.

Data Mining History and Origins

  • During late 1980s and early 1990s, data warehousing, business intelligence, and analytics technologies began to develop.
  • These innovations provided an enhanced capability to evaluate the ever-increasing amounts of data that organisations were creating and gathering.
  • By the year 1995, when the 1st International Conference on Knowledge Discovery and Data Mining was held in Montreal, the phrase “data mining” was already in common usage.
  • The Association for the Advancement of Artificial Intelligence (AARI), was the organisation that was responsible for sponsoring the event.
  • The conference, which has been held annually since 1999 and is commonly referred to as KDD 2021 and so on, is primarily coordinated by Special Interest Group on Knowledge Discovery in Data (SIGKDD), which is part of the Association for Computing Machinery that focuses on knowledge discovery and data mining.
  • In 1997, the first issue of a specialised journal called Data Mining and Knowledge Discovery was released to the public.
  • In 2016, a second publication known as the American Journal of Data Mining and Knowledge Discovery was made available to readers.

Essentials Of Business Analytics

There are numerous different applications for Business Analytics (BA); however, when it comes to commercial enterprises, BA is most commonly used to:

  • Analyse data coming from a range of different sources. Anything from cloud applications to marketing automation tools and customer relationship management software could fall under this category.
  • Find patterns within the data sets by employing more complex analytics and statistical methods. These patterns can assist you in predicting future trends and providing you with new information regarding consumers and the behaviours they engage in.
  • Keep an eye on key performance indicators (KPIs) and trends as they evolve in real time. Because of this, it is much simpler for companies to not only store all of their data in a single location but also draw correct and speedy conclusions from those data.
  • Back and support decisions based on the most recent available facts. Because BA gives us access to such a large amount of data that we can put to use in support of business decisions, we can be certain that we are well-informed not only for one but also for multiple distinct scenarios.

Types Of Analytics

There are four primary approaches to business analysis, and each one is put into practise in succession, beginning with the least complicated.

When you apply these four different types of analytics, your data can be cleansed, examined, and digested in such a way that makes it feasible to produce answers for any difficulties that your organisation may be facing.

  • Descriptive analytics: This method involves the interpretation of historical data and key performance indicators to discover patterns and trends.
  • Diagnostic analytics: This type of analysis focuses on previous performance to understand which factors drive particular trends.
  • Predictive analytics: This is the practice of applying statistics to estimate and evaluate future outcomes by employing statistical models and techniques derived from machine learning.
  • Prescriptive analytics: This approach makes use of data on previous performance to make recommendations for how similar situations should be managed in the future.

Elements Of Business Analytics

When one takes a more in-depth look at business analytics, the method of business analytics that we choose to use is going to be contingent on the end-goal that we establish for ourselves before beginning the process. No matter the approach a person decides to take, they will undoubtedly be rewarded at the end with insights that can be put into practice.  The various elements of business analytics are as follows:

  • Data Mining
  • Text Mining
  • Data Aggregation
  • Forecasting
  • Data Visualisation

Excel Proficiency

  • The ability to edit text documents, develop templates, and automate the generation of tables of content in Microsoft Word is often required to be considered proficient in Microsoft Office.
  • Being proficient with Excel requires being able to execute and create functions, pivot tables, and charts.

The following is a list of the numerous Excel skills that need to be kept up to date: 

Big Data Analytics

  • Big data analytics is the application of more advanced analytical methods to very large, diverse data sets.
  • These data sets might be organised, semi-structured, or unstructured, come from a variety of sources, and range in size from terabytes to zettabytes.
  • Big data is a term that refers to data sets that are so large or complex that typical relational databases are unable to effectively record, manage, or process the data in a timely manner.
  • This form of data is known as unstructured data.
  • Big data can be characterised by high volume, high velocity, or high diversity, or all three of these properties simultaneously.
  • The rise of artificial intelligence (AI), mobile, and social platforms, as well as the Internet of Things (IoT), are all contributing to an increase in the complexity of data through the introduction of new forms and sources of data.

Uses of Big Data Analytics 

Big Data Analytics can be used for the following purposes:

  • Enhancing the integration of the customers Gathering data that is structured, semi-structured, and unstructured from the various touch points that customers have with the firm in order to obtain a comprehensive understanding of the client’s actions and the factors that motivate them so that we may better our personalised marketing. Data sources can include social media, sensors, mobile devices, sentiment and call log data.
  • Detecting and minimising frauds Monitoring transactions in real time and staying on the lookout for strange patterns and behaviours that could indicate fraudulent activity. Companies are able to detect and prevent fraud by utilising the power of big data in conjunction with predictive and prescriptive analytics, as well as the comparison of historical and transactional data.
  • Improving the efficiency of the supply chain. Collecting and examining large amounts of data to figure out how items get to their final destination, highlighting areas of inefficiency as well as opportunities to cut costs and save both time and money.  Tracking vital information from the warehouse to its final destination with the use of sensors, logs, and transactional data is possible.

The use of big data analytics in the following six industries has been explained below:

  • Manufacturing
  • Retail
  • Health Care
  • Oil & Gas
  • Telecommunication
  • Financial Services

Web And Mobile Analytics

Traditional web analytics and mobile web analytics are two different approaches to the same research question:

  • How mobile website users behave. Mobile web analytics is a term that is used in the business world to describe the process of collecting data from customers who browse a website using their mobile phones.
  • It is helpful in determining which aspects of the website work best for mobile traffic and which mobile marketing campaigns work best for the business. Some examples of mobile marketing campaigns include mobile advertising, mobile search marketing, text campaigns, and desktop promotion of mobile websites and services.


Platforms The various platforms are as follows:

  • HTML/JavaScript
  • WordPress Mobile Pack
  • PHP
  • NET
  • Java
  • Python
  • ColdFusion
  • Ruby on Rails
  • js/Connect
  • TypePad Pro

Problems with Tracking 

  • Visitor identification
  • JavaScript page tagging
  • HTTP Cookies
  • Image Tags
  • IP Address

Collecting Mobile Web Analytics Data

  • Packet Sniffing
  • Image Tags or Beacons
  • Link Redirection
  • HTTP Header Analysis
  • IP Address Analysis
  • WAP Gateway Traffic logs


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CAIIB Paper 3 Module D Unit 5 – Business Analytics As Management Tool ( Ambitious_Baba )




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