Module 1 - Overview of Business Analytics PDF

MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 1 MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS INTRODUCTION Business analytics is a relatively new term that is gaining popularity in both business and academic circles like nothing else in recent history. In most general terms, busine...

MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 1 MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS INTRODUCTION Business analytics is a relatively new term that is gaining popularity in both business and academic circles like nothing else in recent history. In most general terms, business analytics is the art and science of discovering insight – by using sophisticated mathematical, statistical, machine learning, and network science methods along with a variety of data and expert knowledge – to support better and faster decision-making. In addition, business analytics can also be thought of as an enabler for decision-making and problem solving. LEARNING OUTCOMES After reading this module, the learner should be able to: 1. Explain what business analytics is; 2. Discuss the basic concept about business analytics; 3. Discuss the business model canvas and its building blocks; and 4. Prepare a business model canvas and value chain model. TIME The time allotted for this module is six (6) hours. LEARNER DESCRIPTION The participants in this module are BSBA second year students. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 2 MODULE CONTENTS LESSON 1.1. Business Analytics Source: https://onlinemasters.ohio.edu/blog/benefits-of-a-business-analytics-course-and-degree/ The word analytics has come into the foreground in last decade or so. The proliferation of the internet and information technology has made analytics very relevant in the current age. Analytics is a field which combines data, information technology, statistical analysis, quantitative methods, and computer-based models into one. These are combined to provide decision makers all the possible scenarios to make well-thought and researched decisions. The computer-based model ensures that decision makers are able to see the performance of such decisions under various scenarios. A subset of Business Intelligence (BI), business analytics is generally implemented with the goal of identifying actionable data. Business intelligence is typically descriptive, focusing on the strategies and tools utilized to acquire, identify, and categorize raw data and report on past or current events. Moreover, it is prescriptive and devoted to the methodology by which any data can be analyzed, patterns can be recognized, and models can be developed to clarify past events, create predictions for future events, and recommend actions to maximize ideal outcomes. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 3 Definition of Business Analytics According to numerous sources, business analytics can be defined as:  The process of transforming data into insights that supports improve, and/or automate business decision.  The skills, technologies, practices for continuous iterative exploration and investigation of 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 (Sharma, 2018; Bentley, 2019).  The study of data through statistical and operations analysis, the formation of predictive models, application of optimization techniques, and the communication of these results to customers, business partners, and college executives (Galleto, 2018).  The acquisition of quantitative methods and evidence- based data for business modeling and decision making.  The practice of iterative, methodical exploration of an organization’s data with emphasis on statistical analysis which can companies committed to data- driven decision making. Importance of Business Analytics  Business analytics is a methodology or tool used to make sound commercial decisions, improve the profitability of the business, increase the market share, and provide better return to a shareholder.  It facilitates better understanding of available primary and secondary data which affects the operational efficiency of several departments.  It provides a competitive advantage to companies. In this digital age, the flow of information is almost equal to all the players. It is the usage of the information that makes the company competitive.  It converts available data into valuable information. This can be presented in any required format comfortable to the decision maker. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 4 User of Business Analytics  Students  Businessman  Accountants  Organization  Companies  Group of industries  Small firm  Auditors Advantages of Business Analytics  Eliminates guesswork  Gets faster answer to one’s question  Gets insight into customer behaviour  Identifies cross selling and up selling opportunities  Gets key business metrics reports when and where one needs them Challenges in Implementing Business Analytics in an Organization  Lack of technical skills in employees  Fussing over acceptable of B.A. by staff  Data security and maintenance  Integrity of data  Inability to address complex issues Why is it Time to Put Analytics to Work? Most companies today have massive amounts of data at their disposal. We can see this by just accessing the net, social media and other surveys out there that hold a very vast amount of data beneficial to the organization. What are the Sources of Data?  Customers  Competitors  Within the organization  Outside the organization  Environment FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  When is Business Analytics Not Practical? MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 5  When there is no data.  When there is no precedent.  When history is misleading.  When the decision maker has no considerable experience.  When the available data is not applicable to the current software or program. Activity 1.1.  Read the online article on “Definition: What is Business Intelligence?” by Business Intelligence the Definitive Guide  Watch online video on “Introduction to Business Analytics” by Cody Baldwin  Read the article on “What is Business Analytics?” by Molly Galleto Study Questions 1. After learning about the different definitions of BA and BI, can you now compare/differentiate BI and BA? 2. Galleto (2016) mentioned that "While Business Intelligence answers what happened, Business Analytics answers why it happened and whether it will happen again". What are your thoughts on this? FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 6 References Definition: what is business intelligence? Retrieved from https://www.microstrategy.com/us/resources/introductory-guides/business- intelligence-the-definitive-guide Baldwin, C. (2016). Introduction to business analytics. Retrieved from https://www.ssyoutube.com/watch?v=9IIgH0hNtgk. Bently, D. (2019). Business analytics: Principles, concepts and applications. Larsen and Keller. Galletto, M. (2016). What is business analytics? Retrieved from https://www.ngdata.com/what-is-business-analytics Sharma, C. H. (2018). Business analytics: Concepts and theories. Random Publications. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 7 LESSON 1.2. Big Data and Business Analytics At this point we have an idea about Business Analytics (BA) and how it differs from Business Intelligence (BI). It is now the time to introduce another concept related to BA which is the Big Data. Sharma (2018) defines big data as the collection of data sets that are so large and complex that software systems are hardly able to process them. SAS however defines it as a term that describes the large volume of structured and unstructured data which can be analyzed for insights needed for better decisions and strategic business moves. IBM, on the other hand, refers to it as data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency. What is the difference? Business Analytics is said to focus on financial and operational analytics of the business while big data involved machine automation to analyze data. The importance of big data is not on how much data you have, but what you do with those data. There are four aspects that define big data which are volume, variety, velocity and veracity (Almodiel & Garcia, 2019). 1. Volume is about how huge the data sets are. 2. Variety includes how many pieces of data we gather together from social media data, government data, financial data, banking data, all sorts of transactions all combined together to make one or more profiles for your customers. 3. Velocity is the speed of data. 4. Veracity means that there is a lot of uncertainty, meaning, there is all of these different data coming together, but the problem is we don’t know what to do with them. Activity 1.2.  Read the online article on "Difference between Big data and Business Analytics"  Watch online video on “Big Data Analytics for Business” Study Questions 1. Upon reading the article how can you relate the above discussion to the article? 2. Compare the discussion above against from the video presentation more specifically on the four aspects the define big data. What have you learn from it? FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 8 References: Big data analytics for business. Retrieved from https://www.ssyoutube.com/watch?v=AIvJbpn3TZM Difference between big data and business analytics. Retrieved from https://talentedge.in/articles/difference-big-data-business-analytics/ Sharma, C. H. (2018). Business analytics: Concepts and theories. Random Publications. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 9 LESSON 1.3. Big-Data Technology Big Data Technology can be defined as a Software-Utility that is designed to Analyse, Process and Extract the information from an extremely complex and large data sets which the Traditional Data Processing Software could never deal with. Types of Big Data Technologies Big Data Technology is mainly classified into two types: 1. Operational Big Data Technologies Operational Big Data is all about the normal day to day data that we generate. This could be the Online Transactions, Social Media, or the data from a Particular Organization etc. Raw Data which is used to feed the Analytical Big Data Technologies. Examples are:  Online ticket bookings, which includes your Rail tickets, Flight tickets, movie tickets etc;  Online shopping which is your Amazon, Flipkart, Walmart, Snap deal and many more;  Data from social media sites like Facebook, Instagram, what’s app and a lot more; and  The employee details of any Multinational Company. 2. Analytical Big Data Technologies Analytical Big Data is like the advanced version of Big Data Technologies. It is a little complex than the Operational Big Data. In short, Analytical big data is where the actual performance part comes into the picture and the crucial real-time business decisions are made by analyzing the Operational Big Data. Examples are:  Stock marketing  Carrying out the Space missions where every single bit of information is crucial.  Weather forecast information  Medical fields where a particular patients health status can Top Big Data Technologies Top big data technologies are divided into 4 fields which are classified as follows:  Data Storage  Data Mining  Data Analytics  Data Visualization FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 10 Activity 1.3.  Read the online article on “Top Big Data Technologies that you Need to know” by Ravi Kiran (2019).  Watch the online video “Top Big Data Technologies | Big Data Tools Tutorial | Big Data Hadoop Training.” Edureka - 2019. Study Question Upon reading the article and watching the video explain how do top Big Data Technologies or tools were classified? FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 11 References: Top big data technologies | big data tools tutorial | big data Hadoop training. Edureka - 2019. Retrieved from https://www.ssyoutube.com/watch?v=Vs9k3FThNic Almodiel, M. & Garcia, Dr. P. G. (2019). Fundamentals of business analytics: A business analytics course. UPOU. Kiran, R. (2019). Top big data technologies that you need to know. Retrieved from https://www.edureka.co/blog/top-big-data-technologies/ FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 12 LESSON 1.4. Business Model Canvas Being exposed from simple to complex business environment, emerging of new business idea is inevitable. A business manager has no choice but to go into competition to sustain the business operation. Being overwhelmed by different business environmental factors, writing a best and suitable business plan is very difficult. Moreover, having it feasible another challenged. Business model canvas is the solution! But what is business model canvas? In this lesson, you’ll have an idea, knowledge, and through continuous practice you’ll able to develop some techniques and skills on creating a business model canvas. Figure 1.4A. Twitter Business Model Source: https://www.ncrypted.net/blog/wp-content/uploads/2018/08/Twitter.jpg FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 13 Figure 1.4B. Twitter Business Model Source: https://businessmodelinnovationmatters.files.wordpress.com/2012/02/twitter-business- model.png A business model canvas describes the rationale of how an organization creates, delivers and captures value. It is a strategic management and lean startup template for developing new or documenting existing business models. A visual chart with a variety of elements, it provides a description of the firm and its products’ value proposition, infrastructure, customers, and finances. Nine Building Blocks in the Business Model Canvas 1. Customer Segment – the different groups of people or organizations an enterprise aims to reach and serve. 2. Value propositions – the bundle of products and services that create value for a specific customer segment. 3. Channels – it refers to how a company communicates with and reaches its customer segments to deliver a value proposition. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 14 4. Customer relationships – the types of relationships a company establishes with specific customer segments. 5. Revenue streams – the cash a company generates from each customer segment (one time or recurring). 6. Key resources – the most important things a company must do to make its business work. 7. Cost structure – all the cost incurred to operate a business model. Activity 1.4.  Read the online articles on “Understanding Twitter Business Model” and "Understanding Business Model" by BUSINESSMODELINNOVATIONMATTERS  Read the article on “The 9 Building Blocks of Business Model Canvas by Strategyzer.” Study Questions 1. After reading the articles, how do business model canvas significantly change their business operations? 2. How importance is the Nine (9) Building Blocks of Business Model Canvas in developing a business model canvas? FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 15 References: The 9 building blocks of business model canvas by Strategyzer. Retrieved from https://www.wemla.co/business-model-canvas/. Understanding business model. Retrieved from https://businessmodelinnovationmatters.wordpress.com/tag/business- model-canvas-examples/ Understanding twitter business model. Retrieved from https://businessmodelinnovationmatters.wordpress.com/2012/02/18/under standing-twitter-business-model-design/ Ebinum, M. (2016). How to: Business model canvas explained. Retrieved from https://medium.com/seed-digital/how-to-business-model-canvas- explained-ad3676b6fe4a FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 16 LESSON 1.5. Value Chain Model An organization is always on the verge of sustaining its competitive advantage – on edge to its competitors in terms of improved efficiency and increased profit margins. Organization analyze its internal activities and enhance its competitive advantages and evaluates its disadvantages based on a model. This will become their basis to perform its activities better than competitors would do. The competitive analysis model that had been the basis by different business organization was introduced by Michael Porter in 1985 which was known as “value chain model”. Figure 1.5A. Value Chain Analysis Source: https://www.smartsheet.com/value-chain-model Figure 1.5B. Value Chain Analysis Source: https://www.managementexchange.com/hack/mapping-porter%e2%80%99s-value- chain-activities-business-functional-units FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 17 A value chain model canvas is a type of business model that describes the full range of activities needed to create a product or service. The purpose of a value- chain analysis in such canvas is to increase production efficiency so that a company can deliver maximum value for the least possible cost. Benefits of Value Chain Modeling A value chain can help an organization identify in-house and outsourcing opportunities that take advantage of cost savings and specialized expertise. Ultimately, value chain modeling offers the following benefits:  Cost reduction  Competitive differentiation  Increased profitability and busines success  Increased efficiency  Decreased waste  Higher quality products at lower costs Activity 1.5.1.  Read the online articles on “The Straightforward Guide to Value Chain Analysis” by Meredith Hart and “How Your Business Can Benefit from Value Chain Modelling” by The Smart Street. Study Question Is there an specific value chain model for all types of industry? Activity 1.5.2. Performance Task (Assignment No. 1) 1. [Group activity consisting of five members]. Create a business model canvas and value chain model for a typical firm suited from the following sector/industry. Choose only one from the following:  Agriculture (Poultry and livestock)  Utilities (Power, water, and telecom)  Public Transportation  Finance and Insurance  Information Services (Media)  Professional Services (Consultations)  Educational Services  Entertainment and Recreation (Sports) FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 18 2. If face-to-face class is already allowed once the lesson is given, the first output is to be written in a yellow paper, while the second one is to be submitted in a short bond paper, its content printed. Otherwise, both of them are to be submitted in LMS, in the Dropbox to be provided. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 19 References: Evans, J. R. (2017). Business Analytics: Methods, Models and Decision. Boston: Pearson Publishing Co. Evans, J. R. (2017). Business Analytics: Principles, Concepts and Applications. Boston: Pearson Publishing Co. Hart, M. (2020). “The Straightforward Guide to Value Chain Analysis.” Retrieved from https://blog.hubspot.com/sales/value-chain-analysis The Smart Sheet (2020). “How Your Business Can Benefit from Value Chain Modelling.” Retrieved from: https://www.smartsheet.com/value-chain- model FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 20 ONLINE READING MATERIALS  “Definition: What is Business Intelligence?” Retrieved from https://www.microstrategy.com/us/resources/introductory-guides/business- intelligence-the-definitive-guide  Galletto, M. (2016). “What is Business Analytics?” Retrieved form https://www.ngdata.com/what-is-business-analytics  "Difference between Big data and Business Analytics." Retrieved form https://talentedge.in/articles/difference-big-data-business-analytics/  Kiran, R. (2019). “Top Big Data Technologies that you Need to know.” Retrieved form https://www.edureka.co/blog/top-big-data-technologies/  “The 9 Building Blocks of Business Model Canvas by Strategyzer.” Retrieved from https://www.wemla.co/business-model-canvas/.  “Understanding Business Model.” Retrieved from https://businessmodelinnovationmatters.wordpress.com/tag/business- model-canvas-examples/  “Understanding Twitter Business Model.” Retrieved from https://businessmodelinnovationmatters.wordpress.com/2012/02/18/under standing-twitter-business-model-design/  Ebinum, M. (2016). How To: Business Model Canvas Explained. Retrieved from https://medium.com/seed-digital/how-to-business-model- canvas-explained-ad3676b6fe4a  Hart, M. (2020). “The Straightforward Guide to Value Chain Analysis.” Retrieved from https://blog.hubspot.com/sales/value-chain-analysis  “How Your Business Can Benefit from Value Chain Modelling.” Retrieved form The Smart Street. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 21 ONLINE VIDEO LINKS AND MATERIALS  “Introduction to Business Analytics.” by Cody Baldwin. Retrieved from https://www.youtube.com/watch?v=9IIgH0hNtgk.  “Big Data Analytics for Business.” Retrieved from https://www.youtube.com/watch?v=AIvJbpn3TZM  “Top Big Data Technologies | Big Data Tools Tutorial | Big Data Hadoop Training” Edureka - 2019. Retrieved from https://www.ssyoutube.com/watch?v=Vs9k3FThNic FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 22 TEST YOUR KNOWLEDGE Online Quiz No. 1 (50 pts) 1. Discuss the current concepts and applications of business analytics in both local and global business environments. 2. Make a comprehensive differentiation on Business Analytics, Business Intelligence and Big Data. 3. Give twelve (8) examples of big data technologies and classified them accordingly based on Edureka video presentation and article by Kiran (2019). 4. Define business model canvas building and enumerate its building blocks. 5. Distinguish the difference between business model canvas and value chain model. Rubrics for Nos. 1., 2., 3., & 5 Knowledge and understanding 2.5 pts. Thinking and Inquiry 2.5 pts. Application 2.5 pts. Communication 2.5 pts. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 23 MODULE REFERENCES Big data analytics for business. Retrieved from https://www.ssyoutube.com/watch?v=AIvJbpn3TZM Definition: What is business intelligence? Retrieved from https://www.microstrategy.com/us/resources/introductory-guides/business- intelligence-the-definitive-guide Difference between big data and business analytics. Retrieved rom https://talentedge.in/articles/difference-big-data-business-analytics/ How your business can benefit from value chain modelling. The Smart Street. Retrieved form www.smartsheet.com/value-chain-model The 9 building blocks of business model canvas by Strategyzer. Retrieved from https://www.wemla.co/business-model-canvas/. top big data technologies | big data tools tutorial | big data Hadoop training” Edureka - 2019. Retrieved from https://www.youtube.com/watch?v=Vs9k3FThNic Understanding business model. Retrieved from https://businessmodelinnovationmatters.wordpress.com/tag/business- model-canvas-examples/ What is big data analytics. Retrieved from https://www.ibm.com/analytics/hadoop/big-data-analytics Almodiel, M. & Garcia, Dr. P. G. (2019). Fundamentals of business analytics: A business analytics course. UPOU. Baldwin, C. (2016). Introduction to business analytics. Retrieved from https://www.ssyoutube.com/watch?v=9IIgH0hNtgk. Bently, D. (2019). Business analytics: principles, concepts and applications. Larsen and Keller. Ebinum, M. (2016). How to: Business model canvas explained. Retrieved from https://medium.com/seed-digital/how-to-business-model-canvas- explained-ad3676b6fe4a Evans, J. R. (2017). Business analytics: methods, models and decision. Pearson Publishing Co. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 1 – OVERVIEW OF BUSINESS ANALYTICS 24 Evans, J. R. (2017). Business analytics: Principles, concepts and applications. Pearson Publishing Co. Galletto, M. (2018). What is business analytics? Retrieved from https://www.ngdata.com/what-is-business-analytics Hart, M. (2020). The straightforward guide to value chain analysis. Retrieved from https://blog.hubspot.com/sales/value-chain-analysis Kiran, R. (2019). Top big data technologies that you need to know. Retrieved from https://www.edureka.co/blog/top-big-data-technologies/ Sharma, C. H. (2018). Business analytics: Concepts and theories. Random Publications. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 2 – BUSINESS ANALYTICS FRAMEWORK 25 MODULE 2 – BUSINESS ANALYTICS FRAMEWORK INTRODUCTION The collections of voluminous data will not be useful in planning and projecting the profit of the organization unless it will be extracted and processed accurately. Ahmad, R. & Khan, R. & Nadeem, A. & Ali, A. (2019) mentioned that the success of organization is mainly dependent upon the quality and effectiveness of the decisions which can be achieved through proper analysis of data. In this module you will learn about the business analytics framework and its importance on the analytical approach in making better decisions using more optimized information- based facts in a more organized way. LEARNING OUTCOMES After reading this module, the learner should be able to: 1. Discuss how data becomes business value. 2. Explain basic concepts on data analysis framework, data extraction, data warehousing and data analytics. 3. Distinguish the types of analytics from its definition and example. 4. Explore how business analytics process relates to a business. TIME The time allotted for this module is three (3) hours. LEARNER DESCRIPTION The participants in this module are BSBA second year students. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 2 – BUSINESS ANALYTICS FRAMEWORK 26 MODULE CONTENTS LESSON 2.1. Framework for Business Analytics The process of turning raw data into business action is the framework for Business Analytics. There are 3 steps in turning data into analytics which are Data Extraction, Data Warehousing and the Extract, Transform, or Load Processes (ETL) (Almodiel & Garcia, 2018). Data extraction Data extraction is a process that involves retrieval of data from various sources (Alley, 2018). This is the first step in turning data into analytics. There are at least 3 sources of data which are the source systems, raw transactions, and from documents and forms (Almodiel & Garcia, 2018). Types of data extraction tools (Alley, 2018)  Batch processing tools: Legacy data extraction tools consolidate your data in batches, typically during off-hours to minimize the impact of using large amounts of compute power. For closed, on-premise environments with a fairly homogeneous set of data sources, a batch extraction solution may be a good approach.  Open source tools: Open source tools can be a good fit for budget- limited applications, assuming the supporting infrastructure and knowledge is in place. Some vendors offer limited or "light" versions of their products as open source as well.  Cloud-based tools: Cloud-based tools are the latest generation of extraction products. Generally the focus is on the real time extraction of data as part of an ETL/ELT process and cloud-based tools excel in this area, helping take advantage of all the cloud has to offer for data storage and analysis. These tools also take the worry out of security and compliance as today's cloud vendors continue to focus on these areas, removing the need for developing this expertise in-house. Data warehousing Data warehousing is the electronic storage of a large amount of information by a business or organization. Data warehousing is a vital component of business intelligence that employs analytical techniques on business data. This concept was introduced in 1988 by IBM researchers Barry Devlin and Paul (Frankenfield & Anderson, 2020). Furthermore, this is where the data is cleaned, curated, organized, and ready for analysis (Almodiel & Garcia, 2018). Data Warehousing vs. Databases A data warehouse is not necessarily the same concept as a standard database. A database is a transactional system that is set to monitor and FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS Extract, Transform, or Load Processes (ETL)  MODULE 2 – BUSINESS ANALYTICS FRAMEWORK 27 update real-time data in order to have only the most recent data available. A data warehouse is programmed to aggregate structured data over a period of time. For example, a database might only have the most recent address of a customer, while a data warehouse might have all the addresses that the customer has lived in for the past 10 years (Frankenfield & Anderson, 2020). This is the process of moving data from source systems to data warehouse to an analytical tool (Almodiel & Garcia, 2018). The ETL process is an integral component in any data-centric project. This process consists of extraction (i.e. reading data from one or more database), transformation (i.e. converting the extracted data from its previous form into the form in which it needs to be so that it can be placed into data warehouse or simply another database), and load (i.e. putting the data into the data warehouse). The purpose of the ETL process is to load the warehouse with integrated and cleansed data. The data used in ETL processes can come from any source: a mainframe application, an ERP application, CRM tool, a flat file, an Excel spreadsheet, or even a message queue (Sharda, Delen, & Turban, 2018). Activity 2.1.  Read the online article on “What is ETL?” Retrieved from https://www.syncsort.com/en/glossary/etl  Watch online video on “Syncsort Connect.” Retrieved from https://www.youtube.com/watch?v=0kIllr2NA14&feature=emb_logo Study Question 1. How does data become business value? 2. What is the importance of having an organized source of data? FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 2 – BUSINESS ANALYTICS FRAMEWORK 28 References: Syncsort connect. Retrieved from https://www.youtube.com/watch?v=0kIllr2NA14&feature=emb_logo What is ETL? Retrieved from https://www.syncsort.com/en/glossary/etl Ahmad, R. & Khan, R. & Nadeem, A. & Ali, A. (2019). Business analytics: A framework. 10. Alley, G. (2018, Novermber 21). What is data extraction. Retrieved July 30, 2020, from alooma: www.alooma.com Almodiel, M., & Garcia, P. G. (2018). Fundamentals of business analytics: A business analytics course. University of the Philippines Open University. Frankenfield, J., & Anderson, S. (2020, June 28). Data warehousing. Retrieved Jul 30, 2020, from Investopedia: www.investopedia.com Sharda, R., Delen, D., & Turban, E. (2018). Business intelligence, analytics, and data science: A managerial perspective (Fourth Edition, Global Edition ed.). Malaysia: Pearson Education Limited. Sharma, C. H. (2018). Business analytics: Concepts and theories. Random Publications. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 2 – BUSINESS ANALYTICS FRAMEWORK 29 LESSON 2.2. Types of Analytics There are many types of analytics, and there is a need to organize these types to understand their uses. According to Institute of Operations Research and Management Science (INFORMS) there are three types of analytics. These types of analytics can be viewed independently as presented in Table 2.2.1. (Sharma, 2018). Table 2.2.1. Types of Analytics Type of Analytics Definition Descriptive The application of simple statistical techniques that describe what is obtained in a data set or database. Example: An age bar chart that is used to depict retail shoppers for a department store that wants to target advertising to customer by age. Predictive An application of advanced statistical, information software, or operations research methods to identify predictive variables and build predictive models to identify trends and relationships not readily observed in a descriptive analysis. Example: Multiple regression is used to show relationship (or lack of relationship) between age, weight, and exercise on diet food sales. Knowing that relationships exist helps explains why one set of independent variables influences dependent variables such as business performance. Prescriptive An application of decision science, management science, and operations research methodologies (applied mathematical techniques) to make best use of allocable resources. Example: A department store has a limited advertising budget to target customers. Linear programming models can be used to optimally allocate the budget to various advertising media. The purposes and methodologies used for each of the three types of analytics differ, as can be seen in Table 2.2.2. It is these differences that distinguish analytics from business analytics. Whereas analytics is focused on generating insightful information from data sources, business analytics goes the extra step FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 2 – BUSINESS ANALYTICS FRAMEWORK 30 to leverage analytics to create an improvement in measurable business performance (Sharma, 2018). Table 2.2.2 Analytic Purpose and Tools Types of Purpose Examples of Methodologies Analytics Descriptive To identify possible trends Descriptive statistics, including in large data sets or measures of central tendency databases. The purpose is (mean, median, mode), to get a rough picture of measures of dispersion what generally the data (standard deviation), charts, looks like and what criteria graphs, sorting methods, might have potential for frequency distributions, identifying trends or future probability distributions, and business behavior sampling methods. (Sharma, 2018; Bentley, 2019) Predictive To build predictive models Statistical methods like designed to identify and multiple regression and predict future trends ANOVA. Information system (Sharma, 2018; Bentley, methods like data mining and 2019). sorting. Operations research methods like forecasting models. Prescriptive To allocate resources Operations research optimally to take advantage methodologies like linear of predicted trends or future programming and decision opportunities. theory. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 2 – BUSINESS ANALYTICS FRAMEWORK 31 Activity 2.2.  Read the online article on “Moving from Descriptive to Predictive and Prescriptive Analytics.” Study Question Compare and differentiate descriptive, predictive, and prescriptive analytics. Types of Analytics Similarities Differences Descriptive Predictive Prescriptive FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 2 – BUSINESS ANALYTICS FRAMEWORK 32 References: Moving from Descriptive to predictive and prescriptive analytics. Retrieved from https://nicoleparmar.com/moving-from-descriptive-to-predictive-and- prescriptive-analytics/ Bentley, D. (2019). Business analytics: Principles, concepts and applications. Larsen & Keller. Sharma, C. H. (2018). Business analytics: concepts and theories. Random Publications. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 2 – BUSINESS ANALYTICS FRAMEWORK 33 LESSON 2.3. Business Analytics Process The complete business analytic process involves the three major component steps applied sequentially to a source of data (see Figure 2.3.1.). The outcome of the business analytic process must relate to business and seek to improve business performance in some way (Sharma, 2018; ). Figure 2.3.1. Business analytic processes Source: http://bampe08.blogspot.com/2015/04/an-overview-into-business-analytics.html The logic of the BA process in Figure 2.3.1 is initially based on a question: What valuable or problem-solving information is locked up in the sources of data that an organization has available? At each of the three steps that make up the BA process, additional questions need to be answered, as shown in Figure 2.3.1. Answering all these questions requires mining the information out of the data via the three steps of analysis that comprise the BA process. The analogy of digging in a mine is appropriate for the BA process because finding new, unique, and valuable information that can lead to a successful strategy is just as good as finding gold in a mine. SAS, a major analytic corporation (www.sas.com), actually has a step in its BA process, Query Drilldown, which refers to the mining effort of questioning and finding answers to pull up useful information in the BA analysis. Many firms routinely undertake BA to solve specific problems, while other firms FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 2 – BUSINESS ANALYTICS FRAMEWORK 34 undertake BA to explore and discover new knowledge to guide organizational planning and decision-making to improve business performance. The size of some data sources can be unmanageable, overly complex, and generally confusing. Sorting out data and trying to make sense of its informational value requires the application of descriptive analytics as a first step in the BA process. One might begin simply by sorting the data into groups using the four possible classifications presented in Table 2.3.1. Also, incorporating some of the data into spreadsheets like Excel and preparing cross tabulations and contingency tables are means of restricting the data into a more manageable data structure. Simple measures of central tendency and dispersion might be computed to try to capture possible opportunities for business improvement. Other descriptive analytic summarization methods, including charting, plotting, and graphing, can help decision makers visualize the data to better understand content opportunities. Table 2.3.1. Types of Data Measurement Classification Scales Type of Data Description Measurement Scale Categorical Data Data that is grouped by one or more characteristics. Categorical data usually involves cardinal numbers counted or expressed as percentages. Example 1: Product markets that can be characterized by categories of “high-end” products or “low-income” products, based on dollar sales. It is common to use this term to apply to data sets that contain items identified by categories as well as observations summarized in cross-tabulations or contingency tables. Ordinal Data Data that is ranked or ordered to show relational preference. Example 1: Football team rankings not based on points scored but on wins. Example 2: Ranking of business firms based on product quality. Interval Data Data that is arranged along a scale where each value is equally distant from others. It is ordinal data. Example 1: A temperature gauge. Example 2: A survey instrument using a Likert scale (that is, 1, 2, 3, 4, 5, 6, 7), where 1 to 2 is perceived as equidistant to the interval from 2 to 3, and so on. Note: In ordinal data, the ranking of firms might vary greatly from first place to second, but in interval data, they would have to be relationally proportional. Ratio Data Data expressed as a ratio on a continuous scale. Example 1: The ratio of firms with green manufacturing programs is FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 2 – BUSINESS ANALYTICS FRAMEWORK 35 Type of Data Description Measurement Scale twice that of firms without such a program. Activity 2.3.  Read the online article on “An Overview into Business Analytics Process” Study Question 1. How important is the familiarization on measurement scale of data in the business analytics process? Activity 2.4. Performance Task ASSIGNMENT NO. 2. 1. [Individual Activity] Applying the business analytics processes model, answer the given problem. A business manager because of data overwhelmed reported to the Board of Directors without reviewing its historical sales for the past five years that the business will eventually hits its return of investment in three years. What would be the consequences of his action? FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 2 – BUSINESS ANALYTICS FRAMEWORK 36 References: An overview into business analytics process. Retrieved from http://bampe08.blogspot.com/2015/04/an-overview-into-business- analytics.html Sharma, C. H. (2018). Business analytics: Concepts and theories. Random Publications. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 2 – BUSINESS ANALYTICS FRAMEWORK 37 ONLINE READING MATERIALS  “Syncsort Connect.” Retrieved from https://www.youtube.com/watch?v=0kIllr2NA14&feature=emb_logo  “What is ETL?” Retrieved from https://www.syncsort.com/en/glossary/etl  “Moving from Descriptive to Predictive and Prescriptive Analytics.” Retrieved from https://nicoleparmar.com/moving-from-descriptive-to-predictive- and-prescriptive-analytics/  An Overview into Business Analytics Process.” Retrieved from http://bampe08.blogspot.com/2015/04/an-overview-into-business- analytics.html ONLINE VIDEO LINKS AND MATERIALS  “Syncsort Connect.” Retrieved from https://www.youtube.com/watch?v=0kIllr2NA14&feature=emb_logo FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 2 – BUSINESS ANALYTICS FRAMEWORK 38 TEST YOUR KNOWLEDGE Online Quiz No. 2 True or False 1. Data warehousing is a process that involves retrieval of data from various sources. (True) 2. The types of data extraction tools can be arranged in any order. (True) 3. Open source tools is a restricted and can be accessed only for a fee. (False) 4. On the part of the end user, cloud-based tools are tangible data storage. (False) 5. The concepts for data warehousing and databases are one and the same (False) 6. The process of moving data from source systems into a data warehouse, and from a data warehouse into an analytical tool is often called ETL (Extract, Transform, or Load processes). (True) 7. In the data extraction, there are at least 3 sources of data which are the source systems, raw transactions, and from documents and forms. (True) 8. Predictive Analytics is the type of analytics where data is used to benchmark or to profile. (False Answer: Descriptive Analytics) 9. In business analytics, you need to follow a process turn data into value. (True) 10. The process of turning raw data into business action is the framework for business analytics. (True) 11. The first step to turning data into analytics is the data warehousing phase. (False) 12. Predictive analytics is used when you want to find relationships between two different types of data and making predictions about future data. (True) 13. For the data to become business value, it has to be extracted from sources, curated and cleansed, and joined in a data warehouse. (True) 14. Big data includes large volumes of structured and unstructured data that inundates a business on a day-to-day basis. (True) 15. Veracity means that there are a lot of uncertainty, meaning, with all of these different data coming altogether but the problem is we don’t know what to do with them. (True) FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 2 – BUSINESS ANALYTICS FRAMEWORK 39 MODULE REFERENCES An overview into business analytics process. Retrieved from http://bampe08.blogspot.com/2015/04/an-overview-into-business- analytics.html Moving from descriptive to predictive and prescriptive analytics. Retrieved from https://nicoleparmar.com/moving-from-descriptive-to-predictive-and- prescriptive-analytics/ Syncsort connect. Retrieved from https://www.youtube.com/watch?v=0kIllr2NA14&feature=emb_logo What is ETL? Retrieved from https://www.syncsort.com/en/glossary/etl Ahmad, R. & Khan, R. & Nadeem, A. & Ali, A. (2019). Business analytics: A framework. 10. Alley, G. (2018, Novermber 21). What is data extraction. Retrieved July 30, 2020, from alooma: www.alooma.com Almodiel, M., & Garcia, P. G. (2018). Fundamentals of business analytics: A business analytics course. Manila: University of the Philippines Open University. Bentley, D. (2019). Business analytics: Principles, concepts and applications. Larsen & Keller. Frankenfield, J., & Anderson, S. (2020, June 28). Data warehousing. Retrieved Jul 30, 2020, from Investopedia: www.investopedia.com Sharda, R., Delen, D., & Turban, E. (2018). Business intelligence, analytics, and data science: A managerial perspective (Fourth Edition, Global Edition ed.). Pearson Education Limited. Sharma, C. H. (2018). Business analytics: Concepts and theories. Random Publications. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 3 – PERFORMANCE MANAGEMENT 40 MODULE 3 – PERFORMANCE MANAGEMENT INTRODUCTION After knowing and understanding the basic concepts of business analytics, you will now be engaging on its different applications. It is always been said that the great asset of a firm and organization are its human capitals and without it all the plans and execution of its systems, vision, mission, goals and objectives will not be feasible at all. That is why performance management had been developed. According to Heathfield (2020) performance management is the process of creating a work environment or setting in which people are enabled to perform to the best of their abilities. This could imply that every organization can develop its own performance management system to fit in the context of their organization. But what must be common is to have a framework that ensures continuous communication within the organization and understanding of the importance of every employee pivotal role to achieve its goals. Another thing that must be common in the performance management is setting up of key performance indicators to measure the set factors translated into numbers or quantified in order to applied appropriate metrics. This will aid the organization to track what really is the current status of the organization on reaching its set target. The Balanced Scorecards (BSC) is probably the best-known and most widely used performance management system. This was first articulated by Kaplan and Norton in their Harvard Business Review article “The Balanced Scorecard: Measures that Drive Performance” in 1992 (Sharda, Delen, & Turban, 2018). LEARNING OUTCOMES After reading this module, the learner should be able to: 1. Discuss the importance of performance management framework. 2. Explain the metric and measurement in the performance management. 3. Explain the feature of a balance scorecard. 4. Give examples of generic scorecards. 5. Formulate a balance scorecard model to different sectors of the industry. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 3 – PERFORMANCE MANAGEMENT 41 TIME The time allotted for this module is three (3) hours. LEARNER DESCRIPTION The participants in this module are BSBA second year students. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 3 – PERFORMANCE MANAGEMENT 42 MODULE CONTENTS LESSON 3.1. Performance Management Framework A solid performance management framework creates a foundation which ties together your company goals, your team members' development, and drives sales. By creating a fluid plan, it will mesh together every department and each employee will know their role within the organization and feel valued. The performance management framework provides a comprehensive, continuous and flexible approach to the management of performance in the office, in teams and individuals that involve planning and monitoring work. It emphasizes on dialogue and feedback between all concerned parties. The functions of such framework depend upon the needs of the organization as shown in the given figure. Figure 3.2.1. Performance Management Framework Components Source: https://images.app.goo.gl/AdUNRkeoT5zeCEtJ9 FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 3 – PERFORMANCE MANAGEMENT 43 Activity 3.1.  Read the online article on “How Predictive Analytics Have Revolutionized Performance Management” by The Human Resource Today. Retrieved from https://www.humanresourcestoday.com/analytics/performance- management/?open-article-id=10723256&article-title=how-predictive- analytics-have-revolutionized-performance-management&blog- domain=rallyware.com&blog-title=rallyware-for-human-resources  Watch online video on “Performance Management – Analytics -1.” Retrieved from https://www.youtube.com/watch?v=_mLCswrTPtg Study Question 1. Based on the article, in what aspect of performance management does predictive analytics has played a major role? 2. Based on the video, how did analytics simplify/automated the work of a CEO and HRD Head? FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 3 – PERFORMANCE MANAGEMENT 44 References: How predictive analytics have revolutionized performance management. The Human Resource Today. Retrieved from https://www.humanresourcestoday.com/analytics/performance- management/?open-article-id=10723256&article-title=how-predictive- analytics-have-revolutionized-performance-management&blog- domain=rallyware.com&blog-title=rallyware-for-human-resources Performance management – Analytics -1. Retrieved from https://www.youtube.com/watch?v=_mLCswrTPtg FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 3 – PERFORMANCE MANAGEMENT 45 LESSON 3.2. Metrics and Measurements Metrics translate the business challenges into operational measures that can be monitored over time, not only for analytics impact, but for the entire company. On the other hand, objective means by which your company can measure progress and business analytics impact. What Are Their Advantages?  Increase productivity and market share  Increase retention and conversion rate  Increase wallet share  Increase customer satisfaction  Increase average order size/number of products  Increase average spend per customer  Decrease operational costs  Decrease time-to-decision  Optimize human capital Figure 3.2.1. Metrics in Fuel Dispensers Source: https://images.app.goo.gl/LWk6q3NuAz43Ah5Y8 Figure 3.2.2. Metrics in Vital Monitors Source: https://images.app.goo.gl/NnxZVxW32gZEeNcP9 Activity 3.2.  Read the online article on 21 Employee performance metrics by Erik van Vulpen. Study Question How did Erik van Vulpen categorized the 21 Employee Performance Metrics? Classify it accordingly. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 3 – PERFORMANCE MANAGEMENT 46 References: Vulpen, E.V. (2020). 21 Employee performance Metrics. AIHR Analytics. Retrieved from https://www.analyticsinhr.com/blog/employee- performance-metrics FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 3 – PERFORMANCE MANAGEMENT 47 LESSON 3.3. Balanced Scorecard A Balanced Scorecard (BSC) is a strategic management performance metric used to identify and improve various internal business functions and their resulting external outcomes. Aside from these, it also provides feedback to organizations and communicate what they are trying to accomplish. The balanced scorecard suggests to view the organization in four perspectives – customer, financial, internal business processes and learning and growth. Integrated each perspectives is to develop objectives or goals, measures, target and initiatives (Sharda, Delen, & Turban, 2018). Figure 3.3.1. Example of Balance Scorecard Source: https://images.app.goo.gl/FpEVqVct51aLxzxQ8 FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 3 – PERFORMANCE MANAGEMENT 48 Figure 3.3.2. City/Municipal Government Scorecard Source: https://images.app.goo.gl/dFdWPqUU2rkVeBDY9 Benefits of Using a Balanced Scorecard (Marr, 2020) Better Strategic Planning The Balanced Scorecard provides a powerful framework for building and communicating strategy. The business model is visualized in a strategy map which helps managers to think about cause-and-effect relationships between the different strategic objectives. The process of creating a strategy map ensures that consensus is reached over a set of interrelated strategic objectives. It means that performance outcomes as well as key enablers or drivers of future performance are identified to create a complete picture of the strategy. Improved Strategic Communication and Execution FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 3 – PERFORMANCE MANAGEMENT 49 Having a one-page picture of the strategy allows companies to easily communicate strategy internally and externally. We have known for a long time that a picture is worth a thousand words. This 'plan on a page' facilitates the understanding of the strategy and helps to engage staff and external stakeholders in the delivery and review of the strategy. Better Alignment of Projects and Initiatives The Balanced Scorecard helps organizations map their projects and initiatives to the different strategic objectives, which in turn ensures that the projects and initiatives are tightly focused on delivering the most strategic objectives. Better Management Information The Balanced Scorecard approach helps organizations design key performance indicators for their various strategic objectives. This ensures that companies are measuring what actually matters. Research shows that companies with a BSC approach tend to report higher quality management information and better decision-making. Improved Performance Reporting The Balanced Scorecard can be used to guide the design of performance reports and dashboards. This ensures that the management reporting focuses on the most important strategic issues and helps companies monitor the execution of their plan. Better Organizational Alignment The Balanced Scorecard enables companies to better align their organizational structure with the strategic objectives. In order to execute a plan well, organizations need to ensure that all business units and support functions are working towards the same goals. Cascading the Balanced Scorecard into those units will help to achieve that and link strategy to operations. Better Process Alignment Well implemented Balanced Scorecards also help to align organizational processes such as budgeting, risk management and analytics with the strategic priorities. This will help to create a truly strategy focused organization. Activity 3.3.1.  Read the online article on Business performance management, Balanced Scorecards and the decision model by Suleiman Shehu. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 3 – PERFORMANCE MANAGEMENT 50  Watch the online video on Seven benefits of a Balanced Scorecard by Bernard Marr. Retrieved from https://www.bernardmarr.com/default.asp?contentID=972  Watch the online video on What are the benefits of the balanced scorecard? by Bernard Marr. Study Questions 1. What is a balanced scorecard (BSC) and where did it come from? 2. Why do we need to define separate objectives, measures, targets, and initiatives for each of these four BSC perspective? Activity 3.3.2. Performance Task ASSIGNMENT NO. 3. 1. [Group activity consisting of five members]. Formulate a balance scorecard model for the enterprise developed in your business model canvas and value chain model in the previous module. 2. If face-to-face class is already allowed once the lesson is given, the output will be submitted in a short bond paper, its content printed. Otherwise, it is to be submitted in LMS, in the Dropbox to be provided. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 3 – PERFORMANCE MANAGEMENT 51 References: Marr, B. (2020). 7 Benefits of a balanced scorecard. Retrieved from https://www.bernardmarr.com/default.asp?contentID=972 Marr, B. (2020). What are the benefits of the balanced scorecard? Retrieved from https://www.ssyoutube.com/watch?time_continue=19&v=UnBR9J0Tq88&f eature=emb_logo Sharda, R., Delen, D., & Turban, E. (2018). Business intelligence, analytics, and data science: A managerial perspective (Fourth Edition, Global Edition ed.). Pearson Education Limited. Shehu, S. (2013). Business performance management, Balanced Scorecards and the decision model. Retrieved from https://bizzdesign.com/blog/business-performance-management- balanced-scorecards-and-the-decision-model/ FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 3 – PERFORMANCE MANAGEMENT 52 ONLINE READING MATERIALS  How Predictive Analytics Have Revolutionized Performance Management. The Human Resource Today. Retrieved from https://www.humanresourcestoday.com/analytics/performance- management/?open-article-id=10723256&article-title=how-predictive- analytics-have-revolutionized-performance-management&blog- domain=rallyware.com&blog-title=rallyware-for-human-resources  21 Employee performance metric by Erik van Vulpen. Retrieved from https://www.analyticsinhr.com/blog/employee-performance-metrics/  7 benefits of a Balanced Scorecard by Bernard Marr. Retrieved from https://www.bernardmarr.com/default.asp?contentID=972  Business performance management, Balanced Scorecards and the decision model by Suleiman Shehu. Retrieved from: https://bizzdesign.com/blog/business-performance-management-balanced- scorecards-and-the-decision-model/ ONLINE VIDEO LINKS AND MATERIALS  Performance management – Analytics -1. Retrieved from https://www.ssyoutube.com/watch?v=_mLCswrTPtg  What are the benefits of the balanced scorecard? by Bernard Marr. Retrieved from https://www.ssyoutube.com/watch?time_continue=19&v=UnBR9J0Tq88&feat ure=emb_logo FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 3 – PERFORMANCE MANAGEMENT 53 TEST YOUR KNOWLEDGE Online Quiz No. 3. (20 pts) 1. The process of creating a work environment setting is ___________. (Performance management) 2. The best-known and most widely used performance management system. (Balanced Scorecard) 3. A _____ performance management framework creates a foundation which ties together your company goals, your team members' development, and drives sales. (solid) 4. There is one standard performance management framework for any organization and industry. (True/False) (False: performance management framework depends upon the needs of the organization) 5. In the article “How Predictive Analytics Have Revolutionized Performance Management” the three factors resulting to low workforce productivity are: _________, ____________, ___________.(The lack of what (objectives and guidance); The lack of how (skills and knowledge); and The lack of why (engagement and motivation) 6. This translates the business challenges into operational measures that can be monitored over time, not only for analytics impact, but for the entire company. (Metrics) 7. The four categorization of employee performance metrics are: _________, ____________, ___________ , ___________. (Work quality metrics, Work quantity metrics, Work efficiency metrics, Organizational performance metrics) 8. Identify the four perspectives of Balanced scorecard. (customer, financial, internal business processes and learning and growth). 9. Enumerate at least four (4) benefits of using a Balanced Scorecard. (Better Strategic Planning, Improved Strategic Communication and Execution, Better Alignment of Projects and Initiatives, Better Management Information, Improved Performance Reporting, Better Organizational Alignment, and Better Process Alignment) FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 3 – PERFORMANCE MANAGEMENT 54 MODULE REFERENCES How predictive analytics have revolutionized performance management. The Human Resource Today. Retrieved from https://www.humanresourcestoday.com/analytics/performance- management/?open-article-id=10723256&article-title=how-predictive- analytics-have-revolutionized-performance-management&blog- domain=rallyware.com&blog-title=rallyware-for-human-resources Evans, J. (2017). Business analytics: Methods, models and decision. Pearson Publishing Co. Evans James R. (2017). Business analytics: Principles, concepts and applications. Pearson Publishing Co. Heathfield, S. M. (2020). Performance panagement: Here’s your quick start learning guide to performance management.” Retrieved from https://www.thebalancecareers.com/performance-management-1918226 Marr, B. (2020). 7 Benefits of a Balanced Scorecard. Retrieved from https://www.bernardmarr.com/default.asp?contentID=972 Marr, B. (2020). What are the benefits of the balanced scorecard?. Retrieved from https://www.ssyoutube.com/watch?time_continue=19&v=UnBR9J0Tq88&f eature=emb_logo Sharda, R., Delen, D., & Turban, E. (2018). Business intelligence, analytics, and data science: A managerial perspective (Fourth Edition, Global Edition ed.). Pearson Education Limited. Shehu, S. (2013). Business performance management, Balanced Scorecards and the decision model. Retrieved from: https://bizzdesign.com/blog/business-performance-management- balanced-scorecards-and-the-decision-model/ Vulpen, E.V. (2020). 21 Employee performance metrics. AIHR Analytics. Retrieved from https://www.analyticsinhr.com/blog/employee- performance-metrics/ FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 4 – BUSINESS ANALYTICS IN DECISION MAKING 33 MODULE 4 – BUSINESS ANALYTICS IN DECISION MAKING INTRODUCTION Business analytics can play a huge part in your business’ decision making, deciphering of patterns from both employees and customers and understanding their interactions, and working with the IT department to further enhance these interactions. Decision making is vital component of small business success. Decision based on a foundation of knowledge and sound reasoning can lead the company into long term prosperity; conversely, decision made on the basis of flawed logic, emotionalism, or incomplete information can quickly put a small business out of commission (Bently, 2019). LEARNING OUTCOMES After reading this module, the learner should be able to: 1. Explain how business analytics can be a part of effective decision making. 2. Differentiate how business analytics can be applied to different fields in business. 3. Examine the different concepts in customer analysis. TIME The time allotted for this module is three (3) hours. LEARNER DESCRIPTION The participants in this module are BSBA second year students. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 4 – BUSINESS ANALYTICS IN DECISION MAKING 34 MODULE CONTENTS LESSON 4.1. Analytics as an Integral Part of Decision Making Data and analytics are disrupting existing business models and ecosystems. Proliferation of new data sets and introduction of massive data migration capabilities are undermining existing information and technological silos. From using granular data to personalize products and services to scaling digital platforms to match buyers and sellers, companies are using business analytics to enable faster and fact-based decision making. Studies show that data driven organizations not only make better strategic decisions, but also enjoy high operational efficiency, improved customer satisfaction, and robust profit and revenue levels. From “descriptive analytics” which involves preparing the data for subsequent analysis, “predictive analytics” that provide advanced models to forecast and predict the future, to the top-notch of analytics called “prescriptive analytics” that utilize machine based learning algorithms and dynamic rule engines in order to provide interpretations and recommendations, it is no longer a surprise that these techniques are now finding way into customer, workforce, supply-chain, finance, and risk strategies at an organizational level. In addition, business analytics can improve the way organizations attract, retain, and develop talent. For example, a consulting group in Asia recently decided to undergo a major restructuring process. As a part of this initiative, the leadership wanted to identify employees with high potential to succeed and gain a greater understanding into key indicators of performance. The analytics team began by streamlining data points such as professional history, education background, performance, age, marital status, and demographics. After running the collated data though multiple regression models, the team was able to identify the employee profiles that had best chances of succeeding in particular roles. The research and analysis also suggested the key roles that had the most impact on the company’s overall growth. As a consequence, the company restructured around the key functional roles and talent groups. Activity 4.1.  Read the online article on “How Business Analytics Can Influence Decision Making” by UNSW – Master of Analytics. Study Questions 1. What are the three main categorization of data analytics according to Dr. Michael Wu? FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 4 – BUSINESS ANALYTICS IN DECISION MAKING 35 2. How did business analytics serve as a tool in decision making? References: How business analytics can influence decision making. UNSW – Master of Analytics. Retrieved from https://www.mybusiness.com.au/management/5648-how-business- analytics-can-influence-decision-making Singh, H. (2018). Using Analytics for Better Decision Making. Retrieved from: https://towardsdatascience.com/using-analytics-for-better-decision- making-ce4f92c4a025 FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 4 – BUSINESS ANALYTICS IN DECISION MAKING 36 LESSON 4.2. Application of Business Analytics Apart from inventory Retail activities, many retailers use analytics to identify customer trends and changing preferences by combining data from different areas. By merging sales data with a variety of factors, businesses can now identify emerging trends and anticipate them better. In order to deal with high expectations from consumers, online retailers need to leverage data being collected and actively integrate analytics to improve their decision-making process. They should use analytics to better understand consumer preferences, and further provide them with right product offerings. Consumer Product It helps business persons to understand their user behavior and grow their product. Examples of this application are digital adoption, product intelligence, retention, and customer analysis. Entertainment By applying this insight, entertainment and media companies can better understand and target consumers, improve the user experience, streamline their business processes, and identify new products and services to offer customers. The media and entertainment industry had to read the tea leaves (TV ratings, blockbuster charts, etc.) to arrive at their investment decisions, but the scenario has changed and the industry is now awash with data. Backed by competent analytics capabilities, data can be accurately sifted and understood to perk up the media and entertainment industry’s telecast and advertising bets. Consumers today are viewing and sharing more content than ever before, highlighting the importance of data analytics in media and entertainment. Hence, the incredible amounts of data open up massive opportunities for the media industry in content planning, bundling, and distribution. Industry players must constantly strive to gauge, spot, and respond to consumers, who watch and listen online. It is imperative for media and entertainment companies to grab the FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 4 – BUSINESS ANALYTICS IN DECISION MAKING 37 opportunities enclosed within data – or watch digital competitors spearhead the industry. The next application, banking Banking analytics or applications of data mining in banking, can help improve how banks segment, target, acquire and retain customers. It also deals with the, improvements needed for risk management, customer understanding, risk and fraud enable banks to maintain and grow a more profitable customer base. Application of Business Analytics in Banking 1. Customer Segmentation Based on a customer’s historical data regarding the customer spending patterns, banks can segment the customers according to the income, expenditure, the risks taken, etc. Cross-selling can be personalized based on this segmentation. It’s important to differentiate between the customers that make you money and the customers that lose you money. By understanding the profitability of certain groups of customers, banks can also analyze each group and extract useful insights. To grow wallet share and create more loyal affluent customers, banks need to concentrate on selling the right product to the right customer. 2. Fraud Management and Prevention Knowing the usual spending patterns of an individual helps raise a red flag if something outrageous happens. If there is a sudden increase in the expenditure of a cautious customer, this might mean the card was stolen and used by fraudsters. Analyzing these types of transactions in real time helps cut down the risk of fraudulent actions greatly. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 4 – BUSINESS ANALYTICS IN DECISION MAKING 38 The key steps to fraud detection include:  Obtaining data samplings for model estimation and preliminary testing  Model estimation  Testing stage and deployment 3. Risk Modeling Risk assessment is of high priority for banks, as it helps to regulate financial activities and in the pricing of financial investments. The financial health of a company can be assessed for corporate financing, facilitating in mergers and acquisitions and for investment purposes. 4. Identifying the Main Channels of Transactions (ATM Withdrawal, Credit/Debit Card Payments) Banks can track the past usage patterns and the daily coordination between the in- and out-payments at their branches and ATM’s, hence predicting the future needs of their potential customers. This also leads to the optimal management of the liquid assets which can result in extra income and can help obtain an overview of future changes in investment and liquidity options. 5. Customer Lifetime Value (LTV) Customer’s lifetime value is how long the organizations are able to retain their customers. Identifying who the best customers are, making them better in different ways, and once you win them over, securing their loyalty, are a few areas that banks are focusing. Predictive analytics helps: 1. Know which customers should be the focus of the new customer engagement efforts. 2. Identify the previous actors that enhanced returns on customer engagements in the past. 3. Use that knowledge to understand why customers responded to certain messages and promotions. 6. Feedback Management Feedback management is really important. Predictive analytics allows banks and financial firms to keep up their relationship with the customers by giving them the right services and products for their needs and matching individual preferences in the most sorted way. To gain competitive advantage, banks must acknowledge the crucial importance of data science, integrate it in their decision-making process, and develop strategies based on the actionable insights from their client’s data. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 4 – BUSINESS ANALYTICS IN DECISION MAKING 39 Learning analysis technology can predict their likelihood of Education academic success and course completion with a high degree of certainty. The software can be used to create unique profiles of students based on their demographic details, assessment results, and data gleaned from their interactions with education services. In many organizations, schools and colleges, teachers have an unmanageable student-related data to handle. Each of the students has a distinct set of qualities and learning abilities and even a wider range of socio- economic background. The teacher also has to figure out what data must be collected for a particular teaching policy, as that is one of the major pillars of developing study plans. Analytics can help Communication communications firms allow to run free consistent, personalized services, strengthen relationships and grow revenue. Communication analysis can help to predict and develop a deeper understanding of customer's likely service needs and the best way to meet them. In connection to this, the Communications Service Provider (CSP) analytics is depicted by economic volatility, the continued challenge of securing customer loyalty, rapidly evolving technologies and reduced churn rates. Capitalizing on the value given by communications analytics and big data can ensure competitive differentiation. To succeed in this environment, CSPs need powerful analytic tools and processes that differ from those utilized by previous generations of providers. Communication analytics can reveal the value in vast and complex network and customer data, allowing real-time decision making that can help the said CSPs in numerous ways such as:  Unleashing consistent services  Grow revenues  Strengthen relationships  Retain network quality and expansion FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 4 – BUSINESS ANALYTICS IN DECISION MAKING 40 Activity 4.2.1. Performance Tasks ASSIGNMENT NO. 4. 1. [Groupwork]. Give at least one common problem that arises in a given nature of business. How can business analytics help solve the problem in your given problem? Criteria: Content 10 Creativity 10 Organization 10 Teamwork 10 2. If face-to-face class is already allowed once the lesson is given, the output will be submitted in a short bond paper, its content written. Otherwise, it is to be submitted in LMS, in the discussion thread to be provided. References: The Soulpage (2019). Six Applications of data analytics in banking. Retrieved from https://soulpageit.com/6-applications-of-data-analytics-in-banking/ Pant, S. (2017). From abundance to targeting – Data analytics in media and entertainment. Retrieved from https://www.sganalytics.com/blog/data- analytics-media-entertainment/ FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 4 – BUSINESS ANALYTICS IN DECISION MAKING 41 LESSON 4.3. Customer Analytics Customer analytics is the process companies uses to capture and analyze customer data and make better decisions. It uses data collection that zero-in on customer’s online order transactions for the purpose of sorting out specific customer demographics, shopping patterns, internet usage and applying predictive analyses (Bently, 2019). Moreover, it often comes in the form of a software that furnishes companies with insights into their users’ behaviors. These insights power businesses’ sales, marketing, and product development efforts. Studies show that companies that use this type of analytics are more profitable. Top Uses for Big Data Analytics in Consumer Products and Retail  Increase demand and basket size of purchases  Increase customer retention and loyalty  Build brand equity  Create a new product innovation  Determine optimal advertising speed  Procure optimization Different Types of Analysis Suit Different Customer Strategies Bently (2019) outlined six styles of customer fitting for three key objectives. Objective: Enable exceptional experience and advocacy For organizations seeking to generate social media “buzz”, to create “customers for life”, and possibly to redefine their industry, there are two styles to consider. Style 1: People Make the Difference Most exceptional customer experiences are driven by exceptional employee behavior. Customer analysis can help by conferring understanding of customer’s preferences and behaviors, so that organizations can match them to the right employees in the right circumstances. Style 2: Deep Listening Customers don’t always know what they want. Analytics technology provides insight into their wants and needs. Analysis of social media and other customer- created content, as well as monitoring of customers’ purchasing and usage data, can yield ideas for new products to develop and new ways to package existing products. Objective: Set Expectation and Minimize Problems Sometimes the objective isn’t to delight customers but to stop annoying them. Two further styles can help to reduce or prevent customer dissatisfaction. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 4 – BUSINESS ANALYTICS IN DECISION MAKING 42 Style 3. Sharing Insights Organizations fails to use much of the customer data they collect, but they could achieve a variety of benefits from sharing some of it with their customers. Sharing data can establish a company as trusted element in customer’s decision- making process, and can even lead to opportunities to monetize data. The data may require little analysis, as the emphasis is on openness and sharing, not on providing deep insights. Style 4: Ensuring “It Just Works” An organization’s value proposition is often built on a few key attributes, such as product reliability, low cost and service consistency. When these fundamentals go wrong, it can be a long and costly process to regain a customer’s trust, especially if his or her needs are met by a competitor in the meantime. Organizations can apply analytics in a variety of ways to determine key factors for customer satisfaction. In some cases, doing this requires feedback from customers, but in other organizations can use even-monitoring system to identify issues before they become visible. Objective: Do the Usual Things, but Better Every Time The final two styles should improve the customer relationship the more consistently they are used. Style 5: Massive Customization In an increasingly digital world, it’s already surprisingly easy to deliver customized products. It should get even easier as the traditional 4Ps of marketing (price, product, promotion and place) can be adjusted to suit customers better, which should lead to it becoming commonplace to create and deliver customized products on the basis of analysis. Style 6: Changing Behaviors The most obvious use of analytics is to encourage changes in customers’ behavior. One can strive to understand and change their behavior at any phase in the customer relationship, and in any context, but most efforts focus on acquisition, cross selling and retention. In some cases, though, the greatest benefit becomes from analysis that is informed by an understanding of customer psychology. Customer Analytics Best Practices By measuring and analyzing data using specific metrics, organizations can create successful customer interactions. Some customer analytics best practices and common metrics that can help drive better business decisions include:  Targeting customers across all channels and analyzing the various ways a product or service can be distributed. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 4 – BUSINESS ANALYTICS IN DECISION MAKING 43  Assessing and understanding customers in relation to the brand and whether a customer is satisfied. This can be achieved through combination of quantitative and qualitative surveys.  Engaging with customers at the right moment through the right channel.  Predicting churn rate and taking actions to extend a customer’s lifetime value.  Spotting trends in big data and analyzing online behavior to increase sales.  Maximizing the customer journey through personalized selling and market segmentation by assessing which customers might buy one type of product versus another. Activity 4.3.  Read the online article on “Complete Overview of Customer Data Analytics” by Alex Bekker. Study Questions How would you relate the different types of analysis suit different customer strategies to the four (4) types of customer data to analyze and their use cases? References: Bekker, A. (2020). Complete overview of customer data analytics by Alex Bekker. Retrieved from https://www.scnsoft.com/blog/customer-data- analytic Bently, D. (2019). Business analytics: Principles, concepts and applications. Larsen & Keller. FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 4 – BUSINESS ANALYTICS IN DECISION MAKING 44 ONLINE READING MATERIALS  “How Business Analytics Can Influence Decision Making by UNSW – Master of Analytics.” Retrieved from https://www.mybusiness.com.au/management/5648-how-business-analytics- can-influence-decision-making  “Complete Overview of Customer Data Analytics” by Alex Bekker. Retrieved from https://www.scnsoft.com/blog/customer-data-analytic ONLINE VIDEO LINKS AND MATERIALS FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 4 – BUSINESS ANALYTICS IN DECISION MAKING 45 TEST YOUR KNOWLEDGE Online Quiz No. 4. 1. Business managers can enhance business analytics technologies by engaging IT personnel. (True/False: True) 2. Decision made on the basis of flawed logic, emotionalism, or incomplete information will result to a sound reasoning. (True/False: False) 3. In what areas can business analytics improve an organization’s talent (attract, retain and develop) 4. Internal and external organizational benefits of applying business analytics in decision making. (Provide a better customer experience, Improve overall performance, Conduct better risk assessment and management) 5. Customer segmentation, fraud management and media and entertainment are some of the areas where business analytics are applied making decision in the banking industry. (True/False: False - media and entertainment is for the entertainment) 6. Outline the six styles of customer. Six Styles of Customers Objectives Style Enable exceptional experience and advocacy 1. People Make the Difference 2. Deep Listening Set Expectation and Minimize Problems 3. Sharing Insights 4. Ensuring “It Just Works” Do the Usual Things, but Better Every Time 5. Massive Customization 6. Changing Behaviors FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS  MODULE 4 – BUSINESS ANALYTICS IN DECISION MAKING 46 MODULE REFERENCES How business analytics can influence decision making. UNSW – Master of Analytics. Retrieved from https://www.mybusiness.com.au/management/5648-how-business- analytics-can-influence-decision-making The Soulpage (2019). Six Applications of data analytics in banking. Retrieved from: https://soulpageit.com/6-applications-of-data-analytics-in-banking/ Bekker, A. (2020). Complete overview of customer data analytics by Alex Bekker. Retrieved from https://www.scnsoft.com/blog/customer-data- analytic Bently, D. (2019). Business analytics: Principles, concepts and applications. Larsen & Keller. Evans, J. (2017). Business analytics: Methods, models and decision. Pearson Publishing Co. Evans James R. (2017). Business analytics: Principles, concepts and applications. Pearson Publishing Co. Pant, S. (2017). From Abundance to targeting – Data analytics in media and entertainment. Retrieved from: https://www.sganalytics.com/blog/data- analytics-media-entertainment/ Singh, H. (2018). Using analytics for better decision making. Retrieved from: https://towardsdatascience.com/using-analytics-for-better-decision- making-ce4f92c4a025 Picture of business analytics. Retrieved from: https://www.uihere.com/free- cliparts/analytics-big-data-search-engine-optimization-marketing-business- office-pattern-1111962 Picture of banking. Retrieved from https://www.uihere.com/free-cliparts/bank- cashier-euclidean-vector-money-vector-bank-counter-1058379 Picture of customer segmentation. Retrieved from https://www.pinclipart.com/pindetail/ibRximJ_vector-graphics-clipart/ Picture of a loudspeaker. Retrieved from https://www.pinclipart.com/pindetail/ibTbJbx_loudspeaker-png-clipart/ FBANA – FUNDAMENTALS OF BUSINESS ANALYTICS