PowerPoint Presentation Contents of this template 1 General about BI 2 BI techniques 3 BI Tools 4 Dataset 5 Analyze the data with the chart and dashboard 6 The legal issues involved in exploiting user[.]
Contents of this template General about BI BI techniques BI Tools Dataset Analyze the data with the chart and dashboard The legal issues involved in exploiting user data for BI Feed-Back survey Conclusion 01 General about BI Define the Business Intelligence • Business intelligence (BI) leverages software and services to transform data into actionable insights that inform an organization’s strategic and tactical business decisions (Fruhlinger and Pratt, 2019) • BI tools access and analyze data sets and present analytical findings in reports, summaries, dashboards, graphs, charts and maps to provide users with detailed intelligence about the state of the business (Fruhlinger and Pratt, 2019) The main component of BI The five primary components of BI include: • OLAP (Online Analytical Processing): This component of BI allows executives to sort and select aggregates of data for strategic monitoring (Villanovau, 2020) • Advanced Analytics or Corporate Performance Management (CPM): This set of tools allows business leaders to look at the statistics of certain products or services (Villanovau, 2020) • Real-time BI: Using software applications, a business can respond to real-time trends in email, messaging system or even digital displays (Villanovau, 2020) • Data Warehousing: Data warehousing lets business leaders sift through subsets of data and examine interrelated components that can help drive business (Villanovau, 2020) • Data Sources: It’s about taking the raw data and using software applications to create meaningful data sources that each division can use to positively impact business (Villanovau, 2020) Benefits of BI 01 02 Faster analysis and reporting Improved data quality 05 Valuable business insights 03 04 Improved operationalComparing data with efficiency competitors 06 Improvement in customization 07 Low costs Example about BI Example 1: The potential use cases for BI extend beyond the typical business performance metrics of improved sales and reduced costs, says Cindi Howson, research vice president at Gartner, an IT research and advisory firm She points to the Columbus, Ohio, school system and its success using BI tools to examine numerous data points – from attendance rates to student performance – to improve student learning and high school graduate rates (Fruhlinger, 2019) Example 2: Coca-Cola’s business intelligence team handles reporting for all sales and delivery operations at the company With their BI platform, the team automated manual reporting processes, saving over 260 hours a year-more than six 40-hour work weeks (Tableau, 2018) 02 —BI Techniques Collection Data cleansing: the process of preparing data for analysis by removing or modifying data that is incorrect, incomplete, irrelevant, duplicated, or improperly formatted This data is usually not necessary or helpful when it comes to analyzing data Find a way to maximize the accuracy of a data set without necessarily deleting information Techniques Collection techniques Advantages Disadvantages • Eliminate errors when multiple data sources are active • Fewer errors make customers happier and employees less frustrated • Ability to map different functions and what your data intends to • Better tracking and reporting to know where the error is coming from, making it easier to fix inaccurate or corrupt data for future applications • Using tools to clean up data will help make business practices more efficient and make decision faster • Dirty data can negatively affect your earnings • Dirty data leads to reduced productivity • Unnecessary expenditure and reduced reliability • There are limitations in the data due to incorrect data Collection techniques Labeling: Data labeling, in the context of machine learning, is the process of detecting and tagging data samples The process can be manual but is usually performed or assisted by software Data labeling is an important aspect of preprocessing data for ML, for supervised learning, where input and output data are labelled for classification to provide a learning basis for future data processing