Business intelligence (BI) refers to processes, technologies and applications used to support data-driven decision making in organizations. Organizations use BI to gain insights into business performance, customers, sales, finances and more. The basic components of BI are gathering data, storing it, analyzing it, and providing access to insights. Leading companies use BI effectively by linking data analysis to strategic objectives, collecting the right types of data, testing assumptions through experiments, communicating insights clearly, and turning insights into actions and decisions.
Business intelligence and IT governance are increasingly important for modern businesses. Business intelligence involves collecting and analyzing large amounts of data to help businesses make better decisions. It has evolved from early attempts by businesses to understand their own information and markets. Modern business intelligence utilizes tools like dashboards, scorecards, and data warehouses. IT governance ensures that business and IT strategies are aligned and that information technology supports business objectives. Business intelligence 2.0 takes analysis a step further by enabling more interactive and flexible analysis of both structured and unstructured data.
Role of business intelligence in knowledge managementShakthi Fernando
This study is fundamentally based on the most common components of a Business Intelligence System, data warehouses, ETL tools, OLAP techniques and data mining, which comfort the decision making function. It further describe about the role of each component in a Business Intelligence System and how Business Intelligence Systems can be used for better business decision making at each level of management.
A treatise on SAP CRM information reportingVijay Raj
This document discusses data extraction from SAP Customer Relationship Management (CRM) 7.0 into SAP Business Warehouse (BW) for business reporting and analytics. It describes the CRM BW Adapter framework used to exchange data between CRM and BW, and the steps to configure and implement it. The document focuses on extracting application database data from CRM into BW, excluding extraction for mobile CRM clients. It provides details on initializing and extracting delta changes from CRM using the BW Adapter data sources.
This document discusses key factors for successful business intelligence (BI) solutions. It outlines that smart companies use BI to gain insights and competitive advantages. BI solutions transform raw data into valuable information and knowledge through extraction, integration, analysis and feedback loops. The document then discusses various aspects of establishing successful BI solutions, including having executive sponsorship, aligning business and IT, prioritizing projects, and building an organizational culture that values data-driven decision making.
Business intelligence (BI) techniques analyze business data to support decision making. BI systems capture enterprise, customer, and competitor data to identify trends and patterns that help strategic, tactical, and operational decisions. This improves productivity. Common BI applications include querying, reporting, online analytical processing, statistical analysis, forecasting, and data mining of data warehouse and ERP system data. When implemented effectively with a focus on business goals, metrics, and processes, BI systems can significantly improve enterprise performance and competitiveness.
Strategic alignment with bi and ROI AffectFarooq Omar
This document discusses the importance of business intelligence and analytics for organizations. It defines business intelligence as activities used to discover, analyze, and assess information to help guide strategic decision making. The main types of intelligence discussed are competitive, market, technological, and strategic intelligence. Effective corporate intelligence involves identifying needs, establishing information sources, analyzing raw data, and disseminating insights within the company. When properly implemented using tools like data mining and knowledge management systems, business intelligence can help organizations improve products, customer relationships, and operations by basing decisions on relevant facts and metrics.
Strategic alignment with Bi and ROI AffectFarooq Omar
Information is a key resource that empowers you to keep up or upgrade your market aggressiveness. Insight is in this manner progressively critical to your business. Here we attempt to ponder on the 'Vital' parameters of Intelligence which is the one of the most basic variables of authoritative development and to support in coherence. We have to realize the accompanying utilitarian segments to make an incentive out of it.
1. The document discusses rational decision making and business intelligence. It defines rational decision making as selecting the optimal alternative based on analyzing past data and considering various performance criteria.
2. It describes the typical cycle of a business intelligence analysis as involving defining objectives, generating insights from data analysis, making decisions based on insights, and evaluating performance.
3. Key components of business intelligence architectures are data sources, data warehouses/marts for storing and processing data, and business intelligence tools for generating insights and supporting decision making.
Business intelligence and IT governance are increasingly important for modern businesses. Business intelligence involves collecting and analyzing large amounts of data to help businesses make better decisions. It has evolved from early attempts by businesses to understand their own information and markets. Modern business intelligence utilizes tools like dashboards, scorecards, and data warehouses. IT governance ensures that business and IT strategies are aligned and that information technology supports business objectives. Business intelligence 2.0 takes analysis a step further by enabling more interactive and flexible analysis of both structured and unstructured data.
Role of business intelligence in knowledge managementShakthi Fernando
This study is fundamentally based on the most common components of a Business Intelligence System, data warehouses, ETL tools, OLAP techniques and data mining, which comfort the decision making function. It further describe about the role of each component in a Business Intelligence System and how Business Intelligence Systems can be used for better business decision making at each level of management.
A treatise on SAP CRM information reportingVijay Raj
This document discusses data extraction from SAP Customer Relationship Management (CRM) 7.0 into SAP Business Warehouse (BW) for business reporting and analytics. It describes the CRM BW Adapter framework used to exchange data between CRM and BW, and the steps to configure and implement it. The document focuses on extracting application database data from CRM into BW, excluding extraction for mobile CRM clients. It provides details on initializing and extracting delta changes from CRM using the BW Adapter data sources.
This document discusses key factors for successful business intelligence (BI) solutions. It outlines that smart companies use BI to gain insights and competitive advantages. BI solutions transform raw data into valuable information and knowledge through extraction, integration, analysis and feedback loops. The document then discusses various aspects of establishing successful BI solutions, including having executive sponsorship, aligning business and IT, prioritizing projects, and building an organizational culture that values data-driven decision making.
Business intelligence (BI) techniques analyze business data to support decision making. BI systems capture enterprise, customer, and competitor data to identify trends and patterns that help strategic, tactical, and operational decisions. This improves productivity. Common BI applications include querying, reporting, online analytical processing, statistical analysis, forecasting, and data mining of data warehouse and ERP system data. When implemented effectively with a focus on business goals, metrics, and processes, BI systems can significantly improve enterprise performance and competitiveness.
Strategic alignment with bi and ROI AffectFarooq Omar
This document discusses the importance of business intelligence and analytics for organizations. It defines business intelligence as activities used to discover, analyze, and assess information to help guide strategic decision making. The main types of intelligence discussed are competitive, market, technological, and strategic intelligence. Effective corporate intelligence involves identifying needs, establishing information sources, analyzing raw data, and disseminating insights within the company. When properly implemented using tools like data mining and knowledge management systems, business intelligence can help organizations improve products, customer relationships, and operations by basing decisions on relevant facts and metrics.
Strategic alignment with Bi and ROI AffectFarooq Omar
Information is a key resource that empowers you to keep up or upgrade your market aggressiveness. Insight is in this manner progressively critical to your business. Here we attempt to ponder on the 'Vital' parameters of Intelligence which is the one of the most basic variables of authoritative development and to support in coherence. We have to realize the accompanying utilitarian segments to make an incentive out of it.
1. The document discusses rational decision making and business intelligence. It defines rational decision making as selecting the optimal alternative based on analyzing past data and considering various performance criteria.
2. It describes the typical cycle of a business intelligence analysis as involving defining objectives, generating insights from data analysis, making decisions based on insights, and evaluating performance.
3. Key components of business intelligence architectures are data sources, data warehouses/marts for storing and processing data, and business intelligence tools for generating insights and supporting decision making.
1. The document discusses shifts in analytics and big data, including that the majority of organizations now realize returns on analytics investments within a year, and that while customer focus remains important, organizations are increasingly using data and analytics to improve operations.
2. It also notes that many organizations are transforming processes by integrating digital capabilities, and that the value driver for big data has shifted from volume to velocity - the ability to quickly move from data to action.
3. Speed is now the key differentiator, as data-driven organizations with capabilities for broad, fast analytics usage and agile technical infrastructure are creating significant business impacts.
This document discusses using business intelligence (BI) strategies and tools to improve human resource (HR) management. It proposes separating personnel data from business data and analyzing employment trends to better screen candidates and improve productivity. BI involves collecting and analyzing large amounts of employee data (profiles, appraisals, compensation) to gain insights for strategic HR decisions. Implementing a BI approach for HR could help translate existing employee data into future-focused actions around candidate screening, cost management, and productivity enhancements.
Emergency Medical Association is a group of 250 emergency physicians responsible for effectively managing emergency departments. They should implement business intelligence values and applications like data-driven decision making to improve patient outcomes, reduce costs, and ensure future success.
Real-time business intelligence involves delivering business operation information as it occurs. Credit card companies use real-time BI by approving purchase amounts via mobile to retain customers longer. The process involves feeding transactions to a system maintaining the current enterprise state in real-time for tactical decisions alongside classic strategic functions.
Business intelligence plays a key role in modern business by processing vast information into understandable formats for strategic, tactical, and operational decision making. This helps decision makers faced with information overload and inconsistent data.
This document discusses metrics that can be used to assess the effectiveness of business analytics initiatives. It summarizes the results of a benchmark study that evaluated organizations on 8 metrics: productivity, governance, timeliness, ROI, accuracy, effectiveness, empowerment and maturity. The study found that on average organizations scored highest in governance and lowest in effectiveness. Certain industries tended to score higher or lower on different metrics. The document recommends evaluating an organization across 64 questions related to the 8 metrics in order to identify strengths and opportunities for improvement.
This document discusses key components of developing a big data strategy, including:
1. Big data initiatives are unique and will likely transform businesses, technologies, and organizations.
2. Companies should identify potentially valuable internal and external data sources, and generate innovative ideas for using big data.
3. Both business and IT strategies are needed to ensure infrastructure is adequate, skills are available, risks are managed, and analytics capabilities are expanded.
Business intelligence (BI) captures electronic data to analyze and report on in order to answer questions and inform business goals, while knowledge management (KM) captures institutional data. The BI process involves developing goals, determining questions, collecting data, cleaning the data, analyzing and reporting on it, and communicating the results. BI tools can help give associations the knowledge to remain relevant to members by collecting members' data and providing insights.
This document discusses business intelligence (BI) in financial institutions. It defines BI as gathering meaningful information to help with analysis and conclusions. An ideal BI system gives employees easy access to needed information and the ability to analyze and share it. The document contrasts traditional reporting with BI and analytic applications. It also discusses identifying BI opportunities by evaluating where it could improve decision making. The benefits of BI include improved operational and strategic decisions from timely information. The document outlines the layers of a BI infrastructure from operational data to delivering intelligence to users.
Leveraging the best of traditional modelling with the latest big data, data profiling & semantic web techniques to accelerate delivery & value realisation
Mis2013 chapter 12 business intelligence and knowledge managementAndi Iswoyo
The document discusses data warehousing and business intelligence systems. It notes that a large amount of new data is being created daily due to factors like Moore's Law, but much of this data is not useful for analysis due to issues like inconsistencies, missing values, and incorrect formats. Data warehouses address these problems by cleaning, integrating, and reformatting data from various sources into a single database optimized for analysis using business intelligence tools. The cleaned and integrated data stored in a data warehouse is then used for reporting, online analytical processing, and data mining to help organizations make better business decisions.
The document discusses how most enterprises are investing in big data and real-time analytics initiatives to gain competitive advantages, but many IT organizations lack strategies to align these technologies with business goals. It describes how new data sources can provide richer customer insights and how real-time analytics can enable more timely operational decisions. However, organizations must evaluate whether their specific use cases require real-time data or would benefit more from traditional BI.
1) The document discusses how organizations can become data-driven by extracting value from big data sources.
2) A key challenge is overcoming managerial and cultural barriers to effectively analyze and link diverse data sources.
3) The document provides several recommendations for organizations, including developing case studies to justify insights from big data, focusing on achievable steps to drive value, and leveraging social media analytics to enable real-time analysis and correlations between data.
Data set Improve your business with your own business dataData-Set
The objective of this module is to gain an overview of how to use the data you already have available in order to improve your business.
Upon completion of this module you will:
-Gain an understanding of how to take advantage of the existing data you already have
-Comprehend the location of where internal data already lies within your company
-Improve your knowledge on how data can help build your brand
Sit717 enterprise business intelligence 2019 t2 copy1NellutlaKishore
This document discusses data mining techniques and business intelligence. It begins with an introduction to different data mining techniques like clustering, statistical analysis, visualization, classification, neural networks, rules, and decision trees. It then provides more detail on statistical techniques, explaining that they help analyze large datasets. The document evaluates how big data and business intelligence are related, concluding that while they are different concepts, they need to work together to effectively analyze data and make smart business decisions. Big data provides the large datasets, while business intelligence extracts useful information from those datasets.
This document discusses implementing a data governance program to address various data challenges. It outlines current data issues like missing information, duplicate data and integration difficulties. A data governance program is proposed to establish policies, processes, roles and data ownership to improve data quality. The presentation recommends starting with a small pilot project, then expanding organization-wide. It provides examples of creating a data dictionary to define data elements and assigning data owners.
Enterprise Data World Webinar: A Strategic Approach to Data Quality DATAVERSITY
We will also explore how to apply the 12 Directives, through a set of tactics to help you assess organizational readiness for data quality strategy. The purpose of such an assessment is to surface priorities for strategic action and to formulate a long-term approach to an organization’s data quality improvement.
AIMLEAP #outsourcebigdata.com is a Trusted Partner for #DigitalIT, #BI #Analytics, #Automation & #DataManagement, #dataprocessing. 20 % of CIOs may lose their jobs if they fail to implement a successful framework for #DataGovernance in their organization Source: Gartner.
1) Organizations want to achieve business value from data-derived insights in four key ways: efficiency/cost reduction, growth of existing business streams, growth through new revenue streams from market disruption, and monetization of data itself through new business lines.
2) Most organizations are adopting an incremental approach to realizing this value, first proving value through use cases, then expanding to pilots in a line of business, and eventually achieving enterprise-wide adoption. This allows them to set a strategic direction while delivering value incrementally.
3) Current business intelligence technology like enterprise data warehouses are not meeting organizations' needs to democratize access to data and analytics. Decision-makers need the ability to rapidly create insights aligned with
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
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Business intelligence environments involve collecting data from various sources, transforming and organizing it using tools like ETL, and storing it in data warehouses or marts. This data is then analyzed using OLAP and reporting tools to provide useful information for business decisions. Setting up an effective BI environment requires understanding business requirements, defining processes, determining data needs, integrating data sources, and selecting appropriate tools and techniques. Careful planning and skilled people are needed to ensure the BI environment supports organizational goals.
Business intelligence (BI) is a system of tools and methods that aid in strategic planning and informed decision-making. This involves collecting data from internal and external sources, analyzing the data to gain insights, and visualizing insights for decision makers. BI helps organizations understand customer behavior, improve products and efficiency, gain competitive advantages, improve sales and marketing, and gain visibility across the organization. Determining if an organization needs BI involves assessing if the organization has data but no useful information, relies solely on IT for reports, or uses spreadsheets without dedicated BI software. Tracking the right metrics like quantitative vs qualitative, actionable vs vanity, reporting vs exploratory, correlated vs causal, and lagging vs leading metrics helps organizations focus on what
1. The document discusses shifts in analytics and big data, including that the majority of organizations now realize returns on analytics investments within a year, and that while customer focus remains important, organizations are increasingly using data and analytics to improve operations.
2. It also notes that many organizations are transforming processes by integrating digital capabilities, and that the value driver for big data has shifted from volume to velocity - the ability to quickly move from data to action.
3. Speed is now the key differentiator, as data-driven organizations with capabilities for broad, fast analytics usage and agile technical infrastructure are creating significant business impacts.
This document discusses using business intelligence (BI) strategies and tools to improve human resource (HR) management. It proposes separating personnel data from business data and analyzing employment trends to better screen candidates and improve productivity. BI involves collecting and analyzing large amounts of employee data (profiles, appraisals, compensation) to gain insights for strategic HR decisions. Implementing a BI approach for HR could help translate existing employee data into future-focused actions around candidate screening, cost management, and productivity enhancements.
Emergency Medical Association is a group of 250 emergency physicians responsible for effectively managing emergency departments. They should implement business intelligence values and applications like data-driven decision making to improve patient outcomes, reduce costs, and ensure future success.
Real-time business intelligence involves delivering business operation information as it occurs. Credit card companies use real-time BI by approving purchase amounts via mobile to retain customers longer. The process involves feeding transactions to a system maintaining the current enterprise state in real-time for tactical decisions alongside classic strategic functions.
Business intelligence plays a key role in modern business by processing vast information into understandable formats for strategic, tactical, and operational decision making. This helps decision makers faced with information overload and inconsistent data.
This document discusses metrics that can be used to assess the effectiveness of business analytics initiatives. It summarizes the results of a benchmark study that evaluated organizations on 8 metrics: productivity, governance, timeliness, ROI, accuracy, effectiveness, empowerment and maturity. The study found that on average organizations scored highest in governance and lowest in effectiveness. Certain industries tended to score higher or lower on different metrics. The document recommends evaluating an organization across 64 questions related to the 8 metrics in order to identify strengths and opportunities for improvement.
This document discusses key components of developing a big data strategy, including:
1. Big data initiatives are unique and will likely transform businesses, technologies, and organizations.
2. Companies should identify potentially valuable internal and external data sources, and generate innovative ideas for using big data.
3. Both business and IT strategies are needed to ensure infrastructure is adequate, skills are available, risks are managed, and analytics capabilities are expanded.
Business intelligence (BI) captures electronic data to analyze and report on in order to answer questions and inform business goals, while knowledge management (KM) captures institutional data. The BI process involves developing goals, determining questions, collecting data, cleaning the data, analyzing and reporting on it, and communicating the results. BI tools can help give associations the knowledge to remain relevant to members by collecting members' data and providing insights.
This document discusses business intelligence (BI) in financial institutions. It defines BI as gathering meaningful information to help with analysis and conclusions. An ideal BI system gives employees easy access to needed information and the ability to analyze and share it. The document contrasts traditional reporting with BI and analytic applications. It also discusses identifying BI opportunities by evaluating where it could improve decision making. The benefits of BI include improved operational and strategic decisions from timely information. The document outlines the layers of a BI infrastructure from operational data to delivering intelligence to users.
Leveraging the best of traditional modelling with the latest big data, data profiling & semantic web techniques to accelerate delivery & value realisation
Mis2013 chapter 12 business intelligence and knowledge managementAndi Iswoyo
The document discusses data warehousing and business intelligence systems. It notes that a large amount of new data is being created daily due to factors like Moore's Law, but much of this data is not useful for analysis due to issues like inconsistencies, missing values, and incorrect formats. Data warehouses address these problems by cleaning, integrating, and reformatting data from various sources into a single database optimized for analysis using business intelligence tools. The cleaned and integrated data stored in a data warehouse is then used for reporting, online analytical processing, and data mining to help organizations make better business decisions.
The document discusses how most enterprises are investing in big data and real-time analytics initiatives to gain competitive advantages, but many IT organizations lack strategies to align these technologies with business goals. It describes how new data sources can provide richer customer insights and how real-time analytics can enable more timely operational decisions. However, organizations must evaluate whether their specific use cases require real-time data or would benefit more from traditional BI.
1) The document discusses how organizations can become data-driven by extracting value from big data sources.
2) A key challenge is overcoming managerial and cultural barriers to effectively analyze and link diverse data sources.
3) The document provides several recommendations for organizations, including developing case studies to justify insights from big data, focusing on achievable steps to drive value, and leveraging social media analytics to enable real-time analysis and correlations between data.
Data set Improve your business with your own business dataData-Set
The objective of this module is to gain an overview of how to use the data you already have available in order to improve your business.
Upon completion of this module you will:
-Gain an understanding of how to take advantage of the existing data you already have
-Comprehend the location of where internal data already lies within your company
-Improve your knowledge on how data can help build your brand
Sit717 enterprise business intelligence 2019 t2 copy1NellutlaKishore
This document discusses data mining techniques and business intelligence. It begins with an introduction to different data mining techniques like clustering, statistical analysis, visualization, classification, neural networks, rules, and decision trees. It then provides more detail on statistical techniques, explaining that they help analyze large datasets. The document evaluates how big data and business intelligence are related, concluding that while they are different concepts, they need to work together to effectively analyze data and make smart business decisions. Big data provides the large datasets, while business intelligence extracts useful information from those datasets.
This document discusses implementing a data governance program to address various data challenges. It outlines current data issues like missing information, duplicate data and integration difficulties. A data governance program is proposed to establish policies, processes, roles and data ownership to improve data quality. The presentation recommends starting with a small pilot project, then expanding organization-wide. It provides examples of creating a data dictionary to define data elements and assigning data owners.
Enterprise Data World Webinar: A Strategic Approach to Data Quality DATAVERSITY
We will also explore how to apply the 12 Directives, through a set of tactics to help you assess organizational readiness for data quality strategy. The purpose of such an assessment is to surface priorities for strategic action and to formulate a long-term approach to an organization’s data quality improvement.
AIMLEAP #outsourcebigdata.com is a Trusted Partner for #DigitalIT, #BI #Analytics, #Automation & #DataManagement, #dataprocessing. 20 % of CIOs may lose their jobs if they fail to implement a successful framework for #DataGovernance in their organization Source: Gartner.
1) Organizations want to achieve business value from data-derived insights in four key ways: efficiency/cost reduction, growth of existing business streams, growth through new revenue streams from market disruption, and monetization of data itself through new business lines.
2) Most organizations are adopting an incremental approach to realizing this value, first proving value through use cases, then expanding to pilots in a line of business, and eventually achieving enterprise-wide adoption. This allows them to set a strategic direction while delivering value incrementally.
3) Current business intelligence technology like enterprise data warehouses are not meeting organizations' needs to democratize access to data and analytics. Decision-makers need the ability to rapidly create insights aligned with
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
Business intelligence environments involve collecting data from various sources, transforming and organizing it using tools like ETL, and storing it in data warehouses or marts. This data is then analyzed using OLAP and reporting tools to provide useful information for business decisions. Setting up an effective BI environment requires understanding business requirements, defining processes, determining data needs, integrating data sources, and selecting appropriate tools and techniques. Careful planning and skilled people are needed to ensure the BI environment supports organizational goals.
Business intelligence (BI) is a system of tools and methods that aid in strategic planning and informed decision-making. This involves collecting data from internal and external sources, analyzing the data to gain insights, and visualizing insights for decision makers. BI helps organizations understand customer behavior, improve products and efficiency, gain competitive advantages, improve sales and marketing, and gain visibility across the organization. Determining if an organization needs BI involves assessing if the organization has data but no useful information, relies solely on IT for reports, or uses spreadsheets without dedicated BI software. Tracking the right metrics like quantitative vs qualitative, actionable vs vanity, reporting vs exploratory, correlated vs causal, and lagging vs leading metrics helps organizations focus on what
Business intelligence involves collecting, storing, and analyzing structured internal data to provide historical, current, and predictive insights. This allows managers to make more informed strategic decisions. Business analytics focuses on using data mining and modeling to generate predictive insights and determine why certain outcomes occur, while data analytics is the technical process of preparing and analyzing large amounts of structured and unstructured data from various sources. Together, these approaches turn raw data into useful information, knowledge, insights, strategic recommendations, and actions.
Business intelligence (BI) transforms raw data into useful information for business purposes by using technologies, methodologies, and processes. BI can analyze large amounts of data to help identify new opportunities and develop effective strategies that provide competitive advantages. While BI and competitive intelligence both support decision making, BI focuses more on analyzing internal data and business processes. BI is leveraged to help make business decisions and recommendations by using data warehouses, data rules engines, statistical analysis tools, and data mining tools. The success of BI implementation depends on factors like business sponsorship, understanding business needs, and having sufficient high quality data.
Enterprize and departmental BusinessIintelligence.pptxHemaSenthil5
Enterprise business intelligence involves collecting, storing, and analyzing business data from across an entire large organization to provide strategic insights. It uses robust, scalable tools to handle large volumes of data from different sources. The goals are to give management a comprehensive view of the business and foster a data-driven culture. Departmental BI focuses more narrowly on specific department needs, empowering teams with custom reports and self-service tools integrated with the overall enterprise strategy.
Business intelligence is a tool that transforms raw data into meaningful information to help businesses make better decisions. It helps managers and executives cut costs, identify opportunities, and improve processes. While the term was coined in 1865, business intelligence has grown more powerful due to increased data collection, storage capacity, and lower storage costs. Companies now use data from various digital sources for business intelligence. It provides benefits like accelerating decision making, optimizing processes, increasing efficiency, and gaining competitive advantages. Common business intelligence software tools are used to analyze historical, current, and predictive data for purposes like performance management and benchmarking against competitors. For successful implementation, companies must have clean data, effective training, clear ROI definitions, and focus on business objectives rather than
This document discusses how business intelligence can benefit financial institutions. It defines business intelligence and describes how it involves collecting and analyzing data to improve business decisions. It then provides examples of how business intelligence can help various parts of the financial industry, including retail banking, insurance, and investment banking, by identifying profitable customers, optimizing marketing, reducing costs and risks, and improving customer service.
Driving Value Through Data Analytics: The Path from Raw Data to Informational...Cognizant
As organizations gather and process colossal amounts of data, analytics is essential for operational and strategic excellence. We offer a guide to the phases of the data analytics journey, from descriptive to diagnostic to predictive to prescriptive, covering intentions, tools and people considerations.
Business analytics uses statistical methods and technologies to analyze historical data and gain new insights to improve strategic decision-making. It refers to skills, technologies, and practices for continuously developing new understandings of business performance based on data analysis. Business analytics is commonly used to analyze various data sources, find patterns within datasets to predict trends and access new consumer insights, monitor key performance indicators in real-time, and support decisions with current information. It provides companies the ability to interpret large volumes of data to make informed decisions supporting organizational growth.
4Emerging Trends in Business IntelligenceITS 531.docxblondellchancy
4
Emerging Trends in Business Intelligence
ITS 531-20 Business Intelligence
Emerging Trends in Business Intelligence
By
Vivek Reddy Chinthakuntla
Soumya Kalakonda
To Professor Dr. Kelly Bruning
University of the Cumberlands
Table of Contents
Abstract.......................................................................................................................................4
Business Intelligence with Data Analytics................................................................................................6
Partial Application of BI with Data Analytics...........................................................................................7
Future of BI and Data Analytics.................................................................................................................8
Positive and negative impacts of BI ..........................................................................................................9
Recommendations ....................................................................................................................................9
Cloud Computing with BI.......................................................................................................................10
Practical Implications..............................................................................................................................10
Future of Cloud Computing with BI........................................................................................................14
Advantages and Disadvantages................................................................................................................15
Recommendations....................................................................................................................................15
Introduction to Business Drive Data Intelligence.....................................................................................16
Data Governance of Self-Service BI ........................................................................................................19
Future of BI depends on Data Governance..............................................................................................19
Conclusion................................................................................................................................................20
References................................................................................................................................................ 22
Abstract:
This paper is based on the proposition used, and the outcomes attained, using data management to expedite the changes in the operation from a conventional old-fashioned practice to an automatic Business Intelligence data analytics system, presenting timely, reliable system production data by using Business Intelligence tools and technologies. This paper explains the importance and productivity of ...
Data-Analytics-Essentials-Building-a-Foundation-for-Informed-Business-Choices...Attitude Tally Academy
Unlock the power of informed decision-making with our guide, "From Data to Decisions: Building a Solid Foundation for Business Success" Explore the essentials of data analytics, empowering your business to thrive in a data-driven era. Discover strategic insights, navigate through information overload, and transform raw data into actionable intelligence.Whether you're a startup or an established enterprise, this resource is your roadmap to making sound business choices and charting a course toward success.Dive into the world of data-backed strategies and position your business for growth in today's competitive landscape.
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Business analytics is the process of using statistical methods and technologies to analyze historical business data in order to gain insights and improve strategic decision making. It helps businesses increase profits, market share, and shareholder returns. Business analytics focuses on developing new understandings of past business performance through continuous data exploration and analysis. The results of business analytics can be used directly for decision making or to drive automated decisions.
Technology-driven process for analyzing data and delivering actionable information that helps executives, managers, and workers make informed business decisions.
How does the business intelligence process work?
A business intelligence architecture includes more than just BI software. Business intelligence data is typically stored in a data warehouse built for an entire organization or in smaller data marts that hold subsets of business information for individual departments and business units, often with ties to an enterprise data warehouse. In addition, data lakes based on Hadoop clusters or other big data systems are increasingly used as repositories or landing pads for BI and analytics data, especially for log files, sensor data, text, and other types of unstructured or semi-structured data.
BI data can include historical information and real-time data gathered from source systems as it’s generated, enabling BI tools to support both strategic and tactical decision-making processes. Before it’s used in BI applications, raw data from different source systems generally must be integrated, consolidated, and cleansed using data integration and data quality management tools to ensure that BI teams and business users are analyzing accurate and consistent information.
From there, the steps in the BI process include the following:
data preparation, in which data sets are organized and modeled for analysis;
analytical querying of the prepared data;
distribution of key performance indicators (KPIs) and other findings to business users; and
use of the information to help influence and drive business decisions.
Initially, BI tools were primarily used by BI and IT professionals who ran queries and produced dashboards and reports for business users. Increasingly, however, business analysts, executives, and workers are using business intelligence platforms themselves, thanks to the development of self-service BI and data discovery tools. Self-service business intelligence environments enable business users to query BI data, create data visualizations, and design dashboards on their own.
BI programs often incorporate forms of advanced analytics, such as data mining, predictive analytics, text mining, statistical analysis, and big data analytics. A common example is predictive modeling which enables what-if analysis of different business scenarios. In most cases, though, advanced analytics projects are conducted by separate teams of data scientists, statisticians, predictive modelers, and other skilled analytics professionals, while BI teams oversee more straightforward querying and analysis of business data.
What is business intelligence (BI)?
It is a tool for transforming data into information and then that information into knowledge using various methodologies. The objective of this process is to optimize the decision-making of the company to the maximum since the acquired knowledge can be used to develop strategic or commercial plans.
"xLogia Tech: Business Intelligence (BI) is a set of technologies, processes, and tools that help organizations collect, analyze, and present business information to support decision-making. The core components of business intelligence are diverse and encompass various aspects of data management and analysis.
1. Data Sources and Integration: BI begins with data. Organizations collect data from various sources such as transactional databases, spreadsheets, and external data providers. Data integration involves combining, cleaning, and transforming this raw data into a unified and consistent format for analysis. This process ensures that decision-makers have access to accurate and reliable information.
2. Data Warehousing: A data warehouse is a central repository that stores large volumes of structured and sometimes unstructured data. It is designed to support reporting and analysis. Data warehouses provide a historical perspective and allow for complex queries to be executed efficiently."
Business intelligence (BI) is the strategies, processes, technologies and architectures used to support the collection, analysis, presentation and dissemination of business information. BI technologies are capable of handling large amounts of structured and unstructured data to help identify new strategic opportunities. Common functions of BI technologies include reporting, analytics, data mining and predictive analytics. The goal of BI is to allow for the easy interpretation of large volumes of data to provide businesses with a competitive advantage.
Business analytics (BA) is the practice of iterative, methodical exploration of an organization's data, with an emphasis on statistical analysis. BA is used by companies committed to data-driven decision-making to gain insights that inform business decisions and can be used to automate and optimize business processes. BA techniques break down into basic business intelligence, which involves collecting and preparing data, and deeper statistical analysis. True data science involves more custom coding and open-ended questions compared to most business analysts.
AI in Business Intelligence Impact use cases and implementationChristopherTHyatt
Data, data everywhere! In the contemporary business landscape, organizations are inundated with a constant stream of data. But without the ability to interpret and analyze this data effectively, it remains just that – data. This is where business intelligence (BI) comes in. As Carly Fiorina aptly stated, “The goal of business intelligence is to turn data into information and information into insight.“Business intelligence tools and strategies enable companies to uncover the latent opportunities hidden within their data, translating it into actionable insights that enhance decision-making processes.
Data is information collected through observations, measurements, or research that is organized into graphs, charts, or tables. Data management involves collecting, organizing, protecting, and storing an organization's data so it can be analyzed to support business decisions. Effective data management provides accurate, available, and accessible data that leads to insights to improve customer value and business performance.
The objective of this module is to gain an overview of how to use the data you already have available in order to improve your business.
Upon completion of this module you will:
Gain an understanding of how to take advantage of the existing data you already have
Comprehend the location of where internal data already lies within your company
Improve your knowledge on how data can help build your brand
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• Manage Sources and Dataset
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• Model Training
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• Best practices
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Just like life, our code must adapt to the ever changing world we live in. From one day coding for the web, to the next for our tablets or APIs or for running serverless applications. Multi-runtime development is the future of coding, the future is to be dynamic. Let us introduce you to BoxLang.
Dynamic. Modular. Productive.
BoxLang redefines development with its dynamic nature, empowering developers to craft expressive and functional code effortlessly. Its modular architecture prioritizes flexibility, allowing for seamless integration into existing ecosystems.
Interoperability at its Core
With 100% interoperability with Java, BoxLang seamlessly bridges the gap between traditional and modern development paradigms, unlocking new possibilities for innovation and collaboration.
Multi-Runtime
From the tiny 2m operating system binary to running on our pure Java web server, CommandBox, Jakarta EE, AWS Lambda, Microsoft Functions, Web Assembly, Android and more. BoxLang has been designed to enhance and adapt according to it's runnable runtime.
The Fusion of Modernity and Tradition
Experience the fusion of modern features inspired by CFML, Node, Ruby, Kotlin, Java, and Clojure, combined with the familiarity of Java bytecode compilation, making BoxLang a language of choice for forward-thinking developers.
Empowering Transition with Transpiler Support
Transitioning from CFML to BoxLang is seamless with our JIT transpiler, facilitating smooth migration and preserving existing code investments.
Unlocking Creativity with IDE Tools
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QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...AlexanderRichford
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation Functions to Prevent Interaction with Malicious QR Codes.
Aim of the Study: The goal of this research was to develop a robust hybrid approach for identifying malicious and insecure URLs derived from QR codes, ensuring safe interactions.
This is achieved through:
Machine Learning Model: Predicts the likelihood of a URL being malicious.
Security Validation Functions: Ensures the derived URL has a valid certificate and proper URL format.
This innovative blend of technology aims to enhance cybersecurity measures and protect users from potential threats hidden within QR codes 🖥 🔒
This study was my first introduction to using ML which has shown me the immense potential of ML in creating more secure digital environments!
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kafka-streams-cassandra-state-store' is a drop-in Kafka Streams State Store implementation that persists data to Apache Cassandra.
By moving the state to an external datastore the stateful streams app (from a deployment point of view) effectively becomes stateless. This greatly improves elasticity and allows for fluent CI/CD (rolling upgrades, security patching, pod eviction, ...).
It also can also help to reduce failure recovery and rebalancing downtimes, with demos showing sporty 100ms rebalancing downtimes for your stateful Kafka Streams application, no matter the size of the application’s state.
As a bonus accessing Cassandra State Stores via 'Interactive Queries' (e.g. exposing via REST API) is simple and efficient since there's no need for an RPC layer proxying and fanning out requests to all instances of your streams application.
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Move Auth, Policy, and Resilience to the PlatformChristian Posta
Developer's time is the most crucial resource in an enterprise IT organization. Too much time is spent on undifferentiated heavy lifting and in the world of APIs and microservices much of that is spent on non-functional, cross-cutting networking requirements like security, observability, and resilience.
As organizations reconcile their DevOps practices into Platform Engineering, tools like Istio help alleviate developer pain. In this talk we dig into what that pain looks like, how much it costs, and how Istio has solved these concerns by examining three real-life use cases. As this space continues to emerge, and innovation has not slowed, we will also discuss the recently announced Istio sidecar-less mode which significantly reduces the hurdles to adopt Istio within Kubernetes or outside Kubernetes.
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Tool Support for Testing as Chapter 6 of ISTQB Foundation 2018. Topics covered are Tool Benefits, Test Tool Classification, Benefits of Test Automation and Risk of Test Automation
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TrustArc Webinar - Your Guide for Smooth Cross-Border Data Transfers and Glob...TrustArc
Global data transfers can be tricky due to different regulations and individual protections in each country. Sharing data with vendors has become such a normal part of business operations that some may not even realize they’re conducting a cross-border data transfer!
The Global CBPR Forum launched the new Global Cross-Border Privacy Rules framework in May 2024 to ensure that privacy compliance and regulatory differences across participating jurisdictions do not block a business's ability to deliver its products and services worldwide.
To benefit consumers and businesses, Global CBPRs promote trust and accountability while moving toward a future where consumer privacy is honored and data can be transferred responsibly across borders.
This webinar will review:
- What is a data transfer and its related risks
- How to manage and mitigate your data transfer risks
- How do different data transfer mechanisms like the EU-US DPF and Global CBPR benefit your business globally
- Globally what are the cross-border data transfer regulations and guidelines
Leveraging AI for Software Developer Productivity.pptxpetabridge
Supercharge your software development productivity with our latest webinar! Discover the powerful capabilities of AI tools like GitHub Copilot and ChatGPT 4.X. We'll show you how these tools can automate tedious tasks, generate complete syntax, and enhance code documentation and debugging.
In this talk, you'll learn how to:
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And these are just a few examples from a vast universe of possibilities!
Packed with practical examples and demos, this presentation offers invaluable insights into optimizing your development process. Don't miss the opportunity to improve your coding efficiency and productivity with AI-driven solutions.
1. Business Intelligence
Business intelligence, or BI for short, is an umbrella term that refers to competencies, processes,
technologies, applications and practices used to support evidence-based decision making in
organisations. In the widest sense it can be defined as a collection of approaches for gathering,
storing, analysing and providing access to data that helps users to gain insights and make better
fact-based business decisions.
What is BI used for?
Organisations use Business Intelligence to gain data-driven insights on anything related to business
performance. It is used to understand and improve performance and to cut costs and identify new
business opportunities, this can include, among many other things:
Analysing customer behaviours, buying patterns and sales trends.
Measuring, tracking and predicting sales and financial performance
Budgeting and financial planning and forecasting
Tracking the performance of marketing campaigns
Optimising processes and operational performance
Improving delivery and supply chain effectiveness
Web and e-commerce analytics
Customer relationship management
Risk analysis
Strategic value driver analysis
The Basics of Business Intelligence
2. The basic components of Business Intelligence are gathering, storing, analysing and providing
access to data (see Figure below).
Gathering Data
Gathering data is concerned with collecting or accessing data which can then be used to inform
decision making. Gathering data can come in many formats and basically refers to the automated
measurement and collection of performance data. For example, these can come from transactional
systems that keep logs of past transactions, point-of-sale systems, web site software, production
systems that measure and track quality, etc. A major challenge of gathering data is making sure that
the relevant data is collected in the right way at the right time. If the data quality is not controlled at
the data gathering stage then it can jeopardise the entire BI efforts that might follow – always
remember the old adage - garbage in garbage out.
Storing Data
Storing Data is concerned with making sure the data is filed and stored in appropriate ways to
ensure it can be found and used for analysis and reporting. When storing data the same basic
principles apply that you would use to store physical goods – say books in a library – you are trying
to find the most logical structure that will allow you to easily find and use the data. The advantages
of modern data-bases (often called data warehouses because of the large volumes of data) is that
they allow multi-dimensional formats so you can store the same data under different categories –
also called data marts or data-warehouse access layers. Like in the physical world, good data
storage starts with the needs and requirements of the end users and a clear understanding of what
they want to use the data for.
Analysing Data
The next component of BI is analysing the data. Here we take the data that has been gathered and
inspect, transform or model it in order to gain new insights that will support our business decision
making. Data analysis comes in many different formats and approaches, both quantitative and
qualitative. Analysis techniques includes the use of statistical tools, data mining approaches as well
as visual analytics or even analysis of unstructured data such as text or pictures.
Providing Access
In order to support decision making the decision makers need to have access to the data. Access is
needed to perform analysis or to view the results of the analysis. The former is provided by the latest
software tools that allow end-users to perform data analysis while the latter is provided through
reporting, dashboard and scorecard applications.
Business Intelligence is more than Software Tools and
Technology
3. The term Business Intelligence is often used in a very narrow way to refer to software applications
used to analyse an organization’s raw data. Terms often associated with BI in an IT sense are data
mining, online analytical processing, querying and reporting. In fact, ownership of BI is very often in
the IT functions of companies. The reason for this is not that they possess the best analytical
capabilities but because they are overseeing the implementation of software tools – so called
Business Intelligence (BI) applications. The problem is that despite huge investments in BI software
and solutions in recent years many organisations are still failing to convert data into strategically
valuable knowledge. IT infrastructure and software alone cannot make this happen – BI must be
owned by business leaders and managers who are supported by IT. Having said this, today’s BI
applications come with a lot of out-of-the-box functionality that allows managers to start analysing
data even without the help of statisticians and IT professionals. Some key BI vendors are IBM
Cognos, Information Builders, Microsoft, Micro Strategy, Oracle, SAP, and SAS. For more
information and our opinions on their products see the software section of our Knowledge Hub.
Becoming an ‘Intelligent Company’
Business Intelligence should sit within the overall approach to enterprise performance management
and help make companies more ‘intelligent’ (see also our outline “What is Performance
Management” for more information or the book “The Intelligent Company”).
Extensive research by the Advanced Performance Institute has identified that there are five essential
steps to becoming a more intelligent company (see Figure below):
Step 1: More intelligent strategies – by identifying strategic priorities and agreeing your real
information needs.
Step 2: More intelligent data – by creating relevant and meaningful performance indicators
as well as qualitative management information linked back to your strategic information
needs.
Step 3: More intelligent insights – by using good evidence to test and prove ideas and by
analysing the data to gain robust and reliable insights.
Step 4: More intelligent communication – by creating informative and engaging management
information packs and dashboards that provide the essential information, packaged in a way
that is targeted and easy-to-understand.
Step 5: More intelligent decision making – by fostering an evidence-based culture of turning
information into actionable knowledge and real decisions.
4. These five steps are taken from my book 'The Intelligent Company: Five Steps to Success with
Evidence-Based Management' in which I discuss and describe the key tools and best-practice skills
that top-performing enterprises apply to become more successful. Here is an overview of each of the
steps:
Step 1: Identify the strategic objective and information
need
In today’s turbulent, unpredictable markets it can prove immensely challenging to identify the core
strategic objectives of an organisation. But it is only by doing so that it becomes possible to ensure
that the analytics we generate are relevant to the organisation’s competitive positioning and support
its greatest information needs. Intelligent organisations such as Tesco or Google have created
strategic performance management frameworks to guide the collection and analysis of data. Tools
such as Balanced Scorecards and in particular Strategy Maps can be used to identify high level
objectives. World-leading retailer Tesco has clearly outlined its strategic priorities in a Balanced
Scorecard called the corporate steering wheel. Once a performance framework is in place any
efforts to use data can be linked back to the strategy of the organisation. That way, organisations
don’t waste valuable time analysing something that doesn’t really matter in the grander scheme of
things. Any efforts in collecting and analysing data need to be focused on:
1. The strategic objectives of the organisation
2. The big un-answered questions in regard to those objectives.
The executive team in Google, for example, has identified the strategic priorities and then formulated
a set of questions they as executives really needed to have an answer to. These questions are now
used to frame the collection and analysis of data. Google’s CEO Eric Schmidt makes it very clear
that any major efforts to collect or analyse data should be linked to the strategic questions the
executive team have formulated.
5. Step 2: Collecting and Organising the Right Data
Once the data and information needs are clear we can start to collect the appropriate data and
performance metrics that, in turn, will help to answer the strategic questions. In this second step
organizations ensure that they gather and organize the right data. The emphasis here is on
meaningful and relevant data to meet the information needs identified in step one. Organizations
need to assess whether the data needed is already held in the organization or how to devise a best
way to collect the data. A typical trap is to think of data as just numbers that come from the
operational or transactional systems or those that are collected using over-simplified surveys or
questionnaires. If organizations want to gain a robust picture of reality then they must keep in mind
that data comes in myriad formats - sounds, text, graphics, and pictures are as much data as are
numbers. Moreover, there are many methodologies for collecting data, which can be quantitative (in
that they are concerned with the collection of numerical data) or qualitative (concerned with the
collection of non-numerical data). What I have learnt over the years is that the richest insights seem
to come from key performance indicators that e.g. are:
1. Unique to the organisation
2. Observing actual behaviour
Take, for example, the insurance firm Progressive Insurance. The company was among the first to
make extensive use of consumer credit scores as input to its automobile insurance underwriting. As
competitors caught up with this technique the company has always found new and innovative ways
of collecting and using data to gain a competitive edge. Tesco is another great example. With its
Clubcard data it can now observe consumer trends in almost real time and gain insights quicker and
in more detail than any of their competitors.
Step 3: Turning Data into Information and Insights
Data has to be analysed and put into context in order to extract information and insights from it. In
the same way as the data collection, analysis must support the core strategic objectives of the
organization (as understood through step one). Central to the analytics process is the running of
experiments to test assumptions. A best practice example of organizations that make good use of
experiments are Yahoo and eBay. These organizations receive many millions of hits to their home
pages each hour. To test new assumptions (in this case that making a certain alteration to the home
page will change behaviours of visitors) they randomly assign one or two hundred thousand users to
an experimental group and have several million other visitors as a control group. In eBay’s case
simple A/B experiments (comparing two versions of a website) can be structured within a few days,
and they typically last at least a week so that they cover full auction periods for selected items.
Step 4: Communicating Information and Insights
This forth step focuses on communicating the information and insights extracted in step three. The
main focus here is to get the information, in its most appropriate format, to the appropriate decision
makers. I always stress the importance of keeping in mind the target audience and their needs when
communicating. Even with proper and tailored analysis it is crucial that the visual presentation tools
are clear, informative and compelling. We need to package information in ways that help recipients
to understand they key messages. Different types of graphs and charts can be used as appropriate,
such as pictographs, tally charts, bar graphs, and histograms, scatter plots, line graphs and pie
charts, as some examples. Moreover, it is important to use more narratives which provide context
6. and meaning to the data. Using graphs and narrative together enable the telling of the story, which
neither can fully do in isolation.
Step 5: Turning information into actionable knowledge
Amassing knowledge, however insightful or compelling, in and of itself is of little value unless it is
turned into action. Decisions have to be made and acted upon. Making this happen often requires a
wholesale reworking of the process for turning knowledge into action. This oftentimes needs a
cultural transformation that might include, as examples, ensuring that:
the organization has a passion for learning and Improvement
there is an unswerving leadership buy-in to the principles of being an intelligent company
there are widespread analytical capabilities within the organization
there is a willingness to share information
The five sequential steps of this framework provide a blueprint for Business Intelligence. However,
the logic of good evidence-based decision making is not just linear (from step one to step five) but
there is a feedback loop between the last and the first step (from step five to step one). Once
learning has taken place and decision have been made they in turn inform future information needs.
Which Companies use BI to become more Intelligent?
There are intelligent retailers such as Wal-Mart and Tesco are heavy users of BI. Take the above
mentioned Tesco, for example, who has become the second most powerful retailer on the planet.
This has not happened by chance but through applying the principles of an intelligent company.
Tesco have identified two factors above all: making sure everyone in the company is actively
engaged in trying to improve performance - all the time; and having the data and analytical skills to
test ideas and turn insights into customer and business relevant actions. Sir Terry Leahy, Tesco's
Chief Executive Officer, puts it in simple terms: "never stop listening to your customers and giving
them what they want". This Tesco philosophy is captured in the phrase 'Every Little Helps' and which
is more than just a slogan: "Every Little Helps is behind everything we do. It's not just something we
say, we really do mean it." To guide their business intelligence activities, Tesco has adopted the
Balanced Scorecard. The Tesco Balanced Scorecard, which they call the Corporate Steering Wheel,
clearly outlines its strategic priorities. With the performance framework in place, any efforts to collect
and analyse data can be linked back to the strategic objectives of the organisation. That way, Tesco
doesn't waste valuable time analysing something that doesn’t really matter in the grander scheme of
things. If you want to read the full case study then you can download it from our case study library.
Restaurant chains such as McDonalds, CKE and T.G.I. Friday’s have successfully applied the
principles of an intelligent company. They use BI to make strategic decisions, such as what new
dishes to add to or remove from their menus and which underperforming stores to close. They also
use BI for tactical matters such as renegotiating contracts with suppliers and identifying opportunities
to improve inefficient processes. At the US-headquartered CKE Restaurants, which includes major
US brands such as the Hardee’s and Carl’s Jr. quick-service restaurant chains, the process for new
product introduction calls for rigorous testing at a certain stage. Testing begins with brainstorming, in
which several cross-functional groups develop a variety of new product ideas. Only some of them
make it past the next phase, judgmental screening, during which a group of marketing, product
development, and operations people will evaluate ideas based on experience and intuition. Those
that make the cut are actually developed and then tested in stores, with well-defined measures and
7. control groups. At that point, executives decide whether to roll out a product system wide, modify it
for retesting, or kill the whole idea. CKE has attained an enviable hit rate in new product
introductions—about one in four new products is successful, versus one in 50 or 60 for consumer
products—and executives say that their rigorous testing process is part of the reason why.
Yahoo Inc. is another intelligent company that used BI effectively to improve their website. The
organization receives many millions of hits to its home page each hour. To test alteration to the
home page they randomly assign one or two hundred thousand users to an experimental group and
have several million other visitors as a control group. By doing so, they can quickly see whether or
not the alterations to the home page leads to the assumed change in the behaviour of the customer.
This in turn allows them to optimize their offerings to enhance revenues and profits. The results of
these experiments can often be seen within minutes, and Yahoo! typically runs about 20
experiments at any given time. This way, the results of the analysis drive behaviours, cutting out
lengthy discussions about website design best practices - which of course can be extremely
subjective and biased.
More Practical Business Intelligence Examples
The API Knowledge Hub features many real-world examples and in-depth best practice case studies
of how organisations have used business intelligence to become more ‘intelligent’ and successful.
Explore the white papers, case studies and articles for more in-depth information on this fascinating
subject. Read free sample chapters of the book ‘The Intelligent Company’ or read our white paper
‘The Basics of Performance Analytics’.