Business Intelligence basics emphasizing the advantages and requirements for SME's of adopting the appropriate BI tool(s) and the rationale for doing so.
This document discusses the essentials of business intelligence (BI). It describes key drivers of BI including understanding customer segments, lifetime customer value, and fraud detection. It also outlines the process of intelligence creation including identifying BI projects, estimating costs and benefits. Finally, it discusses major components of BI systems like data warehousing, business analytics, data mining, and business performance management.
Business intelligence (BI) refers to techniques used to analyze business data and present it to facilitate decision making. BI technologies provide historical, current, and predictive views of business operations to support better decisions. The major components of BI include applications like reporting, analytics, and dashboards. While BI helps improve productivity, decision making, and results, it also faces disadvantages like data piling, costs, and complexity.
Business intelligence- Components, Tools, Need and Applicationsraj
As part of the research project for the course Technical Foundations of Information Systems at the University of Illinois, our team worked on the topic, Business Intelligence. The presentation focuses on what is Business Intelligence, its various components, latest tools, the need of BI as well as applications of this technology. This project deals with the latest development of BI technologies (hardware or software) and includes comprehensive literature survey from Journals, and the Internet.
Business intelligence (BI) refers to transforming raw company data into usable information through specialized computer programs. Raw data from transaction systems can be aggregated and manipulated in BI applications to generate information like sales trend graphs. This helps address challenges where companies have large amounts of raw data but lack tools to exploit it. BI applications read data from transaction systems, transform and present it to decision makers in reports, charts, queries and alerts. For BI projects to succeed, management must be committed, users involved in planning, and systems made easy to use and flexible.
Business intelligence (BI) refers to technologies and processes used to gather, store, analyze and provide access to data to help business users make better decisions. BI systems aggregate data from various sources, enrich it with context and analysis, and present it to inform fact-based decisions. Advanced analytics can also be used to predict customer behavior and business trends. BI is important because it provides timely, reliable data to support decision making rather than relying solely on opinions. Major BI trends include mobile, cloud, social media and advanced analytics. BI systems are used across industries for applications like customer segmentation, inventory forecasting, and predicting customer churn.
This document provides an overview of business intelligence and analytics (BIA). It discusses how BIA uses technologies and practices to analyze critical business data and provide insights to improve decision making. It also covers challenges like accessing diverse data and big data analytics. The stages of BIA include data warehousing, extraction, transformation and loading of data, analytics like descriptive, predictive and prescriptive analysis, and knowledge discovery techniques. BIA provides businesses insights from large and diverse data generated through applications to help in areas like marketing, finance, and human resources.
This document discusses the essentials of business intelligence (BI). It describes key drivers of BI including understanding customer segments, lifetime customer value, and fraud detection. It also outlines the process of intelligence creation including identifying BI projects, estimating costs and benefits. Finally, it discusses major components of BI systems like data warehousing, business analytics, data mining, and business performance management.
Business intelligence (BI) refers to techniques used to analyze business data and present it to facilitate decision making. BI technologies provide historical, current, and predictive views of business operations to support better decisions. The major components of BI include applications like reporting, analytics, and dashboards. While BI helps improve productivity, decision making, and results, it also faces disadvantages like data piling, costs, and complexity.
Business intelligence- Components, Tools, Need and Applicationsraj
As part of the research project for the course Technical Foundations of Information Systems at the University of Illinois, our team worked on the topic, Business Intelligence. The presentation focuses on what is Business Intelligence, its various components, latest tools, the need of BI as well as applications of this technology. This project deals with the latest development of BI technologies (hardware or software) and includes comprehensive literature survey from Journals, and the Internet.
Business intelligence (BI) refers to transforming raw company data into usable information through specialized computer programs. Raw data from transaction systems can be aggregated and manipulated in BI applications to generate information like sales trend graphs. This helps address challenges where companies have large amounts of raw data but lack tools to exploit it. BI applications read data from transaction systems, transform and present it to decision makers in reports, charts, queries and alerts. For BI projects to succeed, management must be committed, users involved in planning, and systems made easy to use and flexible.
Business intelligence (BI) refers to technologies and processes used to gather, store, analyze and provide access to data to help business users make better decisions. BI systems aggregate data from various sources, enrich it with context and analysis, and present it to inform fact-based decisions. Advanced analytics can also be used to predict customer behavior and business trends. BI is important because it provides timely, reliable data to support decision making rather than relying solely on opinions. Major BI trends include mobile, cloud, social media and advanced analytics. BI systems are used across industries for applications like customer segmentation, inventory forecasting, and predicting customer churn.
This document provides an overview of business intelligence and analytics (BIA). It discusses how BIA uses technologies and practices to analyze critical business data and provide insights to improve decision making. It also covers challenges like accessing diverse data and big data analytics. The stages of BIA include data warehousing, extraction, transformation and loading of data, analytics like descriptive, predictive and prescriptive analysis, and knowledge discovery techniques. BIA provides businesses insights from large and diverse data generated through applications to help in areas like marketing, finance, and human resources.
There are a variety of tools used to collect, organize and analyze business data for intelligence purposes. These include spreadsheets for visual data management, reporting and querying software to extract and summarize data, online analytical processing to quickly answer multi-dimensional queries, data mining to uncover patterns, data warehousing for comprehensive reporting, process mining to determine business processes from event logs, digital dashboards for real-time overviews, and performance management tools. Understanding these business intelligence tools is essential for companies to utilize data analytics and make better decisions.
Business intelligence in the real time economyJohan Blomme
1. Business intelligence is evolving from reactive, historical reporting to real-time decision making embedded in business processes. This allows for more proactive responses to changing market conditions.
2. There is a shift towards self-service business intelligence where all employees can access, analyze, and share real-time data to improve decision making. Technologies like in-memory analytics enable faster, interactive analysis.
3. Collaboration and sharing of insights is facilitated by new interactive dashboard and visualization tools with Web 2.0 features. Business intelligence is becoming more user-centric and accessible for all employees.
Download at http://DavidHubbard.net/powerpoint - This Introduction to Business Intelligence gives an overview of how Business Intelligence fits into business strategy in general. It does not go into the specific technologies of Business Intelligence. It is meant to be used to explain Business Intelligence to those not already familiar with Business Intelligence.
Ahmed Rami Elsherif is an experienced IT Project Manager with over 20 years of experience in project management, system development, business analysis, ERP implementation and public transport systems. He has a diverse international work experience including positions in Saudi Arabia, Iraq, and Bolivia. He is PMP certified with expertise in SAP systems, project management, and business process optimization.
Business intelligence (BI) uses data about past and present to help companies make better decisions for the future. BI provides timely, accurate insights that are valuable and can be acted upon. It helps companies operate more efficiently and profitably by supporting better strategic and tactical decision making. As BI systems evolve to deliver analytics to mobile devices in near real-time, more companies are using BI to promote a data-driven culture and rational decision making processes.
Business Intelligence made easy! This is the first part of a two-part presentation I prepared for one of our customers to help them understand what Business Intelligence is and what can it do...
This document provides an overview of business intelligence (BI). It defines BI as collaborating people and technology to enhance decision making by delivering the right insights at the right time. Companies deploy BI to gain faster customer segmentation, understand customer behavior, reduce question response time, and make even simple decisions using integrated data. The document outlines the evolution of BI from basic reporting to predicting and operationalizing insights. It also shows how reports have evolved over time and presents the Gartner BI matrix and typical BI system architecture. Finally, it provides a real-world example of a lifetime segmentation report generated using BI tools and processes.
Top 15 Business Intelligence (BI) SoftwareMopinion
In this slideshare, we will provide you with a rundown of the top 15 best Business Intelligence tools. Keep in mind: these all vary in robustness, integration capabilities, ease-of-use (from a technical perspective) and pricing.
An Overview of Business Intelligence Technology. Business intelligence (BI) software is a collection of decision support technologies for the enterprise aimed at enabling knowledge workers such as executives, managers, and analysts to make better and faster decisions. Take a look on Business Intelligence Process and History!
Business Intelligence and Business Analyticssnehal_152
Business intelligence (BI) involves gathering, storing, and analyzing data to help organizations make better business decisions. It provides a single point of access to timely information to answer business questions. BI tools like dashboards, key performance indicators, graphical reporting, and forecasting help companies adapt quickly to changing customer preferences and market conditions. Implementing an effective BI system removes guesswork from decision making and allows for fact-based decisions through accurate, real-time data.
What is BI,Definition, examples, BI industry, Solutions, Evolution, Catogeries, Key Stages of BI, BI significance, BI technologies, tools, future of BI
Business Intelligence 101 for Business (BI 101)ukdpe
The document discusses business intelligence (BI) tools and techniques that support better decision making. It notes that 90% of organizations fail to execute strategies successfully and 95% of employees do not understand how organizational goals relate to their jobs. It then provides examples of different types of BI tools, including reporting, analyzing, forecasting, and planning tools. These tools use data from sources like ERP, CRM, and SCM systems to turn data into insights. Dimensional data models and online analytical processing (OLAP) are discussed as traditional BI techniques.
The document discusses business analytics and big data. It provides an overview of key concepts like business process analytics, enterprise analytics capability, case studies on implementing analytics, and frameworks for business strategy, IT strategy, business process management, and enterprise architecture. The summaries emphasize linking analytics to business processes and strategy to drive business value from big data.
Knowledge management and business intelligenceAzmi Taufik
1) Business intelligence is a set of tools and processes that analyze raw data to provide useful information to make business decisions. It includes technologies that transform data into meaningful insights.
2) Key aspects of business intelligence include allowing organizations to get a more accurate view of business and customers, increasing visibility, and enabling analysis of customer behavior.
3) Strategic knowledge management helps identify business needs, organize information flow, implement plans, and evaluate to improve by addressing goals, competitive advantage, and organizational performance.
Business intelligence is the process of collecting raw data from various sources, analyzing it to draw meaningful conclusions, and presenting it to drive business decisions. It involves technologies that convert data into useful information to support decision making. Over time, tools like data warehouses, OLAP, and ETL were developed to facilitate analyzing large datasets and generating insights. Business intelligence aims to provide strategic decision support through data exploration, data mining, optimization, and ultimately informing decisions.
The document discusses the need for business intelligence due to the complex business environment companies operate in today. It outlines several key challenges companies face related to data, processes, tracking analytics, people, and geography that make it difficult to obtain reliable and timely information for effective decision making. This can lead to uninformed decisions, inefficient processes, high costs, and data quality issues. The document argues that a business intelligence solution is needed to provide consistent metrics and insights, optimize data management and distribution, and increase sales and marketing effectiveness through real-time reporting, analytics, and visibility into performance and trends.
- Business intelligence (BI) is the set of techniques and tools for transforming raw data into meaningful and useful information for business analysis, and involves a combination of data warehousing and decision support systems.
- The key components of a BI system include user query and reporting, OLAP, data mining, analytics, business performance management, and enterprise management.
- BI solutions help organizations store and analyze data, understand strengths and weaknesses, reduce decision-making time, measure key performance indicators, and avoid guesswork to improve performance.
- Common BI tools include Oracle BI, SAP BusinessObjects, Microsoft BI, Oracle Hyperion, IBM Cognos, and SAS Enterprise BI server. However, Oracle BI Foundation Suite is
Business intelligence (BI) provides tools for exploring, analyzing, and modeling large amounts of complex data. It consists of statistical modeling, data mining, and multidimensional data exploration technologies. BI is built on well-defined data marts and models customer data to provide customer intelligence. It uses several technologies to support decision making, CRM, customer loyalty, campaign management, and marketing. BI requires integrating data from various sources into a data warehouse where advanced analytics can be performed to generate insights.
Business intelligence (BI) provides processes, technologies, and tools to help organizations analyze data and make better business decisions. BI technologies gather, store, analyze and provide access to enterprise data. This helps users understand what happened in the past, what is happening currently, and make plans to achieve desired future outcomes. BI provides a single point of access to information, timely answers to business questions, and allows all departments to use data for decision making. Key BI tools include dashboards, key performance indicators, graphical reporting, forecasting, and data visualization. These tools help analyze trends, customer behavior, market conditions, and support risk analysis and decision making.
The document discusses the business intelligence (BI) lifecycle, which includes 6 key stages: 1) Analyzing business requirements, 2) Designing a data model, 3) Designing the physical schema, 4) Building the data warehouse, 5) Creating project metadata, and 6) Developing BI objects. It also describes the Enterprise Performance Lifecycle (EPLC) framework, which manages project deliverables and reviews across various stages to minimize risk and ensure best practices are followed throughout the project lifecycle.
Decision support systems and business intelligenceShwetabh Jaiswal
This document discusses decision support systems and business intelligence. It describes how the modern business environment requires computerized systems to help with complex decision making. Business intelligence transforms raw data into useful information through methodologies, processes and technologies. Decision support systems couple individual expertise with computer capabilities to improve decision quality for semi-structured problems. Both systems use similar architectures of data warehouses, analytics, and user interfaces to enable analysis and informed decisions.
Business intelligence techniques U2.pptxRenuLamba8
1. A business intelligence strategy aims to help businesses measure and improve performance through analytics solutions and architecture.
2. Business intelligence tools collect, analyze, and transform business data into insights through reports, dashboards, and visualizations to inform business decisions.
3. Developing a clear plan around how the data and analytics will be used, what data will be analyzed, and how staff will make decisions is key to a successful business intelligence strategy.
There are a variety of tools used to collect, organize and analyze business data for intelligence purposes. These include spreadsheets for visual data management, reporting and querying software to extract and summarize data, online analytical processing to quickly answer multi-dimensional queries, data mining to uncover patterns, data warehousing for comprehensive reporting, process mining to determine business processes from event logs, digital dashboards for real-time overviews, and performance management tools. Understanding these business intelligence tools is essential for companies to utilize data analytics and make better decisions.
Business intelligence in the real time economyJohan Blomme
1. Business intelligence is evolving from reactive, historical reporting to real-time decision making embedded in business processes. This allows for more proactive responses to changing market conditions.
2. There is a shift towards self-service business intelligence where all employees can access, analyze, and share real-time data to improve decision making. Technologies like in-memory analytics enable faster, interactive analysis.
3. Collaboration and sharing of insights is facilitated by new interactive dashboard and visualization tools with Web 2.0 features. Business intelligence is becoming more user-centric and accessible for all employees.
Download at http://DavidHubbard.net/powerpoint - This Introduction to Business Intelligence gives an overview of how Business Intelligence fits into business strategy in general. It does not go into the specific technologies of Business Intelligence. It is meant to be used to explain Business Intelligence to those not already familiar with Business Intelligence.
Ahmed Rami Elsherif is an experienced IT Project Manager with over 20 years of experience in project management, system development, business analysis, ERP implementation and public transport systems. He has a diverse international work experience including positions in Saudi Arabia, Iraq, and Bolivia. He is PMP certified with expertise in SAP systems, project management, and business process optimization.
Business intelligence (BI) uses data about past and present to help companies make better decisions for the future. BI provides timely, accurate insights that are valuable and can be acted upon. It helps companies operate more efficiently and profitably by supporting better strategic and tactical decision making. As BI systems evolve to deliver analytics to mobile devices in near real-time, more companies are using BI to promote a data-driven culture and rational decision making processes.
Business Intelligence made easy! This is the first part of a two-part presentation I prepared for one of our customers to help them understand what Business Intelligence is and what can it do...
This document provides an overview of business intelligence (BI). It defines BI as collaborating people and technology to enhance decision making by delivering the right insights at the right time. Companies deploy BI to gain faster customer segmentation, understand customer behavior, reduce question response time, and make even simple decisions using integrated data. The document outlines the evolution of BI from basic reporting to predicting and operationalizing insights. It also shows how reports have evolved over time and presents the Gartner BI matrix and typical BI system architecture. Finally, it provides a real-world example of a lifetime segmentation report generated using BI tools and processes.
Top 15 Business Intelligence (BI) SoftwareMopinion
In this slideshare, we will provide you with a rundown of the top 15 best Business Intelligence tools. Keep in mind: these all vary in robustness, integration capabilities, ease-of-use (from a technical perspective) and pricing.
An Overview of Business Intelligence Technology. Business intelligence (BI) software is a collection of decision support technologies for the enterprise aimed at enabling knowledge workers such as executives, managers, and analysts to make better and faster decisions. Take a look on Business Intelligence Process and History!
Business Intelligence and Business Analyticssnehal_152
Business intelligence (BI) involves gathering, storing, and analyzing data to help organizations make better business decisions. It provides a single point of access to timely information to answer business questions. BI tools like dashboards, key performance indicators, graphical reporting, and forecasting help companies adapt quickly to changing customer preferences and market conditions. Implementing an effective BI system removes guesswork from decision making and allows for fact-based decisions through accurate, real-time data.
What is BI,Definition, examples, BI industry, Solutions, Evolution, Catogeries, Key Stages of BI, BI significance, BI technologies, tools, future of BI
Business Intelligence 101 for Business (BI 101)ukdpe
The document discusses business intelligence (BI) tools and techniques that support better decision making. It notes that 90% of organizations fail to execute strategies successfully and 95% of employees do not understand how organizational goals relate to their jobs. It then provides examples of different types of BI tools, including reporting, analyzing, forecasting, and planning tools. These tools use data from sources like ERP, CRM, and SCM systems to turn data into insights. Dimensional data models and online analytical processing (OLAP) are discussed as traditional BI techniques.
The document discusses business analytics and big data. It provides an overview of key concepts like business process analytics, enterprise analytics capability, case studies on implementing analytics, and frameworks for business strategy, IT strategy, business process management, and enterprise architecture. The summaries emphasize linking analytics to business processes and strategy to drive business value from big data.
Knowledge management and business intelligenceAzmi Taufik
1) Business intelligence is a set of tools and processes that analyze raw data to provide useful information to make business decisions. It includes technologies that transform data into meaningful insights.
2) Key aspects of business intelligence include allowing organizations to get a more accurate view of business and customers, increasing visibility, and enabling analysis of customer behavior.
3) Strategic knowledge management helps identify business needs, organize information flow, implement plans, and evaluate to improve by addressing goals, competitive advantage, and organizational performance.
Business intelligence is the process of collecting raw data from various sources, analyzing it to draw meaningful conclusions, and presenting it to drive business decisions. It involves technologies that convert data into useful information to support decision making. Over time, tools like data warehouses, OLAP, and ETL were developed to facilitate analyzing large datasets and generating insights. Business intelligence aims to provide strategic decision support through data exploration, data mining, optimization, and ultimately informing decisions.
The document discusses the need for business intelligence due to the complex business environment companies operate in today. It outlines several key challenges companies face related to data, processes, tracking analytics, people, and geography that make it difficult to obtain reliable and timely information for effective decision making. This can lead to uninformed decisions, inefficient processes, high costs, and data quality issues. The document argues that a business intelligence solution is needed to provide consistent metrics and insights, optimize data management and distribution, and increase sales and marketing effectiveness through real-time reporting, analytics, and visibility into performance and trends.
- Business intelligence (BI) is the set of techniques and tools for transforming raw data into meaningful and useful information for business analysis, and involves a combination of data warehousing and decision support systems.
- The key components of a BI system include user query and reporting, OLAP, data mining, analytics, business performance management, and enterprise management.
- BI solutions help organizations store and analyze data, understand strengths and weaknesses, reduce decision-making time, measure key performance indicators, and avoid guesswork to improve performance.
- Common BI tools include Oracle BI, SAP BusinessObjects, Microsoft BI, Oracle Hyperion, IBM Cognos, and SAS Enterprise BI server. However, Oracle BI Foundation Suite is
Business intelligence (BI) provides tools for exploring, analyzing, and modeling large amounts of complex data. It consists of statistical modeling, data mining, and multidimensional data exploration technologies. BI is built on well-defined data marts and models customer data to provide customer intelligence. It uses several technologies to support decision making, CRM, customer loyalty, campaign management, and marketing. BI requires integrating data from various sources into a data warehouse where advanced analytics can be performed to generate insights.
Business intelligence (BI) provides processes, technologies, and tools to help organizations analyze data and make better business decisions. BI technologies gather, store, analyze and provide access to enterprise data. This helps users understand what happened in the past, what is happening currently, and make plans to achieve desired future outcomes. BI provides a single point of access to information, timely answers to business questions, and allows all departments to use data for decision making. Key BI tools include dashboards, key performance indicators, graphical reporting, forecasting, and data visualization. These tools help analyze trends, customer behavior, market conditions, and support risk analysis and decision making.
The document discusses the business intelligence (BI) lifecycle, which includes 6 key stages: 1) Analyzing business requirements, 2) Designing a data model, 3) Designing the physical schema, 4) Building the data warehouse, 5) Creating project metadata, and 6) Developing BI objects. It also describes the Enterprise Performance Lifecycle (EPLC) framework, which manages project deliverables and reviews across various stages to minimize risk and ensure best practices are followed throughout the project lifecycle.
Decision support systems and business intelligenceShwetabh Jaiswal
This document discusses decision support systems and business intelligence. It describes how the modern business environment requires computerized systems to help with complex decision making. Business intelligence transforms raw data into useful information through methodologies, processes and technologies. Decision support systems couple individual expertise with computer capabilities to improve decision quality for semi-structured problems. Both systems use similar architectures of data warehouses, analytics, and user interfaces to enable analysis and informed decisions.
Business intelligence techniques U2.pptxRenuLamba8
1. A business intelligence strategy aims to help businesses measure and improve performance through analytics solutions and architecture.
2. Business intelligence tools collect, analyze, and transform business data into insights through reports, dashboards, and visualizations to inform business decisions.
3. Developing a clear plan around how the data and analytics will be used, what data will be analyzed, and how staff will make decisions is key to a successful business intelligence strategy.
Business Intelligence (BI) solutions can help manufacturing business users to analyse cost factors and make appropriate decisions for acquisition of raw material and sold goods.
Decision support systems and business intelligenceShwetabh Jaiswal
The document discusses decision support systems and business intelligence. It describes how business environments have become more complex, requiring faster and better decision-making supported by computerized systems. Business intelligence involves transforming raw data into useful information to enable strategic, tactical and operational insights. Decision support systems couple individual expertise with computer capabilities to improve decision quality for semi-structured problems.
Business intelligence (BI) systems allow companies to gather, store, access, and analyze corporate data to aid in decision-making. These systems illustrate intelligence in areas like customer profiling, market research, and product profitability. A hotel franchise uses BI to compile statistics on metrics like occupancy and room rates to analyze performance and competitive position. Banks also use BI to determine their most profitable customers and which customers to target for new products.
Business intelligence (BI) is a broad set of technologies used to gather, store, analyze and provide access to data to help business users make better decisions. BI technologies include reporting, dashboards, data mining, etc. Business analytics (BA) focuses more on predictive analytics using statistical modeling and machine learning to predict future outcomes and optimize decisions. While BI and BA overlap, BI answers questions about past performance, while BA answers questions about why things are happening, what will happen next, and how to optimize outcomes.
The document provides information on management information systems. It discusses different types of information systems including transaction processing systems, management information systems, decision support systems, executive information systems, and personal and organizational dimensions of information. It also covers topics like business intelligence, Porter's five forces model, generic strategies to manage competitive forces, the impact of the internet, value chain model, and value web.
When the business needs intelligence (15Oct2014)Dipti Patil
When an organization needs to make important decisions, business intelligence can help by analyzing internal and external data to generate knowledge. Business intelligence enables fact-based decisions by aggregating, enriching, and presenting data from sources like ERP systems and databases. The goals of a business intelligence implementation are to capture data from across the business to create a unified view, produce an integrated data warehouse to improve decision making, and enable ongoing analysis of data rather than just collecting it.
The client needed a business intelligence solution to address issues like performance monitoring, revenue leakage identification, and manual report generation. The implemented solution centralized data from multiple sources into a single database, developed an ETL process, and provided online reporting and pre-generated reports. This automated reporting and improved data quality, reducing manual work by 50% and saving 25% on resources. The solution provided consistent, reliable data for improved decision making, cost visibility, and performance management.
The client needed a business intelligence solution to address issues like performance monitoring, revenue leakage identification, and manual report generation. The implemented solution centralized data from multiple sources into a single database, developed an ETL process, and provided online reporting and pre-generated reports. This automated reporting and improved data quality, reducing manual work by 50% and saving 25% on resources. The solution provided consistent, reliable data for improved decision making, cost visibility, and performance management across the client's operations.
Business analytics uses data, statistical analysis, and other quantitative techniques to help understand and optimize business performance. It is becoming a major tool used by many large corporations. There are various tools and techniques for business analytics, including online analytical processing (OLAP), data visualization, data mining, predictive analysis, and geographic information systems (GIS). Real-time business intelligence and automated decision support are also increasingly important for analytics.
The document discusses various types of information systems that support decision making. It describes management information systems that provide routine operational reports, decision support systems that help with semi-structured tactical decisions through modeling and analysis, and executive information systems that provide customized insights to top executives. The document also covers data warehousing, data mining, expert systems, and emerging trends like personalized decision support and what-if scenario analysis.
Business analytics involves using data analysis and quantitative methods to solve business problems and gain a competitive advantage. It requires high quality data, skilled analysts, and using insights to inform decisions. Challenges include integrating diverse data sources, lack of analytic skills, and data storage limitations. Popular tools are Dundas BI, Knime, QlikView, Sisense, Splunk, and Tableau. Benefits include improved efficiency, understanding customers, projections, decision making, performance measurement, and growth. The scope of business analytics is wide, from descriptive to predictive to prescriptive analysis. Key skills are business understanding, critical thinking, communication, and technical skills like programming and databases. Top recruiters are consulting, financial, technology
This document discusses business intelligence (BI) concepts including fundamentals, architecture, and data visualization basics. It provides examples of how BI is used to extract data from various sources, consolidate the information, and allow users to access, analyze, and visualize the data through dashboards and reports. Specific use cases are described for Sabre Airline Solutions and New York Shipping Exchange to improve business insights and efficiency. Advantages of BI for marketing, sales, finance, and supply chain management are also covered.
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 and Analytics .pptxRupaRani28
Business intelligence (BI) refers to technologies and practices used to analyze data and deliver actionable insights for decision-making. BI involves collecting data from various sources, analyzing the data using statistical techniques, visualizing the results, and generating reports. The key goal of BI is to improve decision-making by providing accurate, timely information. Popular BI tools allow users to query data, create reports and dashboards, and perform ad-hoc analysis. Real-time BI uses data analytics on up-to-date data sources to enable even timelier decision-making.
The demand for BI continues to grow, and while there's no question that analytics brings value, there is often uncertainty about how BI initiatives will deliver bottom-line benefits. Your business case for BI should prove ROI, but this is not always a straightforward process.
1) There is a growing gap in capabilities and performance between companies that invest heavily in data and analytics compared to those that invest less. The capability gap is exacerbated by a shortage of analytical talent.
2) The amount of data being created is growing exponentially, estimated at 2.5 quintillion bytes per day globally. However, most organizations are not effectively using the data they already have.
3) Investing in analytics can provide significant financial benefits across industries. For example, leveraging big data in healthcare could capture $300 billion annually and increase retailers' operating margins by 60%.
Agile BI: How to Deliver More Value in Less TimePerficient, Inc.
Learn how to:
Construct a BI and analytical environment that provides the critical functionality that enables your customers to provide timely answers, supporting modern agile business
Leverage agile delivery concepts to deliver value in days rather than in months
Build a support organization that enables your users to create increased value from your company’s information assets
Business Intelligence Data Warehouse SystemKiran kumar
This document provides an overview of data warehousing and business intelligence concepts. It discusses:
- What a data warehouse is and its key properties like being integrated, non-volatile, time-variant and subject-oriented.
- Common data warehouse architectures including dimensional modeling, ETL processes, and different layers like the data storage layer and presentation layer.
- How data marts are subsets of the data warehouse that focus on specific business functions or departments.
- Different types of dimensions tables and slowly changing dimensions.
- How business intelligence uses the data warehouse for analysis, querying, reporting and generating insights to help with decision making.
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The APCO Geopolitical Radar - Q3 2024 The Global Operating Environment for Bu...APCO
The Radar reflects input from APCO’s teams located around the world. It distils a host of interconnected events and trends into insights to inform operational and strategic decisions. Issues covered in this edition include:
Best practices for project execution and deliveryCLIVE MINCHIN
A select set of project management best practices to keep your project on-track, on-cost and aligned to scope. Many firms have don't have the necessary skills, diligence, methods and oversight of their projects; this leads to slippage, higher costs and longer timeframes. Often firms have a history of projects that simply failed to move the needle. These best practices will help your firm avoid these pitfalls but they require fortitude to apply.
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Storytelling is an incredibly valuable tool to share data and information. To get the most impact from stories there are a number of key ingredients. These are based on science and human nature. Using these elements in a story you can deliver information impactfully, ensure action and drive change.
Digital Marketing with a Focus on Sustainabilitysssourabhsharma
Digital Marketing best practices including influencer marketing, content creators, and omnichannel marketing for Sustainable Brands at the Sustainable Cosmetics Summit 2024 in New York
How to Implement a Real Estate CRM SoftwareSalesTown
To implement a CRM for real estate, set clear goals, choose a CRM with key real estate features, and customize it to your needs. Migrate your data, train your team, and use automation to save time. Monitor performance, ensure data security, and use the CRM to enhance marketing. Regularly check its effectiveness to improve your business.
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This PowerPoint compilation offers a comprehensive overview of 20 leading innovation management frameworks and methodologies, selected for their broad applicability across various industries and organizational contexts. These frameworks are valuable resources for a wide range of users, including business professionals, educators, and consultants.
Each framework is presented with visually engaging diagrams and templates, ensuring the content is both informative and appealing. While this compilation is thorough, please note that the slides are intended as supplementary resources and may not be sufficient for standalone instructional purposes.
This compilation is ideal for anyone looking to enhance their understanding of innovation management and drive meaningful change within their organization. Whether you aim to improve product development processes, enhance customer experiences, or drive digital transformation, these frameworks offer valuable insights and tools to help you achieve your goals.
INCLUDED FRAMEWORKS/MODELS:
1. Stanford’s Design Thinking
2. IDEO’s Human-Centered Design
3. Strategyzer’s Business Model Innovation
4. Lean Startup Methodology
5. Agile Innovation Framework
6. Doblin’s Ten Types of Innovation
7. McKinsey’s Three Horizons of Growth
8. Customer Journey Map
9. Christensen’s Disruptive Innovation Theory
10. Blue Ocean Strategy
11. Strategyn’s Jobs-To-Be-Done (JTBD) Framework with Job Map
12. Design Sprint Framework
13. The Double Diamond
14. Lean Six Sigma DMAIC
15. TRIZ Problem-Solving Framework
16. Edward de Bono’s Six Thinking Hats
17. Stage-Gate Model
18. Toyota’s Six Steps of Kaizen
19. Microsoft’s Digital Transformation Framework
20. Design for Six Sigma (DFSS)
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This presentation is a curated compilation of PowerPoint diagrams and templates designed to illustrate 20 different digital transformation frameworks and models. These frameworks are based on recent industry trends and best practices, ensuring that the content remains relevant and up-to-date.
Key highlights include Microsoft's Digital Transformation Framework, which focuses on driving innovation and efficiency, and McKinsey's Ten Guiding Principles, which provide strategic insights for successful digital transformation. Additionally, Forrester's framework emphasizes enhancing customer experiences and modernizing IT infrastructure, while IDC's MaturityScape helps assess and develop organizational digital maturity. MIT's framework explores cutting-edge strategies for achieving digital success.
These materials are perfect for enhancing your business or classroom presentations, offering visual aids to supplement your insights. Please note that while comprehensive, these slides are intended as supplementary resources and may not be complete for standalone instructional purposes.
Frameworks/Models included:
Microsoft’s Digital Transformation Framework
McKinsey’s Ten Guiding Principles of Digital Transformation
Forrester’s Digital Transformation Framework
IDC’s Digital Transformation MaturityScape
MIT’s Digital Transformation Framework
Gartner’s Digital Transformation Framework
Accenture’s Digital Strategy & Enterprise Frameworks
Deloitte’s Digital Industrial Transformation Framework
Capgemini’s Digital Transformation Framework
PwC’s Digital Transformation Framework
Cisco’s Digital Transformation Framework
Cognizant’s Digital Transformation Framework
DXC Technology’s Digital Transformation Framework
The BCG Strategy Palette
McKinsey’s Digital Transformation Framework
Digital Transformation Compass
Four Levels of Digital Maturity
Design Thinking Framework
Business Model Canvas
Customer Journey Map
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1. Getting Actionable Insights –
with Business Intelligence
Eric Anderson, MBA CMC
eanderson@smartpoint.ca
250-882-6543
2. Business Intelligence
• BI / Analytics tools help to integrate existing,
disparate, "stovepiped" data already being
captured by your company
• BI Tools produce more comprehensive and
meaningful reporting and analytics – ie.
ACTIONABLE INSIGHT
• These insights support your effort to increase
productivity by providing:
– decision support
– performance benchmarking, and
– outcomes measurement
3. If you are using any of...
• ...then acquiring and implementing the
appropriate BI tools will provide you with the
ability to merge the information being
captured here
– into usable and meaningful cross-functional
performance information
4. Adding “BI” gives value-add to existing
systems
• ...and ultimately helps to identify nonconformances,
points of wastage, supply chain and process
inefficiencies, and thus...
• ...the critical issues underlying productivity that must
be addressed.
5. BI for an SME
• Reliance on tools like spreadsheets have the
following dangers:
– They don’t scale very well (and introduce risk)
– They don’t provide actionable insights
– They typically only report on particular transaction
types
– They ignore other factors that might affect how
the underlying data should be interpreted
6. Explore BI as one possible “DT” option
• Elicit high-level requirements
• Qualify the "DT" Options
• No “leaping to a solution”
• Arrive at an appropriate solution that
supports:
– Operational effectiveness
– Continuous improvement - costs, quality, and time
– Cost focus vs. flow focus
7. Cost focus vs. flow focus
• Determine where / how the company is
currently:
– generating, storing, using raw data
• How Intelligence can be used to reduce wastage /
exceptions
• Methodologies, processes, architectures, and
range of solutions
• ...that can transform your firm's raw data into
meaningful information that better supports
management's decision-making
8. Other Key Areas of Focus in BI
• Other processes and technologies such as:
• Data integration
• Data quality
• Data warehousing
...this is all about understanding and measuring
your CSF’s, which leads to...
• data preparation and data usage – going into
the actual presentation layer of reporting,
analytics, dashboards, etc.
9. Barriers to Adopting BI
• Tight budgets
• Lack of organizational / institutional
knowledge
• Fewer resources, all of whom are maxed on
other projects
– less time to spend on BI planning
10. But – the Potential Benefits
• Aggregated data from different sources
• Analysis and insight from that data –
automatically and configurable
• Improved decision-making – particularly
around process flows and inefficiencies
• Risk mitigation benefits
• Ultimately – your firm competes more
effectively in marketplace
11. Four Critical Areas
• Information / Data Sources
– Harness data from spreadsheets, financials, contact mgr lists,
payroll systems, asset / inventory / warehouse / production
management systems, any other data sources
• Technology
– Aggressively-priced alternatives have brought comprehensive BI
technology within reach. Some tools available online, on the
Cloud
• Intelligence “rules”
– Determine what needs to be measured, and how to measure it
• Implementation and Communication
– How to use and get intended meaning from the “I” in “BI”
12. SME Manufacturing Case
• Isolated data stored for NC cutting equipment contains
valuable “tag” information (bar-coded lot number,
dates, customer info. etc.)
• Combine this with cutting performance /
configurations (by customer)
• Export, transform, and load information to BI layer, and
then to dashboard, gives:
– Easily distributable, usable information on product
customization, supply chain characteristics
– Keys to process flow improvements
• Same principles can be applied to (say) POS data in
retail setting
13. SME Manufacturing Case 2
• Company gained a much clearer understanding of
which products for which customers were costing
more to produce (and why);
• By taking action on this information, processes
were streamlined, and bottlenecks were reduced
/ eliminated (without moving them somewhere
else).
• Better tracking of work-in-progress, upstream
supply, implemented “kanban” delivery.
• Required new attention to B2B / e-commerce
capabilities