Business intelligence systems help organizations make better decisions by analyzing large amounts of data and presenting the information in a usable way. The document discusses decision support systems and business intelligence, how they help organizational decision making, and reviews several sources that describe the relationship between decision making and business intelligence from different perspectives. Decision support systems use data and models to help managers solve problems, while business intelligence encompasses broader technologies and processes for gathering and analyzing various types of data to support decision making.
A knowledge based collaborative model for the rapid integration of platforms,...paulkfenton
Presentation given at DIA EDM meeting in Washington DC in February 2010.
Presentation introduces the notions of BPM and BI for the life sciences industry.
GDSS and DSS both provide decision support but GDSS focuses on group decisions using networking and technology while DSS focuses on individual decisions without networking. Groupware tools like communication, conferencing and collaboration tools can facilitate knowledge sharing and creation when used with knowledge management systems. Data marts contain subset of data warehouse data for specific teams and are used for business intelligence applications while data warehouses contain enterprise-wide data. Data manipulation languages like SQL are used to insert, delete and update data in databases. Expert systems use knowledge bases and inference engines to provide answers to problems like a human expert.
A Study on 21st Century Business Intelligence Anit Thapaliya
This document provides an analysis of business intelligence (BI). It begins with an introduction that defines BI and discusses its goals and benefits. Section 2 provides background on BI, including its history and factors influencing it. Section 3 contains an analysis, including advantages and disadvantages of BI, examples from companies like Dell and Walmart, and how tools from Microsoft and social media platforms have impacted revenue. The conclusion discusses the future of BI and trends like real-time analytics and increased user access to information.
This presentation explains what data engineering is and describes the data lifecycles phases briefly. I used this presentation during my work as an on-demand instructor at Nooreed.com
What makes it worth becoming a Data Engineer?Hadi Fadlallah
This presentation explains what data engineering is for non-computer science students and why it is worth being a data engineer. I used this presentation while working as an on-demand instructor at Nooreed.com
A knowledge based collaborative model for the rapid integration of platforms,...paulkfenton
Presentation given at DIA EDM meeting in Washington DC in February 2010.
Presentation introduces the notions of BPM and BI for the life sciences industry.
GDSS and DSS both provide decision support but GDSS focuses on group decisions using networking and technology while DSS focuses on individual decisions without networking. Groupware tools like communication, conferencing and collaboration tools can facilitate knowledge sharing and creation when used with knowledge management systems. Data marts contain subset of data warehouse data for specific teams and are used for business intelligence applications while data warehouses contain enterprise-wide data. Data manipulation languages like SQL are used to insert, delete and update data in databases. Expert systems use knowledge bases and inference engines to provide answers to problems like a human expert.
A Study on 21st Century Business Intelligence Anit Thapaliya
This document provides an analysis of business intelligence (BI). It begins with an introduction that defines BI and discusses its goals and benefits. Section 2 provides background on BI, including its history and factors influencing it. Section 3 contains an analysis, including advantages and disadvantages of BI, examples from companies like Dell and Walmart, and how tools from Microsoft and social media platforms have impacted revenue. The conclusion discusses the future of BI and trends like real-time analytics and increased user access to information.
This presentation explains what data engineering is and describes the data lifecycles phases briefly. I used this presentation during my work as an on-demand instructor at Nooreed.com
What makes it worth becoming a Data Engineer?Hadi Fadlallah
This presentation explains what data engineering is for non-computer science students and why it is worth being a data engineer. I used this presentation while working as an on-demand instructor at Nooreed.com
Big Data, Business Intelligence and Data AnalyticsSystems Limited
Business intelligence and data analytics involve analyzing data to extract useful information for making informed decisions. BI technologies provide historical, current, and predictive views of business operations through functions like reporting, OLAP, data mining, and benchmarking. BI architecture organizes data, information management, and technology components to build BI systems, while frameworks provide standards and best practices. Challenges include continuous availability, data security, cost, increasing user numbers, new data sources and areas like operational BI, and performance and scalability. Leading vendors provide solutions like Google, Microsoft, Oracle, SAS, SAP, IBM, EMC, HP, and Teradata.
Data Analytics Role in Digital Business & Business Process ManagementBPMInstitute.org
Discover the role of data analytics in transformation projects and business process management. What are the four key areas of data analytics and what are the types of data analytics. Also explains data visuals.
Presentation by Luc Delanglez (DataLumen) at the Data Vault Modelling and Dat...Patrick Van Renterghem
This document discusses data governance and provides guidance on getting started with a data governance program. It outlines key building blocks like a data catalog, workflows, and business and technical lineage. It also covers challenges like governing a multi-store data environment and ensuring data quality. The document recommends starting with understanding your data and building out foundational elements like a data catalog before operationalizing governance through workflows.
You probably have heard about Big Data, but ever wondered what it exactly is? And why should you care?
Mobile is playing a large part in driving this explosion in data. The data are also created by the apps and other services in the background. As people are moving towards more digital channels, tons of data are being created. This data can be used in a lot of ways for personal and professional use. Big Data and mobile apps are converging in an enterprise and interacting; transforming the whole mobile ecosystem.
The document outlines a presentation about data analytics and business intelligence given by Chris Ortega. The presentation covers:
1. Definitions of data analytics and business intelligence.
2. Why data analytics and business intelligence are important for faster decision making, establishing a learning culture, and exploring opportunities.
3. The decision cycle and how business intelligence tools can automate parts of extracting data, analyzing it, and making decisions.
4. Limitations of current data analytics approaches like manual spreadsheet updates and the difficulty combining multiple data sources.
Data can exist in many forms and analytics involves finding patterns in data to aid decision making. There are different types of analytics like descriptive, predictive, and prescriptive. Data analysis is the process of inspecting, cleaning, and modeling data to provide business insights. It is used to help organizations make faster and better decisions to reduce costs and improve products and marketing. Advanced analytics uses tools like data mining, location intelligence, and predictive analytics to examine historical data and forecast future behaviors.
This document discusses the opportunities and challenges of big data. It defines big data as huge volumes of structured and unstructured data from various sources that require new tools to analyze and extract business insights. Big data provides both statistical and predictive views to help businesses make smarter decisions. While big data allows companies to integrate diverse data sources and gain real-time insights, challenges include processing large and complex data volumes and ensuring data quality, privacy and management. The document outlines the big data lifecycle and how analytics can be used descriptively, predictively and prescriptively.
The document discusses decision support systems and business intelligence, describing how they can help organizations adapt to changing business environments by providing computerized support for managerial decision making. It covers frameworks for decision support and business intelligence, including the concepts of decision support systems, data warehouses, business analytics, and performance management. Additionally, it examines the major tools and techniques used for managerial decision support, such as communication, knowledge management, and business analytics.
Business intelligence (BI) is a set of theories, methodologies, architectures, and technologies that transform raw data into meaningful and useful information for business purposes.
Business Analytics & Big Data Trends and Predictions 2014 - 2015Brad Culbert
Brad Culbert, Executive Director of Strategy & Solutions at Bistech, discusses business analytics trends and predictions for 2014-2015. Some key trends include the consumerization of business IT, disruptive force of cloud computing, and shortage of analytic skills. Visual data discovery and story boarding analytics will gain popularity, while decision management and cognitive computing will emerge. Next generation information management will focus on flexible governance for agile self-service data preparation. Cloud analytics adoption will increase significantly with a focus on cloud benefits. Predictive analytics and mobile analytics will see further mainstream adoption.
This document provides an introduction to data mining. It discusses why data mining is needed due to the explosive growth of data. It defines data mining as the extraction of interesting and previously unknown patterns from large datasets. The document outlines several key aspects of data mining including the types of data that can be mined, patterns that can be discovered, technologies used, applications targeted, and major issues in the field. It also provides a brief history of data mining and discusses how data mining draws from multiple disciplines like machine learning, statistics, and database technology.
Maximizing The Value of Your Structured and Unstructured Data with Data Catal...Molly Alexander
The document discusses how data catalogs can be used to extract value from both structured and unstructured data by providing context about distributed data assets to enable various roles like data scientists and analysts to find and understand relevant datasets, and it recommends implementing an augmented data catalog using machine learning to automatically curate, verify and classify data to improve data quality and insights over time. The document also provides an overview of how to implement a phased data governance approach using a data catalog.
This document provides an overview of future enterprise data architecture. It discusses why enterprise data architecture is important for providing a universal approach to solving business problems, adding uniformity, and encouraging reuse. The document then presents the key topics of why we are here today, what enterprise data architecture means, why it is important, and the target state enterprise data architecture and its principles. It also discusses enterprise data evolution architecture with data virtualization and hybrid solutions. Finally, it introduces the enterprise data architecture framework covering business, information, application, and technology architectures.
This document discusses the growing trend of distributed analytics and taking analytics to where data is located. It notes that as data collection increases, having a single centralized location to store and analyze all data is no longer practical. Instead, distributed networks are needed to analyze data at the edge, such as at IoT devices, branch networks, retail outlets, and remote clusters. This distributed approach is referred to as "distributed network of analytics" or "DNA".
This document discusses governed self-service analytics and proposes a blended approach that combines centralized IT-led analytics with decentralized business-led analytics. It advocates developing an organizational framework that allows businesses more autonomy through self-service tools while still maintaining governance and using centralized data assets. The key aspects of the blended approach include extending global data models to support local needs, incorporating new data sources into certified global datasets, and providing support and training to empower business users.
Presentation by Ivan Schotsmans (DV Community) at the Data Vault Modelling an...Patrick Van Renterghem
The start of GDPR implementations in Europe was, for most organizations, also the start of rethinking their Data Warehouse strategy. The experience of past implementations gave a better view on the do's and don'ts. One of the important lessons learned was the approach of handling information quality. It's not something you handle on top of your data warehouse. To be successful, information quality goes hand in hand with your data warehouse implementation.
Business Intelligence for Better InsightsFrank Silva
The document discusses the need for a business intelligence (BI) program at an organization. It states that the organization currently collects information from various areas in structured and unstructured formats using many different systems, making it difficult for managers to access timely information for decision making. The document recommends developing an enterprise data warehouse to consolidate all information into a single source, along with establishing a BI strategic direction, roadmap, and program to address issues like data quality, the use of unstructured data, and meeting user needs.
The document provides an overview of business intelligence (BI), including definitions, objectives, components, history, needs/benefits, features, uses, and examples. Some key points:
- BI is an umbrella term for architectures, tools, databases, and methods to improve business decision-making through analysis of facts and data-driven systems.
- The goal of BI is to transform raw data into meaningful and useful information through analytics that provides insights and knowledge for impactful decisions.
- Major BI components include data warehousing, extraction/transformation/loading tools, data marts, metrics/key performance indicators, dashboards, and online analytical processing reporting.
- BI has evolved from static reporting systems in
Predictive analytics in Information Systems Research (TSWIM 2015 keynote)Galit Shmueli
Slides from keynote presentation at 3rd Taiwan Summer Workshop in Information Management (TSWIM) by Galit Shmueli on "To Explain or To Predict? Predictive Analytics in Information Systems Research"
This document discusses the type study method of inductive learning, where students study a single representative case to learn about a larger group or system. It explains that studying one typical river system or flower that has all the key characteristics is sufficient to understand rivers or flowers in general, as long as the single case studied is representative of others and includes all relevant parts of the system. The method involves selecting a typical topic, studying its details, comparing it to the group, and generalizing conclusions about the group from the single case.
Big Data, Business Intelligence and Data AnalyticsSystems Limited
Business intelligence and data analytics involve analyzing data to extract useful information for making informed decisions. BI technologies provide historical, current, and predictive views of business operations through functions like reporting, OLAP, data mining, and benchmarking. BI architecture organizes data, information management, and technology components to build BI systems, while frameworks provide standards and best practices. Challenges include continuous availability, data security, cost, increasing user numbers, new data sources and areas like operational BI, and performance and scalability. Leading vendors provide solutions like Google, Microsoft, Oracle, SAS, SAP, IBM, EMC, HP, and Teradata.
Data Analytics Role in Digital Business & Business Process ManagementBPMInstitute.org
Discover the role of data analytics in transformation projects and business process management. What are the four key areas of data analytics and what are the types of data analytics. Also explains data visuals.
Presentation by Luc Delanglez (DataLumen) at the Data Vault Modelling and Dat...Patrick Van Renterghem
This document discusses data governance and provides guidance on getting started with a data governance program. It outlines key building blocks like a data catalog, workflows, and business and technical lineage. It also covers challenges like governing a multi-store data environment and ensuring data quality. The document recommends starting with understanding your data and building out foundational elements like a data catalog before operationalizing governance through workflows.
You probably have heard about Big Data, but ever wondered what it exactly is? And why should you care?
Mobile is playing a large part in driving this explosion in data. The data are also created by the apps and other services in the background. As people are moving towards more digital channels, tons of data are being created. This data can be used in a lot of ways for personal and professional use. Big Data and mobile apps are converging in an enterprise and interacting; transforming the whole mobile ecosystem.
The document outlines a presentation about data analytics and business intelligence given by Chris Ortega. The presentation covers:
1. Definitions of data analytics and business intelligence.
2. Why data analytics and business intelligence are important for faster decision making, establishing a learning culture, and exploring opportunities.
3. The decision cycle and how business intelligence tools can automate parts of extracting data, analyzing it, and making decisions.
4. Limitations of current data analytics approaches like manual spreadsheet updates and the difficulty combining multiple data sources.
Data can exist in many forms and analytics involves finding patterns in data to aid decision making. There are different types of analytics like descriptive, predictive, and prescriptive. Data analysis is the process of inspecting, cleaning, and modeling data to provide business insights. It is used to help organizations make faster and better decisions to reduce costs and improve products and marketing. Advanced analytics uses tools like data mining, location intelligence, and predictive analytics to examine historical data and forecast future behaviors.
This document discusses the opportunities and challenges of big data. It defines big data as huge volumes of structured and unstructured data from various sources that require new tools to analyze and extract business insights. Big data provides both statistical and predictive views to help businesses make smarter decisions. While big data allows companies to integrate diverse data sources and gain real-time insights, challenges include processing large and complex data volumes and ensuring data quality, privacy and management. The document outlines the big data lifecycle and how analytics can be used descriptively, predictively and prescriptively.
The document discusses decision support systems and business intelligence, describing how they can help organizations adapt to changing business environments by providing computerized support for managerial decision making. It covers frameworks for decision support and business intelligence, including the concepts of decision support systems, data warehouses, business analytics, and performance management. Additionally, it examines the major tools and techniques used for managerial decision support, such as communication, knowledge management, and business analytics.
Business intelligence (BI) is a set of theories, methodologies, architectures, and technologies that transform raw data into meaningful and useful information for business purposes.
Business Analytics & Big Data Trends and Predictions 2014 - 2015Brad Culbert
Brad Culbert, Executive Director of Strategy & Solutions at Bistech, discusses business analytics trends and predictions for 2014-2015. Some key trends include the consumerization of business IT, disruptive force of cloud computing, and shortage of analytic skills. Visual data discovery and story boarding analytics will gain popularity, while decision management and cognitive computing will emerge. Next generation information management will focus on flexible governance for agile self-service data preparation. Cloud analytics adoption will increase significantly with a focus on cloud benefits. Predictive analytics and mobile analytics will see further mainstream adoption.
This document provides an introduction to data mining. It discusses why data mining is needed due to the explosive growth of data. It defines data mining as the extraction of interesting and previously unknown patterns from large datasets. The document outlines several key aspects of data mining including the types of data that can be mined, patterns that can be discovered, technologies used, applications targeted, and major issues in the field. It also provides a brief history of data mining and discusses how data mining draws from multiple disciplines like machine learning, statistics, and database technology.
Maximizing The Value of Your Structured and Unstructured Data with Data Catal...Molly Alexander
The document discusses how data catalogs can be used to extract value from both structured and unstructured data by providing context about distributed data assets to enable various roles like data scientists and analysts to find and understand relevant datasets, and it recommends implementing an augmented data catalog using machine learning to automatically curate, verify and classify data to improve data quality and insights over time. The document also provides an overview of how to implement a phased data governance approach using a data catalog.
This document provides an overview of future enterprise data architecture. It discusses why enterprise data architecture is important for providing a universal approach to solving business problems, adding uniformity, and encouraging reuse. The document then presents the key topics of why we are here today, what enterprise data architecture means, why it is important, and the target state enterprise data architecture and its principles. It also discusses enterprise data evolution architecture with data virtualization and hybrid solutions. Finally, it introduces the enterprise data architecture framework covering business, information, application, and technology architectures.
This document discusses the growing trend of distributed analytics and taking analytics to where data is located. It notes that as data collection increases, having a single centralized location to store and analyze all data is no longer practical. Instead, distributed networks are needed to analyze data at the edge, such as at IoT devices, branch networks, retail outlets, and remote clusters. This distributed approach is referred to as "distributed network of analytics" or "DNA".
This document discusses governed self-service analytics and proposes a blended approach that combines centralized IT-led analytics with decentralized business-led analytics. It advocates developing an organizational framework that allows businesses more autonomy through self-service tools while still maintaining governance and using centralized data assets. The key aspects of the blended approach include extending global data models to support local needs, incorporating new data sources into certified global datasets, and providing support and training to empower business users.
Presentation by Ivan Schotsmans (DV Community) at the Data Vault Modelling an...Patrick Van Renterghem
The start of GDPR implementations in Europe was, for most organizations, also the start of rethinking their Data Warehouse strategy. The experience of past implementations gave a better view on the do's and don'ts. One of the important lessons learned was the approach of handling information quality. It's not something you handle on top of your data warehouse. To be successful, information quality goes hand in hand with your data warehouse implementation.
Business Intelligence for Better InsightsFrank Silva
The document discusses the need for a business intelligence (BI) program at an organization. It states that the organization currently collects information from various areas in structured and unstructured formats using many different systems, making it difficult for managers to access timely information for decision making. The document recommends developing an enterprise data warehouse to consolidate all information into a single source, along with establishing a BI strategic direction, roadmap, and program to address issues like data quality, the use of unstructured data, and meeting user needs.
The document provides an overview of business intelligence (BI), including definitions, objectives, components, history, needs/benefits, features, uses, and examples. Some key points:
- BI is an umbrella term for architectures, tools, databases, and methods to improve business decision-making through analysis of facts and data-driven systems.
- The goal of BI is to transform raw data into meaningful and useful information through analytics that provides insights and knowledge for impactful decisions.
- Major BI components include data warehousing, extraction/transformation/loading tools, data marts, metrics/key performance indicators, dashboards, and online analytical processing reporting.
- BI has evolved from static reporting systems in
Predictive analytics in Information Systems Research (TSWIM 2015 keynote)Galit Shmueli
Slides from keynote presentation at 3rd Taiwan Summer Workshop in Information Management (TSWIM) by Galit Shmueli on "To Explain or To Predict? Predictive Analytics in Information Systems Research"
This document discusses the type study method of inductive learning, where students study a single representative case to learn about a larger group or system. It explains that studying one typical river system or flower that has all the key characteristics is sufficient to understand rivers or flowers in general, as long as the single case studied is representative of others and includes all relevant parts of the system. The method involves selecting a typical topic, studying its details, comparing it to the group, and generalizing conclusions about the group from the single case.
CLASSIFICATION OF RESEARCH BY PURPOSE & METHODDr.Shazia Zamir
This document classifies research by purpose and method. For purpose, it discusses basic vs applied research, research and development, and evaluative research. For method, it discusses historical research which describes past conditions, descriptive research which describes present data and characteristics, and experimental research which manipulates variables to discern effects.
There are many ways to classify research, including by purpose, goal, level of investigation, type of analysis, scope, choice of answers to problems, statistical content, and time element. Some of the main classifications are basic/pure research conducted for intellectual purposes versus applied research which tests theories in practice, quantitative research which uses statistics versus non-quantitative, and historical research which describes the past versus descriptive or experimental.
Research is the systematic and objective analysis and recording of controlled observations that may lead to the development of generalizations, principles, or theories, resulting in prediction and possible control of events .
The document discusses research methodology and defines research. It provides examples of what constitutes research and what does not. Research is defined as a systematic, logical process that includes understanding the problem, reviewing literature, collecting and analyzing data, drawing conclusions, and generalizing findings. The document also discusses types of research questions, purposes of research, and common challenges in conducting research.
The document discusses various types of research including applied research, basic research, correlational research, descriptive research, ethnographic research, experimental research, and exploratory research. Applied research seeks practical solutions to problems, while basic research expands knowledge without a direct application. Correlational research examines relationships between variables without determining cause and effect. Descriptive research provides accurate portrayals of characteristics, and ethnographic research involves in-depth study of cultures. Experimental research establishes cause-and-effect through controlled manipulation of variables.
This document provides an overview of key concepts in research methodology, including:
1. It defines research as an organized and systematic process of finding answers to questions through a defined set of steps and procedures.
2. It discusses different types of research including quantitative, qualitative, basic, applied, longitudinal, descriptive, classification, comparative, exploratory, explanatory, causal, theory testing, and theory building research.
3. It also discusses alternatives to research-based knowledge such as relying on authority, tradition, common sense, media, and personal experience.
This document provides an overview of decision support systems and business intelligence. It defines key concepts like decision support frameworks, the types of decisions that systems support, and the evolution of business intelligence tools. The document also explains how decision support systems and business intelligence are related through their architectures and goals of improving access to data and decision making.
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.
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.
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 ...
Analytics, machine e deep learning, data/event streaming
Big data streaming: abilitare la macchina del tempo
Real time event streaming e nuovi paradigmi concettuali:
- Transazioni distribuite
- Consistenza eventuale
- Proiezioni materializzate
Real time event streaming e nuovi paradigmi architetturali:
- Enterprise service bus
- Event store
- Database delle proiezioni
Cenni di Domain Driven Design: una visione strategica della modellazione del proprio dominio di business nell'era dei bi Data.
Analytics, machine e deep learning, data/event streaming
- Big data streaming: abilitare la macchina del tempo
- Real time event streaming e nuovi paradigmi concettuali: transazioni distribuite, consistenza eventuale, proiezioni materializzate
- Real time event streaming e nuovi paradigmi architetturali: Enterprise service bus, Event store, Database delle proiezioni
- Cenni di Domain Driven Design: una visione strategica della modellazione del proprio dominio di business nell'era dei Big Data
1. Top of FormResource Project Systems Acquisition Plan Gradi.docxambersalomon88660
1.
Top of Form
Resource: Project Systems Acquisition Plan Grading Guide
Resources:
· Baltzan, P., and Phillips, A. (2015). Business Driven Information Systems (5th ed).
· Week 3 articles and videos
· It is recommended students search the Internet for a Systems Acquisition Plan template.
Scenario: You are an entrepreneur in the process of researching a business development idea. As you create a high-level Information Technology (IT) strategy for your new enterprise, it is important to consider the acquisition of IT resources. A Systems Acquisition Plan will guide the process of identifying enterprise technology needs and acquiring appropriate information systems in the context of your goal to incorporate business driven IT. The Systems Acquisition Plan is intended to describe a high-level process for acquiring and maintaining IT systems. The Systems Acquisition Plan is a working document, which is expected to change over time as new project details emerge.
Create a high-level Project Systems Acquisition Plan for your project in a minimum of 1,050 words that includes the following information:
· A description and justification of the specific systems design and development approach (SDLC, RAD, Spiral, outsourcing, etc.) the enterprise will employ
· A summary of the steps in the systems acquisition process including initiation, analysis, design, acquisition, and maintenance
· A high-level overview of who will participate in each step of the systems acquisition process
Cite a minimum of 3 peer-reviewed references from the University of Phoenix Library.
Format consistent with APA guidelines.
Submit your assignment.
Resources
· Center for Writing Excellence
· Reference and Citation Generator
· Grammar and Writing Guides
· Learning Team Toolkit
2
CHAPTER
Decisions and Processes: Value Driven Business
CHAPTER OUTLINE
SECTION 2.1
Decision Support Systems
SECTION 2.2
Business Processes
Making Organizational Business Decisions
Measuring Organizational Business Decisions
Using MIS to Make Business Decisions
Using AI to Make Business Decisions
Managing Business Processes
Using MIS to Improve Business Processes
What’s in IT for me?
Working faster and smarter has become a necessity for companies. A firm’s value chain is directly affected by how well it designs and coordinates its business processes. Business processes offer competitive advantages if they enable a firm to lower operating costs, differentiate, or compete in a niche market. They can also be huge burdens if they are outdated, which impedes operations, efficiency, and effectiveness. Thus, the ability of management information systems to improve business processes is a key advantage.
The goal of Chapter 2 is to provide an overview of specific MIS tools managers can use to support the strategies discussed in Chapter 1. After reading this chapter, you, the business student, should have detailed knowledge of the types of information systems that exist to support decision making and business .
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.
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesDerek Kane
This is the first lecture in a series of data analytics topics and geared to individuals and business professionals who have no understand of building modern analytics approaches. This lecture provides an overview of the models and techniques we will address throughout the lecture series, we will discuss Business Intelligence topics, predictive analytics, and big data technologies. Finally, we will walk through a simple yet effective example which showcases the potential of predictive analytics in a business context.
Big Data 101 - Creating Real Value from the Data Lifecycle - Happiest Mindshappiestmindstech
The big impact of Big Data in the post-modern world is
unquestionable, un-ignorable and unstoppable today.
While there are certain discussions around Big Data being
really big, here to stay or just an over hyped fad; there are
facts as shared in the following sections of this whitepaper
that validate one thing - there is no knowing of the limits
and dimensions that data in the digital world can assume.
Traditional approaches to handling disruptive change like big data analytics, such as resisting change or protecting existing business models, are ineffective in today's digital economy. By rapidly processing vast amounts of structured and unstructured data using big data tools, businesses can test new strategies faster through analytical sandboxes to better meet customer demands. Superfast in-memory computing is transforming industries by enabling new data-driven business models in areas like transportation. The ability to analyze unprecedented types and volumes of data in real time using tools like Apache Hadoop and Spark makes it possible to build more accurate predictive models and realize future gains.
1. A strategic information system is an information system aligned with an organization's business strategy to help achieve its objectives and gain a competitive advantage.
2. Strategic information systems differ from other management information systems in that they can change how a firm competes, have an external focus, and involve higher project risk.
3. Strategic information systems support strategic decision making, innovation, responsiveness to market changes, collaboration, customer insight, and introducing new business models.
This document discusses the evolution of information systems from electronic data processing (EDP) systems in the 1960s to today's enterprise systems and e-commerce applications. It outlines the development of different types of information systems including transaction processing systems, management information systems, decision support systems, executive information systems, expert systems, knowledge management systems, and enterprise resource planning systems. It also describes how information systems now support strategic, tactical, and operational management decision-making and enhance the value of information through data warehousing and data mining.
The document is an assignment for a Management Information Systems course. It includes 5 questions related to MIS concepts.
1) The first question defines MIS, lists its characteristics and functions. It also provides disadvantages of MIS such as being highly sensitive and requiring constant monitoring.
2) The second question explains knowledge-based systems and decision support systems (DSS), providing an example of how DSS can be used. It also defines online analytical processing (OLAP).
3) The third question discusses value chain analysis, business process reengineering (BPR), and how data warehousing and data mining are useful for MIS.
4) The fourth question explains data flow diagrams (DFD) and data dictionaries
Assignment mqanagement information system 0047amol_dongare
This document contains an assignment for a Management Information Systems course. It includes 3 questions asking students to:
1) Define MIS, its characteristics and functions, and disadvantages of MIS.
2) Explain knowledge-based systems, decision support systems (DSS), and online analytical processing (OLAP), providing examples of each.
3) Discuss value chain analysis and its significance for MIS, the meaning of business process reengineering (BPR) and its significance, and how data warehousing and data mining are useful for MIS.
The document provides detailed answers for each question, explaining key concepts and terms related to MIS.
This document discusses how to deliver real business impact through analytics by taking a business process view. It recommends understanding end-to-end business processes to design analytics enablement, focusing on providing visibility, managing effectiveness, executing actions, and repeating the process. It also recommends dissecting the data-to-insight process, choosing the right operating model for a shared analytics organization, and ensuring stakeholders are aligned around an agile strategy. Taking this approach can help harness data and analytics to generate material business impact.
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Trusted Execution Environment for Decentralized Process Mining
Business Intelligence System
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Business Intelligence Systems
Table of Contents
Abstract........................................................................................................................................2
Introduction ...............................................................................................................................2
Decision Making Process .................................................................................................................2
Decision Support Systems ...............................................................................................................3
Business Intelligence.........................................................................................................................4
Literature Review.....................................................................................................................4
Annotated Bibliography.........................................................................................................4
Summary of author........................................................................................................................................5
Critical evaluation..........................................................................................................................................5
2.1. Bibliographic Information ..................................................................................................6
2.2. Annotation Explanation .......................................................................................................6
Summary of author........................................................................................................................................6
Critical evaluation..........................................................................................................................................6
3.1. Bibliographic Information ..................................................................................................7
3.2. Annotation Explanation .......................................................................................................7
Summary of author........................................................................................................................................7
Critical evaluation..........................................................................................................................................7
4.1. Bibliographic Information ..................................................................................................8
4.2. Annotation Explanation .......................................................................................................8
Summary of author........................................................................................................................................8
Critical evaluation..........................................................................................................................................8
5.1. Bibliographic Information ..................................................................................................9
5.2. Annotation Explanation .......................................................................................................9
Summary of author........................................................................................................................................9
Critical evaluation..........................................................................................................................................9
Methodology and reflection............................................................................................... 10
Business Intelligence application/ Software............................................................... 11
Concluding ............................................................................................................................... 12
References................................................................................................................................ 13
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Abstract
The business environment is always performing with complexes,
which create opportunities on the one side and confusing to make right
decision on others. A right decision is depending on the business intelligence
of the decision makers.
In this essay discuss about how does business intelligence support
Organizational Decision Making. By observing this essay can understand the
significance of business intelligence, decision-making, Decision support
system and their correlation. This report might be providing idea and
knowledge to gain a complete understands of all the issues facing with
decision-making.
Keywords: business intelligence, decision-making, Decision support system,
data, information
Introduction
Decision Making Process
In every work place as organizations, private and public, are under the
circumstance of making decision for their organizational strategies, tactical,
and operational decisions. A good decision can be support to close the gap
between the current performances of an organization and its goals. The
potential solution is relying on the decision support systems and business
intelligence of the decision maker.
According to Mintzberg (1980), interpersonal figurehead, leader, liaison,
informational monitor, Disseminator, spokesperson, decisional entrepreneur,
disturbance handler, resource allocator and negotiator are ten managerial
roles. To better perform their roles; managers need information that is
efficiency and effectiveness. Every managerial activity in all roles must
perform decision-making process for their every task. High levels
managerial roles are primary decision maker. Some time and some case they
have to make decision in a timely. Managerial level always make decisions
by four- step process of defined the problem, construct a model that describe
the real’ world problem, Identify possible solutions to the modeled problem
and evaluate the solution and compare, choose, and recommend a potential
solution to the problem. For every step must have alternative solution are
being considered. To get alternative solution are difficult because business
environments are growing more and more complex every day. Thus now a
day, making decision is the most complex task for managerial levels.
Technology, information systems, advanced search engines, and
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globalization result in more and more alternatives from which to choose and
government regulations and the need for compliance, political instability and
terrorism, competition and changing consumer demands produce more
uncertainty, marking it more difficult to predict consequences and the future
are the main reason of the complex to make the right decision on time.
Thus, Manager must have more experience and knowledge to use the new
tools and techniques of their fields, especially for decision-making. Those
tools and techniques provide to make effective decisions with efficiency.
For this reason, seventy per cent of the executive levels know that
information technology is vital for their daily business live. They use
Decision support systems to evaluate the daily business transaction
processing and monitoring activities to problem analysis to make right
decision timely.
Decision Support Systems
Speedy computations, improved communication and collaboration,
increased productivity of group member, Improved data management,
managing giant data warehouses, quality support, agility support,
overcoming cognitive limits in processing and storing information, using the
web, anywhere and anytime to get the support are the visual capabilities of
using computerized decision support systems.
The definition of the decision support systems (DSS) as „interactive
computer-based systems, which helps decision makers utilize data and
models to solve unstructured problems” (Gorry and Scott- Morton, 1971).
According to Keen and Scott- Morton (1978), “Decision support systems
couple the intellectual resources of individuals with the capabilities of the
computer to improve the quality of decisions. It is a computer-based support
system for management decision makers who deal with semi structured
problems”.
In my opinion, decision support system (DSS) is a support system, which
based on computerized system for management decision makers to solve
problems. These systems allow the business to gather, store, access,
evaluate and analysis data to make the right decision for their business.
Mostly, customer profiling, customer support, market research, product
profitability, statistical analysis, distribution history, order process are
illustrate in these systems. Data are the first component and models are the
second component of the DSS Architecture. The two major type of Decision
support system are model- oriented DSS and data- oriented DSS.
Quantitative models are used to solve the problem in model – oriented DSS.
Data such as report and queries and other supported information are used in
data- oriented DSS.
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Business Intelligence
Business intelligence (BI) represents theories, methodologies, processes,
architectures, and technologies that transform raw data into meaningful and
useful information for either business purposes or decision-making process
of business. The benefit of Business intelligence is not only can handle
large amounts of information to help identify but also creates new
opportunities for business. The definition of the Business intelligence is
“Business intelligence (BI) is a broad category of applications and
technologies for gathering, storing, analyzing, and providing access to data
to help enterprise users make better business decisions. BI applications
include the activities of decision support systems, query and reporting,
online analytical processing (OLAP), statistical analysis, forecasting, and
data mining.” (Luca 2006, Searchdatamanagement.techtarget.com)
In my point of view the term business intelligence (BI) refer to the set of
theories, methodologies, processes, tools and systems that play a high level
in the organization process. Also business Intelligence (BI) is a conceptual
framework for decision making that contains all the information executives
need such as architectures, tools, data-bases, analytical tools, applications
and methodologies. Data warehouse, business analysis, business
performance management and user interface are four major components of a
BI system. The data-warehousing environment is concern relaying on
technical staff. Business analysis is the field of business users. Business
performance management processes include planning and forecasting of a
business strategy. Dashboards and other information broadcasting tools are
including in user interface that provide a comprehensive visual view of
business performance, trends and exceptions. According to Thompson
(2004) report, faster, more accurate reporting, improved decision making,
improved customer service and increased revenue are the major benefits of
the business intelligence.
Literature Review
In this essay, five sources will be discussed as annotated bibliography
to understand how Business intelligence supports Organizational Decision-
making. All sources are providing how BI supports Organizational Decision-
making from their different point of view.
Annotated Bibliography
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1.1. Bibliographic Information
Turban,E., Sharda, R., and Delen, D., Decision support and business
intelligence systems, 2011 (9th ed), Person Education International, Upper
Saddle River, and USA.
1.2. Annotation Explanation
Summary of author
In this book, 14 chapters are organized into seven parts as part1: Decision
support and business intelligence, Part II; computerized decision support,
Part III; Business intelligence, Part IV: collaboration, communication, group
support systems, and knowledge management, Part V: Intelligent systems,
Part VI: implementing decision support system and business intelligence and
Part VII: online supplements.
Critical evaluation
Turban,E., Sharda, R., and Delen, D describe how organizations
survive by using business intelligence as computerized support for
managerial decision making in such a business environment. This book
concentrates not only the theoretical and conceptual foundations of decision
support but also the commercial tools and techniques that are available.
Chapter 1 has provided an overview of decision support systems and
business intelligence. Chapter 2 and 3 are included in Part II computerized
decision support and describes both structured models and modeling tools.
Chapter 5 to 9 are several distinct components of business intelligence.
These three parts are the main stream of how BI support decision-making.
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2.1. Bibliographic Information
Farnaz M and Nastaran H write this book. The title of the book is A
Framework Correlating Decision Making Style and Business Intelligence
Aspect. Publication year was 2012 (3rd Ed). Published by International
Conference on e-Education, e-Business, e-Management and e Learning,
IPEDR vol.27, (2012) IACSIT Press, Singapore.
2.2. Annotation Explanation
Summary of author
This research paper proposes a framework with appropriate BI
capabilities to get the best result for each of the decision-making styles’
requirements. The concept of business intelligence systems and its
capabilities are discussed in first part. After that decision making style
concept and how decision-making styles are impact on BI are discussed.
Critical evaluation
This paper highlights the bidirectional relation between decision-
making styles’ concept and Business intelligence capabilities.
This paper give the logic of, application of BI in organizational
decision making is important to consider the manner of decision maker to
fulfill the information of their organization needs. This paper mentions that
BI capabilities change according to decision making style and finding
appropriate BI capabilities for the entire decision maker has limited.
This research paper brings not only established theories from the decision
sciences but also research for information systems areas.
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3.1. Bibliographic Information
Hana, K and Marketa S, Decision Support Systems or Business Intelligence:
What can help in Decision Making? Publication year was 2006. Published
by Institute of System Engineering and Informatics, Faculty of Economic
and Administration, University of Pardubice.
3.2. Annotation Explanation
Summary of author
In this paper discuss about two basic types of supporting system to
make effective decision-making; DSS- decision support system and BI-
business intelligence. This paper provides comparison definitions, and an
overview of decision support systems and business intelligence.
Critical evaluation
The main discussion of this paper is comparing between the business
intelligence and decision support system.
Business intelligence is a broad category of applications and technologies.
Business intelligence is not stable and their ranges of concepts are
developing and future components can be absolutely different.
A decision support system is a computer program application. It is
help to make decisions more easily for decision makers through direct
interaction with data and analysis models.
Both of particular concepts are based on data to make a decision in a better
and easier way.
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4.1. Bibliographic Information
Chartered Instituted of Management Accountants (executive report),
Improving decision making in organizations, unlocking business
intelligence. Publication year was October 2009. Published by Chartered
Instituted of Management Accountants (CIMA).
4.2. Annotation Explanation
Summary of author
This business intelligence system is perspective from the role of the
management accountant. The decisions making process, business
intelligence and the role of the management accountant in business
intelligence are the main areas of discussion in this paper.
Critical evaluation
According to this paper, business intelligence is not just about
hardware and software. Business intelligence perform vital role in better
decision making in business. Business intelligence will always be a demand
for accountants with technical expertise. Management accounts should have
business intelligence in their business. If they have the Business intelligence
they can help ensure data quality for problem area, they can help to
articulate the business’s information needs for decision making, they can
work with information technology or decision support system to develop
business intelligence strategy and so on. A lot of figures demonstrate to
understand easily.
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5.1. Bibliographic Information
Peter, GWK & Michael, S.S.M write this book. The title of the book is
Decision Support Systems an Organizational Perspective 1978. Published by
Addison-Wesley publishing company, Sydney.
5.2. Annotation Explanation
Summary of author
“Decision support systems an organizational perspective” is a book,
which is the Addison-Wesley series on Decision Support focus on
managers’ decision-making activities. This book has shaped by nine
categories, which address the main issue of decision support.
Critical evaluation
The book explores systematic approaches to improving the
effectiveness and efficiencies of decision processes. The theoretical studies
of organizational decision making and technical work in interactive
computer systems have evolved in the concept of decision support. The
intention of the series brings together the various theoretical, behavioral,
practical and technical point of views gained so far.
One interesting aspect of the work of authors is indicates a major role
for computers as a tool for individual decision-making. Although there was a
time more than thirty five year ago where the idea of using, their idea is still
leading in decision support systems concepts.
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Methodology and reflection
Above five sources are used in research as the quantitative method. All
resources discuss how decision support system and business intelligence are
impact on decision-making process from their point of view by using the
reference of other authors.
Decision support system and business intelligence tools are essential
component of the decision making process in this ages is the main
methodology of the essay.
Poor data and information quality are key issue of the decision making
process. If base on poor quality data and information, neither the right
analysis nor decisions are made. Poor quality data and information leads to
poor quality decision and its leads waste of the time and organization
resources.
Furthermore, experience and intelligence to use these tools of the decision
maker are another issue of the decision making process. If the decision
makers not have the sufficient experience and intelligence, the decisions
cannot the best one.
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Business Intelligence application/ Software
OLAP online analytical processing is a computer processing. That enables a
user to easily and selectively extract and view data from different points of
view. OLAP data is stored in a multidimensional database. OLAP software
can locate the intersection of dimensions and display. OLAP can be use for
data mining or the discovery of previously undiscerned relationships
between data items. OLAP database does not need to be as large data
warehouse.(Margaret 2007)OLAP applications span a variety of
organizational functions. Finance departments use OLAP for applications
such as budgeting, activity-based costing (allocations), financial
performance analysis, and financial modeling. Among other applications,
marketing departments use OLAP for market research analysis,
salesforecasting, promotions analysis, customer analysis, and
market/customer segmentation. Typical manufacturing OLAP applications
include production planning and defect analysis. OLAP applications are
found in widely divergent functional areas, they all require the following key
features Multidimensional views of data, Calculation-intensive capabilities
and Time intelligence
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Concluding
In globalization ages, the environment of the organizations is more and more
complex with opportunities and problem or pressures. The degrees of the
successful of the organization are depending on decision-making skill of the
managerial levels. Decision-making skills are relying on the managerial
levels’ BI and DSS. According to the annotated bibliographies, decision
support system is a technology that can directly help make intelligence
business decision faster. Business intelligence is an umbrella term and new
tools as business analytics, data minding, intelligent systems, delivered via
web technology, BI capabilities, model and data for computer-aided decision
making are appear under the BI. (OLAP Council, 1997)
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References
CIMA Chartered Institute of Management Accountants. Cimaglobal.com,
2011, improving decision making in organizations, Unlocking business
intelligence, Executive report October 2009, viewed 27 February 2013,
http://www.cimaglobal.com/Documents/Thought_leadership_docs/cid_execr
ep_unlocking_business_intelligence_Oct09.pdf
Farnaz, M and Nastaraan, H 2012, A Framework Correlating Decision
Making Style and Business Intelligence Aspect, IPEDR 2013, viewed 27
February 2013, http://www.ipedr.com/vol27/18-IC4E%202012-F00024.pdf
Gorry, G.A., and M.S. Scott- Morton. (1971). “A Framework for
Management Information Systems” Sloan Management Review,
Vol.13,No1, pp 55-7.
H. A. Mintzberg, The Nature of Managerial Work, Prentice Hall, Englewood
Cliffs, NJ 1980
H. A. Mintzberg, The Rise and Fall of Strategic Planning.The free press,
New York, 1993
Hana, K and Marketa, S 2006, Decision Support Systems or Business
Intelligence: What can help in decision making?, Dspace.upce.cz 2009,
viewed 3 March 2013,
http://dspace.upce.cz/bitstream/10195/32436/1/CL585.pdf
Keen, P. G. W., and M. S. Scott- Morton. (1978). Decision Support
Systems: An Organizational Perspective. Reading, MA;Addison- Wesley.
Luca, R 2006, Tech Target, Business Intelligence (BI), viewed 29 April
2013, http://searchdatamanagement.techtarget.com/definition/business-
intelligence
Margaret, R 2007, Tech Target, OLAP(Online analytical processing),
viewed 29 April 2013,
http://searchdatamanagement.techtarget.com/definition/OLAP
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OLAP Council, 1997, olapcouncil.org viewed 19 April 2013,
http://www.olapcouncil.org/research/whtpaply.htm
Peter, GWK & Michael, S.S.M 1978, Decision Support Systems an
Organizational Perspective, Addison-Wesley publishing company, Sydney.
Thompson,O. (2004, October). “Business Intelligence Success, lesson
Learned”. Technologyevaluation.com (accessed June 2009).
Turban, E., Sharda, R., &Delen, D. 2011, Decision support and business
intelligence system, 9th
ed., pp. 2-30, viewed 26 February 2013, University
of Canberra E-Reserve.