Project analytics in Project ManagementKetan Gandhi
Project managers can use this predictive information to make better decisions and keep projects on schedule and on budget. Analytics does more than simply enable project managers to capture data and mark the tasks done when completed.
The document discusses several key challenges in adopting predictive analytics in healthcare:
1) Lack of quality data due to incomplete, inconsistent, or non-standardized data from different sources.
2) Difficulty incorporating analytics into clinical workflows and ensuring usability for clinicians.
3) Privacy concerns around sharing and integrating patient data from different organizations.
4) Need for interdisciplinary teams including data scientists, clinicians, and other stakeholders to design effective predictive solutions.
Advanced analytics uses sophisticated techniques beyond traditional business intelligence to discover deeper insights from data. It includes techniques like machine learning, data mining, and neural networks. While many major companies invest in analytics, some hesitate due to a lack of structured data or past failures. The document provides suggestions for effective advanced analytics, including choosing the right data sources, building models to optimize business outcomes, and embedding analytics in tools to generate maximum profit. However, companies must set boundaries on data use and consider ethics to avoid illegal or reputation-damaging practices.
The Business Analytics Value PropositionEric Stephens
Presentation made to the Nashville Technology Council Analytics Peer Network meeting on May 30, 2013. Discussion of the impact of analytics to an organization, along with use cases that can help convey the value of the practice to executives and other managers.
This document discusses analytics and its classifications and types. It defines analytics as a fact-based approach used for business planning that includes data validation, root cause analysis, and strategic prediction. Analytics in business is described as a continuous iterative process that investigates past performance to provide better insight for planning. The document outlines descriptive analytics methods like association analysis and clustering, as well as predictive analytics roles in generating theories, measures, and models. It also discusses the rapid growth of the analytics industry and its major domains like marketing, IT, and customer analytics. Finally, the document notes both views on the impact of analytics and differences between analytics and scientific approaches.
This document discusses business analytics. It defines business analytics as using data, statistical and quantitative analysis, explanatory and predictive models to gain insights and support decision-making. The document outlines the typical business analytics process, including understanding the business objectives, assessing the situation, collecting and preparing data, developing analytic models, evaluating and reporting results, and deploying the outcomes. It provides examples of how analytics can be used to drive personalized customer services, optimize people management decisions, and conduct real-time sentiment analysis of social media data for an FMCG company. The document concludes with lessons learned, emphasizing the importance of continuous learning, gaining experience through projects and mentoring, and having confidence in one's abilities.
phd admission. phd in computer science phd thesisresearch paper formatresearch paper how to writepaper publicationpaper publication journalpaper publication journal paper publication journal paper publication in journalsresearch topics research paper topics research questions
Project analytics in Project ManagementKetan Gandhi
Project managers can use this predictive information to make better decisions and keep projects on schedule and on budget. Analytics does more than simply enable project managers to capture data and mark the tasks done when completed.
The document discusses several key challenges in adopting predictive analytics in healthcare:
1) Lack of quality data due to incomplete, inconsistent, or non-standardized data from different sources.
2) Difficulty incorporating analytics into clinical workflows and ensuring usability for clinicians.
3) Privacy concerns around sharing and integrating patient data from different organizations.
4) Need for interdisciplinary teams including data scientists, clinicians, and other stakeholders to design effective predictive solutions.
Advanced analytics uses sophisticated techniques beyond traditional business intelligence to discover deeper insights from data. It includes techniques like machine learning, data mining, and neural networks. While many major companies invest in analytics, some hesitate due to a lack of structured data or past failures. The document provides suggestions for effective advanced analytics, including choosing the right data sources, building models to optimize business outcomes, and embedding analytics in tools to generate maximum profit. However, companies must set boundaries on data use and consider ethics to avoid illegal or reputation-damaging practices.
The Business Analytics Value PropositionEric Stephens
Presentation made to the Nashville Technology Council Analytics Peer Network meeting on May 30, 2013. Discussion of the impact of analytics to an organization, along with use cases that can help convey the value of the practice to executives and other managers.
This document discusses analytics and its classifications and types. It defines analytics as a fact-based approach used for business planning that includes data validation, root cause analysis, and strategic prediction. Analytics in business is described as a continuous iterative process that investigates past performance to provide better insight for planning. The document outlines descriptive analytics methods like association analysis and clustering, as well as predictive analytics roles in generating theories, measures, and models. It also discusses the rapid growth of the analytics industry and its major domains like marketing, IT, and customer analytics. Finally, the document notes both views on the impact of analytics and differences between analytics and scientific approaches.
This document discusses business analytics. It defines business analytics as using data, statistical and quantitative analysis, explanatory and predictive models to gain insights and support decision-making. The document outlines the typical business analytics process, including understanding the business objectives, assessing the situation, collecting and preparing data, developing analytic models, evaluating and reporting results, and deploying the outcomes. It provides examples of how analytics can be used to drive personalized customer services, optimize people management decisions, and conduct real-time sentiment analysis of social media data for an FMCG company. The document concludes with lessons learned, emphasizing the importance of continuous learning, gaining experience through projects and mentoring, and having confidence in one's abilities.
phd admission. phd in computer science phd thesisresearch paper formatresearch paper how to writepaper publicationpaper publication journalpaper publication journal paper publication journal paper publication in journalsresearch topics research paper topics research questions
Data Warehousing Implementation Issues
Implementing a data warehouse is generally a massive effort that must be planned and executed according to established methods
There are many facts to the project lifecycle, and no single person can be an expert in each area
Some best practices for implementing a data warehouse (Weir, 2002):
Project must fit with corporate strategy and business objectives
It is important to manage user expectations about the completed project
The data warehouse must be built incrementally
Build in adaptability
Implementing a data warehouse is a massive effort that must be planned and executed according to established methods. There are many factors involved in the project lifecycle, so no single person can be an expert in all areas. Some best practices for a successful implementation include managing user expectations, building the warehouse incrementally and with adaptability, and ensuring involvement from both IT and business professionals. Risks that can undermine the project include cultural issues not being addressed, unclear objectives, unrealistic expectations, and low data quality.
DEC Business Intelligence for NSW Public SchoolsNSWCESE
The document discusses Business Intelligence (BI) and its implementation within the NSW Department of Education and Communities (DEC). BI allows disparate data sources to be combined to deliver key information to users, enabling evidence-driven strategic decision making. The DEC is providing training to principals, directors, and other power users on accessing BI reports. These reports pull from various student data systems to address information needs like determining which primary schools students originate from. Governance of BI is through the Information Governance Group while the Centre for Education Statistics and Evaluation sponsors and stewards the program.
This document discusses using agile project management approaches within a financial industry. It presents a case study of a project delivered through 5 iterations, with 2 iterations for planning and design and 3 for agile delivery. The project aimed to deliver both back-office benefits like reduced processing times and front-office benefits like increased access to information. It emphasizes the importance of involving business representatives, delivering value iteratively, and loosely coupling business and IT teams while ensuring business continuity during project execution.
"Implementing AI for New Business Models and Efficiencies" - Parag Shrivastav...Grid Dynamics
Dynamic talks Seattle: Artificial Intelligence (AI) and data are foundational to ideation of new business models that bring growth and efficiencies. This is in action in pharmaceutical supply chain for reducing costs, increasing volumes, and optimizing the contracts with suppliers. The implementation involves process and tools that make data usable, and overcome the challenges of culture, ethics, data scalability, compute and engineering, and. Learn about data collection, data management, and metadata management tools implementation and modern data architecture to support them. Discuss machine learning algorithms for growth and efficiency scenarios.
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 discusses key aspects of healthcare information technology (HIT) strategy and service delivery. It outlines the role of the chief information officer (CIO) in developing and executing enterprise-wide HIT strategy, prioritizing infrastructure and applications, and ensuring governance and security. The document also describes several core areas of focus for IT services, including business intelligence, cybersecurity, innovation, and strategic analytic options in healthcare.
This paper proposes a method for jointly training an active feature acquisition policy network and classifier to perform cost-effective instance-based active feature acquisition for diagnosis. The method sequentially asks questions or examines the patient based on the current state of available information, acquiring only necessary features. It was tested on the physioNet 2012 medical dataset and aims to model how doctors make diagnoses by acquiring features as needed rather than statically.
Data Science in Action for an Insurance Product - Shawn JinMolly Alexander
This document summarizes how an insurance company uses data science across its operations, from marketing to pricing to claims processing. Data from various sources is used to separate good and bad risks and test analytical models. Data scientists work closely with product teams to develop new strategies and continuously improve the product. A standard analytical process is enforced to institutionalize best practices. The company also builds a platform for individuals to develop technical skills and advance their careers in data science.
The document discusses advanced analytics and its growing importance for businesses. It notes that advanced analytics uses sophisticated techniques like machine learning and data mining to generate deeper insights from data. While big data and analytics are becoming increasingly important, many companies are unsure how to implement them effectively. The document recommends targeted efforts to work with data and build models, rather than massive overhauls, to maintain flexibility as technologies evolve. It also acknowledges challenges like risk of failure and need for expertise in interpreting probabilistic results.
This document provides an overview of how business analytics tools and methods can be used in corporate finance to maximize shareholder value. It discusses how capital budgeting and working capital management are used to allocate financial resources through investment analysis. It then defines business analytics and describes how it involves statistically interpreting historical transaction data to make fact-based decisions. Various categories and types of analytics are outlined, along with basic techniques, system analysis methods, analytical techniques, decision making methods, and simulation/optimization methods that are commonly used in corporate finance through business analytics.
Given the complexity of creating RFPs, organisations are not
always fully equipped to ask the correct questions, and might even
lack the required technical knowledge. This talk will demonstrate
the importance of considering input from others when setting
up your own RFP as this can save time and money – the risks
of not identifying all potential pitfalls at RFP stage can be time-
consuming and costly a few years later. This talk will apply to
all in the industry, from publishers seeking out new platform
providers to libraries sourcing vendors for specific projects.
Tracy Gardner, Renew Publishing Consultants
Yann Amouroux, Bioscientifica
Making advanced analytics work for you.
Big data and analytics have rocketed to the top of the corporate agenda. Executives look with admiration at how Google, Amazon, and others have eclipsed competitors with powerful new business models that derive from an ability to exploit data....
This document discusses the Excel add-in for data mining. It allows users to mine data with a few clicks using advanced algorithms without needing experience in data mining or SQL server configuration. The add-in contains sections for data preparation, modeling, accuracy validation, and connection. Data can be explored, cleaned, and prepared for modeling. Common modeling algorithms like decision trees, clustering, and association rules are available. Accuracy and validation tools allow testing models on real data. The add-in combines the power of SQL Server Analysis Services with the ease of use of Excel.
This document summarizes the benefits of a data warehouse and business intelligence solutions. It discusses how a data warehouse can consolidate an organization's data into a single source of truth to allow analysis and insights. It then outlines how business intelligence solutions can translate data into actionable results to foster better decision making. Finally, it provides an overview of sele q tech's offerings and methodology for delivering end-to-end data warehouse and business intelligence projects.
Swaroop Vakkalanka provides an overview of starting a discovery-based R&D company in India. He discusses the background of pharmaceutical R&D globally and in India, opportunities for a business model, gaps that need to be addressed, the importance of licensing, and factors for success regarding infrastructure and operations. Key points include diminishing R&D productivity globally, India's progression from generics to biotech, gaps in India's capabilities, and the need for management experience, flexibility, financial support, and a focus on science in early-stage R&D.
This document discusses best practices for working in the gig economy as an independent contractor on data and analytics projects. It recommends finding the right fit between contractor skills and project needs, committing to an agile or waterfall project management approach, setting quantitative goals, creating extensible code and documentation, and over-communicating through frequent updates rather than relying on emails. The document concludes with case studies comparing two different broadcaster clients' projects that illustrates these principles in action and contrasts their outcomes.
The document discusses the importance of use-inspired research and collaborative infrastructure to drive innovation. It notes that modern research requires large, multidisciplinary teams and access to world-class infrastructure. While Australia invests in collaborative research centers and infrastructure, there are still gaps, including in areas like ICT and bioinformatics. The document calls for defining infrastructure needs, improving access and governance, and strategically investing in capabilities to support priority research areas.
Lecture 01 Introduction to Business Research Methods.pptJunaidrazaq
This document provides an introduction to business research methods. It defines business research as the systematic and objective process of gathering, recording, and analyzing data to aid in making business decisions. The document outlines the course objectives, which are to introduce core research methods concepts and stimulate interest in business research. It also lists and describes several common business research methods and techniques. Additionally, it provides examples of fields where business research is often used, such as sales and marketing research, financial research, and management research. Finally, the document gives examples of real-life business situations where research may be applied.
The document provides an overview of business research methods from Dr. Shriram Dawkhar of Sinhgad Institute of Management. It discusses defining research and the scientific method, formulating research questions in a management hierarchy from management dilemmas to investigative questions, the steps in the research process, and selecting an appropriate research problem based on criteria.
Data Warehousing Implementation Issues
Implementing a data warehouse is generally a massive effort that must be planned and executed according to established methods
There are many facts to the project lifecycle, and no single person can be an expert in each area
Some best practices for implementing a data warehouse (Weir, 2002):
Project must fit with corporate strategy and business objectives
It is important to manage user expectations about the completed project
The data warehouse must be built incrementally
Build in adaptability
Implementing a data warehouse is a massive effort that must be planned and executed according to established methods. There are many factors involved in the project lifecycle, so no single person can be an expert in all areas. Some best practices for a successful implementation include managing user expectations, building the warehouse incrementally and with adaptability, and ensuring involvement from both IT and business professionals. Risks that can undermine the project include cultural issues not being addressed, unclear objectives, unrealistic expectations, and low data quality.
DEC Business Intelligence for NSW Public SchoolsNSWCESE
The document discusses Business Intelligence (BI) and its implementation within the NSW Department of Education and Communities (DEC). BI allows disparate data sources to be combined to deliver key information to users, enabling evidence-driven strategic decision making. The DEC is providing training to principals, directors, and other power users on accessing BI reports. These reports pull from various student data systems to address information needs like determining which primary schools students originate from. Governance of BI is through the Information Governance Group while the Centre for Education Statistics and Evaluation sponsors and stewards the program.
This document discusses using agile project management approaches within a financial industry. It presents a case study of a project delivered through 5 iterations, with 2 iterations for planning and design and 3 for agile delivery. The project aimed to deliver both back-office benefits like reduced processing times and front-office benefits like increased access to information. It emphasizes the importance of involving business representatives, delivering value iteratively, and loosely coupling business and IT teams while ensuring business continuity during project execution.
"Implementing AI for New Business Models and Efficiencies" - Parag Shrivastav...Grid Dynamics
Dynamic talks Seattle: Artificial Intelligence (AI) and data are foundational to ideation of new business models that bring growth and efficiencies. This is in action in pharmaceutical supply chain for reducing costs, increasing volumes, and optimizing the contracts with suppliers. The implementation involves process and tools that make data usable, and overcome the challenges of culture, ethics, data scalability, compute and engineering, and. Learn about data collection, data management, and metadata management tools implementation and modern data architecture to support them. Discuss machine learning algorithms for growth and efficiency scenarios.
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 discusses key aspects of healthcare information technology (HIT) strategy and service delivery. It outlines the role of the chief information officer (CIO) in developing and executing enterprise-wide HIT strategy, prioritizing infrastructure and applications, and ensuring governance and security. The document also describes several core areas of focus for IT services, including business intelligence, cybersecurity, innovation, and strategic analytic options in healthcare.
This paper proposes a method for jointly training an active feature acquisition policy network and classifier to perform cost-effective instance-based active feature acquisition for diagnosis. The method sequentially asks questions or examines the patient based on the current state of available information, acquiring only necessary features. It was tested on the physioNet 2012 medical dataset and aims to model how doctors make diagnoses by acquiring features as needed rather than statically.
Data Science in Action for an Insurance Product - Shawn JinMolly Alexander
This document summarizes how an insurance company uses data science across its operations, from marketing to pricing to claims processing. Data from various sources is used to separate good and bad risks and test analytical models. Data scientists work closely with product teams to develop new strategies and continuously improve the product. A standard analytical process is enforced to institutionalize best practices. The company also builds a platform for individuals to develop technical skills and advance their careers in data science.
The document discusses advanced analytics and its growing importance for businesses. It notes that advanced analytics uses sophisticated techniques like machine learning and data mining to generate deeper insights from data. While big data and analytics are becoming increasingly important, many companies are unsure how to implement them effectively. The document recommends targeted efforts to work with data and build models, rather than massive overhauls, to maintain flexibility as technologies evolve. It also acknowledges challenges like risk of failure and need for expertise in interpreting probabilistic results.
This document provides an overview of how business analytics tools and methods can be used in corporate finance to maximize shareholder value. It discusses how capital budgeting and working capital management are used to allocate financial resources through investment analysis. It then defines business analytics and describes how it involves statistically interpreting historical transaction data to make fact-based decisions. Various categories and types of analytics are outlined, along with basic techniques, system analysis methods, analytical techniques, decision making methods, and simulation/optimization methods that are commonly used in corporate finance through business analytics.
Given the complexity of creating RFPs, organisations are not
always fully equipped to ask the correct questions, and might even
lack the required technical knowledge. This talk will demonstrate
the importance of considering input from others when setting
up your own RFP as this can save time and money – the risks
of not identifying all potential pitfalls at RFP stage can be time-
consuming and costly a few years later. This talk will apply to
all in the industry, from publishers seeking out new platform
providers to libraries sourcing vendors for specific projects.
Tracy Gardner, Renew Publishing Consultants
Yann Amouroux, Bioscientifica
Making advanced analytics work for you.
Big data and analytics have rocketed to the top of the corporate agenda. Executives look with admiration at how Google, Amazon, and others have eclipsed competitors with powerful new business models that derive from an ability to exploit data....
This document discusses the Excel add-in for data mining. It allows users to mine data with a few clicks using advanced algorithms without needing experience in data mining or SQL server configuration. The add-in contains sections for data preparation, modeling, accuracy validation, and connection. Data can be explored, cleaned, and prepared for modeling. Common modeling algorithms like decision trees, clustering, and association rules are available. Accuracy and validation tools allow testing models on real data. The add-in combines the power of SQL Server Analysis Services with the ease of use of Excel.
This document summarizes the benefits of a data warehouse and business intelligence solutions. It discusses how a data warehouse can consolidate an organization's data into a single source of truth to allow analysis and insights. It then outlines how business intelligence solutions can translate data into actionable results to foster better decision making. Finally, it provides an overview of sele q tech's offerings and methodology for delivering end-to-end data warehouse and business intelligence projects.
Swaroop Vakkalanka provides an overview of starting a discovery-based R&D company in India. He discusses the background of pharmaceutical R&D globally and in India, opportunities for a business model, gaps that need to be addressed, the importance of licensing, and factors for success regarding infrastructure and operations. Key points include diminishing R&D productivity globally, India's progression from generics to biotech, gaps in India's capabilities, and the need for management experience, flexibility, financial support, and a focus on science in early-stage R&D.
This document discusses best practices for working in the gig economy as an independent contractor on data and analytics projects. It recommends finding the right fit between contractor skills and project needs, committing to an agile or waterfall project management approach, setting quantitative goals, creating extensible code and documentation, and over-communicating through frequent updates rather than relying on emails. The document concludes with case studies comparing two different broadcaster clients' projects that illustrates these principles in action and contrasts their outcomes.
The document discusses the importance of use-inspired research and collaborative infrastructure to drive innovation. It notes that modern research requires large, multidisciplinary teams and access to world-class infrastructure. While Australia invests in collaborative research centers and infrastructure, there are still gaps, including in areas like ICT and bioinformatics. The document calls for defining infrastructure needs, improving access and governance, and strategically investing in capabilities to support priority research areas.
Lecture 01 Introduction to Business Research Methods.pptJunaidrazaq
This document provides an introduction to business research methods. It defines business research as the systematic and objective process of gathering, recording, and analyzing data to aid in making business decisions. The document outlines the course objectives, which are to introduce core research methods concepts and stimulate interest in business research. It also lists and describes several common business research methods and techniques. Additionally, it provides examples of fields where business research is often used, such as sales and marketing research, financial research, and management research. Finally, the document gives examples of real-life business situations where research may be applied.
The document provides an overview of business research methods from Dr. Shriram Dawkhar of Sinhgad Institute of Management. It discusses defining research and the scientific method, formulating research questions in a management hierarchy from management dilemmas to investigative questions, the steps in the research process, and selecting an appropriate research problem based on criteria.
This document provides an introduction to a course on business analytics. It outlines the course objectives to build students' knowledge of applying analytics in various industry settings. It discusses administrative details like the grading structure and course schedule. It also introduces fundamental concepts in data science and analytics, including common techniques. The document describes the case study methodology that will be used, involving analyzing organizations' data-driven business models and decision-making processes.
This document provides an overview of business research methods and processes. It discusses key topics including the scientific method, types of research, data collection and analysis techniques. Examples are given of how business research is used in areas such as marketing, finance, and management. Guidelines are provided on when research should be undertaken and how it can help reduce uncertainty and improve decision-making for organizations.
The document summarizes research on the silent killers of strategy implementation in quantity surveying (QS) firms. It begins with an introduction and literature review on strategy implementation and why strategic plans fail. It then describes the methodology used, which was a quantitative survey of QS firm directors. The results identified five categories of obstacles to strategy implementation: environmental, planning consequences, organizational, management, and individual personnel obstacles. Key obstacles included inadequate commitment, communication, and consistency between strategy and organizational structure. The conclusions call for troubleshooting planning, flexibility, communication, and buy-in to address barriers to effective strategy execution.
This document provides an overview of a business research methods course. It discusses topics that will be covered in the course like research terminology, designing research projects, ethics in research, types of research, and measurements in research. It outlines course objectives like acquainting students with research methodology and showing how to apply the knowledge to real-life situations. The document also lists expectations of students like acquiring and applying knowledge critically. Assessment methods are discussed including quizzes, projects, and exams. Examples of fields where business research is commonly used and real-life situations where it applies are also provided.
Stop the madness - Never doubt the quality of BI again using Data GovernanceMary Levins, PMP
Does this sound familiar? "Are you sure those numbers are right?" "Why are your numbers different than theirs?"
We've all heard it and had that gut wrenching feeling of doubt that comes with uncertainty around the quality of the numbers.
Stop the madness! Presented in Dunwoody on April 18 by industry leading expert Mary Levins who discusseses what it takes to successfully take control of your data using the Data Governance Framework. This framework is proven to improve the quality of your BI solutions.
Mary is the founder of Sierra Creek Consulting
Data analytics is used to make better business decisions by combining data and insights. There are four aspects to an effective data analytics framework: discovery, insights, actions, and outcomes. Discovery involves defining problems, developing hypotheses, and collecting relevant data. Insights are generated by exploring and analyzing the data. Actions link the insights to recommendations and plans. The desired outcomes are improved decisions and performance. Different types of analytics include descriptive (what happened), diagnostic (why), predictive (what could happen), and prescriptive (what should be done). Tools used include SQL, Hadoop, machine learning libraries, and optimization or simulation software.
The document discusses strategic planning and management. It outlines the typical planning cycle which includes analyzing the current situation, forecasting future trends, setting objectives, developing an action plan, implementing the plan, monitoring progress, and starting a new cycle. It also describes the different levels of management - operational, tactical, and strategic - and their respective information needs and decision-making responsibilities.
Why Predictive Analytics Should Be Part of Your 2015 Strategy FinalJoe Brandenburg
This document discusses how predictive analytics should be part of business intelligence strategies in 2015. It begins with an introduction of the speaker, Joe Brandenburg, and his experience with predictive analytics. The rest of the document discusses what predictive analytics is, why it is important for companies to stay competitive, how it can help decision makers improve business decisions, how organizations can incorporate it into their BI strategies to reduce costs, how technologies make implementation easier, and real-world examples of significant ROI from predictive analytics.
This document discusses barriers to effective strategy implementation in quantity surveying (QS) firms in South Africa. It identifies the top barriers based on a literature review and survey of QS firm directors. The most prominent barriers are environmental factors outside firms' control, obstacles related to how strategies are planned and communicated, and organizational issues like inconsistent structures and poor communication. Compounding problems include inconsistent strategy formulation, a lack of managerial commitment, and ineffective communication. The researcher calls for QS firms to better identify, inform employees of, involve them in, incentivize, and monitor implementation efforts to overcome these barriers and successfully execute strategies.
Entrepreneurship & Innovation – a new DNA to SuccessDipti Chhatrapati
Take a tour of the Entrepreneurship & Innovation ecosystem. Discover what elements foster innovation by understanding new business models that are emerging in the market. This will provide a dual perspective that can be useful for businesses that want to infuse innovation into organization DNA for success.
The document provides details about a survey being conducted by the Australian Information Industry Association (AIIA) Data & Analytics Special Interest Group. The survey aims to assess organizational maturity levels in effectively utilizing data and analytics. It will involve distributing a survey to members of partner organizations, analyzing the results, and conducting executive briefings. The timeline outlines plans to develop the survey, analyze responses in August-September, and hold briefings in October. Background information establishes the business issues motivating the survey and hypotheses about success factors related to data usage that will be tested. An overview of McKinsey and Gartner maturity models is also provided, as well as draft survey questions.
Chap 6 IMplementation of Information SystemSanat Maharjan
The document discusses the implementation of information systems and provides details on key concepts. It begins with defining what an information system is and its key components. It then discusses the types of information systems, examples of systems, and considerations for implementation in Nepal and the US. It also covers theories related to behavioral science and managing change when implementing new systems. Finally, it discusses critical success factors for information system projects and introducing next generation balanced scorecard concepts to improve performance measurement.
Delivered at the University of Bristol in September 2023, this talk outlines several considerations for privacy engineering, including the process of instilling personal privacy values, best practice for privacy engineering, and overall research findings.
Research & development strategies across different industriesVaishakh PV
This document discusses research and development (R&D) strategies for various industries. It begins by defining R&D and providing examples of R&D strategies used in the automotive, pharmaceutical, food and beverage, and technology industries. Specific R&D approaches and elements of an effective R&D strategy are described, including architecture, processes, people, and portfolio. The document also discusses Toyota's global R&D vision and activities focused on environmental technology, safety technology, and intelligent transport systems.
Revolutionizing R&D with OnePlan by Streamlining Innovation and EfficiencyOnePlan Solutions
Join us for an insightful webinar where we explore how OnePlan can transform Research and Development (R&D) departments. Discover how our comprehensive Project Portfolio Management software empowers R&D teams to innovate faster, manage resources more effectively, and deliver breakthrough results.
In this webinar, you'll learn how to:
Enhance project visibility and alignment with strategic goals
Facilitate efficient resource allocation and management
Streamline project tracking and reporting for better decision-making
Integrate seamlessly with existing tools and systems
Provide advanced risk management and mitigation strategies
Support collaboration and communication across dispersed teams
1 question minimum 750 words and APA stylewell be focusing on.docxoswald1horne84988
1 question / minimum 750 words and APA style
we'll be focusing on the notion of human perception as both a biological construct and a design consideration. In what ways has HCI historically engaged perception in research and design trends? In what way is HCI now engaging our understanding of perception, and what are some of the major goals and directions for the combination of HCI and Perception?
Requirements:3 Discrete Examples/Arguments
1 Source Each Minimum
General Tips:Directly engage source, theory, and practice
Discuss method, application, understanding, and solution-orientationDiscuss historically, contemporaneously, and project into the future
2 question / minimum 750 words and APA style
the foundation of HCI/HCD research as it stems from human cognitive ability. The notion of how humans (users) think, perceive, and make decisions is critical to developing an understanding of how we can best design to fit their needs. In doing so, we must take into consideration a variety of individual differences, context-based choices on learning approaches, and how our understanding of memory and cognition suggest particular modes of design and insight for our development projects.
For your reflection this week, I want you to find 3 discrete examples of media/tech and break them down with regard to how they allow learning to occur, how they map interactions/tasks according to human cognition and learning models, how they use visual affordances to suggest more functional elements, and how (if at all) they encourage expertise development within their product/service. These examples can be anything from office software and video games to handheld devices and advanced machinery. Whatever you'd like.
Requirements:Minimum 5 uses of HCI terminology (evidence understanding of some cognitive concepts)
3 Different Cases
Give at least 3 different examples per case in your writing, distinctly discussing how they fit into the lecture content
General Tips:Directly engage source, theory, and practice
Discuss how new understandings of the human role and cognitive functions inform practice
Apply HCI understanding to observable design practice
WHAT BINDS WELL-FORMED IT SECURITY POLICIES together is a sense of shared beliefs, purpose, and urgency. Within your organization, you will achieve
that, in part, by establishing principles that create a shared vision, by empowering others to act, and by institutionalizing support processes. It’s important that the
implementation of IT security policies become second nature to the organization. That is, business processes should be designed with the controls needed to
implement and maintain security policies built in.
For example, consider the issue of emergency access to a server in the middle of the night. Gaining access may require going through a firecall system that will issue
an ID and password only when approval by the manager is obtained. In that way security policies are enforced and cannot be bypassed. .
Exploring the role of the online customer experience in the firm’s multichann...philippklaus
The concept of online customer service experience (OCSE) has recently received great interest from academia and businesses alike. Despite the belief that providing superb online experiences will influence customers’ behaviour, most of the research focuses solely on the customer perspective rather than the firm’s strategic viewpoint. This study investigates current strategies of retail banking service, developing a much-needed typology of such practices. Based on in-depth interviews with senior executives, using the Emerging Consensus Technique (ECT) method, we explore firms offering more and more sophisticated online tools to compete for contemporary, digitalized customers. We propose a typology of online channel strategies and management based on five dimensions: (1) key objectives; (2) business processes; (3) benefits; (4) integration; and (5) outlook. Three emerging categories differentiate strategies and practice into introducers, converters and integrators. We highlight current and future roles of online channels, social media and their strategic implications for the financial services sector.
Similar to A Framework for Corporate Artificial Intelligence Strategy (20)
Two faces of the same coin: Exploring the multilateral perspective of informa...ICDEcCnferenece
Adriana AnaMaria Davidescu, Professor, PhD, Department of Statistics and Econometrics. Two faces of the same coin: Exploring the multilateral perspective of informality in relation to Sustainable Development Goals. Fostering formal work with digital tools. (ICDEc 2022)
The document summarizes an upcoming special issue of the Journal of Telecommunications and the Digital Economy on the topic of digital technologies and innovation. It provides details on the topics covered in the special issue such as banking/finance, business data, and social media. It also outlines the submission process and acceptance rates. Additionally, it discusses future special issues that will focus on areas like AI technologies for smart cities and women's participation in the digital economy.
Possibilities and limitations of the Croatian police in communication via soc...ICDEcCnferenece
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A Maturity Model for Open Educational Resources in Higher Education Instituti...ICDEcCnferenece
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AI-based Business Models in Healthcare: An Empirical Study of Clinical Decisi...ICDEcCnferenece
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This document summarizes research into the determinants of customer satisfaction with fitness technology innovations. It reviews theories of diffusion of innovation, planned behavior, and technology acceptance. The research methodology combined independent variables like service quality, device friendliness and helpfulness, and quickness with the dependent variable of customer satisfaction. The results found that all hypotheses about positive relationships between the independent and dependent variables were accepted. Practical implications are that focusing on customer satisfaction through service quality, device usability factors, and other determinants can improve research and development, product/service quality, and customer satisfaction with fitness technologies.
Closing session: awards for best papers and reviewersICDEcCnferenece
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The document outlines a proposed IoT monitoring healthcare system for patients with COPD. It discusses:
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HijackLoader Evolution: Interactive Process HollowingDonato Onofri
CrowdStrike researchers have identified a HijackLoader (aka IDAT Loader) sample that employs sophisticated evasion techniques to enhance the complexity of the threat. HijackLoader, an increasingly popular tool among adversaries for deploying additional payloads and tooling, continues to evolve as its developers experiment and enhance its capabilities.
In their analysis of a recent HijackLoader sample, CrowdStrike researchers discovered new techniques designed to increase the defense evasion capabilities of the loader. The malware developer used a standard process hollowing technique coupled with an additional trigger that was activated by the parent process writing to a pipe. This new approach, called "Interactive Process Hollowing", has the potential to make defense evasion stealthier.
2. Kajetan Schuler & Dennis Schlegel
Introduction
• Problem statement and research gap
• Increasing use of AI in corporate context to enhance productivity,
customer experience and decision making
• Lack of clear AI strategy from holistic point of view in many
organizations
• Need to establish an AI strategy to systematically exploit the
emerging opportunities of the technology
• Little discussion in the academic community about how to build such
a strategy
• Research aim
• Development of a conceptual framework for corporate AI strategy
2
3. Kajetan Schuler & Dennis Schlegel
Research Approach
3
Systematic
literature review
Factor extraction
Inductive coding/
codes-to-theory
model
Modeling
relationships
Papers
Factors
Concepts/ Themes
Framework
4. Kajetan Schuler & Dennis Schlegel
Results: Inductive Coding
Factor / Code Category Theme
Single source of truth
Data storage
Data
Lack of standardization
Data platform
Data availability
Data
management
Data quality
Data collection
Data sources
Data management
Data culture Data
governance
Data security
Culture
Corporate
culture
Organization
Mindset
User resistance
High expectations
Trust in technology
Top management support
Leadership
Top-down Guidance
Leadership skills
Communication
Communication
Integrate stakeholders
Visualization of strategy
Understanding of strategy
Business to IT
communication
Change Management
Collaboration
Integration of C level
Organizational
structure
Central analytics team
Organizational structure
Appoint CDO
Clear responsibilities
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Factor / Code Category Theme
Mainstream vendors Technology Infrastructure
Compatibility
Implementation process
Understandable technology
IT resources Technical
architecture
Deploy in existing systems
Silo-oriented systems
Flexible infrastructure
Complexity Sourcing
Make or buy
IT capabilities Organizational
capabilities
Capabilities
IT not a core competency
Training Human resources
HR strategy
Lack of employees with AI
skills
Lack of understanding
Individual skills
AI knowledge
Ethical conditions Ethical conditions Constraints
Legal conditions Legal conditions
Identify use cases Use Cases Use Cases
Business as driver of use
cases
Financial justification
Repetitive tasks
Decision process for AI-
technologies
Decision
processes
Managerial
processes
Selection process for use
cases
Alignment of strategies Strategic
alignment
6. Kajetan Schuler & Dennis Schlegel
Summary & Implications
• Framework can serve as an aid for practitioners to develop AI strategy
• Companies should consider diverse aspects when formulating an AI strategy
• Not only technology, but organizational and managerial aspects seem to
very important to ensure a successful AI strategy
• Alignment of AI strategy with overall business strategy and other domain-
specific strategies is crucial
• The framework shows the necessity for interdisciplinary collaboration in practical
and academic environments
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7. Kajetan Schuler & Dennis Schlegel
Limitations & Further Research
• The study‘s analysis relies on prior studies that were conducted based on
different methodologies
• Possible source of bias
• Findings may not be transferable to other contexts
• This study was meant to give a holistic overview, therefore it does not cover
detailed advice regarding the different themes
• It is therefore recommended to corroborate and expand this research
• Possible research designs: Interviews or case studies
• Detailed recommendations and guidance for the themes of the framework
should be elaborated
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