The document discusses the concept of a cognitive engine and argues that true cognitive technology involves identifying the underlying algorithms or "performance thinking" that drive human behavior and productivity, rather than just analyzing aggregated behavioral data. It proposes that developing technologies that can emulate these cognitive algorithms/performance drivers could lead to applications that automate and improve business processes, uncover new insights, and predict and enhance human performance and outcomes. The authors believe this approach represents the next generation of cognitive technologies and has significant potential to transform businesses and drive innovation.
This takes a look at the architectural constructs that are used for building business intelligence systems and how they are used in business processes to improve marketing, better serve customers, and maximize organizational efficiency.
This document discusses moving from business intelligence to predictive analytics. It introduces predictive analytics and how they can automatically discover patterns in data to predict trends or future behavior. Predictive analytics turn uncertainty about the future into usable probabilities. The document also discusses how predictive analytics can be applied in operations through decision management, which is a proven approach to deploy and apply predictive analytics at decision points.
Disruptive Data Science Series: Transforming Your Company into a Data Science...EMC
Big Data is the latest technology wave impacting C-Level executives across all areas of business, but amid the hype, there remains confusion about what it all means. The name emphasizes the exponential growth of data volumes worldwide (collectively, 2.5 Exabytes/ day in the latest estimate I saw from IDC), but more nuanced definitions of Big Data incorporate the following key tenets: diversification, low latency, and ubiquity. In the current developmental-phase of Big Data, CIOs are investing in platforms to “manage” Big Data.
Mattel implemented a shallow-dive analytics approach to gain visibility into key metrics and drive a more data-driven supply chain culture. Employees were overwhelmed by large amounts of data, so Mattel focused on a select few critical metrics in real-time, such as on-time delivery rates. This allowed executives to quickly identify issues and take action. The shallow-dive approach helped Mattel steer its large, complex supply chain and reinforce strategic goals using data rather than feelings. It also engaged employees by giving them access to the same real-time metrics seen by executives.
Living in a data economy: Transforming the role of HRMartin Sutherland
In a data economy, wealth is defined by extracting value from good quality data. The challenge is how to ensure a sustainable source of good quality HR data and how to turn that data into a compelling story that engages business leaders and creates a competitive advantage through talent.
This takes a look at the architectural constructs that are used for building business intelligence systems and how they are used in business processes to improve marketing, better serve customers, and maximize organizational efficiency.
This document discusses moving from business intelligence to predictive analytics. It introduces predictive analytics and how they can automatically discover patterns in data to predict trends or future behavior. Predictive analytics turn uncertainty about the future into usable probabilities. The document also discusses how predictive analytics can be applied in operations through decision management, which is a proven approach to deploy and apply predictive analytics at decision points.
Disruptive Data Science Series: Transforming Your Company into a Data Science...EMC
Big Data is the latest technology wave impacting C-Level executives across all areas of business, but amid the hype, there remains confusion about what it all means. The name emphasizes the exponential growth of data volumes worldwide (collectively, 2.5 Exabytes/ day in the latest estimate I saw from IDC), but more nuanced definitions of Big Data incorporate the following key tenets: diversification, low latency, and ubiquity. In the current developmental-phase of Big Data, CIOs are investing in platforms to “manage” Big Data.
Mattel implemented a shallow-dive analytics approach to gain visibility into key metrics and drive a more data-driven supply chain culture. Employees were overwhelmed by large amounts of data, so Mattel focused on a select few critical metrics in real-time, such as on-time delivery rates. This allowed executives to quickly identify issues and take action. The shallow-dive approach helped Mattel steer its large, complex supply chain and reinforce strategic goals using data rather than feelings. It also engaged employees by giving them access to the same real-time metrics seen by executives.
Living in a data economy: Transforming the role of HRMartin Sutherland
In a data economy, wealth is defined by extracting value from good quality data. The challenge is how to ensure a sustainable source of good quality HR data and how to turn that data into a compelling story that engages business leaders and creates a competitive advantage through talent.
Analytical Thinking is a fortnightly newsletter from the UK Business Analytics team.
The purpose of the newsletter is to raise awareness about why analytics is a hot topic at the moment, where is analytics being referenced in the press and in what ways are organisations using analytics.
Business Analytics (Operational Research) is part of the Digital Transformation team in Capgemini Consulting UK
Analytics is all about course correcting the future. While this starts with accurate predictions of the future, without resultant actions steering the future toward company goals, knowing that future is academic. Successful companies must be grounded in successful data-based prescription. In this webinar, William will present a data maturity model with a focus on how analytic competitors outdo the competition by looking forward to a data-influenced future.
In this document, the five disruptive trends shaping the corporate IT landscape today are layed out. Out of the five, Big Data has the biggest potential to generate new sustainable competitive advantages. But the benefits will remain out of reach of many organizations as they struggle to adopt the technology, develop new capabilities, and manage the cultural change associated with the use of big data. This document offers a pragmatic approach to generating business value.
This document discusses Oracle's approach to big data and information architecture. It begins by explaining what makes big data different from traditional data, noting that big data refers to large datasets that are challenging to store, search, share, visualize, and analyze due to their volume, velocity, and variety. It then provides an overview of big data architecture capabilities and describes how to integrate big data capabilities into an organization's overall information architecture. The document concludes by outlining some key big data architecture considerations and best practices.
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
Information 3.0 - Data + Technology + PeopleHubbard One
The document provides an overview of big data and its transformational value. It discusses how big data can drive value through case studies in technology and collaboration between CTOs and CMOs. It also identifies impediments to realizing big data's transformational value and provides recommendations to overcome these impediments through enhanced data policies and security, infrastructure improvements, organizational change, access to data, and CTO-CMO collaboration.
MIT report: How data analytics and machine learning reap competitive advantage.Nicolas Valenzuela
How Analytics and Machine Learning Help Organizations Reap Competitive Advantage
Produced MIT Technology Review, in Partnership with Google Analytics 360 Suite
Unlocking the Value of Big Data (Innovation Summit 2014)Dun & Bradstreet
Big Data is central to the strategic thinking of today’s innovators and business executives as companies are scrambling to figure out the secret to transforming Big Data to Big Insight and that Insight into Action. As many companies struggle with the emerging technologies and nascent capabilities to discover and curate massive quantities of highly dynamic data, new problems are emerging in the form of how to ask meaningful questions that leverage the “V’s” of large amounts of data (e.g. volume, variety, velocity, veracity). In the Business-to-Business space, these challenges are creating both significant opportunity and ominous new types of risk. This presentation discusses how companies are reacting to these changes and provide valuable insight into new ways of thinking in a world with overwhelming quantities of data.
The document provides advice on successfully managing predictive analytics programs. It discusses the importance of having an open organizational mindset that embraces new ideas and change. It also emphasizes having a clear business strategy and objectives when developing predictive models. Regularly testing and updating models is key to ensuring optimal predictive accuracy over time as business needs and available data evolve.
This document discusses integrated planning and the role of technology in linking operational and financial data streams. It explains that integrated planning allows companies to synchronize financial and operational planning processes to better predict the financial implications of business decisions. Technology is needed to integrate these previously separate data streams and break down silos through collaborative planning platforms. The document outlines several key aspects of new integrated planning solutions, including democratizing knowledge across departments, enabling more granular analysis at the transaction level, taking a hypothesis-driven approach, and connecting relationships between multiple parameters for more accurate predictions. Companies can realize measurable benefits from integrated planning through improved revenue and profitability.
The document discusses how most enterprises are investing in big data and real-time analytics initiatives to gain competitive advantages, but many IT organizations lack strategies to align these technologies with business goals. It describes how new data sources can provide richer customer insights and how real-time analytics can enable more timely operational decisions. However, organizations must evaluate whether their specific use cases require real-time data or would benefit more from traditional BI.
This document provides an overview of predictive analytics. It discusses what predictive analytics is and how it is used by organizations to make smarter decisions about customers. Predictive analytics uses historical data and statistical techniques to predict future outcomes and automate decisions. Examples are given of how predictive analytics has helped industries like financial services, insurance, telecommunications, retail, and healthcare improve customer decisions and outcomes.
This white paper discusses criteria for evaluating strategic analytics platforms. It identifies 5 key questions: 1) Does the platform combine consumer-like cloud services with sophisticated analytics? 2) Can it access all relevant data? 3) Can the entire analytical process be completed in a single tool? 4) Does it allow for analysis of big data? 5) Does it provide the right analytics for decision making? The document argues that an ideal platform seamlessly integrates data access, analysis, and sharing capabilities to support rapid, data-driven decisions.
David Bernstein of eQuest, the global leader in job-posting delivery and job board performance analytics, discusses how Big Data analysis provides organizations with greater recruitment marketing effectivenss than ever before. By not only delivering predictive information on job postings but by also taking a holistic look at your talent pipeline, Big Data analysis provides the insight organizations need to make better-informed decisions more quickly, reducing time-to-hire, costs and administrative burden.
Predictive Data Analytics to Help Your CustomersExperian_US
The @ExperianDataLab hosts a #DataTalk on Thursdays at 5 p.m. ET on Twitter. Join us.
This week, we talked about data preparation, model evaluation, testing effectiveness of predictive analytics, challenges, and trends in predictive analytics.
We learned from Michael Beygelman, Co-founder and CEO of Joberate and Berry Diepeveen, Partner and Enterprise Intelligence Leader at EY in South Africa, and Chuck Robida, Chief Scientist for Experian Decision Analytics.
Learn about past and upcoming chats at:
http://experian.com/datatalk
spocto's unique machine learning algorithms & artificial learning which provides solution to create persona.
spocto analytics has improved the Contact Rate, Debt Collections and Non Performing Asset Management for a leading Bank in India
Data Con LA 2020
Description
The People at any organization are one of the most important stakeholders in the business. People Analytics & Research is the broad discipline in which employee data is leveraged to inform organizational decision-making. In current times, data science has found its way into People Analytics and Research with individuals using AI to predict or diagnose important metrics like turnover. However, it is only through ethical, context-driven, and inclusive methods that data science can continue to intelligently augment human resources. This talk will help attendees recognize and describe People Analytical challenges within their organizations and teams. Further, through a discussion of real-world examples, attendees will appreciate the need for inclusive and ethical context-driven best practices for People Analytics. Finally, attendees will be able to explore applications of AI/ML to problem solving for the People Analytics space. This is an interactive session, so please bring your questions, and get ready to put your thinking hats on!
Speaker
Sreyoshi Bhaduri, McGraw Hill, Manager, Global People Research and Analytics
Business is running ever faster—generating, collecting and using increas-ing volumes of data about every aspect of the interactions between sup-pliers, manufacturers, retailers and customers. Within these mountains of data are seams of gold—patterns of behavior that can be interpreted, classified and analyzed to allow predictions of real value. Which treat-ment is likely to be most effective for this patient? What can we offer that this particular customer is more likely to buy? Can we identify if that transaction is fraudulent before the sale is closed?
Huntel global webinar aligning data talent with your analytics needsHuntel Global
The document is a presentation by Wayne Hinds of Huntel Global about aligning data talent with analytics needs. It discusses where organizations are along the analytics continuum from descriptive to predictive analytics. It addresses common challenges organizations face with data and talent, and provides considerations for developing an analytics plan and identifying metrics and questions to guide analysis. The presentation also covers common analytics roles, qualities of strong analytics talent, and tools data scientists typically use. Huntel Global is introduced as a firm that helps clients find the right talent for analytics and related fields, and special offers are provided to attendees.
This document provides an overview of data analytics including:
- The basics of data analytics including analytics definitions and the need for data analytics due to increasing data volumes.
- Descriptions of different types of analytics including descriptive, diagnostic, predictive, and prescriptive analytics and their purposes.
- An overview of the data analytics lifecycle including phases such as data preparation, model planning, model building, and communication of results.
Data science is the study of where information comes from, what it represents and how it can be turned into a valuable resource in the creation of business and IT strategies. Mining large amounts of structured and unstructured data to identify patterns can help an organization rein in costs, increase efficiencies, recognize new market opportunities and increase the organization's competitive advantage.
Cognitive Explorers have adopted cognitive systems to gain competitive advantages in areas like revenue forecasting, supply chain management, and customer service. While only 4% of organizations currently have cognitive systems operational, 74% have the data and analytics capabilities needed to implement cognitive approaches. Cognitive Explorers outperform competitors on metrics like revenue, effectiveness, profitability, and innovation. Building a cognitive mindset through strategy and governance is key to successfully adopting cognitive systems. Cognitive Explorers also invest more in technologies that support data ingestion, integration, and analysis from a variety of sources needed for cognitive applications.
Analytical Thinking is a fortnightly newsletter from the UK Business Analytics team.
The purpose of the newsletter is to raise awareness about why analytics is a hot topic at the moment, where is analytics being referenced in the press and in what ways are organisations using analytics.
Business Analytics (Operational Research) is part of the Digital Transformation team in Capgemini Consulting UK
Analytics is all about course correcting the future. While this starts with accurate predictions of the future, without resultant actions steering the future toward company goals, knowing that future is academic. Successful companies must be grounded in successful data-based prescription. In this webinar, William will present a data maturity model with a focus on how analytic competitors outdo the competition by looking forward to a data-influenced future.
In this document, the five disruptive trends shaping the corporate IT landscape today are layed out. Out of the five, Big Data has the biggest potential to generate new sustainable competitive advantages. But the benefits will remain out of reach of many organizations as they struggle to adopt the technology, develop new capabilities, and manage the cultural change associated with the use of big data. This document offers a pragmatic approach to generating business value.
This document discusses Oracle's approach to big data and information architecture. It begins by explaining what makes big data different from traditional data, noting that big data refers to large datasets that are challenging to store, search, share, visualize, and analyze due to their volume, velocity, and variety. It then provides an overview of big data architecture capabilities and describes how to integrate big data capabilities into an organization's overall information architecture. The document concludes by outlining some key big data architecture considerations and best practices.
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
Information 3.0 - Data + Technology + PeopleHubbard One
The document provides an overview of big data and its transformational value. It discusses how big data can drive value through case studies in technology and collaboration between CTOs and CMOs. It also identifies impediments to realizing big data's transformational value and provides recommendations to overcome these impediments through enhanced data policies and security, infrastructure improvements, organizational change, access to data, and CTO-CMO collaboration.
MIT report: How data analytics and machine learning reap competitive advantage.Nicolas Valenzuela
How Analytics and Machine Learning Help Organizations Reap Competitive Advantage
Produced MIT Technology Review, in Partnership with Google Analytics 360 Suite
Unlocking the Value of Big Data (Innovation Summit 2014)Dun & Bradstreet
Big Data is central to the strategic thinking of today’s innovators and business executives as companies are scrambling to figure out the secret to transforming Big Data to Big Insight and that Insight into Action. As many companies struggle with the emerging technologies and nascent capabilities to discover and curate massive quantities of highly dynamic data, new problems are emerging in the form of how to ask meaningful questions that leverage the “V’s” of large amounts of data (e.g. volume, variety, velocity, veracity). In the Business-to-Business space, these challenges are creating both significant opportunity and ominous new types of risk. This presentation discusses how companies are reacting to these changes and provide valuable insight into new ways of thinking in a world with overwhelming quantities of data.
The document provides advice on successfully managing predictive analytics programs. It discusses the importance of having an open organizational mindset that embraces new ideas and change. It also emphasizes having a clear business strategy and objectives when developing predictive models. Regularly testing and updating models is key to ensuring optimal predictive accuracy over time as business needs and available data evolve.
This document discusses integrated planning and the role of technology in linking operational and financial data streams. It explains that integrated planning allows companies to synchronize financial and operational planning processes to better predict the financial implications of business decisions. Technology is needed to integrate these previously separate data streams and break down silos through collaborative planning platforms. The document outlines several key aspects of new integrated planning solutions, including democratizing knowledge across departments, enabling more granular analysis at the transaction level, taking a hypothesis-driven approach, and connecting relationships between multiple parameters for more accurate predictions. Companies can realize measurable benefits from integrated planning through improved revenue and profitability.
The document discusses how most enterprises are investing in big data and real-time analytics initiatives to gain competitive advantages, but many IT organizations lack strategies to align these technologies with business goals. It describes how new data sources can provide richer customer insights and how real-time analytics can enable more timely operational decisions. However, organizations must evaluate whether their specific use cases require real-time data or would benefit more from traditional BI.
This document provides an overview of predictive analytics. It discusses what predictive analytics is and how it is used by organizations to make smarter decisions about customers. Predictive analytics uses historical data and statistical techniques to predict future outcomes and automate decisions. Examples are given of how predictive analytics has helped industries like financial services, insurance, telecommunications, retail, and healthcare improve customer decisions and outcomes.
This white paper discusses criteria for evaluating strategic analytics platforms. It identifies 5 key questions: 1) Does the platform combine consumer-like cloud services with sophisticated analytics? 2) Can it access all relevant data? 3) Can the entire analytical process be completed in a single tool? 4) Does it allow for analysis of big data? 5) Does it provide the right analytics for decision making? The document argues that an ideal platform seamlessly integrates data access, analysis, and sharing capabilities to support rapid, data-driven decisions.
David Bernstein of eQuest, the global leader in job-posting delivery and job board performance analytics, discusses how Big Data analysis provides organizations with greater recruitment marketing effectivenss than ever before. By not only delivering predictive information on job postings but by also taking a holistic look at your talent pipeline, Big Data analysis provides the insight organizations need to make better-informed decisions more quickly, reducing time-to-hire, costs and administrative burden.
Predictive Data Analytics to Help Your CustomersExperian_US
The @ExperianDataLab hosts a #DataTalk on Thursdays at 5 p.m. ET on Twitter. Join us.
This week, we talked about data preparation, model evaluation, testing effectiveness of predictive analytics, challenges, and trends in predictive analytics.
We learned from Michael Beygelman, Co-founder and CEO of Joberate and Berry Diepeveen, Partner and Enterprise Intelligence Leader at EY in South Africa, and Chuck Robida, Chief Scientist for Experian Decision Analytics.
Learn about past and upcoming chats at:
http://experian.com/datatalk
spocto's unique machine learning algorithms & artificial learning which provides solution to create persona.
spocto analytics has improved the Contact Rate, Debt Collections and Non Performing Asset Management for a leading Bank in India
Data Con LA 2020
Description
The People at any organization are one of the most important stakeholders in the business. People Analytics & Research is the broad discipline in which employee data is leveraged to inform organizational decision-making. In current times, data science has found its way into People Analytics and Research with individuals using AI to predict or diagnose important metrics like turnover. However, it is only through ethical, context-driven, and inclusive methods that data science can continue to intelligently augment human resources. This talk will help attendees recognize and describe People Analytical challenges within their organizations and teams. Further, through a discussion of real-world examples, attendees will appreciate the need for inclusive and ethical context-driven best practices for People Analytics. Finally, attendees will be able to explore applications of AI/ML to problem solving for the People Analytics space. This is an interactive session, so please bring your questions, and get ready to put your thinking hats on!
Speaker
Sreyoshi Bhaduri, McGraw Hill, Manager, Global People Research and Analytics
Business is running ever faster—generating, collecting and using increas-ing volumes of data about every aspect of the interactions between sup-pliers, manufacturers, retailers and customers. Within these mountains of data are seams of gold—patterns of behavior that can be interpreted, classified and analyzed to allow predictions of real value. Which treat-ment is likely to be most effective for this patient? What can we offer that this particular customer is more likely to buy? Can we identify if that transaction is fraudulent before the sale is closed?
Huntel global webinar aligning data talent with your analytics needsHuntel Global
The document is a presentation by Wayne Hinds of Huntel Global about aligning data talent with analytics needs. It discusses where organizations are along the analytics continuum from descriptive to predictive analytics. It addresses common challenges organizations face with data and talent, and provides considerations for developing an analytics plan and identifying metrics and questions to guide analysis. The presentation also covers common analytics roles, qualities of strong analytics talent, and tools data scientists typically use. Huntel Global is introduced as a firm that helps clients find the right talent for analytics and related fields, and special offers are provided to attendees.
This document provides an overview of data analytics including:
- The basics of data analytics including analytics definitions and the need for data analytics due to increasing data volumes.
- Descriptions of different types of analytics including descriptive, diagnostic, predictive, and prescriptive analytics and their purposes.
- An overview of the data analytics lifecycle including phases such as data preparation, model planning, model building, and communication of results.
Data science is the study of where information comes from, what it represents and how it can be turned into a valuable resource in the creation of business and IT strategies. Mining large amounts of structured and unstructured data to identify patterns can help an organization rein in costs, increase efficiencies, recognize new market opportunities and increase the organization's competitive advantage.
Cognitive Explorers have adopted cognitive systems to gain competitive advantages in areas like revenue forecasting, supply chain management, and customer service. While only 4% of organizations currently have cognitive systems operational, 74% have the data and analytics capabilities needed to implement cognitive approaches. Cognitive Explorers outperform competitors on metrics like revenue, effectiveness, profitability, and innovation. Building a cognitive mindset through strategy and governance is key to successfully adopting cognitive systems. Cognitive Explorers also invest more in technologies that support data ingestion, integration, and analysis from a variety of sources needed for cognitive applications.
Running title TRENDS IN COMPUTER INFORMATION SYSTEMS1TRENDS I.docxanhlodge
Running title: TRENDS IN COMPUTER INFORMATION SYSTEMS 1
TRENDS IN COMPUTER INFORMATION SYSTEMS 4
Trends in Computer Information Systems, and the Rise to Business Intelligence
Shad Martin
School for Professional Studies
St. Louis University
ENG 2005 Dr. Rebecca Wood
November 23, 2016
Introduction
Our quest to increase our knowledge of Computer Information Systems has produced a number of benefits to humanity. The innovation humans have discovered in Computer Information Systems has led to new sub-areas of study for students and professionals to continue their progression to master all that Computer Information Systems has to offer. Amy Web of the Harvard Business Review reported 8 Tech Trends to Watch in 2016, She noted, “In order to chart the best way forward, you must understand emerging trends: what they are, what they aren’t, and how they operate. Such trends are more than shiny objects; they’re manifestations of sustained changes within an industry sector, society, or human behavior. Trends are a way of seeing and interpreting our current reality, providing a useful framework to organize our thinking, especially when we’re hunting for the unknown. Fads pass. Trends help us forecast the future” (Harvard Business Review, 2015). In short, Amy’s reference to understanding the emerging trends in Computer Information can provide a framework from which, students, professionals, and scientists to conscientiously create a path towards optimizing their efforts. Ensuring we have a fundamental approach to analyze data will enhance our understanding of this subject further.
In this paper I will expound on three of the top trends used to provide insight into the data produced from the advancements in Computer Information Systems. These trends or methods are taking place in my workplace within a financial institution, and in many other industries. It is important to note this paper does not provide an inclusive list of all methodologies that exist. Individuals can now leverage analytics to synthesize insights from data to identify emerging risk, manage operational risks, identify trends, improve compliance, and customer satisfaction. Data in and by itself is not always useful. Regardless of the data source, trained professional must understand the best approach to structure the data to make it more useful. In this paper, I will touch on three popular methodology trends occurring in Computer Information Systems. Students and professionals who work with large data would benefit from having a solid understanding of the fundamental principles of Business Intelligence as data scientific approach and when to use these methodologies.
The rise of Business Intelligence
Computer Information Systems allow many companies to gather and generate large amounts of data on their customers, business activities, potential merger targets, and risks found in their organization. These large sets of data have given rise to vari.
This document discusses using business intelligence (BI) strategies and tools to improve human resource (HR) management. It proposes separating personnel data from business data and analyzing employment trends to better screen candidates and improve productivity. BI involves collecting and analyzing large amounts of employee data (profiles, appraisals, compensation) to gain insights for strategic HR decisions. Implementing a BI approach for HR could help translate existing employee data into future-focused actions around candidate screening, cost management, and productivity enhancements.
The Future of Analytics: Predict, Optimize, SucceedUncodemy
In today's data-driven world, the importance of analytics cannot be overstated. Businesses across industries are realizing the power of harnessing data to gain valuable insights, make informed decisions, and drive growth.
Business analytics helps organizations make informed decisions by deriving insights from data. While computers can process and analyze large amounts of data, humans play important roles in business analytics by understanding context, asking relevant questions, and interpreting results. Effective human analysts have skills in data analysis, statistics, communicating insights, and critical thinking. It is also important to acknowledge potential human bias and mitigate it through diverse teams and transparent processes.
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.
Uncover Trends and Patterns with Data Science.pdfUncodemy
In today's data-driven world, the vast amount of information generated every second presents both challenges and opportunities for businesses and researchers alike. Harnessing this data effectively can provide valuable insights, unlock hidden trends, and identify patterns that drive innovation and strategic decision-making.
Data science and data analytics major similarities and distinctions (1)Robert Smith
Those working in the field of technology hear the terms ‘Data Science’ and ‘Data Analytics’ probably all the time. These two words are often used interchangeably. Big data is a major component in the tech world today due to the actionable insights and results it offers for businesses. In order to study the data that your organization is producing, it is important to use the proper tools needed to comprehend big data to uncover the right information. To help you optimize your analytics, it is important for you to examine both the similarities and differences of data science and data analytics.
1Dr. LaMar D. Brown PhD, MBAExecutive MSITUnivEttaBenton28
1
Dr. LaMar D. Brown PhD, MBA
Executive MSIT
University of the Cumberlands
Course: 2019-SPR-IG-ITS530-21: 2019_SPR_IG_Analyzing and Visualizing Data_21
Chapter Readings Reflections Journal
Chapter 1: Defining Data Visualization
Summary
In Chapter 1, the author Mr. Kirk describes about the concept of Data Visualization. Data visualization was defined as the visual analysis and communication of data. The chapter also included the historical background survey definition of data visualization by various other authors.
Also, in the book was a set of fascinating recipes that of the components in that involve in the definition. The type of data that is required to be visually analyzed is important before it is being subjected to further processing before visualization.
Mr. Kirk also emphasized the significance of the art and science of making data analysis a fun filled technical and an analytical reading that encourages the use of human perception to make decisions in assistance of visual treats that come in the form of graphs, pie charts among others. The science of data visualization is defined with the implication of truth, evidence and rules that govern the process of visualizing a set of data that can be quintessential in determining the path of an enterprise or an organization.
Highlights:
Upon reading the chapter 1 in this book that was in depth into data visualization, I was able to grasp essential technical and analytical definitions and can say they are quiet telling in terms of the importance on the concept and visual representation of the definitions. The use of some of the citations was a key indicator that data visualization can be defined in various ways and can assist in technical improvements if used in way that is beneficial to all parties.
Ideas and thoughts:
The chapter was a thorough analysis of the concept. However, I was also keen on looking for live examples of visual tools or results of analysis inculcated in this defining place of the book. The big positive is the use of the concept of science and art that can be implemented in the day to day activities to introduce data visualization in any area and can help in making decisions that can set a trend for the growth of an organization. In terms of the course, it was a great read to write this review journal and can hopefully add a firm base to the things to come.
Application:
The concept of data visualization can be implemented in my current work environment. As an IT personnel, I deal with the network infrastructure and constantly come across large chunk of data that will need to be analyzed for its usage stats, bandwidth, performance and benefits of choosing the hardware or software accordingly. To best impact this, the monitoring tools such a s NetFlow helps us in verifying bandwidth over utilization or underutilization to perform a set of tasks before troubleshooting any related issues. Now, the concept of data visualization can be implemented here ...
what is ..how to process types and methods involved in data analysisData analysis ireland
Data analysis is the process of cleaning, transforming, and processing raw data in order to extract useful and actionable information that can assist businesses in making better decisions.
The dynamic synergy between data analytics and cognitive process automation embodies the very essence of data-driven decision-making. In this latest piece from the E42 Blog, we dive deep into the very synergy that has firmly established itself as the bedrock of modern business strategy, ushering in precision, efficiency, and growth for enterprises.
Cost & benefits of business analytics marshall sponderMarshall Sponder
The document discusses turning data into useful business insights through business intelligence and data enablement approaches. It advocates starting with departmental BI systems and linking them together, while also taking an "enablement" approach to integrate data from different silos. The document recommends conducting a data enablement audit to map data sources, identify measurement gaps, and develop standardized reporting to provide insights for objectives like sales, lead generation, and brand awareness. It emphasizes selecting the right team and approach to optimize the degree of insights that can be gained from enterprise data.
HR analytics provides valuable insights for organizations by analyzing employee-related data using statistical tools. It has two main components: descriptive analytics which measures past performance, and predictive analytics which provides insights into future outcomes. The increased focus on HR analytics stems from both necessity and opportunity. Necessity arises from the growing importance of human capital to organizational value creation. Opportunity comes from the vast amounts of employee data now available that can be transformed into useful insights using analytics. When done effectively, HR analytics can help organizations improve performance, better link business objectives to workforce strategies, and increase returns on investment in human resources.
This white paper discusses how companies can apply data science insights to improve products and operations. It describes the typical data science project lifecycle, including problem definition, data collection, model building and testing. However, many companies struggle to deploy models into production applications. The paper argues that data science teams need tools that allow models to be easily updated and redeployed without disrupting operations. The Yhat platform aims to streamline this process and help companies more quickly turn insights into data-driven products.
Big Data & Business Analytics: Understanding the MarketspaceBala Iyer
This document provides an overview of big data and business analytics. It discusses the growth of data and importance of analytics to businesses. The key topics covered include defining big data and data science, analyzing the analytics ecosystem and key players, examining use cases of analytics at companies like Target and Whirlpool, and providing recommendations for building an analytics capability and working with analytics vendors. The presentation emphasizes how data-driven decisions can improve business performance but also notes challenges to overcome like skills shortages and changing organizational culture.
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Goodbuzz Inc.
Driving Tangible Value for Business. Briefing Paper. Interest in AI/ML is soaring, but confusion and hype can mask the real benefits of these technologies. Organizations need to identify use cases that will produce value for them, especially in the areas of enhancing processes, detecting anomalies and enabling predictive analytics.
Mission Critical Use Cases Show How Analytics Architectures Usher in an Artif...Dana Gardner
A discussion on how artificial intelligence and advanced analytics solutions coalesce into top competitive differentiators that prove indispensable for digital business transformation.
This document provides an introduction to data literacy for beginners. It defines key terms like data science, data analytics, and data literacy. It explains that data science involves building and structuring datasets, while data analytics refers to analyzing data to gain insights. The document then covers foundational concepts like the data ecosystem and lifecycle, data privacy and ethics, and data integrity. Finally, it discusses seven skills needed for data and analytics success, such as critical thinking, data visualization, and machine learning, and how readers can improve their skills. The overall document aims to give beginners a foundational understanding of data concepts to build their data literacy.
Similar to -- The Cognitive Engine - 10RULE WHITE PAPER (20)
1. WRITTEN BY
Gary Morais, Bottom Line Results, LLC
Dennis Cagan, Caganco Incorporated
Chris Spivey, Spivey & Co. LLC
The Cognitive Engine:
a white paper
2. 1
TECHNOLOGY MEETS BRAIN SCIENCE
What is a cognitive engine and what is the right approach to developing one? The
chemistry or electronics of the mind are the source of thinking and the originating
cause that generates human behavior and actions. Manifestations of the brain’s
thought patterns, actions and behaviors comprise a human being’s performance in
any endeavor. We call this “performance thinking” In a workplace environment, this
equates to competency, contributions, performance, skills, and outcomes – which
may be either good or bad. To use a digital circuitry analogy, this performance
thinking – think of it as wiring or algorithms, are the original creation of the
impulses that generate the other performance behaviors and results. To continue, this
performance thinking, and its resulting behaviors, can be impacted and modified by
various factors over time.
Often, we see that demographics are used as a substitute for performance thinking.
What are demographics? Demographics are simply statistical data relating to the
population and particular groups within it. Demographics do not represent actions, or
behaviors, or performance, or thought processes. The statistical information includes
things like age, gender, geography, education levels, ethnicity, and perhaps even the
statistical proportion of people that have performed certain actions or exhibited
specific observable behaviors. But, demographics are not the actions or behaviors, or
their cause or root. Performance thinking is.
LOOKING WHERE THE LIGHT IS SHINING
One of today’s biggest buzzwords is Big Data – pun intended. Big Data and Data
Analytics go hand-in-hand. Collect it, and analyze it. We should not for a moment
confuse cognitive technology with the observation of actions and behavior, and
demographic statistics, massively compiled is a big database, and then analyzed and
dissected for measurable correlations and causality.
We propose that what most computer scientists and software vendors term a
cognitive engine is the massive aggregation, compiling, cross referencing, and
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correlation of observable actions and demographic data sets. In this approach, we
find “streetlight” bias.
According to Quote Investigator (www.quoteinvestigator.com) there is a brilliant
comical allegory that depicts the biases inherent in many types of scientific research.
The allegory is illustrated by a tale whose publication in slightly different forms
traces back to at least 1924, if not earlier.
A police officer sees a drunken man intently searching the ground
near a lamppost and asks him the goal of his quest. The inebriate
replies that he is looking for his car keys, and the officer helps for
a few minutes without success then he asks whether the man is
certain that he dropped the keys near the lamppost.
“No,” is the reply, “I lost the keys somewhere across the street.”
“Why look here?” asks the surprised and irritated officer. “The
light is much better here,” the intoxicated man responds with
aplomb.
Some sound scientific research is shaped by the need to perform and verify
replicable measurements. But these measurements do not always accurately reflect
the phenomenon that is being investigated. The term “streetlight effect” is sometimes
used to name this form of observational bias. Because the data is available, and
statistical correlations can be drawn, the industry is arguably looking for the keys
under the “Big Data streetlight”.
TRADITIONAL APPROACH IN REVERSE
In our extensive research, traditional investigative approaches are not truly
cognitive; rather it is specifically investigative research compiling multi-source data
sets, analytics, actions, outcomes and demographical data, in the hope of finding
ways to automate tasks that would mimic the perceptual and cognitive skills of
humans.
Many have approached talent engagement by assessing or researching styles,
personality, preferences, talent themes, or by using competencies and values-based
approaches, which fall short because these all rely on static identifiable data, and not
alterable coefficient of human behavior. They make it difficult to measure a
sustainable ROI. Their failure is based on a lack of baseline scientific performance
algorithms, and proven dynamic clinical tools, which can allow for the modification
of existing algorithmic brain functions, reforming a brain’s formulas to accelerate
measurable positive changes in productivity.
True cognitive science is much deeper than collecting external observable behavioral
data sets and correlation research.
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The secret is ‘between the ears’, the human brain science, real cognitive thinking. It
is the discovery of the ‘root source algorithms’ that provide what we will term
performance thinking. These are the driving force that impacts all aspects of human
behavior and workforce productivity, from leadership, sales, customer service,
meeting goals, following processes, procedures, working effectively in teams, and
building a high performance culture.
An algorithm is defined as a problem-solving procedure, or in the case of a computer
or brain, a program. The brain’s programs/algorithms that comprise thinking -
performance thinking, are the root source of all behavior and decisions in a human’s
workplace functions – therefore, the lifeblood of any business. By identifying and
emulating these, you have the true cognitive brain ‘files’, which we call performance
drivers. With these in hand, and coded into the proper software platform, you can
then predict outcomes, execution performance, and the results of people’s
engagement.
ARTIFICIAL INTELLIGENCE VS. COGNITIVE BRAIN
PERFORMANCE DRIVERS
Without debating the similarities or differences between artificial intelligence and a
cognitive engine, hopefully we can agree that the integration of the cognitive
formulas behind what drives behavior - performance drivers, has the potential to
contribute vital value added data and predictive analytics into current and new
technologies and applications. After over twenty years of research in this field – not
where the light is shining, but where impulses originate – we believe that as they are
identified and emulated, these cognitive brain algorithms will be the breakthrough
force for today's new disruptive technology companies. This leap forward in
integrating true cognitive performance thinking could be the ‘holy grail’ for future
applications and solutions.
THE REAL OPPORTUNITIES FOR BUSINESS
In a recent article in the Deloitte Review, Issue 16, titled Cognitive technologies:
The real opportunities for business, David Schatsky, Craig Muraskin, & Ragu
Gurumurthy noted: "We found that applications of cognitive technologies fall into
three main categories: product, process, or insight." They go on to summarize
“…Product applications can provide end-customer benefits, while Process
applications can automate or improve operations, while Insight applications uncover
insights that can inform operational and strategic decisions across an organization."
Given that performance thinking is the root source of all human productivity, the
brain’s performance drivers (performance thinking algorithms) are certainly the
source for new Product applications, or services, which deliver greater efficiencies
and added value productivity. Imagine being able to predict the productivity of an
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entire organization or being able to sustainably replicate the top performers in
organizations such as project or sales teams.
With regard to Process applications, applying these performance drivers to automate
or improve organizational development, will also allow transformational business
operations by getting to the ‘root source’ for solving diverse business engagement
challenges, and it will build sustainable high performance cultures faster and with
greater efficiencies.
With regard to Insight applications, applying performance drivers to a company’s
talent audit management technology solution can capture the critical talent
engagement data to uncover insights that can inform and shape operational and
strategic decisions, structures, and initiatives across an entire organization.
THE NEW FUTURE OF COGNITIVE TECHNOLOGIES
Cognitive technologies with embedded performance thinking algorithms will be the
new products in the field of artificial intelligence. One can imagine being able to
positively change human behavior and engagement that traditionally only humans
used to be able to do. Or being able to identify top performing talent before you open
a resume, or mining an entire organizations’ or nations performance capabilities.
Wise and appropriate investments in embedding true cognitive brain algorithms (as
opposed to data analytics) can dramatically improve current enterprise applications
and future innovative technologies.
Imagine actually automating the mind’s own thinking patterns to build future
innovative and disruptive technologies.
AUTHORS
Gary Morais is a psychotherapist and the inventor of Performance Drivers™
(Performance Thinking Algorithms) and the 10Rule® business performance
transformation suite of cloud-based software product (www.10Rule.com). Morais is
the managing partner of Bottom Line Results, LLC. and a business strategist
applying the 10Rule’s Hybrid Cognitive Technology strategy and solutions for
company CEO’s and President’s for over 25 years.
Dennis Cagan is a noted high-technology industry figure. He has founded or co-
founded over a dozen different companies, and has served as CEO of both public
and private companies, a consultant, venture investor, mentor and professional board
member – 53 fiduciary corporate boards. In 2011 he was elected to the IT Hall of
Fame. He has authored numerous articles in magazines including NACD
Directorship, Directors & Boards, Private Company Director, and Family Business.