Visit this link to complete the quiz - https://mix.office.com/watch/ays9xktksvjb The Data Asset Introduction - Databases, Business Intelligence, Analytics, Big Data, and Competitive Advantage
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.
You probably have heard about Big Data, but ever wondered what it exactly is? And why should you care?
Mobile is playing a large part in driving this explosion in data. The data are also created by the apps and other services in the background. As people are moving towards more digital channels, tons of data are being created. This data can be used in a lot of ways for personal and professional use. Big Data and mobile apps are converging in an enterprise and interacting; transforming the whole mobile ecosystem.
Big Data, Business Intelligence and Data AnalyticsSystems Limited
Business intelligence and data analytics involve analyzing data to extract useful information for making informed decisions. BI technologies provide historical, current, and predictive views of business operations through functions like reporting, OLAP, data mining, and benchmarking. BI architecture organizes data, information management, and technology components to build BI systems, while frameworks provide standards and best practices. Challenges include continuous availability, data security, cost, increasing user numbers, new data sources and areas like operational BI, and performance and scalability. Leading vendors provide solutions like Google, Microsoft, Oracle, SAS, SAP, IBM, EMC, HP, and Teradata.
Data scientists and IT push the limits of what's possible -- whether that's operating more efficiently, taking advantage of new opportunities, or innovating. Here are 5 ways businesses can boost their effectiveness.
For more: http://blog.tyronesystems.com/
Tamr works with companies to improve business decisions through data science and spend analytics. It helped one large, diversified manufacturing client identify over $100 million in savings by cleaning and classifying messy supplier and spend data from multiple sources using machine learning and input from domain experts. This created a centralized data asset that enabled various analytics and initiatives across procurement, including contract renegotiation, supplier risk assessment, and inventory rationalization. Tamr automates much of the data preparation process so analytics teams can quickly gain insights and identify opportunities across the business.
Four Techniques to Run AI on Your Business DataHyoun Park
This webinar explores key topics for preparing AI, including
Is Your Data Ready for AI?
Why Do We Mean By AI?
Practical AI for Business Profit
Preparing Data for AI
From BI to AI
Key Techniques for Running AI on Business Data
Attend The Data Science Course in Bangalore From ExcelR. Practical Data Science Course in Bangalore Sessions With Assured Placement Support From Experienced Faculty. ExcelR Offers The Data Science Course in Bangalore.
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.
You probably have heard about Big Data, but ever wondered what it exactly is? And why should you care?
Mobile is playing a large part in driving this explosion in data. The data are also created by the apps and other services in the background. As people are moving towards more digital channels, tons of data are being created. This data can be used in a lot of ways for personal and professional use. Big Data and mobile apps are converging in an enterprise and interacting; transforming the whole mobile ecosystem.
Big Data, Business Intelligence and Data AnalyticsSystems Limited
Business intelligence and data analytics involve analyzing data to extract useful information for making informed decisions. BI technologies provide historical, current, and predictive views of business operations through functions like reporting, OLAP, data mining, and benchmarking. BI architecture organizes data, information management, and technology components to build BI systems, while frameworks provide standards and best practices. Challenges include continuous availability, data security, cost, increasing user numbers, new data sources and areas like operational BI, and performance and scalability. Leading vendors provide solutions like Google, Microsoft, Oracle, SAS, SAP, IBM, EMC, HP, and Teradata.
Data scientists and IT push the limits of what's possible -- whether that's operating more efficiently, taking advantage of new opportunities, or innovating. Here are 5 ways businesses can boost their effectiveness.
For more: http://blog.tyronesystems.com/
Tamr works with companies to improve business decisions through data science and spend analytics. It helped one large, diversified manufacturing client identify over $100 million in savings by cleaning and classifying messy supplier and spend data from multiple sources using machine learning and input from domain experts. This created a centralized data asset that enabled various analytics and initiatives across procurement, including contract renegotiation, supplier risk assessment, and inventory rationalization. Tamr automates much of the data preparation process so analytics teams can quickly gain insights and identify opportunities across the business.
Four Techniques to Run AI on Your Business DataHyoun Park
This webinar explores key topics for preparing AI, including
Is Your Data Ready for AI?
Why Do We Mean By AI?
Practical AI for Business Profit
Preparing Data for AI
From BI to AI
Key Techniques for Running AI on Business Data
Attend The Data Science Course in Bangalore From ExcelR. Practical Data Science Course in Bangalore Sessions With Assured Placement Support From Experienced Faculty. ExcelR Offers The Data Science Course in Bangalore.
This document discusses the opportunities and challenges of big data. It defines big data as huge volumes of structured and unstructured data from various sources that require new tools to analyze and extract business insights. Big data provides both statistical and predictive views to help businesses make smarter decisions. While big data allows companies to integrate diverse data sources and gain real-time insights, challenges include processing large and complex data volumes and ensuring data quality, privacy and management. The document outlines the big data lifecycle and how analytics can be used descriptively, predictively and prescriptively.
Beyond the Classroom consists of events, workshops and presentations meant to introduce Computer Science students to learning opportunities in addition to their regular classroom experiences. Beyond the Classroom events are free and open to all NHCC CSci students.
This presentation is about Big Data, how it changes the traditional data landscape, how different companies are using it, and which skills are in demand.
This document provides tips for aspiring data scientists. It advises them to start by focusing on a topic that interests them and to clearly define their objectives and data collection process. It also recommends that they visualize their data, understand the context, look for additional insights, evaluate results, and find effective uses of the data. The document notes that data is becoming increasingly important in all industries and companies without data-savvy managers will be at a disadvantage.
This document discusses how organizations can leverage big data and analytics for competitive advantage. It recommends that leaders 1) build a data-driven culture, 2) apply analytics to core business functions, 3) invest in software-driven analytics capabilities, 4) ensure strong privacy, security and governance, and 5) understand how to differentiate based on data and analytics. The document emphasizes becoming more data-driven, scaling analytics use cases, and establishing governance and an architecture to make data and insights accessible across an organization.
Big Data Analytics and a Chartered AccountantBharath Rao
Big Data Analytics is a growing field and currently being capitalized by many businesses. Businesses leverage on Big Data to gain a keen understanding of the Consumer Behavior and Market Understanding. Additionally Big Data can be used different fields such as Financial Audit, Control Assurance and Forensics.
This presentation is made to provide an insight regarding what opportunities reside for a Chartered Accountant in order to provide suitable value creation with regards to Big Data Analytics.
This presentation was made during my GMCS 2 Course at Mangalore branch of SIRC of ICAI and hence has limited number of slides.
BI / POWER BI - Key Concepts Business FeaturesSamer Fouad
This document provides an overview and agenda for a presentation on Power BI key concepts. It discusses:
1) What business intelligence is and how it helps businesses make better decisions by transforming data into meaningful information.
2) How Power BI allows users to get started quickly by creating interactive reports from spreadsheets using Power BI Desktop and publishing live dashboards and reports.
3) The presentation agenda which covers an introduction to Power BI, identifying business questions, working with datasets, data transformations, data warehousing, and demonstrations of the tools.
The document discusses big data and big data analytics in banking. It defines big data as large, complex datasets that are difficult to process and store using traditional databases. Sources of big data include social media, sensors, transportation services, online shopping, and mobile apps. Characteristics of big data include volume, velocity, and variety. Hadoop is presented as an open source framework for analyzing big data using HDFS for storage and MapReduce for processing. The benefits of big data analytics in banking include fraud detection, risk management, customer segmentation, churn analysis, and sentiment analysis to improve customer experience.
Looking at what is driving Big Data. Market projections to 2017 plus what is are customer and infrastructure priorities. What drove BD in 2013 and what were barriers. Introduction to Business Analytics, Types, Building Analytics approach and ten steps to build your analytics platform within your company plus key takeaways.
The Present - the History of Business IntelligencePhocas Software
Learn the history of business intelligence in this three part series. In part one, we discussed how business intelligence software used to be (the past). In part two, we discuss business intelligence as it is in the present.
In this SlideShare, we present our take on the future of the business intelligence industry. See where the industry is going and the aspects of business intelligence that will become more prominent in the coming years.
The document discusses opportunities for using big data in statistics. It describes how large amounts of digital data are being generated daily and how traditional tools cannot handle this volume of data. Significant knowledge is hidden in big data that can help address important issues. The document outlines how statistics play a key role in economic and political decisions and proposes using big data, such as telecom data, as a new source for statistics to enrich decision making. This would provide a low-cost, endless source of data. The document advocates designing systems to support various analysis techniques and tailoring approaches to specific domains using open standards.
Data can exist in many forms and analytics involves finding patterns in data to aid decision making. There are different types of analytics like descriptive, predictive, and prescriptive. Data analysis is the process of inspecting, cleaning, and modeling data to provide business insights. It is used to help organizations make faster and better decisions to reduce costs and improve products and marketing. Advanced analytics uses tools like data mining, location intelligence, and predictive analytics to examine historical data and forecast future behaviors.
This document discusses the big data analytics market opportunity. It notes that the volume of data from various sources is growing exponentially. It then outlines the life cycle of big data, reference architectures, and characteristics of big data. It discusses drivers of big data, pain points for enterprises, and the market opportunity for big data analytics. It predicts strong growth in spending on big data analytics and outlines types of analytics initiatives and trends in big data technology.
Organizations want to use all the data available to them for analytics. But they’ve been thwarted by data silos and top-down, mostly manual approaches to unifying data for analytics. A new approach, based on machine learning combined with human expert sourcing, dramatically speeds analytics’ time-to-value. It automates data unification end-to end: from finding and connecting diverse data to interactive consumption by virtually anyone using any analytic tool.
This document introduces PatentStrategiesSM, a tool from LexisNexis that correlates over 90 million worldwide patents with various data sources to provide a comprehensive view of the intellectual property landscape. It analyzes patent and business data using over 50 visualizations to help with research and innovation, licensing, competitive analysis, litigation, acquisitions, risk management, and strategic planning. Key capabilities include examining patent strength, creating datasets based on semantics or other criteria, understanding patent expiration status, analyzing markets and technology areas through various maps and visualizations, and clustering related patents. The tool aims to provide insights that previously took days or weeks to uncover in just minutes.
The key to the cognitive business is putting data to work. What is needed is a platform, an ecosystem, and a method.
Learn more about http://ibm.co/dataworks
Big Data Analytic with Hadoop: Customer StoriesYellowfin
Why watch?
Looking to analyze your growing data assets to unlock real business benefits today? But, are you sick of all the Big Data hype and whoopla?
Watch this on-demand Webinar from Actian and Yellowfin – Big Data Analytics with Hadoop – to discover how we’re making Big Data Analytics fast and easy:
Learn how a telecommunications provider has already transformed its business using Big Data Analytics with Hadoop.
Hold on as we go from data in Hadoop to predictive analytics in just 40-minutes.
Learn how to combine Hadoop with the most advanced Big Data technologies, and world’s easiest BI solution, to quickly generate real business value from Big Data Analytics.
What will you learn?
Discover how Actian’s market-leading Big Data Analytics technologies, combined with Yellowfin’s consumer-oriented platform for reporting and analytics, makes generating value from Big Data Analytics faster and easier than you thought possible.
Join us as we demonstrate how to:
• Connect to, prepare and optimize Big Data in Hadoop for reporting and analytics.
• Perform predictive analytics on streaming Big Data: Learn how to empower all your analytics stakeholders to move from historical reports to predictive analytics and gain a sustainable competitive advantage.
• Communicate insights attained from Big Data: Optimize the value of your Big Data insights by learning how to effectively communicate analytical information to defined user groups and types.
This Webinar is ideal if…
• You want to act on more data and data types in shorter timeframes
• You want to understand the steps involved in achieving Big Data success – both front and back end
• You want to see how market leaders are leveraging Big Data to become data-driven organizations today
Looking to analyze and exploit Big Data assets stored in Hadoop? Then this Webinar is a must.
Presentation: Big Data – From Strategy to Production - Mario Meir-Huber, Big Data Leader Eastern Europe, Teradata GmbH (AT), at the European Data Economy Workshop taking place back to back to SEMANTiCS2015 on 15 September 2015 in Vienna
This presentation is an Introduction to the importance of Data Analytics in Product Management. During this talk Etugo Nwokah, former Chief Product Officer for WellMatch, covered how to define Data Analytics why it should be a first class citizen in any software organization
The document summarizes several local news stories from a newspaper:
1) The city is holding a flag design contest and the winning design could become the official city flag. The university is also improving Title IX training and procedures but may prioritize mental health later.
2) A local high school student is running for homecoming queen and raising money for Alzheimer's while describing how much she loves her grandmother who has the disease.
3) An engineering firm has been approved to design replacements for two aging sewer projects in the Flat Branch watershed that currently overflow after heavy rain.
Professor David Hopkins presented on England's education system reforms over the past decade. He outlined four key drivers that helped raise student achievement and build school capacity: 1) personalizing learning, 2) professionalizing teaching, 3) building intelligent accountability, and 4) innovation and networking. Hopkins argued these drivers should be shaped by system leadership to create sustainable reform where every school is great.
This document discusses the opportunities and challenges of big data. It defines big data as huge volumes of structured and unstructured data from various sources that require new tools to analyze and extract business insights. Big data provides both statistical and predictive views to help businesses make smarter decisions. While big data allows companies to integrate diverse data sources and gain real-time insights, challenges include processing large and complex data volumes and ensuring data quality, privacy and management. The document outlines the big data lifecycle and how analytics can be used descriptively, predictively and prescriptively.
Beyond the Classroom consists of events, workshops and presentations meant to introduce Computer Science students to learning opportunities in addition to their regular classroom experiences. Beyond the Classroom events are free and open to all NHCC CSci students.
This presentation is about Big Data, how it changes the traditional data landscape, how different companies are using it, and which skills are in demand.
This document provides tips for aspiring data scientists. It advises them to start by focusing on a topic that interests them and to clearly define their objectives and data collection process. It also recommends that they visualize their data, understand the context, look for additional insights, evaluate results, and find effective uses of the data. The document notes that data is becoming increasingly important in all industries and companies without data-savvy managers will be at a disadvantage.
This document discusses how organizations can leverage big data and analytics for competitive advantage. It recommends that leaders 1) build a data-driven culture, 2) apply analytics to core business functions, 3) invest in software-driven analytics capabilities, 4) ensure strong privacy, security and governance, and 5) understand how to differentiate based on data and analytics. The document emphasizes becoming more data-driven, scaling analytics use cases, and establishing governance and an architecture to make data and insights accessible across an organization.
Big Data Analytics and a Chartered AccountantBharath Rao
Big Data Analytics is a growing field and currently being capitalized by many businesses. Businesses leverage on Big Data to gain a keen understanding of the Consumer Behavior and Market Understanding. Additionally Big Data can be used different fields such as Financial Audit, Control Assurance and Forensics.
This presentation is made to provide an insight regarding what opportunities reside for a Chartered Accountant in order to provide suitable value creation with regards to Big Data Analytics.
This presentation was made during my GMCS 2 Course at Mangalore branch of SIRC of ICAI and hence has limited number of slides.
BI / POWER BI - Key Concepts Business FeaturesSamer Fouad
This document provides an overview and agenda for a presentation on Power BI key concepts. It discusses:
1) What business intelligence is and how it helps businesses make better decisions by transforming data into meaningful information.
2) How Power BI allows users to get started quickly by creating interactive reports from spreadsheets using Power BI Desktop and publishing live dashboards and reports.
3) The presentation agenda which covers an introduction to Power BI, identifying business questions, working with datasets, data transformations, data warehousing, and demonstrations of the tools.
The document discusses big data and big data analytics in banking. It defines big data as large, complex datasets that are difficult to process and store using traditional databases. Sources of big data include social media, sensors, transportation services, online shopping, and mobile apps. Characteristics of big data include volume, velocity, and variety. Hadoop is presented as an open source framework for analyzing big data using HDFS for storage and MapReduce for processing. The benefits of big data analytics in banking include fraud detection, risk management, customer segmentation, churn analysis, and sentiment analysis to improve customer experience.
Looking at what is driving Big Data. Market projections to 2017 plus what is are customer and infrastructure priorities. What drove BD in 2013 and what were barriers. Introduction to Business Analytics, Types, Building Analytics approach and ten steps to build your analytics platform within your company plus key takeaways.
The Present - the History of Business IntelligencePhocas Software
Learn the history of business intelligence in this three part series. In part one, we discussed how business intelligence software used to be (the past). In part two, we discuss business intelligence as it is in the present.
In this SlideShare, we present our take on the future of the business intelligence industry. See where the industry is going and the aspects of business intelligence that will become more prominent in the coming years.
The document discusses opportunities for using big data in statistics. It describes how large amounts of digital data are being generated daily and how traditional tools cannot handle this volume of data. Significant knowledge is hidden in big data that can help address important issues. The document outlines how statistics play a key role in economic and political decisions and proposes using big data, such as telecom data, as a new source for statistics to enrich decision making. This would provide a low-cost, endless source of data. The document advocates designing systems to support various analysis techniques and tailoring approaches to specific domains using open standards.
Data can exist in many forms and analytics involves finding patterns in data to aid decision making. There are different types of analytics like descriptive, predictive, and prescriptive. Data analysis is the process of inspecting, cleaning, and modeling data to provide business insights. It is used to help organizations make faster and better decisions to reduce costs and improve products and marketing. Advanced analytics uses tools like data mining, location intelligence, and predictive analytics to examine historical data and forecast future behaviors.
This document discusses the big data analytics market opportunity. It notes that the volume of data from various sources is growing exponentially. It then outlines the life cycle of big data, reference architectures, and characteristics of big data. It discusses drivers of big data, pain points for enterprises, and the market opportunity for big data analytics. It predicts strong growth in spending on big data analytics and outlines types of analytics initiatives and trends in big data technology.
Organizations want to use all the data available to them for analytics. But they’ve been thwarted by data silos and top-down, mostly manual approaches to unifying data for analytics. A new approach, based on machine learning combined with human expert sourcing, dramatically speeds analytics’ time-to-value. It automates data unification end-to end: from finding and connecting diverse data to interactive consumption by virtually anyone using any analytic tool.
This document introduces PatentStrategiesSM, a tool from LexisNexis that correlates over 90 million worldwide patents with various data sources to provide a comprehensive view of the intellectual property landscape. It analyzes patent and business data using over 50 visualizations to help with research and innovation, licensing, competitive analysis, litigation, acquisitions, risk management, and strategic planning. Key capabilities include examining patent strength, creating datasets based on semantics or other criteria, understanding patent expiration status, analyzing markets and technology areas through various maps and visualizations, and clustering related patents. The tool aims to provide insights that previously took days or weeks to uncover in just minutes.
The key to the cognitive business is putting data to work. What is needed is a platform, an ecosystem, and a method.
Learn more about http://ibm.co/dataworks
Big Data Analytic with Hadoop: Customer StoriesYellowfin
Why watch?
Looking to analyze your growing data assets to unlock real business benefits today? But, are you sick of all the Big Data hype and whoopla?
Watch this on-demand Webinar from Actian and Yellowfin – Big Data Analytics with Hadoop – to discover how we’re making Big Data Analytics fast and easy:
Learn how a telecommunications provider has already transformed its business using Big Data Analytics with Hadoop.
Hold on as we go from data in Hadoop to predictive analytics in just 40-minutes.
Learn how to combine Hadoop with the most advanced Big Data technologies, and world’s easiest BI solution, to quickly generate real business value from Big Data Analytics.
What will you learn?
Discover how Actian’s market-leading Big Data Analytics technologies, combined with Yellowfin’s consumer-oriented platform for reporting and analytics, makes generating value from Big Data Analytics faster and easier than you thought possible.
Join us as we demonstrate how to:
• Connect to, prepare and optimize Big Data in Hadoop for reporting and analytics.
• Perform predictive analytics on streaming Big Data: Learn how to empower all your analytics stakeholders to move from historical reports to predictive analytics and gain a sustainable competitive advantage.
• Communicate insights attained from Big Data: Optimize the value of your Big Data insights by learning how to effectively communicate analytical information to defined user groups and types.
This Webinar is ideal if…
• You want to act on more data and data types in shorter timeframes
• You want to understand the steps involved in achieving Big Data success – both front and back end
• You want to see how market leaders are leveraging Big Data to become data-driven organizations today
Looking to analyze and exploit Big Data assets stored in Hadoop? Then this Webinar is a must.
Presentation: Big Data – From Strategy to Production - Mario Meir-Huber, Big Data Leader Eastern Europe, Teradata GmbH (AT), at the European Data Economy Workshop taking place back to back to SEMANTiCS2015 on 15 September 2015 in Vienna
This presentation is an Introduction to the importance of Data Analytics in Product Management. During this talk Etugo Nwokah, former Chief Product Officer for WellMatch, covered how to define Data Analytics why it should be a first class citizen in any software organization
The document summarizes several local news stories from a newspaper:
1) The city is holding a flag design contest and the winning design could become the official city flag. The university is also improving Title IX training and procedures but may prioritize mental health later.
2) A local high school student is running for homecoming queen and raising money for Alzheimer's while describing how much she loves her grandmother who has the disease.
3) An engineering firm has been approved to design replacements for two aging sewer projects in the Flat Branch watershed that currently overflow after heavy rain.
Professor David Hopkins presented on England's education system reforms over the past decade. He outlined four key drivers that helped raise student achievement and build school capacity: 1) personalizing learning, 2) professionalizing teaching, 3) building intelligent accountability, and 4) innovation and networking. Hopkins argued these drivers should be shaped by system leadership to create sustainable reform where every school is great.
Dal convegno "Alimentazione, stili di vita e salute dei bambini" - 4 maggio 2010, Roma. Giocampus L'alleanza educativa pubblico-privata per il benessere delle future generazioni - Paolo Buzzi
The student learned that they need to think more curiously and quickly when speaking to people from different cultures and backgrounds. During an interview with an international student from Zimbabwe, the student realized they had made incorrect assumptions and struggled to ask follow up questions. The student also realized their Chinese language skills are poor for conversations. They will need immersion experiences abroad to improve their spoken Chinese abilities.
Using FME to Refine Electric Company Facility DataSafe Software
LSS utilized FME to create a workspace designed to dramatically reduce the facility footprint of an electric company. Using the workspace, the company reduced their One Call ticket volume by roughly 25%. This presentation will provide an overview of the problem and how FME was used as the solution.
This document provides contact information for the National Endowment for Science, Technology and the Arts (NESTA). It summarizes that NESTA is funded by the UK lottery and invests in scientists, artists, and inventors so they can use their skills to help build tomorrow's world.
This document provides instructions for finding a journal either electronically or in print format. It explains that you should first check if the journal is available electronically on the web by searching the library's journal portal. If it is not available online, you should search the library's catalogue (Aleph) to find the print version, where the holdings and shelf number will indicate what volumes are available. Step-by-step directions are given for both searching the electronic journals and using the catalogue to locate print journals. Assistance from a librarian is also offered.
This document summarizes an investment analysis of a Class B office building located at 70 Franklin Street in Boston, Massachusetts. It outlines the positive factors including below market rents that are 20% lower than recent leases, the opportunity to lease up vacant space as the building is currently only 51% occupied, and the desirable location between the financial district and downtown shopping area. The document also notes investment risks such as uncertain market conditions slowing lease up and an upcoming lease expiration for the largest tenant. Financial analysis projects a 13% internal rate of return on the $15 million acquisition based on leasing up vacant space and annual cash flows.
This document discusses different methods for embedding fonts on web pages, including their advantages and disadvantages. The font tag allowed using images of text for any font but had performance issues. CSS font stacks provide flexibility but not all fonts will be visible to users. JavaScript methods like SIFR and Cufón let any font be used but have limitations. The @font-face rule allows dynamic font usage through TrueType, OpenType, Embedded OpenType and SVG formats, but font licensing and performance need to be considered. Services exist to help with font hosting and subsetting can reduce file sizes but may cause issues. Overall font embedding remains challenging due to technical and licensing restrictions.
Farm tractors should be equipped with rollover protective structures and seat belts to protect operators in the event of an accident. They should also have proper shields around power take-off parts to prevent entanglement. Slow moving vehicle emblems and emergency lighting are required to warn other drivers when sharing roads. Equipment should only be hitched to the drawbar and extra passengers are prohibited for safety. Operators should check for hydraulic leaks and use hand signals instead of verbal commands due to noise.
Este documento describe diferentes tipos de técnicas de respiración, incluyendo la respiración diafragmática, pulmonar y clavicular. Explica que la respiración diafragmática es esencial ya que involucra el descenso del diafragma para llenar la parte baja de los pulmones con aire. La respiración pulmonar separa las costillas para llenar la región media de los pulmones. Y la respiración clavicular intenta levantar las clavículas al inspirar sin levantar los h
Oracle is a leading technology company focused on database software and cloud computing. It generates revenue from software licenses and cloud services. While Oracle faces competition from other large tech companies, its strengths include consulting services, global sales channels, and expertise in data storage and applications. The rise of big data presents both opportunities and challenges for Oracle to leverage new types and volumes of customer information through its products.
Modern Analytics And The Future Of Quality And Performance ExcellenceICFAI Business School
This document discusses modern business analytics and its applications. It defines analytics as using data, technology and analysis to help managers make better decisions. It outlines common analytics tools like Excel, SPSS and R. It traces the history and evolution of analytics from the 1950s to today. It describes the three main disciplines of analytics as business intelligence, quantitative methods, and statistics. It discusses descriptive, predictive and prescriptive analytics approaches. Finally, it discusses challenges and advantages of modern analytics for quality and strategic management.
Business intelligence techniques U2.pptxRenuLamba8
1. A business intelligence strategy aims to help businesses measure and improve performance through analytics solutions and architecture.
2. Business intelligence tools collect, analyze, and transform business data into insights through reports, dashboards, and visualizations to inform business decisions.
3. Developing a clear plan around how the data and analytics will be used, what data will be analyzed, and how staff will make decisions is key to a successful business intelligence strategy.
This document provides an overview of big data and big data analytics. It defines big data as large, complex datasets that grow quickly in volume and variety. Big data analytics involves examining these large datasets to find patterns and useful information. The challenges of big data include increased storage needs and handling diverse data formats. Hadoop is a framework that allows distributed processing of big data across clusters of computers. Common big data analytics tools include MapReduce, Spark, HBase and Hive. The benefits of big data analytics include improved decision making, customer service and efficiency.
Introduction to Big Data
Big Data is a massive collection of data that is growing exponentially over time.
It is a data set that is so large and complex that traditional data management tools cannot store or process it efficiently.
Big data is a type of data that is extremely large in size.
The presentation includes the introduction to the topic, the various dimensions of big data, its evolution from big data 1.0 to bid data 3.0 and its impact on various industries, uses as well as the challenges it faces. The concluding slide gives a brief on the future of big data.
There are ten areas in Data Science which are a key part of a project, and you need to master those to be able to work as a Data Scientist in much big organization.
This document provides an introduction to big data and data science from Amity Institute of Information Technology. It defines big data and data science, highlighting that big data is a subset of data science. The key differences between big data and data science are described. Examples of applications of big data in various domains like social media, healthcare, finance, ecommerce and education are outlined. Finally, the skills required to become a data scientist or big data specialist are summarized.
Data and analytic strategies for developing ethical itHyoun Park
Suggested audience: CIO, Enterprise Architects, Data Managers, Analytics Managers, Data Scientists
IT is broken. Bad data assumptions, legacy technology, poor business decisions, and weak IT management have changed IT from a superstar to a second-rate department that struggles to maintain its seat at the CEO's table.
With AI, personal data, & business ethics all in ascendence, the need for ethical IT policies has never been greater. Otherwise, companies risk building services and products that fall short of the ethics and trust that they have been given by employees.
In this webinar, Amalgam Insights explores how current data, BI, analytics, and machine learning technologies threaten ethical IT and provides guidance based on other rules-based frameworks that derive business outcomes, such as the law and corporate legislation.
Business Intelligence and Analytics .pptxRupaRani28
Business intelligence (BI) refers to technologies and practices used to analyze data and deliver actionable insights for decision-making. BI involves collecting data from various sources, analyzing the data using statistical techniques, visualizing the results, and generating reports. The key goal of BI is to improve decision-making by providing accurate, timely information. Popular BI tools allow users to query data, create reports and dashboards, and perform ad-hoc analysis. Real-time BI uses data analytics on up-to-date data sources to enable even timelier decision-making.
This document discusses business analytics and data analytics capabilities. It covers key concepts like data warehouses, data marts, ETL processes, business intelligence, data mining techniques, and how organizations can use analytics to gain insights from data to support decision making and gain a competitive advantage. The document provides examples of how companies like IHG and retailers use analytics to improve operations and customer understanding.
Business intelligence (BI) refers to technologies and processes used to gather, store, analyze and provide access to data to help business users make better decisions. BI systems aggregate data from various sources, enrich it with context and analysis, and present it to inform fact-based decisions. Advanced analytics can also be used to predict customer behavior and business trends. BI is important because it provides timely, reliable data to support decision making rather than relying solely on opinions. Major BI trends include mobile, cloud, social media and advanced analytics. BI systems are used across industries for applications like customer segmentation, inventory forecasting, and predicting customer churn.
Ch1-Introduction to Business Intelligence.pptxsommaikhantong
The document discusses business intelligence systems (BIS). It defines BIS as an analytical information system built on a data warehouse that uses tools like multidimensional analysis and data mining. The main components of BIS are the data warehouse, business analytics tools, business performance management, and user interfaces. BIS applications include accounting, inventory control, production management, and human resources. The document also discusses data warehousing, business analytics tools, and how technology changes have enabled more widespread use of BI.
Business intelligence and analytics both refer to maximize the value of your data to make better decisions, ALTEN CAlsoft Labs helps
enterprises accelerate business intelligence by providing the most comprehensive, integrated and easy-to-use reporting and analytics features with its industry specific analytics solutions and best in-class technology.
Top 5 Business Intelligence (BI) Trends in 2013Siva Shanmugam
Below are a few trends that we believe are going to gain momentum this year.
Agile IM
Cloud BI / SaaS BI
Mobile Business Intelligence
Analytics
Big Data
Business intelligence (BI) involves strategies and technologies used to analyze business data and present information to support decision-making. Big data refers to extremely large datasets that require advanced analytics to derive insights. BI technologies provide historical, current, and predictive views of business operations through reporting, analytics, and data mining. While BI helps with reporting, budgeting, forecasting, and promotions, it can be costly and expose information to risks. Big data allows for detecting fraud, gaining competitive insights, and improving customer service and profits through real-time analysis, but poses logistical and privacy challenges.
The document discusses how utilities are increasingly collecting and generating large amounts of data from smart meters and other sensors. It notes that utilities must learn to leverage this "big data" by acquiring, organizing, and analyzing different types of structured and unstructured data from various sources in order to make more informed operational and business decisions. Effective use of big data can help utilities optimize operations, improve customer experience, and increase business performance. However, most utilities currently underutilize data analytics capabilities and face challenges in integrating diverse data sources and systems. The document advocates for a well-designed data management platform that can consolidate utility data to facilitate deeper analysis and more valuable insights.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
1. THE DATA ASSET
INTRODUCTION - DATABASES, BUSINESS INTELLIGENCE, ANALYTICS, BIG DATA,
AND COMPETITIVE ADVANTAGE
2. INTRODUCTION
1. Understand how increasingly standardized data, access to
third-party data sets, cheap, fast computing and easier-to-
use software are collectively enabling a new age of decision
making.
2. Be familiar with some of the enterprises that have benefited
from data-driven, fact-based decision making.
3. INTRODUCTION
• Big Data
• Big data is a general term used to describe the massive amount of data
available to today’s managers. Big data are often unstructured and are
too big and costly to easily work through use of conventional databases,
but new tools are making these massive datasets available for analysis
and insight.
• Business Intelligence (BI)
• A term combining aspects of reporting, data exploration and ad hoc
queries, and sophisticated data modeling and analysis.
4. INTRODUCTION
• Analytics
• A term describing the extensive use of data, statistical and quantitative
analysis, explanatory and predictive models, and fact-based
management to drive decisions and actions.
• Machine Learning
• A type of artificial intelligence that leverages massive amounts of data so
that computers can improve the accuracy of actions and predictions on
their own without additional programming.
6. INTRODUCTION
Major League Baseball’s At the Ballpark app will use iBeacon technology to distribute deals and guide y
Source - http://www.technologytell.com/apple/130140/mlb-utilizing-ibeacon-technology-in-ballpar
7. INTRODUCTION
• What have we discussed?
• The amount of data being created doubles every two years.
• In many organizations, available data is not exploited to advantage.
However new tools supporting big data, business intelligence, and
analytics are helping managers make sense of this data torrent.
• Data is oftentimes considered a defensible source of competitive
advantage; however, advantages based on capabilities and data that
others can acquire will be short-lived.
LEARNING OBJECTIVES
Understand how increasingly standardized data, access to third-party data sets, cheap, fast computing and easier-to-use software are collectively enabling a new age of decision making.
Be familiar with some of the enterprises that have benefited from data-driven, fact-based decision making
The planet is awash in data. Cash registers ring up transactions worldwide. Web browsers leave a trail of cookie crumbs nearly everywhere they go. Fitness trackers, health monitors, and smartphone apps arecollectingdataonthebehaviorofmillions.Andwithradiofrequencyidentification(RFID),inventory can literally announce its presence so that firms can precisely journal every hop their products make along the value chain: “I’m arriving in the warehouse,” “I’m on the store shelf,” “I’m leaving out the front door.” A study by Gartner Research claims that the amount of data on corporate hard drives doubles everysixmonths,[1]whileIDCstatesthatthecollectivenumberofthosebitsalreadyexceedsthenumber of stars in the universe.[2] Walmart alone boasts a data volume well over 125 times as large as the entire print collection of the US Library of Congress, and rising.[3]It’s further noted that the Walmart figure is just for data stored on systems provided by the vendor Teradata. Walmart has many systems outside its Teradata-sourced warehouses, too. You’ll hear managers today broadly refer to this torrent of bits as “big data.” Andwiththisfloodofdatacomesatidalwaveofopportunity.Researchhasfoundthatcompanies ranked in the top third of their industry in the use of data-driven decision making were on average 5 percent more productive and 6 percent more profitable than competitors.[4]Increasingly standardized corporate data, and access to rich, third-party data sets—all leveraged by cheap, fast computing and easier-to-usesoftware—arecollectivelyenablinganewageofdata-driven,fact-baseddecisionmaking. You’relesslikelytohearold-schooltermslike“decisionsupportsystems”usedtodescribewhat’sgoing onhere.Thephraseofthedayisbusinessintelligence(BI),acatchalltermcombiningaspectsofreporting, data exploration and ad hoc queries, and sophisticated data modeling and analysis.
Alongside business intelligence in the new managerial lexicon is the phrase analytics, a term describing the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and factbased management to drive decisions and actions (and in our ever-imprecise world of business buzzwords,you’lloftenhearbusinessintelligenceandanalyticsusedinterchangeablytomeanthesame thing—usingdataforbetterdecisionmaking).[5] Anothertrend,machinelearning,referstoasophisticated category of software applications known as artificial intelligence that leverage massive amountsofdatasothatcomputerscan“learn”andimprovetheaccuracyofactionsandpredictionson their own without additional programming.[6] The benefits of all this data and number crunching are very real, indeed. Data leverage lies at the center of competitive advantage in many of the firms that we’ve studied, including Amazon, Netflix and Zara. Data mastery has helped vault Walmart to the top of the Fortune 500 list. It helps Spotify craft you a killer playlist for your run. And it helps make Google’s voice recognition a better listener andGooglesearchabetterdetective,sothattheservicecanaccuratelyshowyouwhatyou’veaskeditto lookup.There’sevensomethinghereforPoliScimajors,sincedata-driveninsightsareincreasinglybeing credited with helping politicians win elections.[7] To quote from a BusinessWeek cover story on analytics, “Math Will Rock Your World!”[8] Soundsgreat,butitcanbeatoughsloggettinganorganizationtothepointwhereithasaleveragable data asset. In many organizations data lies dormant, spread across inconsistent formats and incompatible systems, unable to be turned into anything of value. Many firms have been shocked at the amount of work and complexity required to pull together an infrastructure that empowers its managers.Butnotonlycanthisbedone,itmustbedone.Firmsthatarebasingdecisionsonhunchesaren’t managing, they’re gambling. And today’s markets have no tolerance for uninformed managerial dice rolling. Whilewe’llstudytechnologyinthischapter,ourfocusisn’tasmuchonthetechnologyitselfasitis onwhatyoucandowiththattechnology.ConsumerproductsgiantP&Gbelievesinthisdistinctionso thoroughlythatthefirmrenameditsITfunctionas“InformationandDecisionSolutions.”[9]Solutions drive technology decisions, not the other way around. Inthischapterwe’llstudythedataasset,howit’screated,howit’sstored,andhowit’saccessedand leveraged.We’llalsostudymanyofthefirmsmentionedabove,andmore,providingacontextforunderstanding how managers are leveraging data to create winning models, and how those that have failed to realize the power of data have been left in the dust.
Data, Analytics, and Competitive Advantage
Anyone can acquire technology—but data is oftentimes considered a defensible source of competitive advantage. The data a firm can leverage is a true strategic asset when it’s rare, valuable, imperfectly imitable, and lacking in substitutes (seeChapter 2). If more data brings more accurate modeling, moving early to capture this rare asset can be the difference between a dominating firm and an also-ran. But be forewarned, there’s no monopoly on math. Advantages based on formulas, algorithms, and data that others can also acquire will be short-lived. Moneyball advances in sports analytics originally pioneered by the Oakland A’s and are now used by nearly every team in the major leagues. This doesn’t mean that firms can ignore the importance data can play in lowering costs, increasing customer service, and other ways that boost performance. But differentiation will be key in distinguishing operationally effective data use from those efforts that can yield true strategic positioning.
That Seat Will Cost You $8−Wait, Make That $45.50
For some games it’s tough to fill the stands. A Wednesday night game against a mediocre rival will prompt thousands to stay home unless they get a really compelling deal. But many fans are ready to pay big bucks for a rivalry game on a weekend. To optimize demand, over thirty teams in Major League Baseball (MLB), the National Basketball Association (NBA), National Hockey League (NHL), and Major League Soccer (MLS) are using data analytics from Austin-based Qcue to fill seats and maximize revenue.[10] Take the San Francisco Giants as an example. The baseball standout draws big crowds when playing crosstown, interleague rivals, the Oakland As. A seat in the left field, upper deck of AT&T Park will cost above $45 for a Saturday afternoon game. But when the Diamondbacks are in town on a work or school night, that verysameseatcanbehadfor$8.Changingpricingbasedondemandconditionsisknownasdynamicpricing, and the Giants credits analytics-driven demand pricing with helping bump ticket revenues by at least 6 percent in a single year[11]and fuel a 250-plus sellout streak.[12]And getting fans in the stands is critical since once there, those fans usually rack up even more revenue in the form of concessions and merchandise sales. Dynamic pricing can be tricky. In some cases, it can leave consumers feeling taken advantage of (it is especially tricky in situations where consumers make repeated purchases and are more likely to remember past prices, and when they have alternative choices, like grocery or department store shopping). But dynamic pricing often works in markets where supply is constrained and subject to demand spikes. Firms from old-school airlines toapp-savvyUberregularlyletdataanalyticssetasupply-demandequilibriumthroughdynamicpricing,while also helping boost their bottom line. Sports teams are even leveraging weather insights and other data to drive the pricing of concession specials and to set the cost of a beer. New technologies, such as iBeacon (a tech that sends messages to iPhones using a low-energy Bluetooth signal) are being rolled out throughout MLB, making it easier to let consumers know a deal is in effect and guiding them to the quickest counter for quenching thirst and satisfying cravings.[13] FIGURE 15.1 Major League Baseball’s At the Ballpark app will use iBeacon technology to distribute deals and guide you to concessions.
Source: Alex Colon, “MLB Completes iBeacon Installations at Dodger Stadium and Petco Park,” GigaOM, February 14, 2014, https://gigaom.com/2014/02/14/mlb-completes-ibeacon-installations-at-dodger-stadium-and-petco-park.
KEY TAKEAWAYS
< The amount of data being created doubles every two years. < In many organizations, available data is not exploited to advantage. However new tools supportingbig data, business intelligence, and analytics are helping managers make sense of this data torrent. < Data is oftentimes considered a defensible source of competitive advantage; however, advantages based on capabilities and data that others can acquire will be short-lived.
1. Name and define the terms that are supplanting discussions of decision support systems in the modern IS lexicon.
2. Is data a source of competitive advantage? Describe situations in which data might be a source for sustainable competitive advantage. When might data not yield sustainable advantage?
3. Are advantages based on analytics and modeling potentially sustainable? Why or why not?
4. Think about the amount of data that is collected about you every day. Make a list of various technologies and information systems you engage with and the organizations that use these technologies, systems, and services to learn more about you. Does this information serve you better as a consumer? What, if any, concerns does broad data collection leave you with?
5. What role do technology and timing play in realizing advantages from the data asset?
6. What do you think about dynamic pricing? Is it good or bad for consumers? Is it good or bad for businesses? Explain your answer.
7. Have you visited a retailer or other venue using iBeacons? If so, describe your experience. If not, research the technology and come to class prepared to discuss its implications for collecting data and for driving consumer actions.