LinkedIn's Director of Business Analytics Simon Zhang's talk from industry event Big Analytics 2012 in San Francisco. http://www.biganalytics2012.com
Creating Big Data Success with the Collaboration of Business and ITEdward Chenard
This document discusses the importance of collaboration between business and IT teams for successful big data projects. It notes that many big data projects fail due to a lack of alignment between business and IT perspectives, siloed data access, and an inability to achieve enterprise adoption. Common reasons for failure include focusing on technology over business opportunities, not providing data access to subject matter experts, and failing to gain widespread adoption. The document advocates for improved collaboration between business, analytics, and IT teams in order to properly define problems, align stakeholders, and achieve true multi-disciplinary collaboration needed for big data success.
Thomas Davenport has written numerous books, articles, and delivered presentations on "Competing on Analytics". He is considered by many the leading authority on the subject. I created this presentation to articulate many of the concepts he established in his book with the same title.
1) While data has become more abundant, organizations must ensure they extract useful information from data to drive better decisions.
2) The rise of instrumented, interconnected and intelligent systems allows organizations to gain real-time insights from vast amounts of structured and unstructured data.
3) Leveraging predictive analytics and content analytics can help organizations move from reactive to predictive decision-making to optimize performance.
Business intelligence and analytics solutions can help organizations turn abundant data into actionable insights to make better business decisions. As data increases 650% over the next five years, these solutions can leverage big data to build customer insights, enhance customer experience, and understand customer behaviors. Enfathom helps organizations identify areas for new revenue and performance improvement by delivering data solutions to derive insight from data and achieve transformative business results such as improved customer relationships, profitability, and margins. Enfathom has delivered hundreds of solutions to large companies to improve processes like customer acquisition, reduce reporting redundancy by 70%, and conduct predictive modeling and statistical analysis.
Business intelligence and analytics solutions can help organizations turn abundant data into actionable insights to make better business decisions. As data increases 650% over the next five years, these solutions can leverage big data to build customer insights, enhance customer experience, and understand customer behaviors. Enfathom helps clients unlock the power of their data to identify new revenue opportunities and performance improvements through delivering solutions that derive insight from data and achieve transformative business results. They have delivered hundreds of solutions to large companies to improve customer relationships, profitability, and reduce time-to-value through transforming data into actionable insights.
Material for the 26 Oct 2015 lecture I held for Aalto University business students. The lecture focuses on the high level topics in analytics and Big Data that are either central to the subject or just highly visible in the media.
The main messages of the lecture are:
- The purpose of analytics and of the data analyst is to solve business problems
- Big Data brings over some very special traits to doing analytics that don't exist when working working with smaller datasets. Understanding these traits is a must for successful analytics.
- Deploying analytics is more dependent on humans than on technology
- Data and analytics are nowadays significant assets to many companies. Therefore they need their own strategy and need to be managed just like any other business critical assets.
This document discusses how business intelligence (BI) can help hospitality companies by integrating information. BI involves analyzing data that companies already collect on customers, operations, and competitors to provide insights. It allows companies to quickly address problems, take advantage of opportunities, and improve operations. While BI can boost profits and efficiency, hospitality companies face challenges in implementing it like dispersed data sources and a need to integrate systems. The document describes how one hotel franchise works with Cognizant to develop data cleansing processes to ensure accurate information for effective BI.
Data summit presentation may 28 2019 hcmIgor Gonchar
This document discusses building a data-driven organization by turning data into a strategic asset. It covers several topics:
- Monetizing data through advanced analytics like predictive modeling and machine learning.
- Evolving information management practices from basic data collection and reporting to treating information as a strategic differentiator.
- Developing an "information maturity model" to optimize business performance through real-time access to a single version of the truth.
- Leveraging technologies like data warehousing, streaming analytics, and artificial intelligence to transform data insights into intelligent actions.
The overall message is that organizations can gain competitive advantages by establishing modern information management practices that turn their data into a strategic asset.
This document discusses how organizations can harness big data as a game changing asset. It begins by setting the context on the impact of big data and how the volume of digital information is growing exponentially. It then covers analytical layers for deriving value from big data, including the data layer, emerging trends in real analytics, and shifting the focus from hindsight to foresight. Two case studies from India are presented on applying analytics in retail and HR. Finally, it discusses how big data is changing the paradigm for analytics by enabling closer monitoring, simulation and optimization. Harnessing big data requires asking focused questions to solve specific business problems.
Day 2 aziz apj aziz_big_datakeynote_pressIntelAPAC
This document summarizes Aziz Safa's presentation on Intel's adoption of big data, cloud, and IoT technologies. It discusses how innovative companies are leveraging big data to create disruptive business models and enhance customer experience. Lower costs of computing and storage as well as the growth of unstructured data are driving big data adoption. However, only a small percentage of available data is currently being analyzed due to legacy techniques being insufficient. Intel proposes a unified big data approach to capture, store, manage, and analyze all data types. Advanced analytics applied to big data can provide a competitive advantage if companies build the right skills and move quickly.
The document discusses data discovery and its role in helping organizations gain insights from data. It notes three key trends - agile analytics, big data, and data visualization. Data discovery fits within these trends by providing flexible tools for rapid analysis of diverse data sources and delivering insights visually. The document outlines the journey from data to results, noting that data discovery supports data acquisition, governance, quality, and analysis. It presents a cycle of analysis and provides an example for customer analytics. Key success factors for data discovery include goal-centricity, agility, fast iteration, and cross-functional collaboration.
Why Everything You Know About bigdata Is A LieSunil Ranka
As a big data technologist, you can bet that you have heard it all: every crazy claim, myth, and outright lie about what big data is and what it isn't that you can imagine, and probably a few that you can't.If your company has a big data initiative or is considering one, you should be aware of these false statements and the reasons why they are wrong.
The document discusses three approaches to business intelligence (BI) that organizations can take to improve decision making:
1. IT-centric - Focuses on analyzing historical data to understand what happened in the past. Asks "What happened?"
2. Information management - Enables real-time decision making by integrating data sources. Asks "How are we doing and what can we tweak now?"
3. Predictive insight - Adds advanced analytics to anticipate the future and identify opportunities. Asks "What will happen next and how can we optimize outcomes?" More advanced organizations use this approach.
Big Data : From HindSight to Insight to ForesightSunil Ranka
When it comes to Analytics and Reporting , There is a fine line between HindSight to Insight to Foresight . With the evolution of BigData technology, there is a need in deriving value out of the larger datasets, not available in the past. Even before we can start using the new shiny technologies, there is a need of understanding what is categorized as reporting or business intelligence or Big Data and Analytics. Based on my experience, people struggle to distinguish between reporting, Analytics, and Business Intelligence.
Dynamics Business Conference 2015: Creating a Business Intelligence Strategym-hance
What is a Business Intelligence (BI) strategy? What are the benefits? And how do I go about creating a BI strategy for my business? By the end of this session you should have a good idea of where you can start and the steps you need to take to move your BI strategy forwards.
This document discusses real-time analytics and its benefits. It explains that real-time analytics aims to improve decision making by providing data in real-time and closing the gap between data analysts and business operations. While real-time analytics provides benefits like enabling immediate decisions, it also has challenges like high costs and not being applicable to all use cases. The document provides examples of industries that use real-time analytics and discusses best practices.
The document discusses the evolution of enterprise IT from systems of record to systems of engagement. It notes that while systems of record provide efficiency, systems of engagement focus on effectiveness through communication, coordination and collaboration. It argues that consumer technologies will disrupt enterprise IT by demanding more engaging applications. Finally, it outlines how systems of engagement can empower employees through globally accessible mobile, social and interactive technologies and analytics to improve business performance.
How do you get a job in data science? Knowing enough statistics, machine learning, programming, etc to be able to get a job is difficult. One thing I have found lately is quite a few people may have the required skills to get a job, but no portfolio. While a resume matters, having a portfolio of public evidence of your data science skills can do wonders for your job prospects. Even if you have a referral, the ability to show potential employers what you can do instead of just telling them you can do something is important. This is a talk based on my original blog on Building a Data Science Portfolio: https://towardsdatascience.com/how-to-build-a-data-science-portfolio-5f566517c79c
This document summarizes the evolution of Merial's business intelligence capabilities from 2004 to the present. It describes how Merial moved from siloed business units with inconsistent data to a single source of truth. Key steps included taking a disciplined approach, learning from past implementations, and gaining buy-in from business leaders. The approach improved sales reporting, visibility, and productivity. Areas for future improvement were also discussed.
In an era of Big Data organizations are looking to use analytic insight to improve
their business. Rapidly changing competitive landscapes and the need to evaluate and
adopt new business models is pushing organizations to become more adaptive. How
can these imperatives be reflected in the way we build systems? In response to these imperatives, organizations are increasingly buying or building a new class of systems - Decision Management Systems. Decision Management Systems leverage the growing power of predictive analytics to create agile, analytic and adaptive processes and systems.
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.
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
Giving Organisations new capabilities to ask the right business questions 1.7OReillyStrata
This presentation takes the seminal work structured analytic techniques work pioneered within US intelligence, and proposes adaptions and simplifications for use within commercial enterprises
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.
Lean Business Intelligence - How and Why Organizations Are Moving to Self-Ser...FindWhitePapers
Learn why and how enterprises are moving to self-service business intelligence (BI). Find out how to get the right data now, while maintaining information quality and operational security. By reviewing requirements and specific use cases for a controlled self-service BI application, Forrester identifies five key findings that can transform your business.
Analytics in Financial Services: Keynote Presentation for TDWI and NY Tech Co...Fitzgerald Analytics, Inc.
Keynote Presentation Given in New York City on March 30th, at a joint event of The Data Warehousing Institute (TDWI) and the New York Technology Council. This keynote presentation by Jaime Fitzgerald focused on "Bridging the Gap" between business goals in the data and analytic enablers of achieving these goals.
Harvesting business Value with Data ScienceInfoFarm
Slidedeck from our seminar on "Harvesting Business Value with Data Science" (18/03/2015)
Topics covered:
- What is Data Science?
- Data Science: Tools and Techniques
- Data Science examples:
- Market segmentation
- Impact analysis
- Recommendations
- Water treatment
- Damage type research
- Call center aid
- Personalized client mailing (Essent)
- What do people write about us
- Fraud detection: Gotch’All (KU Leuven)
Data summit presentation may 28 2019 hcmIgor Gonchar
This document discusses building a data-driven organization by turning data into a strategic asset. It covers several topics:
- Monetizing data through advanced analytics like predictive modeling and machine learning.
- Evolving information management practices from basic data collection and reporting to treating information as a strategic differentiator.
- Developing an "information maturity model" to optimize business performance through real-time access to a single version of the truth.
- Leveraging technologies like data warehousing, streaming analytics, and artificial intelligence to transform data insights into intelligent actions.
The overall message is that organizations can gain competitive advantages by establishing modern information management practices that turn their data into a strategic asset.
This document discusses how organizations can harness big data as a game changing asset. It begins by setting the context on the impact of big data and how the volume of digital information is growing exponentially. It then covers analytical layers for deriving value from big data, including the data layer, emerging trends in real analytics, and shifting the focus from hindsight to foresight. Two case studies from India are presented on applying analytics in retail and HR. Finally, it discusses how big data is changing the paradigm for analytics by enabling closer monitoring, simulation and optimization. Harnessing big data requires asking focused questions to solve specific business problems.
Day 2 aziz apj aziz_big_datakeynote_pressIntelAPAC
This document summarizes Aziz Safa's presentation on Intel's adoption of big data, cloud, and IoT technologies. It discusses how innovative companies are leveraging big data to create disruptive business models and enhance customer experience. Lower costs of computing and storage as well as the growth of unstructured data are driving big data adoption. However, only a small percentage of available data is currently being analyzed due to legacy techniques being insufficient. Intel proposes a unified big data approach to capture, store, manage, and analyze all data types. Advanced analytics applied to big data can provide a competitive advantage if companies build the right skills and move quickly.
The document discusses data discovery and its role in helping organizations gain insights from data. It notes three key trends - agile analytics, big data, and data visualization. Data discovery fits within these trends by providing flexible tools for rapid analysis of diverse data sources and delivering insights visually. The document outlines the journey from data to results, noting that data discovery supports data acquisition, governance, quality, and analysis. It presents a cycle of analysis and provides an example for customer analytics. Key success factors for data discovery include goal-centricity, agility, fast iteration, and cross-functional collaboration.
Why Everything You Know About bigdata Is A LieSunil Ranka
As a big data technologist, you can bet that you have heard it all: every crazy claim, myth, and outright lie about what big data is and what it isn't that you can imagine, and probably a few that you can't.If your company has a big data initiative or is considering one, you should be aware of these false statements and the reasons why they are wrong.
The document discusses three approaches to business intelligence (BI) that organizations can take to improve decision making:
1. IT-centric - Focuses on analyzing historical data to understand what happened in the past. Asks "What happened?"
2. Information management - Enables real-time decision making by integrating data sources. Asks "How are we doing and what can we tweak now?"
3. Predictive insight - Adds advanced analytics to anticipate the future and identify opportunities. Asks "What will happen next and how can we optimize outcomes?" More advanced organizations use this approach.
Big Data : From HindSight to Insight to ForesightSunil Ranka
When it comes to Analytics and Reporting , There is a fine line between HindSight to Insight to Foresight . With the evolution of BigData technology, there is a need in deriving value out of the larger datasets, not available in the past. Even before we can start using the new shiny technologies, there is a need of understanding what is categorized as reporting or business intelligence or Big Data and Analytics. Based on my experience, people struggle to distinguish between reporting, Analytics, and Business Intelligence.
Dynamics Business Conference 2015: Creating a Business Intelligence Strategym-hance
What is a Business Intelligence (BI) strategy? What are the benefits? And how do I go about creating a BI strategy for my business? By the end of this session you should have a good idea of where you can start and the steps you need to take to move your BI strategy forwards.
This document discusses real-time analytics and its benefits. It explains that real-time analytics aims to improve decision making by providing data in real-time and closing the gap between data analysts and business operations. While real-time analytics provides benefits like enabling immediate decisions, it also has challenges like high costs and not being applicable to all use cases. The document provides examples of industries that use real-time analytics and discusses best practices.
The document discusses the evolution of enterprise IT from systems of record to systems of engagement. It notes that while systems of record provide efficiency, systems of engagement focus on effectiveness through communication, coordination and collaboration. It argues that consumer technologies will disrupt enterprise IT by demanding more engaging applications. Finally, it outlines how systems of engagement can empower employees through globally accessible mobile, social and interactive technologies and analytics to improve business performance.
How do you get a job in data science? Knowing enough statistics, machine learning, programming, etc to be able to get a job is difficult. One thing I have found lately is quite a few people may have the required skills to get a job, but no portfolio. While a resume matters, having a portfolio of public evidence of your data science skills can do wonders for your job prospects. Even if you have a referral, the ability to show potential employers what you can do instead of just telling them you can do something is important. This is a talk based on my original blog on Building a Data Science Portfolio: https://towardsdatascience.com/how-to-build-a-data-science-portfolio-5f566517c79c
This document summarizes the evolution of Merial's business intelligence capabilities from 2004 to the present. It describes how Merial moved from siloed business units with inconsistent data to a single source of truth. Key steps included taking a disciplined approach, learning from past implementations, and gaining buy-in from business leaders. The approach improved sales reporting, visibility, and productivity. Areas for future improvement were also discussed.
In an era of Big Data organizations are looking to use analytic insight to improve
their business. Rapidly changing competitive landscapes and the need to evaluate and
adopt new business models is pushing organizations to become more adaptive. How
can these imperatives be reflected in the way we build systems? In response to these imperatives, organizations are increasingly buying or building a new class of systems - Decision Management Systems. Decision Management Systems leverage the growing power of predictive analytics to create agile, analytic and adaptive processes and systems.
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.
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
Giving Organisations new capabilities to ask the right business questions 1.7OReillyStrata
This presentation takes the seminal work structured analytic techniques work pioneered within US intelligence, and proposes adaptions and simplifications for use within commercial enterprises
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.
Lean Business Intelligence - How and Why Organizations Are Moving to Self-Ser...FindWhitePapers
Learn why and how enterprises are moving to self-service business intelligence (BI). Find out how to get the right data now, while maintaining information quality and operational security. By reviewing requirements and specific use cases for a controlled self-service BI application, Forrester identifies five key findings that can transform your business.
Analytics in Financial Services: Keynote Presentation for TDWI and NY Tech Co...Fitzgerald Analytics, Inc.
Keynote Presentation Given in New York City on March 30th, at a joint event of The Data Warehousing Institute (TDWI) and the New York Technology Council. This keynote presentation by Jaime Fitzgerald focused on "Bridging the Gap" between business goals in the data and analytic enablers of achieving these goals.
Harvesting business Value with Data ScienceInfoFarm
Slidedeck from our seminar on "Harvesting Business Value with Data Science" (18/03/2015)
Topics covered:
- What is Data Science?
- Data Science: Tools and Techniques
- Data Science examples:
- Market segmentation
- Impact analysis
- Recommendations
- Water treatment
- Damage type research
- Call center aid
- Personalized client mailing (Essent)
- What do people write about us
- Fraud detection: Gotch’All (KU Leuven)
El documento describe la historia y el funcionamiento de la transmisión inalámbrica de energía eléctrica a través de ondas electromagnéticas, conocida como el efecto Tesla. Nikola Tesla desarrolló este concepto en el siglo XIX y demostró que la energía podía transmitirse sin cables utilizando campos magnéticos resonantes. Actualmente, investigadores como el profesor Marín Soljacic del MIT están continuando este trabajo para desarrollar aplicaciones prácticas de la transmisión de energía inalámbrica.
Albert Einstein nació en 1879 en Alemania. Desarrolló teorías revolucionarias como la relatividad especial y general que cambiaron la física moderna. Pasó sus últimos años en Estados Unidos donde continuó trabajando en una teoría unificada de la gravedad y el electromagnetismo hasta su muerte en 1955.
The document outlines the 9 building blocks of a business model canvas which are: customer segments, value propositions, channels, customer relationships, revenue stream, key resources, key activities, key partners, and cost structure. These building blocks are the core components that define how a company operates and creates value for customers. The document was presented by Hamza JOUNAIDI and provides resources for further information on the business model canvas.
This document provides an overview of the skills, tools, and techniques needed for big data science. It discusses infrastructure requirements like Hadoop and NoSQL, as well as necessary talent and analytic capabilities. A case study is presented using data from Stack Overflow to demonstrate the end-to-end process of exploring data, building features, creating structured and unstructured models, and ensembling models to solve a business problem. The document emphasizes that achieving early success in big data science requires a blend of analysis and scripting skills along with an understanding of relevant techniques, but large teams of PhDs or major investments are not necessarily needed.
How can Data Science benefit your business?Peadar Coyle
This document provides an overview of how data science can benefit businesses and examples of how data science has been applied in different industries. It discusses how data scientists can help businesses harness big data by performing tasks like customer segmentation, predictive analytics, forecasting, and developing data products. The document also provides several case studies and examples of how specific companies have used data science for applications such as optimizing marketing strategies, reducing customer churn, and improving supply chain management.
Data Science for Business Managers by TektosDataMaurício Garcia
This document summarizes a 6-hour course on data science for business managers. The course is divided into 3 modules that combine theory, case studies, and practical exercises. Module 1 covers current trends in data science and applications. Module 2 teaches fundamental data science concepts and problem types. Module 3 shows how to build and manage data-driven projects and transform businesses through data-focused product and process design. The goal is for participants to understand data science processes, manage data projects, and apply data to drive business innovation.
1) The document discusses how marketing has shifted from an "art" to a "science" due to changes in technology and customer expectations. Advances like social media, mobile devices, and cloud computing have given customers more control over brand conversations and empowered them to expect personalized, seamless experiences across channels.
2) It proposes a "Customer Value model" with four interconnecting layers: customer value at the core, surrounded by customer journey, customer value analytics, and finally company value. This model aims to continuously link customer insights and data to business decisions in real-time in order to maximize value for both customers and the company.
3) Achieving customer value now requires understanding customer needs, behaviors, and motiv
Data Science & Data Products at Neue Zürcher ZeitungRené Pfitzner
1) The document discusses data science and data products at NZZ, a Swiss media company.
2) NZZ uses data science to build data products like article recommendations and the NZZ News Companion app to address challenges from declining newspaper revenues and readership.
3) Key aspects of NZZ's data stack include REST APIs, Spark for scalable data processing, and deploying products on-premise, in the cloud, or with microservices.
This document discusses Pirelli's smart manufacturing initiatives including Industry 4.0 technologies like cyber-physical systems, IoT, and cloud computing. It outlines Pirelli's smart manufacturing architecture with local analytics at each factory and a central data hub. It provides examples of data products like anomaly detection algorithms, trend analysis visualizations, and machine learning models to optimize quality and efficiency. Finally, it discusses Python/R integration and deploying visualizations for factory workers using Domino and Plotly.
Business model innovation for the digital ageChanade Hemming
Here's the slide deck from a talk I gave at ProductTank in May 2016. This explores failures and successes, real world examples of companies that are trying to attack new entrants (and failing) and some useful resources to start thinking about business model/product innovation.
Applied analytics is all about creating actionable insights that can be injected back into a business process at the point of highest impact. This slideshow walks you through the "11 Principles of Applied Analytics" from Georgian Partners.
On Big Data Analytics - opportunities and challengesPetteri Alahuhta
This document discusses big data analytics and its opportunities and challenges. It defines big data and explains the increasing number of "V's" that characterize big data, such as volume, velocity, variety, and veracity. It also outlines some common uses of big data analytics including customer insights, security and risk analysis, and resource optimization. Additionally, it discusses challenges of big data adoption like skills shortages and infrastructure limitations, as well as trends in big data and areas of expertise related to big data that VTT focuses on.
Malang Digital Core - Business Model NavigatorEvans Winata
The document discusses various business model strategies such as add-on, affiliation, auction, cash machine, crowdsourcing, and more. For each strategy, it provides a brief description, examples of how it works, and any prerequisites needed to implement the strategy successfully.
UCD Smurfit: Digital Merchants Business Model AnalysisLara Zaccaria
This document analyzes different digital merchandising business models including bricks and clicks, catalogue merchants, pure players, and bit vendors. It discusses the key features and challenges of each model. It also examines the characteristics of e-merchants like ubiquity, global reach, information density, and personalization. Finally, it provides an overview of Tesco's online business model focusing on their key activities, resources, value propositions, channels, and customer segments.
The Value of Data for Digital Business ModelsBoris Otto
This presentation outlines the challenges and opportunities of digitization and highlights the important role of data quality. The presentation uses a well-known business model conceptualization to illustrate successful digitization examples.
Applying Data Science to Your Business ProblemCA Technologies
This document discusses applying data science techniques to solve business problems. It outlines key steps such as identifying a high-value business problem, determining what type of data is available, choosing appropriate metrics to measure success, developing predictive models, and evaluating models through iterative testing and refinement. Payment fraud detection and predicting mainframe issues are used as examples to illustrate how to analyze problems, leverage available data sources, and develop classification or anomaly detection models to provide business value.
The New Normal: Predictive Power on the Front LinesInside Analysis
The Briefing Room with Mike Ferguson and Alteryx
Live Webcast on Feb. 12, 2013
Today's savvy organizations know that a streamlined approach to data and applications can put the power of predictive analytics right where it needs to be: in the hands of the user. Sure, training is still required, but a real revolution is underway for the graphic design of such user interfaces. Central to this overhaul of design is the concept of intelligent, simple workflow, which enables users to get things done in an orderly fashion.
Check out the slides for this episode of The Briefing Room to hear analyst Mike Ferguson of Intelligent Business Strategies as he explains why interface design and workflow must go hand-in-hand. He will be briefed by Matt Madden of Alteryx, who will tout his company’s predictive platform, a solution that leverages an array of traditional and Big Data analytics applications, designed for problem solvers and decision makers. Madden will also provide several customer use cases that demonstrate the new normal in predictive analytics.
Find how to add more value to your Business Intelligence and Performance Management solutions by incorporating predictive analytics using IBM Cognos 10. Learn about the integration of Predictive Analytics and SPSS functionality, and how it fits with the Cognos platform. View the video recording and download this deck: http://www.senturus.com/resources/ibm-cognos-10-demo-predictive-analytics/.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
IBM Cognos - Kombinera BI med prediktiv analys för att minimera risker och nå...IBM Sverige
Vi visar hur du kan öka värdet på IBM Cognos lösningarna genom integration med IBM SPSS lösning för prediktiv analys. Detta ger användare på olika nivåer möjligheten att agera mer proaktivt genom att integrera intelligent underbyggda resultat som föutsäger och ger en djupare insikt till existerande IBM Cognos BI och Performance Management lösning. Denna presentation hölls på IBM Cognos Performance 2010 av Robert Moberg, Solution Architect, IBM
The document is an issue of the SAP Executive Insight Paper that discusses how real-time analytics are enabling high-resolution management by removing bottlenecks, rethinking businesses, and driving innovative products and services through granular insights. It provides an overview of SAP's database and analytics strategies and products, including predictive analysis and SAP HANA, and how customers are using these technologies to transform industries and business models. The paper also includes a tool to assess readers' business intelligence strategies and highlights upcoming SAP events.
Datawarehouse på System z (IBM Systems z)IBM Danmark
Lær om datawarehouse-systemer baseret på system z og om, hvilken udviklingsstrategi IBM følger for fortsat at være først med lanceringen af næste generations platformløsninger.
Læs mere her: bit.ly/softwaredagsystemz5
Using Business Intelligence to Bring Your Data to LifeInnoTech
Using business intelligence (BI) tools allows companies to analyze and visualize their data to identify opportunities and make better business decisions. BI provides interactive dashboards and visualizations that make it easy to see patterns and trends in the data. Its easy-to-use tools allow users to convert data into charts and tables with just a few clicks to process and analyze information faster. Companies implement BI software to improve how they connect with customers, find opportunities, and give their teams insights from the data.
This document discusses IBM's business analytics development strategy as part of its "Smarter Planet" initiative. It provides examples of how IBM analytics solutions have helped organizations gain insights from large amounts of data, improve business performance, and optimize processes like marketing campaigns. The solutions discussed use IBM analytics software like SPSS Modeler to perform tasks like customer segmentation, predictive modeling, and data mining. Overall the document promotes IBM's full suite of business intelligence and predictive analytics capabilities.
The document discusses data-driven decision making and data management. It introduces Deployments Factory SA, which provides data-driven project, program, and portfolio management solutions. The presentation covers:
1) How data-driven decision making can improve organizational performance
2) Challenges of managing large, diverse data sources
3) The "information virtuous cycle" and how the DataFactory concept transforms raw data into useful information to support better decisions
4) Examples of real applications in different domains like project management, risk management, and strategic execution.
Leverage IBM Business Analytics with PMSquarePM square
This document discusses how business intelligence (BI) solutions can meet the analysis needs of different types of business users. It provides an overview of common business questions around performance, factors influencing it, and necessary actions. The document then maps these questions to BI capabilities for reporting, analytics, trend analysis, modeling and planning that can provide insights. It argues that empowering all users with analysis can help address issues more quickly and make better decisions. An example shows how understanding production shortfalls and their drivers allows modeling scenarios to avoid future shortages.
Business in the Moment: From Reactive to ProactiveSAP Analytics
Keynote presentation from ANZ SAP Innovation Forums and BI Briefings Tour: An overview of SAP’s five market categories. Learn more at: http://bit.ly/Lj18rw
This document provides information about BusinessIntelligenze Company and their product JumboAnalysisTM. It discusses how JumboAnalysisTM collates, analyzes, and reports operational performance data for customers in an innovative way. It then describes the key features and benefits of JumboAnalysisTM, including executive scorecards, operations dashboards, and performance dashboards. The document also outlines the technical architecture and development approach.
Lean kanban university presentatie tri ict Hartman & Strooajhartman
The document summarizes two cases of applying Lean principles to improve IT operational processes.
Case 1 describes improving incident management at a logistics company's IT service desk. Various Lean tools were used to identify issues and implement improvements like reducing non-value-add steps.
Case 2 focuses on redesigning the incident and problem management process at a high tech company. The changes included establishing process "war rooms" and setting KPIs to measure and control quality improvements over time.
Manthan provides solutions and services across various domains including analytics, information management, big data, social media intelligence, mobile dashboards, master data management, and data quality. It has over 700 associates with expertise in research and development, different engagement models, and over 350 accelerators and solution templates. Services include consulting, implementation, custom development, and managed services.
Business intelligence and analytics solutions can help organizations turn abundant data into actionable insights to make better business decisions. As data increases 650% over the next five years, these solutions can leverage big data to build customer insights, enhance customer experience, and understand customer behaviors. Enfathom helps organizations unlock the power of their data through solutions that allow deriving insights, gaining clarity, and achieving transformative business results such as improving customer relationships, profitability, and reducing time-to-value.
IBM Cognos 10.1 User Interface Tools OverviewSenturus
Learn what tools will be available to users with Cognos 10.1, and what’s covered by your licenses. Download this deck: http://www.senturus.com/resources/overview-of-user-interface-tools-in-cognos-10-1-2/.
Senturus senior solutions architects provide clear visuals and concise descriptions to help you assess the IBM Cognos 10 platform in the context of a Corporate Performance Management (CPM) solution and determine how to take your performance management initiative to the next level.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
This document discusses faster and cheaper approaches to information governance. It suggests starting small with a tangible project that addresses an acute business problem. Use existing resources and focus on quick success metrics to prove value. Grow the initiative over time by publicizing results, establishing executive support, and developing a growth plan around key technologies like data quality, master data management and information lifecycle management. The goal is more effective yet lower cost information governance.
IBM Cognos - Vad handlar egentligen prediktiv analys om?IBM Sverige
This document provides a 3-paragraph summary of an IBM performance event about predictive analytics:
Paragraph 1 introduces predictive analytics and how it can help decision makers predict things like infection in newborns, credit line adjustments, customer purchases, and social relationships to prevent customer churn.
Paragraph 2 describes how predictive analytics can optimize transactions, processes, and decisions in real-time using data and predictive models, and how it helps everyone make better decisions rather than just analytical experts.
Paragraph 3 gives examples of how a telecommunications company uses predictive analytics to proactively target at-risk customers and determine the best retention offers to reduce churn.
Razorfish Multi-Channel Marketing: Better Customer Segmentation and TargetingTeradata Aster
Matt Comstock, Vice President Business Intelligence Office, Razorfish, presents at the Big Analytics 2012 Roadshow.
From search to email to social, customers are interacting with your brand across a variety of channels. But what do people do once they view an advertisement or get an email? What common behaviors are displayed once they’re on your site? By combining media exposure/behavior, site-side media, and in-store purchase data, you can understand better the impact media has on driving value to your business. Come to this session to learn how better data-driven multi-channel analysis lets you see what consumers do before they become a customer to understand what content influences which segments of users by media audience. Discover new segmentation and targeting strategies to improve engagement with your brand and increase advertising lift. See how a leader in digital marketing uses a combination of technologies including Teradata Aster, Hadoop, and Amazon Web Services to handle big data and provide big analytics to improve business value.
Gayatri Patel, eBay, presents at the Big Analytics 2012 Roadshow
The wonders of what data can do for an organization is measured in the productivity and competitiveness of their team's decisions. Some believe more data is the key. Agreed...but good decisions require more than just deriving intelligence from big data. In this dynamic market, the need to socialize and evolve ideas with other teams, quickly correlate information across sources, and test ideas to fail fast early are strong enablers to gain competitive footing. eBay¹s analytic and technology advancements garners insights and approaches that continue to help our employees tell their "data stories" and make better decisions.
Using Data to Manage in Today’s Chaotic EnvironmentTeradata Aster
Data science is a field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. It aims to discover patterns in large data sets involving methods at the intersection of mathematics, statistics, and computer science. The key steps typically include data collection, data cleaning, data analysis, model building, and communicating results.
This data is from registrants for the Big Analytics 2012 events. The survey asked participants to classify themselves as “business” or “IT”.
Survey details:
Number of survey respondents and date -
San Francisco (April) = 507
Boston (May)= 322
Chicago (June) = 441
New York (Dec) = 894
TOTAL = 2164
Bill Franks, Chief Analytics Officer, Teradata, presents at the 2012 Big Analytics Roadshow.
As enterprises come to understand the value of analytics, more support and funding is being allocated to build these departments. Managers are now faced with the challenge of who to hire. What exactly makes a great analytic professional? Is a Data Scientist a "must have"? Should a candidate have a PhD? Is prior experience in a specific industry vital? Just what is the right fit when creating a successful team? The answers to these questions are still unclear as the value of analytics continues to grow.
In this session, Bill Franks, author of the book, Taming the Big Data Tidal Wave, addresses these and many more questions as he defines the characteristics of high-performing data scientists and great analytics teams.
Practical Applications of Visual AnalyticsTeradata Aster
Dustin Smith, Community Manager, Tableau Software, presents at the 2012 Big Analytics Roadshow.
Organizations now have the ability to store and process massive amounts of data like never before. And there are huge expectations for turning data into a fundamental driver for business transformation and competitive advantage.
Visual analytics is helping everyday employees gain insight into data in order to solve unexpected problems and challenges, it is changing the way people interact with data and the way business intelligence is defined in organizations. In this presentation, we will share real-world examples of how everyday people can and are using visual analytics to solve some of businesses most challenging issues.
Trust and Influence in the Complex Network of Social MediaTeradata Aster
William Rand, University of Maryland, presents at the 2012 Big Analytics Roadshow.
The dramatic feature of social media is that it gives everyone a voice; anyone can speak out and express their opinion to a crowd of followers with little or no cost or effort, which creates a loud and potentially overwhelming marketplace of ideas. The good news is that the organizations have more data than ever about what their consumers are saying about their brand. The bad news is that this huge amount of data is difficult to sift through. We will look at developing methods that can help sift through this torrent of data and examine important questions, such as who do users trust to provide them with the information and the recommendations that they want? Which tastemakers have the greatest influence on social media users? Using agent-based modeling, machine learning and network analysis we begin to examine and shed light on these questions and develop a deeper understanding of the complex system of social media.
Mohanbir Sawhney, Robert R. McCormick Tribune Foundation Clinical Professor of Technology Kellogg School of Management, Northwestern University presents at the 2012 Big Analytics Roadshow.
Companies are drinking from a fire hydrant of data that is too big, moving too fast and is too diverse to be analyzed by conventional database systems. Big Data is like a giant gold mine with large quantities of ore that is difficult to extract. To get value out of Big Data, enterprises need a new mindset and a new set of tools. They also need to know how to extract actionable insights from Big Data that can lead to competitive advantage. The Big Story of Big Data is not what Big Data is, but what it means for business value and competitive advantage.... read more: http://www.biganalytics2012.com/sessions.html#mohan_sawhney
Big Brands Meet Big Data – The Newest Innovator’s DilemmaTeradata Aster
Big brands face risks from big data moving too fast for them to keep up. Most brands stick to traditional data practices rather than taking advantage of growing customer data. Meanwhile, data is exploding in amount and value as it is increasingly generated by consumers. Well-managed companies can fail when they cannot adapt to disruptive technologies, allowing others to steal their market. To avoid this, brands must learn to better analyze and use customer data for competitive advantage before disruptors emerge to do so.
Simplifying Big Data Analytics for the BusinessTeradata Aster
Tasso Argyros, Co-Founder & Co-President, Teradata Aster presents at the 2012 Big Analytics Roadshow.
The opportunity exists for organizations in every industry to unlock the power of iterative, big data analysis with new applications such as digital marketing optimization and social network analysis to improve their bottom line. Big data analysis is not just the ability to analyze large volumes of data, but the ability to analyze more varieties of data by performing more complex analysis than is possible with more traditional technologies. This session will demonstrate how to bring the science of data to the art of business by empowering more business users and analysts with operationalized insights that drive results. See how data science is making emerging analytic technologies more accessible to businesses while providing better manageability to enterprise architects across retail, financial services, and media companies.
Evaluating Big Data Predictive Analytics PlatformsTeradata Aster
Mike Gualtieri, Principal Analyst, Forrester Research, presents at the Big Analytics Roadshow, 2012 in New York City on December 12, 2012
Presentation title: Evaluating Big Data Predictive Analytics Platforms
Abstract: Great. You have Big Data. Now what? You have to analyze it to find game-changing predictive models that you can use to make smart decisions, reduce risk, or deliver breakthrough customer experiences. Big Data Predictive Analytics solutions are software and/or hardware solutions that allow firms to discover, evaluate, optimize, and deploy predictive models by analyzing big data sources. In this session, Forrester Principal Analyst Mike Gualtieri will discuss the key criteria you should use to evaluate Big Data Predictive Analytics platforms to meet your specific needs.
Keynote: Cross Industry Lessons from Moneyball AnalyticsTeradata Aster
Ari Kaplan keynote presentation at the Big Analytics Roadshow, 2012 in New York City on December 12, 2012
Presentation title: "Cross Industry Lessons from Moneyball Analytics", by Ari Kaplan, "Moneyball" advisor to Major League Baseball teams and President of AriBall
Ari Kaplan is a leading figure in sports analytics. Known throughout the Major Leagues for revolutionizing and modernizing player assessment, Ari's use of analytics and technology helps coaches prepare for games, players understand their strengths and weaknesses, General Managers forecast future performance and risk of player contracts and draft picks, and more.
In this presentation, Kaplan discusses how professional sports teams and players use analytics and data visualization in the Major Leagues. Through his 23 years of experience in over half of all MLB organizations, he will discuss the changes that took place and where analytics will continue to innovate in the future.
Technology Strategies for Big Data Analytics, Teradata Aster
SAS Presentation delivered at the Big Analytics Roadshow, 2012 in New York City on December 12, 2012
Presentation title: Technology Strategies for Big Data Analytics, by Bernard Blais, Global Strategist and Principal Manager, SAS
The exploding volume, complexity and velocity of big data present an increasing challenge to organizations, but also a significant opportunity to derive valuable insights. As organizations are tasked with managing massive data sets, it’s clear that the value of big data will be derived from the analytics that can be performed on it. Analytics is the key to identifying patterns, managing risks and tackling previously unsolvable problems. This presentation provides an overview of how to comprehensively tackle big data, including emerging strategies for information management, analytics, and high performance analytics.
This document discusses Apache Hadoop, its current state and future direction. It provides an overview of Hadoop as an open source platform for storing and analyzing large amounts of data across distributed systems. The document outlines Hortonworks' vision of making Hadoop an enterprise-ready platform that can power data-driven businesses and unify both traditional and big data analytics methods. It also announces an upcoming Hadoop conference in June 2012 with sessions showcasing real-world Hadoop uses.
Solving the Education Crisis with Big DataTeradata Aster
Presentation by Crystal Hutter, COO at Edmodo. Part of the Big Analytics 2012 roadshow series.
http://www.biganalytics2012.com
Using SQL-MapReduce for Advanced AnalyticsTeradata Aster
This document discusses using SQL-MapReduce (SQL-MR) for advanced analytical queries. SQL-MR allows parallelization of complex SQL queries using MapReduce functions. It simplifies architectures by eliminating the need for separate data warehouses, datamarts and cubes. SQL-MR enables new forms of analytics like deep, complex, operational and self-service analytics without restricting queries. The marriage of SQL and MapReduce offers significant potential by parallelizing analytical logic processing.
SAS aster data big data dc presentation publicTeradata Aster
This document discusses SAS In-Database, which allows SAS functions, models, and code to run directly inside databases. Key points include:
- SAS In-Database aims to streamline analytics workflows, improve performance, and ensure data consistency.
- It works by embedding SAS capabilities like scoring functions, modeling, and data preparation directly into databases.
- SAS has partnered with Aster Data to enable these in-database analytics using Aster's nCluster platform.
Utilizing Aster nCluster to support processing in excess of 100 Billion rows ...Teradata Aster
The document proposes a plan to utilize Aster nCluster to support processing over 100 billion rows of data per month. It discusses comScore's need to scale its data analytics capabilities to handle growing volumes of data and more advanced analysis. Key aspects of the plan include using Aster nCluster to store 3 months of data, support 150 analysts, and provide SQL access to data while handling potential growth.
The document discusses comScore's plan to utilize Aster nCluster to support processing over 100 billion rows of data per month. It outlines comScore's existing data analytics systems and challenges in scaling them to this level of data. The plan is to build a new Aster nCluster production environment with 70 workers and 350TB of storage to meet their growing analytics needs.
20100506 aster data big data summit - microstrategy (shareable)Teradata Aster
The document discusses business intelligence with MicroStrategy. It provides an overview of MicroStrategy, highlighting that it is the largest independent public BI vendor. It notes that most analyst evaluations conclude MicroStrategy has the best overall BI technology. It also discusses how MicroStrategy is database-aware and offers specific benefits in integrating with database platforms like database-specific SQL generation and optimizations. Finally, it covers some key trends in enterprise BI like extending BI to operational users.
With Claude 4, Anthropic redefines AI capabilities, effectively unleashing a ...SOFTTECHHUB
With the introduction of Claude Opus 4 and Sonnet 4, Anthropic's newest generation of AI models is not just an incremental step but a pivotal moment, fundamentally reshaping what's possible in software development, complex problem-solving, and intelligent business automation.
Master tester AI toolbox - Kari Kakkonen at Testaus ja AI 2025 ProfessioKari Kakkonen
My slides at Professio Testaus ja AI 2025 seminar in Espoo, Finland.
Deck in English, even though I talked in Finnish this time, in addition to chairing the event.
I discuss the different motivations for testing to use AI tools to help in testing, and give several examples in each categories, some open source, some commercial.
New Ways to Reduce Database Costs with ScyllaDBScyllaDB
How ScyllaDB’s latest capabilities can reduce your infrastructure costs
ScyllaDB has been obsessed with price-performance from day 1. Our core database is architected with low-level engineering optimizations that squeeze every ounce of power from the underlying infrastructure. And we just completed a multi-year effort to introduce a set of new capabilities for additional savings.
Join this webinar to learn about these new capabilities: the underlying challenges we wanted to address, the workloads that will benefit most from each, and how to get started. We’ll cover ways to:
- Avoid overprovisioning with “just-in-time” scaling
- Safely operate at up to ~90% storage utilization
- Cut network costs with new compression strategies and file-based streaming
We’ll also highlight a “hidden gem” capability that lets you safely balance multiple workloads in a single cluster. To conclude, we will share the efficiency-focused capabilities on our short-term and long-term roadmaps.
As data privacy regulations become more pervasive across the globe and organizations increasingly handle and transfer (including across borders) meaningful volumes of personal and confidential information, the need for robust contracts to be in place is more important than ever.
This webinar will provide a deep dive into privacy contracting, covering essential terms and concepts, negotiation strategies, and key practices for managing data privacy risks.
Whether you're in legal, privacy, security, compliance, GRC, procurement, or otherwise, this session will include actionable insights and practical strategies to help you enhance your agreements, reduce risk, and enable your business to move fast while protecting itself.
This webinar will review key aspects and considerations in privacy contracting, including:
- Data processing addenda, cross-border transfer terms including EU Model Clauses/Standard Contractual Clauses, etc.
- Certain legally-required provisions (as well as how to ensure compliance with those provisions)
- Negotiation tactics and common issues
- Recent lessons from recent regulatory actions and disputes
Adtran’s new Ensemble Cloudlet vRouter solution gives service providers a smarter way to replace aging edge routers. With virtual routing, cloud-hosted management and optional design services, the platform makes it easy to deliver high-performance Layer 3 services at lower cost. Discover how this turnkey, subscription-based solution accelerates deployment, supports hosted VNFs and helps boost enterprise ARPU.
What’s New in Web3 Development Trends to Watch in 2025.pptxLisa ward
Emerging Web3 development trends in 2025 include AI integration, enhanced scalability, decentralized identity, and increased enterprise adoption of blockchain technologies.
SAP Sapphire 2025 ERP1612 Enhancing User Experience with SAP Fiori and AIPeter Spielvogel
Explore how AI in SAP Fiori apps enhances productivity and collaboration. Learn best practices for SAPUI5, Fiori elements, and tools to build enterprise-grade apps efficiently. Discover practical tips to deploy apps quickly, leveraging AI, and bring your questions for a deep dive into innovative solutions.
Content and eLearning Standards: Finding the Best Fit for Your-TrainingRustici Software
Tammy Rutherford, Managing Director of Rustici Software, walks through the pros and cons of different standards to better understand which standard is best for your content and chosen technologies.
Dev Dives: System-to-system integration with UiPath API WorkflowsUiPathCommunity
Join the next Dev Dives webinar on May 29 for a first contact with UiPath API Workflows, a powerful tool purpose-fit for API integration and data manipulation!
This session will guide you through the technical aspects of automating communication between applications, systems and data sources using API workflows.
📕 We'll delve into:
- How this feature delivers API integration as a first-party concept of the UiPath Platform.
- How to design, implement, and debug API workflows to integrate with your existing systems seamlessly and securely.
- How to optimize your API integrations with runtime built for speed and scalability.
This session is ideal for developers looking to solve API integration use cases with the power of the UiPath Platform.
👨🏫 Speakers:
Gunter De Souter, Sr. Director, Product Manager @UiPath
Ramsay Grove, Product Manager @UiPath
This session streamed live on May 29, 2025, 16:00 CET.
Check out all our upcoming UiPath Dev Dives sessions:
👉 https://community.uipath.com/dev-dives-automation-developer-2025/
Measuring Microsoft 365 Copilot and Gen AI SuccessNikki Chapple
Session | Measuring Microsoft 365 Copilot and Gen AI Success with Viva Insights and Purview
Presenter | Nikki Chapple 2 x MVP and Principal Cloud Architect at CloudWay
Event | European Collaboration Conference 2025
Format | In person Germany
Date | 28 May 2025
📊 Measuring Copilot and Gen AI Success with Viva Insights and Purview
Presented by Nikki Chapple – Microsoft 365 MVP & Principal Cloud Architect, CloudWay
How do you measure the success—and manage the risks—of Microsoft 365 Copilot and Generative AI (Gen AI)? In this ECS 2025 session, Microsoft MVP and Principal Cloud Architect Nikki Chapple explores how to go beyond basic usage metrics to gain full-spectrum visibility into AI adoption, business impact, user sentiment, and data security.
🎯 Key Topics Covered:
Microsoft 365 Copilot usage and adoption metrics
Viva Insights Copilot Analytics and Dashboard
Microsoft Purview Data Security Posture Management (DSPM) for AI
Measuring AI readiness, impact, and sentiment
Identifying and mitigating risks from third-party Gen AI tools
Shadow IT, oversharing, and compliance risks
Microsoft 365 Admin Center reports and Copilot Readiness
Power BI-based Copilot Business Impact Report (Preview)
📊 Why AI Measurement Matters: Without meaningful measurement, organizations risk operating in the dark—unable to prove ROI, identify friction points, or detect compliance violations. Nikki presents a unified framework combining quantitative metrics, qualitative insights, and risk monitoring to help organizations:
Prove ROI on AI investments
Drive responsible adoption
Protect sensitive data
Ensure compliance and governance
🔍 Tools and Reports Highlighted:
Microsoft 365 Admin Center: Copilot Overview, Usage, Readiness, Agents, Chat, and Adoption Score
Viva Insights Copilot Dashboard: Readiness, Adoption, Impact, Sentiment
Copilot Business Impact Report: Power BI integration for business outcome mapping
Microsoft Purview DSPM for AI: Discover and govern Copilot and third-party Gen AI usage
🔐 Security and Compliance Insights: Learn how to detect unsanctioned Gen AI tools like ChatGPT, Gemini, and Claude, track oversharing, and apply eDLP and Insider Risk Management (IRM) policies. Understand how to use Microsoft Purview—even without E5 Compliance—to monitor Copilot usage and protect sensitive data.
📈 Who Should Watch: This session is ideal for IT leaders, security professionals, compliance officers, and Microsoft 365 admins looking to:
Maximize the value of Microsoft Copilot
Build a secure, measurable AI strategy
Align AI usage with business goals and compliance requirements
🔗 Read the blog https://nikkichapple.com/measuring-copilot-gen-ai/
UiPath Community Berlin: Studio Tips & Tricks and UiPath InsightsUiPathCommunity
Join the UiPath Community Berlin (Virtual) meetup on May 27 to discover handy Studio Tips & Tricks and get introduced to UiPath Insights. Learn how to boost your development workflow, improve efficiency, and gain visibility into your automation performance.
📕 Agenda:
- Welcome & Introductions
- UiPath Studio Tips & Tricks for Efficient Development
- Best Practices for Workflow Design
- Introduction to UiPath Insights
- Creating Dashboards & Tracking KPIs (Demo)
- Q&A and Open Discussion
Perfect for developers, analysts, and automation enthusiasts!
This session streamed live on May 27, 18:00 CET.
Check out all our upcoming UiPath Community sessions at:
👉 https://community.uipath.com/events/
Join our UiPath Community Berlin chapter:
👉 https://community.uipath.com/berlin/
Agentic AI - The New Era of IntelligenceMuzammil Shah
This presentation is specifically designed to introduce final-year university students to the foundational principles of Agentic Artificial Intelligence (AI). It aims to provide a clear understanding of how Agentic AI systems function, their key components, and the underlying technologies that empower them. By exploring real-world applications and emerging trends, the session will equip students with essential knowledge to engage with this rapidly evolving area of AI, preparing them for further study or professional work in the field.
Supercharge Your AI Development with Local LLMsFrancesco Corti
In today's AI development landscape, developers face significant challenges when building applications that leverage powerful large language models (LLMs) through SaaS platforms like ChatGPT, Gemini, and others. While these services offer impressive capabilities, they come with substantial costs that can quickly escalate especially during the development lifecycle. Additionally, the inherent latency of web-based APIs creates frustrating bottlenecks during the critical testing and iteration phases of development, slowing down innovation and frustrating developers.
This talk will introduce the transformative approach of integrating local LLMs directly into their development environments. By bringing these models closer to where the code lives, developers can dramatically accelerate development lifecycles while maintaining complete control over model selection and configuration. This methodology effectively reduces costs to zero by eliminating dependency on pay-per-use SaaS services, while opening new possibilities for comprehensive integration testing, rapid prototyping, and specialized use cases.
Introducing the OSA 3200 SP and OSA 3250 ePRCAdtran
Adtran's latest Oscilloquartz solutions make optical pumping cesium timing more accessible than ever. Discover how the new OSA 3200 SP and OSA 3250 ePRC deliver superior stability, simplified deployment and lower total cost of ownership. Built on a shared platform and engineered for scalable, future-ready networks, these models are ideal for telecom, defense, metrology and more.
nnual (33 years) study of the Israeli Enterprise / public IT market. Covering sections on Israeli Economy, IT trends 2026-28, several surveys (AI, CDOs, OCIO, CTO, staffing cyber, operations and infra) plus rankings of 760 vendors on 160 markets (market sizes and trends) and comparison of products according to support and market penetration.
From Data Science to Business Value - Analytics Applied
1. From Data Science to Business Value
- Analytics Applied
Simon Zhang
Business Analytics
April 2012
1
2. Simon Zhang 1st
Director, Business Analytics at LinkedIn
San Francisco Bay Area | Internet Brief Introduction
Send Message View Profile
Simon Zhang, Business Analytics 2
3. Overview of Business Analytics @
Support 70% of total 2100+ internal LinkedIn employees
Cover 3 major diverse, scalable and growing revenue
streams
Hiring Solutions
Providing passive recruiting at scale and adding
social relevancy to active job searches
Marketing Solutions
Delivering marketers targeted access to one of the most
influential, affluent and educated audiences on the web
Premium Subscriptions
Enabling professionals to be more productive with
premium tools tailored by customer segment
3
Simon Zhang, Business Analytics
4. Today’s topics
• Insights •Value
• Analytics •Action
• Data •Energy
4
Simon Zhang, Business Analytics
5. Let’s take a tour to visit the Great Pyramid of Giza
Simon Zhang, Business Analytics 5
6. Traditional Framework of Analytics
And why it does not work at LinkedIn
Insights
Analytics
Layers Deep
Analysis
Ad-hoc Analysis The BI layer and Ad-
Hoc Analytical layers
Technology are misplaced.
BI & Reporting
Layers
Data Mgmt & Data Quality Mgmt
Tracking
6
Simon Zhang, Business Analytics
7. Let’s go deeper, and expand the pyramid…
Decision
Decision
6. Value!
Insights 5. “Interesting” is not enough, always ask “so what”.
Deep Analysis
4. Do the actual work…...
BI & Reporting
Ad-hoc Analysis
3. Make sure data is deployed efficiently
Data Mgmt & Data Quality Mgmt
with good quality.
2. Implement tracking to ensure data
Tracking
is useful.
Products 1. Understand the products first.
7
Simon Zhang, Business Analytics
8. Why it is still not enough?
Decision
1. Functional layers 2. Bottom layers consume
Insights
could be disconnected! 90% of analysts’ time
Deep Analysis
BI & Reporting
Ad-hoc Analysis
Data Mgmt & Data Quality Mgmt
Tracking
Products
8
Simon Zhang, Business Analytics
9. Business Analytics Evolution at LinkedIn
Decision
Decision
Insights
and Action
Deep
Analysis
BI & Reporting Easy,
Fast,
& Scalable
Ad-hoc Analysis Solutions
Data Mgmt & Data
Quality Mgmt
Tracking
……
Products
Past Current
9
Simon Zhang, Business Analytics
10. “Leverage” is our priority
In the past!
Current!
Moving forward…
Simon Zhang, Business Analytics 10
11. Next play: Unified Analytics
Products
Decision Tracking
Data
Insights
Mgmt
Deep Ad-hoc
Analysis Analysis
BI &
Reporting
11
Simon Zhang, Business Analytics
12. Hire the right people with the right……
+X%
80%
100% ?
15%
5%
0%
Skills + IQ & EQ + Passion = Good Analyst Great Analyst?
What else makes a great analyst?
Simon Zhang, Business Analytics 12
13. The power of belief.
Simon Zhang, Business Analytics 13
14. We are looking for people beyond “Get It Done” mindset!
100%
50%-
100%
0%
Almost Done Get It Done! ABC
What is ‘ABC’?
Simon Zhang, Business Analytics 14
15. ABC = Always Be Closing!
Simon Zhang, Business Analytics 15
16. Working as ONE person concept!
Decision
Insights
Deep Analysis
BI & Reporting
Ad-hoc Analysis
Data Mgmt & Data Quality Mgmt
Tracking
Products
The importance of team work!
Simon Zhang, Business Analytics 16
17. A very useful tool to solve complex analytical problems
• Make an offer no one can refuse!
LinkedIn Me
Team Team
Me
LinkedIn
• If this is your own business, are you going to do it in this way?
• We are not consultants/advisors, we are business owners!
Simon Zhang, Business Analytics 17
18. The power of LinkedIn’s networking effects
The power of LinkedIn data!
Member growth
and engagement
Relevant and
valuable products Critical mass
Technology of data
& services
platform
Simon Zhang, Business Analytics 18
19. Some comments from LinkedIn internal teams who are
supported by Business Analytics team……
To VP of Sales: "Want to shoot you a quick
note, w/o our analytics team’s work, I would
not have this vacation with my family now!”
“Now, I believe.”
“We believe!”
……
19
LinkedIn Property. Confidential
Simon Zhang, Business Analytics
20. Next Generation Data Science?
or
Albert Einstein Nikola Tesla
A Great An Amazing
Theoretical Proposer Applied Science Provider
Our job is to provide “energy”, so we go with Tesla
20
Simon Zhang, Business Analytics