Juice's Data Monetization Workshop helps product managers and business leaders consider the opportunities and challenges of transforming their valuable data into customer-facing products.
Is big data handicapped by "design"? Seven design principles for communicatin...Zach Gemignani
Is big data handicapped by "design"? This presentation shares the seven design principles for effective data communication. Good and bad examples for data visualizations highlight the choices designers make in helping non-analytical audiences understand the meaning in data.
Launching Data Products for Fun and ProfitZach Gemignani
You've made your big data investments, but where is the ROI. The answer may be in data products -- using your data assets to build customer-facing solutions that differentiate and generate new revenue streams. This presentation explains the opportunity and best practices for designing, building, and launching data products.
The document discusses building a data fluent organization. It defines data fluency as the ability to fluidly exchange and explore ideas within an organization using the language of data. It presents a data fluency framework that identifies the key elements needed for an organization to unlock the value in their data, including having data literate consumers who can understand and draw meaning from data, data producers who can create effective data products, and an ecosystem that supports sharing data products. The framework emphasizes that for data to have value, an organization needs the right skills, culture, processes, and technologies to allow fluid movement and sharing of ideas through data.
From the MarTech Conference in London, UK, October 20-21, 2015. SESSION: The Human Side of Analytics. PRESENTATION: The Human Side of Data - Given by Colin Strong - @colinstrong - Managing Director - Verve, Author of Humanizing Big Data. #MarTech DAY2
In the age of information overload, having a social media measurement practice is the key to successful execution of your social strategy. In this session, Debra Askanase looked at what data points tell you that your community cares and is willing to take action, a methodology to figuring what data is relevant to your outcomes, where to find the metrics that matter, and why setting up the right metrics can make the difference between knowing that people visited a page on your website, and if your social media actions sent them there.
Big Data: The Force That’s Good for Consumers and SocietyExperian_US
Craig Boundy, CEO of Experian North America, discusses how big data is being used as a force for good. Good for consumers, good for business, and good for society. He shares his perspective how Experian’s work in data and analytics has real-life applications.
GGV Capital: Venture Investing and the Cloud (2012)GGV Capital
This document discusses venture investing in cloud computing. It provides an overview of why VCs continue to see opportunities in the cloud sector. The presentation agenda covers trends disrupting the cloud like mobile and big data, as well as opportunities in serving small and medium businesses. The document concludes with advice for cloud startups on effectively approaching VCs for funding, emphasizing differentiation, market size, scalability, financial model, and chemistry over legal terms.
Is big data handicapped by "design"? Seven design principles for communicatin...Zach Gemignani
Is big data handicapped by "design"? This presentation shares the seven design principles for effective data communication. Good and bad examples for data visualizations highlight the choices designers make in helping non-analytical audiences understand the meaning in data.
Launching Data Products for Fun and ProfitZach Gemignani
You've made your big data investments, but where is the ROI. The answer may be in data products -- using your data assets to build customer-facing solutions that differentiate and generate new revenue streams. This presentation explains the opportunity and best practices for designing, building, and launching data products.
The document discusses building a data fluent organization. It defines data fluency as the ability to fluidly exchange and explore ideas within an organization using the language of data. It presents a data fluency framework that identifies the key elements needed for an organization to unlock the value in their data, including having data literate consumers who can understand and draw meaning from data, data producers who can create effective data products, and an ecosystem that supports sharing data products. The framework emphasizes that for data to have value, an organization needs the right skills, culture, processes, and technologies to allow fluid movement and sharing of ideas through data.
From the MarTech Conference in London, UK, October 20-21, 2015. SESSION: The Human Side of Analytics. PRESENTATION: The Human Side of Data - Given by Colin Strong - @colinstrong - Managing Director - Verve, Author of Humanizing Big Data. #MarTech DAY2
In the age of information overload, having a social media measurement practice is the key to successful execution of your social strategy. In this session, Debra Askanase looked at what data points tell you that your community cares and is willing to take action, a methodology to figuring what data is relevant to your outcomes, where to find the metrics that matter, and why setting up the right metrics can make the difference between knowing that people visited a page on your website, and if your social media actions sent them there.
Big Data: The Force That’s Good for Consumers and SocietyExperian_US
Craig Boundy, CEO of Experian North America, discusses how big data is being used as a force for good. Good for consumers, good for business, and good for society. He shares his perspective how Experian’s work in data and analytics has real-life applications.
GGV Capital: Venture Investing and the Cloud (2012)GGV Capital
This document discusses venture investing in cloud computing. It provides an overview of why VCs continue to see opportunities in the cloud sector. The presentation agenda covers trends disrupting the cloud like mobile and big data, as well as opportunities in serving small and medium businesses. The document concludes with advice for cloud startups on effectively approaching VCs for funding, emphasizing differentiation, market size, scalability, financial model, and chemistry over legal terms.
Data-Ed Webinar: Demystifying Big Data DATAVERSITY
We are in the middle of a data flood and we need to figure out how to tame it without drowning. Most of what has been written about Big Data is focused on selling hardware and services. But what about a Big Data Strategy that guides hardware and software decisions? While virtually every major organization is faced with the challenge of figuring out the approach for and the requirements of this new development, jumping into the fray hastily and unprepared will only reproduce the same dismal IT project results as previously experienced. Join Dr. Peter Aiken as he will debunk a number of misconceptions about Big Data as your un-typical IT project. He will provide guidance on how to establish realistic Big Data management plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers without getting lost in the hype.
Takeaways:
- The means by which Big Data techniques can complement existing data management practices
- The prototyping nature of practicing Big Data techniques
- The distinct ways in which utilizing Big Data can generate business value
- Bigger Data isn’t always Better Data
2015 is knocking on the door and will be an exciting and surprising year for the BI industry. However, not everything will be a surprise for Panorama as we are always on top of the latest trends influencing the Business Intelligence community.
• What will the future hold for the industry?
• What are our BI experts thoughts, predictions and internal assessments on what new directions the Business Intelligence community will see in the coming year?
• Countdown of the most important trends in the industry
Whitepaper: Thriving in the Big Data era Manage Data before Data Manages you Intellectyx Inc
Paper Overview -
Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone.
Data comes from everywhere and we are generating data more than ever before.
This white paper will explain what Big Data is and provide practical examples, concluding with a message how to put data your data to work.
The ABC of Data Governance: driving Information ExcellenceAlan D. Duncan
Overview of Data Governance requirements, techniques and outcomes. Presented at 5th Annual Records & Information Officers' Forum, Melbourne 19-20 Feb 2014.
This document discusses how to build a data-driven organization by collecting and analyzing metrics. It emphasizes that data is important for making decisions, hitting goals, and knowing if systems are working properly. The author promotes their tool called Larimar, which aims to automate data collection and analysis at the application level to provide insights without configuration. Building a data culture where employees are inspired to act on insights is key to success.
Managing for Effective Data Governance: workshop for DQ Asia Pacific Congress...Alan D. Duncan
This session reflects on the human aspects of Data Governance and examines what it takes to be successful in implementing effective information-enabled business transformation:
* Do we need to rethink our Data Governance strategies?
* Is enterprise-wide Data Management & Governance really achievable?
* What techniques and capabilities do we need to focus on?
* What skills and personal attributes does a Data Governance Manager need?
a whistlestop tour through some of the ethical dilemmas and challenges that arise in this "Big Data Age" and the various approaches to considering them, if not solving them.
In this 10 minute "lightning talk" delegates will get insights into some of the research agenda and issues being considered in this area, touching on Business Analytics, Data Quality, analytic risks, ethics and evidence-based decision-making culture
What every product manager needs to know about data science (ProductCamp Bost...ProductCamp Boston
Product management and data science work in close partnership at many of the highest performing organizations, which use analytics to develop better products, drive customer acquisition and retention, and create additional revenue streams. Not all organizations have the resources or desire to build an internal data science competency, but all can benefit from some degree of analytical orientation. When armed with basic quantitative literacy, product managers are often among the best positioned individuals in their organizations to recognize and build strong business cases around data related opportunities worth pursuing.
This presentation covers, through practical case studies, the information that all product managers should know about data science. Attendees will leave better able to recognize data related opportunities, understand the potential benefits and risks, build business cases for analytics, and learn more.
About Trevor Bass
Trevor Bass is a data scientist with a decade of experience building highly successful and innovative products and teams. He runs Bitten Labs, a data science management consultancy, education provider, and innovation lab. Prior to Bitten Labs, Trevor founded and built the data science function at payment processor Litle & Co (acquired by Vantiv), which performed product R&D, drove customer acquisition, retention, and upselling, provided quantitative consulting throughout the company and for its end customers, and established clear industry thought leadership. Trevor holds a Master's degree from Rutgers University and a Bachelor's degree magna cum laude from Harvard University, both in mathematics.
Great quotes from historical leading thinkers (Einstein) to contemporary (Tim O’Reilly & DJ Patil) have to say about the power, use and analysis of data.
Big data is impacting individuals in several ways based on their digital footprint and interactions online. As more data is collected through mobile devices, social media, and ecommerce sites about things like demographics, preferences, and behaviors, companies can use analytics to better target marketing and tailor product offerings. This results in personalized ads and discounts. Additionally, employers are able to gain deeper insights into current and potential employees by analyzing data from HR systems, social profiles, and other sources to inform talent management, succession planning, and career predictions. So in many areas of life, big data collected from individuals online is being used to shape the experiences, opportunities, and interactions they receive.
1. Sepa exactamente de donde provienen sus datos.
2. Asegure que todos en la organización comparten los mismos datos, con un acceso fácil y libre de complejidades.
3. Gobernabilidad de la información: mantenga a su equipo capacitado en procesos simples y transparentes.
The document discusses Pivotal's platform and strategy. It notes that Pivotal's platform allows for agile application development, access to big data solutions, and infrastructure flexibility. Examples are given of how companies like GE have used Pivotal's technologies to innovate faster using data and applications. The document promotes Pivotal's platform as uniquely positioned to help enterprises modernize their use of applications, data, and analytics.
Improving Business Intelligence Through DataLevelwing
While many businesses are actively collecting powerful data from various sources, they do not always have the internal resources or knowledge to maximize the potential at their fingertips. Bryan Cherok’s presentation will explore 1) sources of data, 2) data relationships, 3) business questions that can be answered through data, 4) Actionable results (both online and offline) that can be leveraged from data.
On February 21, 2013, Bryan Cherok, Group Director at Levelwing spoke to members of the Association of Information Technology Professionals (AITP) about taking a data driven approach to Business Analytics + Intelligence. Learn more about our recent speaking engagements and how you can book to speak: http://ow.ly/i1O1h
Data Culture and the Future of Analytics #CIAEX Exchange Jan 2016Jonathan Woodward
The document discusses the future of analytics and the importance of developing a data culture. It emphasizes the need to move beyond simply collecting big data, to leveraging all available data and using predictive and prescriptive analytics to gain insights. Developing machine intelligence and experimenting will be key to informing personalized customer experiences across all channels. Building trust through an integrated, customer-centered approach will be important for organizations going forward.
Nonprofits are undergoing a trend of increased transparency, accountability, and information-sharing. Institutional funders require data demonstrating need and impact, policy-makers rely on data to guide their course and decision-making processes, and media and donors demand more transparency. This workshop will show you how you can use this trend to your advantage.
In this training session you will learn to find data that can help promote your cause and turn it into meaningful communication. What you create can be used to guide internal efforts, inform policy-makers and media, and/or support fund seeking from institutional funders and donors.
The workshop will take you from data to story in 90 minutes including.
1. Where to find data that supports your cause
2. How to prepare your data using Excel (Including shortcuts and tips)
3. How to find meaningful insights in your data
4. Publishing engaging stories based on your data using LiveStories and Haiku Deck
About the presenter: Anders Maul has a background in marketing and international business. Anders worked for 5 years in business controlling for a large pharmaceutical company where he managed and analyzed $100M+ budgets and spent "a lot of time" looking at Excel spreadsheets. Anders Maul currently works for LiveStories, a technology startup that helps nonprofits, foundations, and governments to become more data-driven.
Seven Trends in Government Business IntelligenceTableau Software
Modern business intelligence (BI) trends in government for 2017 include:
1. Modern BI becoming the new normal as more governments adopt self-service analytics platforms.
2. The era of open data in government arriving as more data is released to the public.
3. Collaborative analytics growing from niche to essential as data becomes more accessible and cloud technology enables easier sharing.
4. Data-driven decision making exploding as people can more easily access and explore data to improve outcomes.
Architecting a Data Platform For Enterprise Use (Strata NY 2018)mark madsen
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT.
Long:
The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This session will discuss hidden design assumptions, review design principles to apply when building multi-use data infrastructure, and provide a reference architecture to use as you work to unify your analytics infrastructure.
The focus in our market has been on acquiring technology, and that ignores the more important part: the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform then it should be a designed rather than accidental architecture.
Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. What are the design principles that lead to good design and a functional data architecture? What are the assumptions that limit older approaches? How can one integrate with, migrate from or modernize an existing data environment? How will this affect an organization's data management practices? This tutorial will help you answer these questions.
Topics covered:
* A brief history of data infrastructure and past design assumptions
* Categories of data and data use in organizations
* Data architecture
* Functional architecture
* Technology planning assumptions and guidance
What is Your Data Worth? - Data Science Pop-up SeattleDomino Data Lab
With all the attention that big data’s place in the enterprise has been getting in the press as well as the coverage of a number of high-value data purchases and exchanges, people are beginning to wonder, “How much is my data worth?” Despite the volume of money being invested in data and data technology, methods for answering this question are severely lacking. In this talk, we will discuss relevant concepts to data’s valuation from related fields, the characteristics of data that make it unique from other economic goods, and practical considerations for how to begin thinking about the general value of data for the enterprise. Presented by Chloe Mawer, Data Scientist at SVDS.
This document outlines a 4-step process for turning data into dollars by monetizing data through data products. It covers: (1) understanding data as a product; (2) designing products that meet audience needs; (3) designing the product for purpose, usability and actionability; and (4) launching through selling, delivering and supporting the product. Examples of existing data products are also provided. The overall goal is to help organizations design and launch profitable data products.
Adaptive apps as the name suggests, anticipate and adapt to the needs of each customer to deliver more relevant and profitable interactions. By combining predictive analytics, big data, and APIs, they deliver individualized experiences that build strong, lasting relationships with customers. Adaptive apps promise to revolutionize how we imagine, design, and build apps and APIs for a wide variety of use cases.
In this track keynote, we’ll introduce the concept of a adaptive app, describe the opportunities they present, and discuss how you can start taking advantage of predictive analytics and APIs to accelerate your business.
Data-Ed Webinar: Demystifying Big Data DATAVERSITY
We are in the middle of a data flood and we need to figure out how to tame it without drowning. Most of what has been written about Big Data is focused on selling hardware and services. But what about a Big Data Strategy that guides hardware and software decisions? While virtually every major organization is faced with the challenge of figuring out the approach for and the requirements of this new development, jumping into the fray hastily and unprepared will only reproduce the same dismal IT project results as previously experienced. Join Dr. Peter Aiken as he will debunk a number of misconceptions about Big Data as your un-typical IT project. He will provide guidance on how to establish realistic Big Data management plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers without getting lost in the hype.
Takeaways:
- The means by which Big Data techniques can complement existing data management practices
- The prototyping nature of practicing Big Data techniques
- The distinct ways in which utilizing Big Data can generate business value
- Bigger Data isn’t always Better Data
2015 is knocking on the door and will be an exciting and surprising year for the BI industry. However, not everything will be a surprise for Panorama as we are always on top of the latest trends influencing the Business Intelligence community.
• What will the future hold for the industry?
• What are our BI experts thoughts, predictions and internal assessments on what new directions the Business Intelligence community will see in the coming year?
• Countdown of the most important trends in the industry
Whitepaper: Thriving in the Big Data era Manage Data before Data Manages you Intellectyx Inc
Paper Overview -
Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone.
Data comes from everywhere and we are generating data more than ever before.
This white paper will explain what Big Data is and provide practical examples, concluding with a message how to put data your data to work.
The ABC of Data Governance: driving Information ExcellenceAlan D. Duncan
Overview of Data Governance requirements, techniques and outcomes. Presented at 5th Annual Records & Information Officers' Forum, Melbourne 19-20 Feb 2014.
This document discusses how to build a data-driven organization by collecting and analyzing metrics. It emphasizes that data is important for making decisions, hitting goals, and knowing if systems are working properly. The author promotes their tool called Larimar, which aims to automate data collection and analysis at the application level to provide insights without configuration. Building a data culture where employees are inspired to act on insights is key to success.
Managing for Effective Data Governance: workshop for DQ Asia Pacific Congress...Alan D. Duncan
This session reflects on the human aspects of Data Governance and examines what it takes to be successful in implementing effective information-enabled business transformation:
* Do we need to rethink our Data Governance strategies?
* Is enterprise-wide Data Management & Governance really achievable?
* What techniques and capabilities do we need to focus on?
* What skills and personal attributes does a Data Governance Manager need?
a whistlestop tour through some of the ethical dilemmas and challenges that arise in this "Big Data Age" and the various approaches to considering them, if not solving them.
In this 10 minute "lightning talk" delegates will get insights into some of the research agenda and issues being considered in this area, touching on Business Analytics, Data Quality, analytic risks, ethics and evidence-based decision-making culture
What every product manager needs to know about data science (ProductCamp Bost...ProductCamp Boston
Product management and data science work in close partnership at many of the highest performing organizations, which use analytics to develop better products, drive customer acquisition and retention, and create additional revenue streams. Not all organizations have the resources or desire to build an internal data science competency, but all can benefit from some degree of analytical orientation. When armed with basic quantitative literacy, product managers are often among the best positioned individuals in their organizations to recognize and build strong business cases around data related opportunities worth pursuing.
This presentation covers, through practical case studies, the information that all product managers should know about data science. Attendees will leave better able to recognize data related opportunities, understand the potential benefits and risks, build business cases for analytics, and learn more.
About Trevor Bass
Trevor Bass is a data scientist with a decade of experience building highly successful and innovative products and teams. He runs Bitten Labs, a data science management consultancy, education provider, and innovation lab. Prior to Bitten Labs, Trevor founded and built the data science function at payment processor Litle & Co (acquired by Vantiv), which performed product R&D, drove customer acquisition, retention, and upselling, provided quantitative consulting throughout the company and for its end customers, and established clear industry thought leadership. Trevor holds a Master's degree from Rutgers University and a Bachelor's degree magna cum laude from Harvard University, both in mathematics.
Great quotes from historical leading thinkers (Einstein) to contemporary (Tim O’Reilly & DJ Patil) have to say about the power, use and analysis of data.
Big data is impacting individuals in several ways based on their digital footprint and interactions online. As more data is collected through mobile devices, social media, and ecommerce sites about things like demographics, preferences, and behaviors, companies can use analytics to better target marketing and tailor product offerings. This results in personalized ads and discounts. Additionally, employers are able to gain deeper insights into current and potential employees by analyzing data from HR systems, social profiles, and other sources to inform talent management, succession planning, and career predictions. So in many areas of life, big data collected from individuals online is being used to shape the experiences, opportunities, and interactions they receive.
1. Sepa exactamente de donde provienen sus datos.
2. Asegure que todos en la organización comparten los mismos datos, con un acceso fácil y libre de complejidades.
3. Gobernabilidad de la información: mantenga a su equipo capacitado en procesos simples y transparentes.
The document discusses Pivotal's platform and strategy. It notes that Pivotal's platform allows for agile application development, access to big data solutions, and infrastructure flexibility. Examples are given of how companies like GE have used Pivotal's technologies to innovate faster using data and applications. The document promotes Pivotal's platform as uniquely positioned to help enterprises modernize their use of applications, data, and analytics.
Improving Business Intelligence Through DataLevelwing
While many businesses are actively collecting powerful data from various sources, they do not always have the internal resources or knowledge to maximize the potential at their fingertips. Bryan Cherok’s presentation will explore 1) sources of data, 2) data relationships, 3) business questions that can be answered through data, 4) Actionable results (both online and offline) that can be leveraged from data.
On February 21, 2013, Bryan Cherok, Group Director at Levelwing spoke to members of the Association of Information Technology Professionals (AITP) about taking a data driven approach to Business Analytics + Intelligence. Learn more about our recent speaking engagements and how you can book to speak: http://ow.ly/i1O1h
Data Culture and the Future of Analytics #CIAEX Exchange Jan 2016Jonathan Woodward
The document discusses the future of analytics and the importance of developing a data culture. It emphasizes the need to move beyond simply collecting big data, to leveraging all available data and using predictive and prescriptive analytics to gain insights. Developing machine intelligence and experimenting will be key to informing personalized customer experiences across all channels. Building trust through an integrated, customer-centered approach will be important for organizations going forward.
Nonprofits are undergoing a trend of increased transparency, accountability, and information-sharing. Institutional funders require data demonstrating need and impact, policy-makers rely on data to guide their course and decision-making processes, and media and donors demand more transparency. This workshop will show you how you can use this trend to your advantage.
In this training session you will learn to find data that can help promote your cause and turn it into meaningful communication. What you create can be used to guide internal efforts, inform policy-makers and media, and/or support fund seeking from institutional funders and donors.
The workshop will take you from data to story in 90 minutes including.
1. Where to find data that supports your cause
2. How to prepare your data using Excel (Including shortcuts and tips)
3. How to find meaningful insights in your data
4. Publishing engaging stories based on your data using LiveStories and Haiku Deck
About the presenter: Anders Maul has a background in marketing and international business. Anders worked for 5 years in business controlling for a large pharmaceutical company where he managed and analyzed $100M+ budgets and spent "a lot of time" looking at Excel spreadsheets. Anders Maul currently works for LiveStories, a technology startup that helps nonprofits, foundations, and governments to become more data-driven.
Seven Trends in Government Business IntelligenceTableau Software
Modern business intelligence (BI) trends in government for 2017 include:
1. Modern BI becoming the new normal as more governments adopt self-service analytics platforms.
2. The era of open data in government arriving as more data is released to the public.
3. Collaborative analytics growing from niche to essential as data becomes more accessible and cloud technology enables easier sharing.
4. Data-driven decision making exploding as people can more easily access and explore data to improve outcomes.
Architecting a Data Platform For Enterprise Use (Strata NY 2018)mark madsen
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT.
Long:
The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This session will discuss hidden design assumptions, review design principles to apply when building multi-use data infrastructure, and provide a reference architecture to use as you work to unify your analytics infrastructure.
The focus in our market has been on acquiring technology, and that ignores the more important part: the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform then it should be a designed rather than accidental architecture.
Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. What are the design principles that lead to good design and a functional data architecture? What are the assumptions that limit older approaches? How can one integrate with, migrate from or modernize an existing data environment? How will this affect an organization's data management practices? This tutorial will help you answer these questions.
Topics covered:
* A brief history of data infrastructure and past design assumptions
* Categories of data and data use in organizations
* Data architecture
* Functional architecture
* Technology planning assumptions and guidance
What is Your Data Worth? - Data Science Pop-up SeattleDomino Data Lab
With all the attention that big data’s place in the enterprise has been getting in the press as well as the coverage of a number of high-value data purchases and exchanges, people are beginning to wonder, “How much is my data worth?” Despite the volume of money being invested in data and data technology, methods for answering this question are severely lacking. In this talk, we will discuss relevant concepts to data’s valuation from related fields, the characteristics of data that make it unique from other economic goods, and practical considerations for how to begin thinking about the general value of data for the enterprise. Presented by Chloe Mawer, Data Scientist at SVDS.
This document outlines a 4-step process for turning data into dollars by monetizing data through data products. It covers: (1) understanding data as a product; (2) designing products that meet audience needs; (3) designing the product for purpose, usability and actionability; and (4) launching through selling, delivering and supporting the product. Examples of existing data products are also provided. The overall goal is to help organizations design and launch profitable data products.
Adaptive apps as the name suggests, anticipate and adapt to the needs of each customer to deliver more relevant and profitable interactions. By combining predictive analytics, big data, and APIs, they deliver individualized experiences that build strong, lasting relationships with customers. Adaptive apps promise to revolutionize how we imagine, design, and build apps and APIs for a wide variety of use cases.
In this track keynote, we’ll introduce the concept of a adaptive app, describe the opportunities they present, and discuss how you can start taking advantage of predictive analytics and APIs to accelerate your business.
Hortonworks and Clarity Solution Group Hortonworks
Many organizations are leveraging social media to understand consumer sentiment and opinions about brands and products. Analytics in this area, however, is in its infancy and does not always provide a compelling result for effective business impact. Learn how consumer organizations can benefit by integrating social data with enterprise data to drive more profitable consumer relationships. This webinar is presented by Hortonworks and Clarity Solution Group, and will focus on the evolution of Hadoop, the clear advantage of Hortonworks distribution, and business challenges solved by “Consumer720.”
This document provides a beginner's guide to understanding web analytics. It discusses defining business objectives and key performance indicators to measure according to the business model. It recommends choosing 2 metrics each for acquisition, behavior, and outcomes for owned and rented digital properties, for a total of 24 metrics. Frameworks from Avinash Kaushik and Eric Peterson are presented for determining which metrics to track. General advice includes starting small and focusing on actionable analysis over perfecting data. Common web analytics tools are also mentioned.
Harness Your Product Data: Better Understanding User Behavior Across Channels...Aggregage
As a product leader, you are tasked with collecting and synthesizing your customer’s interactions with your product.The great news is that there are many tools used across your company which collect unique parts of your user’s journey. Whoops, did we say great news? As your sources of data increase, so do the complexities of unifying the data in a meaningful way. Join our webinar on October 17th with Segment and Looker to hear how they have solved these complex data issues. Plus, hear from SpotHero’s Product Manager and Business Intelligence Lead, Megan Bubley and Kate Owens, as they describe how they not only unified their siloed data but aligned their team on what it all means. They thought their challenge was improving conversion rate, but found that it was really defining a single source of truth and understanding how to track users in light of their increasingly complex product.
Modern Product Data Workflows: Harness Your Product Data: Better Understandin...Hannah Flynn
As a product leader, you are tasked with collecting and synthesizing your customer’s interactions with your product.The great news is that there are many tools used across your company which collect unique parts of your user’s journey. Whoops, did we say great news? As your sources of data increase, so do the complexities of unifying the data in a meaningful way.
Join Segment and Looker to hear how they have solved these complex data issues.
Plus, hear from SpotHero’s Product Manager and Business Intelligence Lead, Megan Bubley and Kate Owens, as they describe how they not only unified their siloed data but aligned their team on what it all means. They thought their challenge was improving conversion rate, but found that it was really defining a single source of truth and understanding how to track users in light of their increasingly complex product.
4 Easy Steps on how to Super Charge your Contact Center, EiG 2011 Milano KeynoteJoakim Nilsson
4 steps how to super charge your contact center is an introduction to the theory of social media monitoring.
Joakim Nilsson, Betclic Everest at EiG Milano 2011.
Digital Leadership Series : Shawn O'Neal Capgemini
Shawn O’Neal is VP of Global Marketing Data and Analytics at Unilever, part of the Consumer & Marketing Insights (CMI) team, and he leads the company’s Global People Data Program.
The ultimate objective of the program is to enable 1 billion relationships through digital data analysis and new ways of
connecting with people.
In his 12 years at Unilever, Shawn has worked across a range of roles in customer development
and consumer & marketing insights, with a particular focus on strategy, analysis, and the optimal use of information for
decision-making.
How Companies Turn Data Into Business ValueJamie Hribal
This document discusses how businesses can capture, combine, and turn data into actionable insights. It summarizes Umbric Data Services, a company that provides data solutions to help businesses harness data to improve strategies, operations, and revenue. The document outlines common misconceptions about big data, how to ask the right questions to examine customer value, and ways companies are using data analytics, including to find new customers, increase retention, improve service, manage marketing, and track social media.
The document is a magazine from Walter Analytics that discusses digital analytics and trends. It contains the following key points:
- Shaun Ernst reports on the growth of analytics in 2014 and tools like Universal Analytics, Enhanced Ecommerce, and demographic segmenting that provide more insights.
- Neil Walter discusses how to optimize pay-per-click acquisition through Google AdWords, noting it generated over $50 billion in 2013. Testing, refinement and constant innovation are key to success.
- Personalized content and real-time personalization using browser data is becoming more widely adopted by companies like Dropbox and Google to target messages to users.
Big Data Analytics for Banking, a Point of ViewPietro Leo
This document discusses how big data and analytics can transform the banking industry. It notes that digital transformation, enabled by big data and analytics, is creating pressures on banks from new digital native customers, large amounts of new data, new channels like mobile, and new competitors. It argues that to succeed in this new environment, banks need to build a 360-degree integrated customer view using big data, and ensure analytics are part of closed-loop business processes to create value. New applications and platforms like IBM Watson Analytics aim to make analytics more accessible and valuable to more users.
Pivotal Tech Talk - Using data to inform product decisions (22.10.14)Marc Abraham
Why is important to use data to inform product decisions? How can you best use data as part of the product lifecycle? This talk about the role that data can play in informing decisions (and answering questions) at different stages of the product lifecycle.
Age Friendly Economy - Improving your business with dataAgeFriendlyEconomy
The objective of this module is to gain an overview how you can use the data you already have available to improve your business.
Upon completion of this module you will:
- Learn the tips of how take advantage of the existing data you already have
- Be able to locate where internal data already lies within your company
- See how data can help you to build your brand
Verifeed open analytics_3min deck_071713_finalOpen Analytics
The document discusses Verifeed, a company that analyzes social media conversations to provide insights for enterprises. It highlights large potential markets, example use cases showing benefits, plans to grow revenue through an initial product launch and expansion. Key points include:
- Verifeed's platform allows customers to filter social data to identify relevant information, engage customers, and make better business decisions.
- There is significant potential demand totaling billions from industries like consumer goods, sports, financial services, and more.
- Early pilots showed benefits like increased engagement for sports and identifying customer attitudes for a dog food brand.
- The company plans an initial product launch, adding customers, and expanding its capabilities and markets over time.
This document discusses how technologies like big data and social media are changing product management. It provides examples of how companies like Vitaminwater, eBay, and Netflix use big data and social media to test new products and features. The key points are that these new tools allow for faster and cheaper A/B testing of new products, greater customer engagement during development, and the ability to analyze large amounts of user data to identify trends and spot new opportunities. The future will involve more customer involvement in development through signaling and personal data, and combining behavioral and attribute data from multiple sources.
4-Step Guide to Mobile Engagement - presented by Forrester Research & Message...SparkPost
The constant evolution of today’s mobile environment can seem complicated—even overwhelming. Your audience is on the move, making them favor businesses that can keep information relevant, and in quickly consumable form.
Hear from guest speaker Julie A. Ask, Vice President and Principal Analyst for Forester Research, as she outlines a simple four-step process for creating an engaging mobile experience.
Learn how to:
Engage expertly by identifying context
Build branding with a well-designed engagement experience
Optimize marketing dollars leveraging the entire mobile ecosystem
Refine strategy by monitoring performance
Anil Kaul, CEO and Co-Founder, AbsolutData delivered a session on institutionalizing Big Data analytics for organizations, at the Big Data Innovation Summit, London on 1st May, 2013.
AbsolutData is a global leader in applying analytics to drive sales and increase profits for its customers. AbsolutData has built strong expertise and traction with Fortune 1000 companies across 40 countries. We specialize in big data, high end business analytics, predictive modeling, research, reporting, social media analytics and data management services. AbsolutData delivers world class analytics solutions by combining their expertise in industry domains, analytical techniques and sophisticated tools.
Visit us here : www.absolutdata.com
Capitalize On Social Media With Big Data AnalyticsHassan Keshavarz
This document discusses how companies can capitalize on social media through big data analytics. It notes that while social media promises benefits, most companies struggle to measure the true value and impact. To leverage social media effectively, the entire business must be aligned in their interactions. The document also discusses how analyzing large datasets through big data analytics can provide strategic insights for success, maximize product performance, and deliver real business value. It emphasizes the need for companies to measure social media's impact on key metrics and business goals.
The Star Trek Guide to Better Analytics.pdfZach Gemignani
A lesson in better analytics brought you by the characters of Star Trek:
* Set your mission
* Bring together the right team
* Follow the phase(r)s of the voyage(r)
A comparison of pie chart visualizations provided by leading data visualization solutions. Each tool is compared across 4 categories:
1. INTERPRET-ABILITY even with many data elements
2. COMPARABILITY of differences in segment values
3. INTERACT-ABILITY to enable data exploration
4. SCAN-ABILITY design of color, contrast, layout
Learn more here: https://www.juiceanalytics.com/writing/a-better-pie-chart
Measures and Dimensions: Your Data IngredientsZach Gemignani
Data fields can either be measures or dimensions. This presentation provides an overview of these data types and what you want to know before you begin visualizing your data.
Data is a collection of information about a group of things and/or activities. For example, data can be collected about the many characters in the Harry Potter series, including their name, skills, house, wand details, and patronus. This information can be organized into a data table with each row representing a character and each column representing a different data point. Once this character data is collected and organized, it can be analyzed to gain new insights, such as sorting characters by house to see which has the most members or examining gender imbalance among the houses. While data is not a perfect reflection of reality, organizing real-world information into a collectable format allows for analysis that can lead to a better understanding.
A Practical Guide to the Art of Data StorytellingZach Gemignani
When only 18% of companies believe they can gather and use data insights effectively (McKinsey), there has to be a better way. This presentation shares our principles and framework for designing impactful data stories.
Beyond Data Visualization: What's next in communicating with data?Zach Gemignani
We've made great progress in learning how to visualize data, yet a gap still remains between the data experts and the data consumers who might take action on the data. This presentation, shared at the Nashville Analytics Summit, explains how we can bring people into the process of communicating data and guide them to informed actions.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!