Product Management in the Era of Data ScienceMandar Parikh
My slide-deck from a webinar on the same topic for the Institute of Product Leadership, April 4th, 2017
What does it take to build killer products in the “AI-first” era? What makes for a great Data Science-driven product and how do great Product Managers leverage Data Science to drive value for customers? Find out how to avoid the pitfalls of hype-chasing Data Science tactics. Learn how to work with Data Science and Engineering to build a compelling product and solve real problems.
Mandar takes a practitioner’s approach to present his recipe for success for building Data Science-driven products that drive enduring value for customers.
The document discusses UX field research basics in three sections. Section 1 covers planning and preparation, including developing test plans, recruitment screeners, interview guides, and logistics. Section 2 discusses facilitating research through introductions, managing flow, improvisation, body language, and energy levels. Section 3 is about analyzing and reporting research findings by consolidating data, finding the overall story, and determining what story to tell from the research. The overall message is that thorough planning, proper facilitation in the field, and identifying patterns in the data are key to effective UX field research.
Lean Analytics is a set of rules to make data science more streamlined and productive. It touches on many aspects of what a data scientist should be and how a data science project should be defined to be successful. During this presentation Richard will present where data science projects go wrong, how you should think of data science projects, what constitutes success in data science and how you can measure progress. This session will be loaded with terms, stories and descriptions of project successes and failures. If you're wondering whether you're getting value out of data science, how to get more value out of it and even whether you need it then this talk is for you!
What you will take away from this session
Learn how to make your data science projects successful
Evaluate how to track progress and report on the efficacy of data science solutions
Understand the role of engineering and data scientists
Understand your options for processes and software
Get It Right the First Time Through Cheap and Easy DIY Usability Testing - Dr...DesignHammer
“If you want a great site, you’ve got to test.” - Steve Krug, Usability Expert
At it’s most basic, usability is about insuring something, such as a website, works well. Without usability testing results, design and functionality decisions are based on opinion. Despite the understood importance, many organizations believe usability testing is too expensive and time consuming to fit their budget and schedule. We will show how usability tests can be performed both quickly and inexpensively using popular DIY usability techniques. We will cover both analog and online tools for user surveys, card sorting, tree testing, first click testing, and user testing. You don’t need a lot of participants either—many valuable tests may be performed with as few as five subjects.
Takeaways:
What is usability testing?
What, when, and who to test?
List of free and/or inexpensive usability tools
How to plan and run your own usability test?
What to do with the data when you are done?
Website Redesign: Are You Planning To Succeed Or Succeeding To Fail? It All S...DesignHammer
Don’t let planning your next website project become a full-time second job. Join us for a fresh look at the planning, RFP writing, and hiring process. We’ll provide a “from the trenches” look at common points of failure and provide tactics for avoiding them through guidelines, tips, case studies, and role-play.
Presented at: Capital Camp and Gov Days
Presented by: David Minton and Stephen Pashby
Date: August 1, 2014
Link: http://2014.capitalcampandgovdays.com/capital-camp-and-gov-days/sessions/website-redesign-are-you-planning-succeed-or-succeeding-fail-it
Website Redesign in Drupal: are you planning to succeed or succeeding to fail...DesignHammer
Are you currently using, or considering Drupal? Whether you are looking to develop an organization's initial website or upgrade an existing one, managing a website development project can be a daunting task, especially if your organization lacks internal website design and development expertise. Drupal provides an extremely flexible platform, but determining an appropriate approach to best fit your organization's needs and budget often involves navigating the benefits and costs of different providers, approaches, and technologies.
This document discusses how analytics and data science projects can benefit from adopting agile principles and methods. It notes that analytics problems are often non-linear like scientific problems, requiring an agile approach with rapid experimentation and refinement of models and insights over time based on feedback. Adapting agile practices like user stories and incremental improvements can help analytics teams discover valuable insights and continuously learn from their work and data. The document also promotes the use of new technologies like data lakes and data virtualization to help provision agile data architectures that support rapid analytics experimentation.
How to Use Website Strategy to Rise to New HeightsDesignHammer
Through years of experience working with clients developing websites to overcome organizational obstacles we have refined a process for gathering critical information. By determining what the website needs to do, we can design a blueprint to build a website with measurable success.
Product Management in the Era of Data ScienceMandar Parikh
My slide-deck from a webinar on the same topic for the Institute of Product Leadership, April 4th, 2017
What does it take to build killer products in the “AI-first” era? What makes for a great Data Science-driven product and how do great Product Managers leverage Data Science to drive value for customers? Find out how to avoid the pitfalls of hype-chasing Data Science tactics. Learn how to work with Data Science and Engineering to build a compelling product and solve real problems.
Mandar takes a practitioner’s approach to present his recipe for success for building Data Science-driven products that drive enduring value for customers.
The document discusses UX field research basics in three sections. Section 1 covers planning and preparation, including developing test plans, recruitment screeners, interview guides, and logistics. Section 2 discusses facilitating research through introductions, managing flow, improvisation, body language, and energy levels. Section 3 is about analyzing and reporting research findings by consolidating data, finding the overall story, and determining what story to tell from the research. The overall message is that thorough planning, proper facilitation in the field, and identifying patterns in the data are key to effective UX field research.
Lean Analytics is a set of rules to make data science more streamlined and productive. It touches on many aspects of what a data scientist should be and how a data science project should be defined to be successful. During this presentation Richard will present where data science projects go wrong, how you should think of data science projects, what constitutes success in data science and how you can measure progress. This session will be loaded with terms, stories and descriptions of project successes and failures. If you're wondering whether you're getting value out of data science, how to get more value out of it and even whether you need it then this talk is for you!
What you will take away from this session
Learn how to make your data science projects successful
Evaluate how to track progress and report on the efficacy of data science solutions
Understand the role of engineering and data scientists
Understand your options for processes and software
Get It Right the First Time Through Cheap and Easy DIY Usability Testing - Dr...DesignHammer
“If you want a great site, you’ve got to test.” - Steve Krug, Usability Expert
At it’s most basic, usability is about insuring something, such as a website, works well. Without usability testing results, design and functionality decisions are based on opinion. Despite the understood importance, many organizations believe usability testing is too expensive and time consuming to fit their budget and schedule. We will show how usability tests can be performed both quickly and inexpensively using popular DIY usability techniques. We will cover both analog and online tools for user surveys, card sorting, tree testing, first click testing, and user testing. You don’t need a lot of participants either—many valuable tests may be performed with as few as five subjects.
Takeaways:
What is usability testing?
What, when, and who to test?
List of free and/or inexpensive usability tools
How to plan and run your own usability test?
What to do with the data when you are done?
Website Redesign: Are You Planning To Succeed Or Succeeding To Fail? It All S...DesignHammer
Don’t let planning your next website project become a full-time second job. Join us for a fresh look at the planning, RFP writing, and hiring process. We’ll provide a “from the trenches” look at common points of failure and provide tactics for avoiding them through guidelines, tips, case studies, and role-play.
Presented at: Capital Camp and Gov Days
Presented by: David Minton and Stephen Pashby
Date: August 1, 2014
Link: http://2014.capitalcampandgovdays.com/capital-camp-and-gov-days/sessions/website-redesign-are-you-planning-succeed-or-succeeding-fail-it
Website Redesign in Drupal: are you planning to succeed or succeeding to fail...DesignHammer
Are you currently using, or considering Drupal? Whether you are looking to develop an organization's initial website or upgrade an existing one, managing a website development project can be a daunting task, especially if your organization lacks internal website design and development expertise. Drupal provides an extremely flexible platform, but determining an appropriate approach to best fit your organization's needs and budget often involves navigating the benefits and costs of different providers, approaches, and technologies.
This document discusses how analytics and data science projects can benefit from adopting agile principles and methods. It notes that analytics problems are often non-linear like scientific problems, requiring an agile approach with rapid experimentation and refinement of models and insights over time based on feedback. Adapting agile practices like user stories and incremental improvements can help analytics teams discover valuable insights and continuously learn from their work and data. The document also promotes the use of new technologies like data lakes and data virtualization to help provision agile data architectures that support rapid analytics experimentation.
How to Use Website Strategy to Rise to New HeightsDesignHammer
Through years of experience working with clients developing websites to overcome organizational obstacles we have refined a process for gathering critical information. By determining what the website needs to do, we can design a blueprint to build a website with measurable success.
5 Proven Strategies For a Successful Analytics Product LaunchGoodData
Here are the key things to consider from a legal perspective when launching an analytics product:
- Review your standard customer contracts and determine if any changes are needed to address things like data ownership, sharing analytics with 3rd parties, limitations of liability related to analytics outputs, etc.
- Consider creating separate contracts or terms for the analytics offering to clearly delineate responsibilities related to the core product vs the analytics.
- Ensure you have appropriate data security, privacy and confidentiality terms in place that address things like where data is stored and accessed, who has rights to use it, obligations around breaches, etc.
- Determine if any regulatory issues like GDPR need to be addressed depending on the data being used and customers/regions
How to Speak Human - Turning Big Data Insights into Actionable Business StrategyLuciano Pesci, PhD
Big Data has failed to deliver on its promise because decision-makers and technical practitioners aren't speaking the same language. Cryptic data outputs have to be translated into simple strategy recommendations to turn this trend around.
Pdf analytics-and-witch-doctoring -why-executives-succumb-to-the-black-box-me...OrateTeam
This document discusses common issues ("pathologies") that organizations face when adopting analytics and data-driven approaches. It summarizes these pathologies in 3 sentences or less:
Organizations often treat analytics as a "black box" without understanding how it works due to the technical nature of analytics and lack of transparency in algorithms and methods. Many projects fail because organizations jump into analytics without properly preparing their data, validating results, or planning how insights will be implemented and drive business changes. To successfully adopt analytics, organizations must ask critical questions about data quality, intended use cases, and consequences of results in order to focus efforts and avoid wasting resources on initiatives that do not provide value.
Agile Analytics: Delivering on Promises by Atif Abdul RahmanAgile ME
Big Data is all the hype in town yet the real value still remain with delivering analytics that create business impact. Agile Analytics sets out to unleash the true promise usually lost in lengthy, elephantine projects and years of data management purists' pursuits of perfection. That is exactly what separates these big data technologies: They promise greater agility. But is a supportive technology enough or even mandatory to become more agile? We will go through the value chain of delivering high impact analytics using agile practices and devise a jumpstarter kit for you to adopt and adapt.
Webinar: Business Intelligence From The Inside OutCorSourceTechPDX
There are a lot of terms thrown around in the world of business intelligence and analytics. Presented as a webinar, this deck is an introduction to the terminology and power of business intelligence to transform companies.
Activate 2019 Opening Keynote, Will Hayes, CEO, LucidworksLucidworks
Hear Will Hayes, CEO of Lucidworks deliver the opening keynote at ACTIVATE 2019, the Search and AI Conference hosted by Lucidworks. http://www.activate-conf.com
How to take the stress out of writing case studiesDesignHammer
Case studies are great sales tools. But how many times have you gotten ready to submit a pitch, and realized you were missing case studies for the projects you want to highlight? You assumed somebody on the team would write one after the projected deployed, but everyone got reassigned to other projects, and it slipped through the cracks, again. And now you are sad. But it doesn’t have to be this way.
Learn why we draft our project case studies before writing the first line of code, and how the team uses the case study draft as a quality assurance step to help ensure a successful project outcome.
Waterstons’ Business analytics specialists Dan, Chris and Michael will present Waterstons’ latest thinking and experience around the drivers behind analytics and intelligence in the business environment, and the current business analytics marketplace.
They will discuss Waterstons’ Business Insights Maturity Model, which sets out the methodology we use to help our customers derive competitive advantage, improve productivity and management control, and provide support for better business decision making, before using case studies to explain how real businesses are leveraging the power of modern analytics tools.
Data Collection for Research Based Organizations to Aid Research!NTEN
The document discusses the Population Council's use of electronic data capture to conduct research surveys. Some key points:
- The Council partnered with their IT group to develop an audio computer-assisted self-interview (ACASI) system to anonymously collect sensitive data from research participants.
- Using electronic surveys rather than in-person interviews was found to reduce bias and get more honest answers to sensitive questions.
- However, they did not capture some useful data like the total number of surveys completed and indicators over time.
- Moving forward, the IT group recommends exploring unconventional tools like SMS and capturing survey metadata to help with monitoring, evaluation and future projects.
Agile Analytics: The Secret to Test, Improve, Fail & Succeed Quickly.Venveo
The document discusses agile analytics and its benefits for businesses. Agile analytics involves rapidly testing hypotheses, analyzing results, and making improvements to gain a better understanding of customers, get results more quickly, and reduce risks. It recommends businesses focus on a single problem, develop small testable hypotheses, iterate testing every 2-4 weeks with specific changes, and use findings to direct the next round of improvements. Practicing agile analytics allows organizations to test, improve, fail, and succeed quickly.
How Would You Start? - starting projects on the right footIIBA UK Chapter
This document proposes a new framework called SADD (Strategic Analysis Discovery Design) for pre-project analysis. It argues that most projects fail to meet objectives because they lack proper upfront analysis and design. SADD involves fact-finding, analysis, ideation and high-level design before a project begins. It is presented as an alternative to traditional project management approaches with a stronger emphasis on understanding problems, exploring options creatively and establishing a holistic strategic direction and high-level solution design. The framework has both a process model and principles like design thinking. It aims to establish the right conditions for success before a project is initiated.
Results from the Enterprise Search and Findability Survey 2012Findwise
The survey found that many organizations struggle with findability due to a lack of search strategy and standards. Over half of respondents said it is moderately or very hard to find the right information, and less than 20% were satisfied with their search capabilities. Issues included poor search functionality, inconsistent tagging, and not knowing where to look. While most organizations recognize that findability is important, many do not allocate dedicated budgets, resources, or ownership to search. The report recommends that organizations prioritize improving metadata, taxonomy, and strategy to enhance the value of their existing information assets.
The Next Generation of AI-Powered SearchLucidworks
Trey Grainger discusses the next generation of AI-powered search. He covers many techniques used in modern search systems including signals boosting, learning to rank, semantic search, collaborative filtering, personalized search, and neural search. Grainger also discusses using knowledge graphs, domain modeling, multimodal learning, and thought vectors to power conceptual scoring and merged content understanding.
Data Science-Why?What?How? By Hari PrasadHari Prasad
This document provides an overview of data science from several perspectives:
- It introduces the presenter and their background/experience in fields related to data science such as social network analysis, big data analytics, and machine learning applications.
- The agenda outlines exploring why data science is important, what it involves technically, and how the data science process works using a standardized approach.
- Key aspects of what data science involves are discussed like machine learning algorithms, the data science skillset, and how machine learning techniques can be demystified and applied to problems.
- The process of data science is reviewed using a popular CRISP-DM framework and an IBM methodology, with examples of how questions can initiate a
The Data Greenhouse DevOps Measurement at Scalesparkagility
This document summarizes a presentation on developing a "Data Greenhouse" to integrate measurement into DevOps programs. The presentation covers:
- Why program leaders often miss targets for data collection due to issues like unstructured data and lack of integration
- Generating leadership interest in unknowns by communicating initial data findings and insights
- Whether measurement efforts should be their own initiative given barriers to improvement
- Signs that measurement is paying off such as teams independently problem-solving and requesting data
- Next steps like partnering with teams on analysis and an improved measurement platform
Ew conversation feb28 2012 mind mapping and mind jetRon Burns
The document discusses an upcoming webinar titled "The Conversation - Telling Your Story... Visually" featuring Marty Levy and Kai Van de Carr from Mindjet who will discuss how their mind mapping software Mindjet can be used to visually plan strategic accounts, complex sales, projects, and more by easily capturing and organizing information, dependencies, and risks. The webinar will demonstrate how Mindjet allows users to turn analysis into action when planning accounts and projects in a visual, collaborative way compared to traditional methods.
UX Field Research Basics Chicago Camp 2017David Farkas
David Farkas gave a presentation on UX field research basics. He covered three main sections: planning and preparation, which included developing test plans, recruitment screeners, and interview guides; facilitating research, such as introductions, managing session flow, and body language; and analysis and reporting, like consolidating data, finding themes in the research, and determining what story to tell with the findings. The overall presentation provided guidance on how to properly plan for, conduct, and analyze UX field research.
UX Field Research Basics, Abstractions 2019David Farkas
This document discusses UX field research basics. It covers planning and preparation, conducting research in the field, and analyzing findings. In the planning section, it describes creating documents like test plans, interview guides, and recruitment materials. For fieldwork, it discusses facilitating sessions, using improvisation techniques, and managing logistics. Finally, the analysis section explores consolidating data, identifying themes, and determining the best way to tell the research story. The overall message is that thorough planning and preparation are essential for high-quality field research.
5 Proven Strategies For a Successful Analytics Product LaunchGoodData
Here are the key things to consider from a legal perspective when launching an analytics product:
- Review your standard customer contracts and determine if any changes are needed to address things like data ownership, sharing analytics with 3rd parties, limitations of liability related to analytics outputs, etc.
- Consider creating separate contracts or terms for the analytics offering to clearly delineate responsibilities related to the core product vs the analytics.
- Ensure you have appropriate data security, privacy and confidentiality terms in place that address things like where data is stored and accessed, who has rights to use it, obligations around breaches, etc.
- Determine if any regulatory issues like GDPR need to be addressed depending on the data being used and customers/regions
How to Speak Human - Turning Big Data Insights into Actionable Business StrategyLuciano Pesci, PhD
Big Data has failed to deliver on its promise because decision-makers and technical practitioners aren't speaking the same language. Cryptic data outputs have to be translated into simple strategy recommendations to turn this trend around.
Pdf analytics-and-witch-doctoring -why-executives-succumb-to-the-black-box-me...OrateTeam
This document discusses common issues ("pathologies") that organizations face when adopting analytics and data-driven approaches. It summarizes these pathologies in 3 sentences or less:
Organizations often treat analytics as a "black box" without understanding how it works due to the technical nature of analytics and lack of transparency in algorithms and methods. Many projects fail because organizations jump into analytics without properly preparing their data, validating results, or planning how insights will be implemented and drive business changes. To successfully adopt analytics, organizations must ask critical questions about data quality, intended use cases, and consequences of results in order to focus efforts and avoid wasting resources on initiatives that do not provide value.
Agile Analytics: Delivering on Promises by Atif Abdul RahmanAgile ME
Big Data is all the hype in town yet the real value still remain with delivering analytics that create business impact. Agile Analytics sets out to unleash the true promise usually lost in lengthy, elephantine projects and years of data management purists' pursuits of perfection. That is exactly what separates these big data technologies: They promise greater agility. But is a supportive technology enough or even mandatory to become more agile? We will go through the value chain of delivering high impact analytics using agile practices and devise a jumpstarter kit for you to adopt and adapt.
Webinar: Business Intelligence From The Inside OutCorSourceTechPDX
There are a lot of terms thrown around in the world of business intelligence and analytics. Presented as a webinar, this deck is an introduction to the terminology and power of business intelligence to transform companies.
Activate 2019 Opening Keynote, Will Hayes, CEO, LucidworksLucidworks
Hear Will Hayes, CEO of Lucidworks deliver the opening keynote at ACTIVATE 2019, the Search and AI Conference hosted by Lucidworks. http://www.activate-conf.com
How to take the stress out of writing case studiesDesignHammer
Case studies are great sales tools. But how many times have you gotten ready to submit a pitch, and realized you were missing case studies for the projects you want to highlight? You assumed somebody on the team would write one after the projected deployed, but everyone got reassigned to other projects, and it slipped through the cracks, again. And now you are sad. But it doesn’t have to be this way.
Learn why we draft our project case studies before writing the first line of code, and how the team uses the case study draft as a quality assurance step to help ensure a successful project outcome.
Waterstons’ Business analytics specialists Dan, Chris and Michael will present Waterstons’ latest thinking and experience around the drivers behind analytics and intelligence in the business environment, and the current business analytics marketplace.
They will discuss Waterstons’ Business Insights Maturity Model, which sets out the methodology we use to help our customers derive competitive advantage, improve productivity and management control, and provide support for better business decision making, before using case studies to explain how real businesses are leveraging the power of modern analytics tools.
Data Collection for Research Based Organizations to Aid Research!NTEN
The document discusses the Population Council's use of electronic data capture to conduct research surveys. Some key points:
- The Council partnered with their IT group to develop an audio computer-assisted self-interview (ACASI) system to anonymously collect sensitive data from research participants.
- Using electronic surveys rather than in-person interviews was found to reduce bias and get more honest answers to sensitive questions.
- However, they did not capture some useful data like the total number of surveys completed and indicators over time.
- Moving forward, the IT group recommends exploring unconventional tools like SMS and capturing survey metadata to help with monitoring, evaluation and future projects.
Agile Analytics: The Secret to Test, Improve, Fail & Succeed Quickly.Venveo
The document discusses agile analytics and its benefits for businesses. Agile analytics involves rapidly testing hypotheses, analyzing results, and making improvements to gain a better understanding of customers, get results more quickly, and reduce risks. It recommends businesses focus on a single problem, develop small testable hypotheses, iterate testing every 2-4 weeks with specific changes, and use findings to direct the next round of improvements. Practicing agile analytics allows organizations to test, improve, fail, and succeed quickly.
How Would You Start? - starting projects on the right footIIBA UK Chapter
This document proposes a new framework called SADD (Strategic Analysis Discovery Design) for pre-project analysis. It argues that most projects fail to meet objectives because they lack proper upfront analysis and design. SADD involves fact-finding, analysis, ideation and high-level design before a project begins. It is presented as an alternative to traditional project management approaches with a stronger emphasis on understanding problems, exploring options creatively and establishing a holistic strategic direction and high-level solution design. The framework has both a process model and principles like design thinking. It aims to establish the right conditions for success before a project is initiated.
Results from the Enterprise Search and Findability Survey 2012Findwise
The survey found that many organizations struggle with findability due to a lack of search strategy and standards. Over half of respondents said it is moderately or very hard to find the right information, and less than 20% were satisfied with their search capabilities. Issues included poor search functionality, inconsistent tagging, and not knowing where to look. While most organizations recognize that findability is important, many do not allocate dedicated budgets, resources, or ownership to search. The report recommends that organizations prioritize improving metadata, taxonomy, and strategy to enhance the value of their existing information assets.
The Next Generation of AI-Powered SearchLucidworks
Trey Grainger discusses the next generation of AI-powered search. He covers many techniques used in modern search systems including signals boosting, learning to rank, semantic search, collaborative filtering, personalized search, and neural search. Grainger also discusses using knowledge graphs, domain modeling, multimodal learning, and thought vectors to power conceptual scoring and merged content understanding.
Data Science-Why?What?How? By Hari PrasadHari Prasad
This document provides an overview of data science from several perspectives:
- It introduces the presenter and their background/experience in fields related to data science such as social network analysis, big data analytics, and machine learning applications.
- The agenda outlines exploring why data science is important, what it involves technically, and how the data science process works using a standardized approach.
- Key aspects of what data science involves are discussed like machine learning algorithms, the data science skillset, and how machine learning techniques can be demystified and applied to problems.
- The process of data science is reviewed using a popular CRISP-DM framework and an IBM methodology, with examples of how questions can initiate a
The Data Greenhouse DevOps Measurement at Scalesparkagility
This document summarizes a presentation on developing a "Data Greenhouse" to integrate measurement into DevOps programs. The presentation covers:
- Why program leaders often miss targets for data collection due to issues like unstructured data and lack of integration
- Generating leadership interest in unknowns by communicating initial data findings and insights
- Whether measurement efforts should be their own initiative given barriers to improvement
- Signs that measurement is paying off such as teams independently problem-solving and requesting data
- Next steps like partnering with teams on analysis and an improved measurement platform
Ew conversation feb28 2012 mind mapping and mind jetRon Burns
The document discusses an upcoming webinar titled "The Conversation - Telling Your Story... Visually" featuring Marty Levy and Kai Van de Carr from Mindjet who will discuss how their mind mapping software Mindjet can be used to visually plan strategic accounts, complex sales, projects, and more by easily capturing and organizing information, dependencies, and risks. The webinar will demonstrate how Mindjet allows users to turn analysis into action when planning accounts and projects in a visual, collaborative way compared to traditional methods.
UX Field Research Basics Chicago Camp 2017David Farkas
David Farkas gave a presentation on UX field research basics. He covered three main sections: planning and preparation, which included developing test plans, recruitment screeners, and interview guides; facilitating research, such as introductions, managing session flow, and body language; and analysis and reporting, like consolidating data, finding themes in the research, and determining what story to tell with the findings. The overall presentation provided guidance on how to properly plan for, conduct, and analyze UX field research.
UX Field Research Basics, Abstractions 2019David Farkas
This document discusses UX field research basics. It covers planning and preparation, conducting research in the field, and analyzing findings. In the planning section, it describes creating documents like test plans, interview guides, and recruitment materials. For fieldwork, it discusses facilitating sessions, using improvisation techniques, and managing logistics. Finally, the analysis section explores consolidating data, identifying themes, and determining the best way to tell the research story. The overall message is that thorough planning and preparation are essential for high-quality field research.
Trendspotting: Helping you make sense of large information sourcesMarieke Guy
This document provides an overview of a presentation on trendspotting and making sense of large information sources. The presentation introduces qualitative data analysis and thematic coding. It discusses collecting and organizing qualitative data, identifying themes and patterns through coding, and presenting findings through reports, visualizations and infographics. Practical exercises are included to have participants analyze text data by identifying codes and themes in small groups. Resources on qualitative analysis techniques are also provided.
Deliver:agile2018 - basic user research skills for non-designersSophie Freiermuth
The document describes a workshop on user research skills for teams without dedicated designers. It covers the five steps of user research: identifying needs, preparing sessions, running sessions, analyzing results, and sharing findings. It provides guidance on note-taking, facilitating sessions, and incorporating research into agile processes. The workshop demonstrates how to write a discussion guide and practice facilitation. It emphasizes that research should provide actionable insights and add value to the product.
JavaZone 2018 - A Practical(ish) Introduction to Data ScienceMark West
Code: https://github.com/markwest1972/titanic
Video: https://vimeo.com/289705893
Data Science has been described as the sexiest job of the 21st Century. But what is Data Science? And what has Machine Learning got to do with all of this?
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
1. I’ll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
2. Next up we’ll run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
NDC Oslo : A Practical Introduction to Data ScienceMark West
Data Science has been described as the sexiest job of the 21st Century. But what is Data Science? And what has Machine Learning got to do with all this?
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
(1) I’ll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
(2) Next up we’ll run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
(3) The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
Ofer Ron, senior data scientist at LivePerson.
Recently, I've had the pleasure of presenting an introduction to Data Science and data driven products at DevconTLV
I focused this talk around the basic ideas of data science, not the technology used, since I thought that far too many times companies and developers rush to play around with "big data" related technologies, instead of figuring out what questions they want to answer, and whether these answers form a successful product.
Many of us data science and business analytics practitioners perform research and analysis for decision makers on a regular basis. The deliverable of such analysis often results in a Power Point presentation, and/or a model that needs to be productionalized. The code used to produce the analysis also needs to be considered a deliverable.
Many of us perform analysis without reproducibility in mind. With the increasing democratization of data, it is becoming more and more important for people that may not have scientific training to be able to create analysis that can be picked up by somebody else who can then reproduce your results. That, and creating reproducible research is just solid science.
We are going to spend an evening walking though the various tools available to create reproducible research on Big Data. You will get introduced to the Tidyverse of R packages and how to use them. We will discuss the ins and outs of various notebook technologies like Jupyter, and Zeppelin. You will have an opportunity to learn how to get up and running with R and Spark and the various options you have to learn on real clusters instead of just your local environment. There also be a quick introduction to source control and the various options you have around using Git.
The theme of the evening will be “getting started”. We will go over various training resources and show you the optimal path to go from zero to master. Some commentary will be provided around the current state of the job market and intel from the front lines of the data science language wars. This is a large topic and the evening will be fairly dynamic and responsive to the needs of the audience.
Bob Wakefield has spent the better part of 16 years building data systems for many organizations across various industries. He has been running Hadoop in a lab environment for 3 years. He is the principal of Mass Street Analytics, LLC a boutique data consultancy. Mass Street is a Hortonworks Consultant Partner and Confluent Partner.
In his spare time, he likes to work on an equity investment application that combines various sources of information to automatically arrive at investing decisions. When he is not doing that, you’ll find him flying his A-10 simulator. Full CV can be found here: https://www.linkedin.com/in/bobwakefieldmba/
This document discusses project management and big data analytics. It covers two main topics: 1) Project management of analytics and analytics of project management. It discusses the roles of data scientists and project managers. It also identifies common myths and facts regarding big data/analytics projects and project management. Key reasons for big data project failures are discussed, along with success factors such as getting a data scientist involved, developing a clear business case, using appropriate tools and methodologies, and adopting agile principles.
This document discusses project management and big data analytics. It covers two main topics: 1) Project management of analytics and analytics of project management. It discusses the roles of data scientists and project managers. It also addresses common myths and facts about big data/analytics projects and project management. Key reasons for big data project failure include unclear objectives, lack of talent, wrong tool selection, poor planning, and ownership issues. The document emphasizes getting a data scientist involved, properly defining business objectives, using appropriate tools and methodologies, and accepting requirements volatility.
This document discusses the growing importance of business intelligence and data analytics. It introduces the roles of the data detective, who bridges business and IT to uncover missed opportunities and improve processes. Two use cases are described: 1) A detective helped a retailer increase layaway sales through targeted promotions. 2) A detective analyzed pricing data to identify the "sweet spot" and increase sales for a manufacturer. Throughout, the document emphasizes that data needs context to be useful and stresses the detective's focus on both technical skills and business understanding.
This document outlines a 10 step framework for developing data science applications. It begins with articulating the business problem and data questions. Next steps include developing a data acquisition and preparation strategy, exploring and formatting the data, defining the goal, and shortlisting techniques. Later steps evaluate constraints, establish evaluation criteria, fine tune algorithms, and plan for deployment and monitoring. The document also provides background on the speaker and organization. They offer data science, quant finance, and machine learning programs and consulting using Python, R, and MATLAB on their online sandbox platform.
This document discusses research debt and ResearchOps. It provides tips for avoiding research debt through proper organization and reuse of research findings. Research creates both tangible and intangible outputs that should be preserved. Best practices include setting aside time at the end of projects to organize findings, identify insights, and document learnings. This allows the research to benefit future work rather than creating extra work later. The workshop presented ideas for ResearchOps sprints and project close-downs to systematically pay down research debt.
Reaktor is a data science company with 8 PhDs and expertise in using data to optimize business challenges through personalization, recommendations, marketing impact analysis, and other uses. They follow an agile data science project model of optimizing actions using big and small data from various sources to generate insights and information for business drivers through iterative modelling, data wrangling, and testing. Some ideals of being data-driven include being curious, active in testing solutions rather than just observing, understanding uncertainties, acting on evidence courageously but also learning quickly through an agile process while maintaining transparency.
Taking portfolio benefits management to the next level with modern analytics webinar
Wednesday 13 June 2018
presented by Ian Stuart, Altis Consulting, Principal
hosted by Merv Wyeth, Benefits Management SIG Secretary
The link to the write up page and resources of this webinar:
https://www.apm.org.uk/news/taking-portfolio-benefits-management-to-the-next-level-with-modern-analytics-webinar/
GeeCon Prague 2018 - A Practical-ish Introduction to Data ScienceMark West
This document provides an introduction to data science. It begins with defining data science and its interdisciplinary nature, drawing from fields like computer science, mathematics, statistics, and domain-specific knowledge. It then discusses machine learning as a tool in data science and provides examples of common machine learning algorithms like linear regression, decision trees, and k-means clustering. It also outlines different roles required for data science projects. The document aims to give a practical overview of key concepts in data science.
A Practical-ish Introduction to Data ScienceMark West
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
1. I'll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
2. Next up well run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
1) The document provides an introduction to a course on data analytics with Python. It outlines the objectives of the course, which are to conceptually understand data analytics using practical examples rather than just procedures.
2) The course will cover defining data and its importance, different types of data analytics, why analytics is important today, how statistics, analytics and data science are related, and why Python is used. It will also explain the four levels of data measurement.
3) The levels of data from lowest to highest are nominal, ordinal, interval, and ratio. The type of data affects what operations and statistical methods can be meaningfully applied. Descriptive, diagnostic, predictive, and prescriptive analytics will also
This document provides product management tips for business intelligence platforms and applications. It discusses measuring success through the value analytics provides to business, considering the data pyramid from raw data to reports to analytics. Ten tips are provided for creating a successful BI product, including planning for data early, understanding user needs, prioritizing user stories, embracing design principles, and validating frequently with users. The tips are grouped by product phases like discovery, planning, and definition.
Collaborative Improv: Building Better Products Through ImprovisationDavid Farkas
Improv is a collaborative storytelling technique that can be applied in various business contexts like sales, presentations, and product development. The key rules of improv include accepting offers, listening, supporting other participants, and focusing on details. Improv helps build trust and consensus through open body language and mirroring others. It encourages participants to say "yes and" to contribute to the evolving story instead of blocking or criticizing ideas.
Collaborative Improv presentation shared at PixelUp conference, March 2019. Exploring collaboration and product design through the lens of improvisation.
For more info, check out https://collaborativeimprov.com
UX Research: Half Day Workshop IAS 2018, ChicagoDavid Farkas
This document outlines an agenda for a UX research training session. It covers the history of UX research and different research methods including qualitative and quantitative approaches. Specific techniques are discussed like contextual inquiry, card sorting, and various analysis methods. The importance of asking good questions, choosing appropriate methods, and effectively communicating findings is emphasized. Research is framed as an ongoing process that should inform every stage of product development.
This document discusses how improvisational acting techniques can be applied to product development processes. It covers some basic principles of improv like listening, reacting in the moment, and accepting offers from others. It then gives examples of how improv can be used in idea generation, requirements gathering, presentations, and product validation. Some specific improv games and exercises are also presented like bodystorming and walking in the park that could help teams collaborate and think creatively.
Improvised IA: Going Beyond the WhiteboardDavid Farkas
The need to adapt and be flexible within project schedules and meetings has never been greater, but this is a soft skill not easily taught or quickly learned. It starts with team collaboration and trust while ultimately leading to idea generation and problem solving. Yield to the highest offer. Always say YES. Alway raise the bar. These are three of the core components to improvisation in comedy. They are also three pillars to a good collaborative environment.
This hands on session will explore the fundamentals to improv as a means to strengthen teams across organizations. Participants will walk away with:
An understanding to the fundamentals to improv
An understanding of applications to the field of UX as both a team building tool and idea generation
Real world practice and sample exercises
We’re looking to get up and shake the cobwebs off our bodies. Through Bodystorming and other improv games participants will engage with the space around them and will learn the basics of improvisational comedy and how it can directly translate back to work in the office and with clients alike.
Giant 2015: CTRL Z, A Practitioner's Support GroupDavid Farkas
A discussion in how we can better ask and offer support within our teams when projects and situations occur that are unexpected or non-ideal. This presentation was paired with a live-demo and discussion.
Silver Linings, When Building a Team FailsDavid Farkas
My five-minute lightning talk presented at Pro/Design Conference January 30, 2015. Hosted by Nasdaq, this talk shares a story and lessons learned building a design team within a larger organization.
A guest lecture presentation on Interaction Design Best Practices given at Penn's campus in Philadelphia.
Includes heuristics from Jacob Nielsen, Abby Covert, Erik Dahl's UX Axioms.
Interaction Design Through Mixology IxDA-DC 2012David Farkas
David Farkas gave a presentation on interaction design through mixology at the August 2012 IxDA DC conference. He discussed how mixology, like interaction design, involves experimenting with ingredients and processes to create new recipes/experiences. Farkas presented several original cocktail recipes he developed through an iterative process involving education on techniques and ingredients, experimentation, and user testing to refine flavors that were pleasing but masked the taste of alcohol. He argued that interaction designers can apply similar approaches used in mixology to their work.
David Farkas presented on using mixology and cocktail design as an analogy for interaction design. Mixology involves experimenting with ingredients and recipes to create new cocktails, similar to how interaction designers iterate on a design by testing with users. Farkas discussed how the process of learning mixology through education and practice is comparable to learning design and provided several examples of original cocktail recipes he created with different flavor combinations and ingredients. He argued that mixology serves as a useful metaphor for interaction design processes like user validation, iteration and designing experiences that are intuitive to users.
International Upcycling Research Network advisory board meeting 4Kyungeun Sung
Slides used for the International Upcycling Research Network advisory board 4 (last one). The project is based at De Montfort University in Leicester, UK, and funded by the Arts and Humanities Research Council.
Discovering the Best Indian Architects A Spotlight on Design Forum Internatio...Designforuminternational
India’s architectural landscape is a vibrant tapestry that weaves together the country's rich cultural heritage and its modern aspirations. From majestic historical structures to cutting-edge contemporary designs, the work of Indian architects is celebrated worldwide. Among the many firms shaping this dynamic field, Design Forum International stands out as a leader in innovative and sustainable architecture. This blog explores some of the best Indian architects, highlighting their contributions and showcasing the most famous architects in India.
ARENA - Young adults in the workplace (Knight Moves).pdfKnight Moves
Presentations of Bavo Raeymaekers (Project lead youth unemployment at the City of Antwerp), Suzan Martens (Service designer at Knight Moves) and Adriaan De Keersmaeker (Community manager at Talk to C)
during the 'Arena • Young adults in the workplace' conference hosted by Knight Moves.
Practical eLearning Makeovers for EveryoneBianca Woods
Welcome to Practical eLearning Makeovers for Everyone. In this presentation, we’ll take a look at a bunch of easy-to-use visual design tips and tricks. And we’ll do this by using them to spruce up some eLearning screens that are in dire need of a new look.
Architectural and constructions management experience since 2003 including 18 years located in UAE.
Coordinate and oversee all technical activities relating to architectural and construction projects,
including directing the design team, reviewing drafts and computer models, and approving design
changes.
Organize and typically develop, and review building plans, ensuring that a project meets all safety and
environmental standards.
Prepare feasibility studies, construction contracts, and tender documents with specifications and
tender analyses.
Consulting with clients, work on formulating equipment and labor cost estimates, ensuring a project
meets environmental, safety, structural, zoning, and aesthetic standards.
Monitoring the progress of a project to assess whether or not it is in compliance with building plans
and project deadlines.
Attention to detail, exceptional time management, and strong problem-solving and communication
skills are required for this role.
Explore the essential graphic design tools and software that can elevate your creative projects. Discover industry favorites and innovative solutions for stunning design results.
2. #PracticalResearch @dafark8
Who is this guy?
EPAM
@dafark8
Co-Author of UX Research
Research, Strategy, Workshops,
Improvisation
CollaborativeImprov.com
#PracticalResearch
3. #PracticalResearch @dafark8
History of Research & Good Questions
Quantitative & Qualitative Methods
Method Selection
Choosing Methods
Mind Map Exploration
Break
Making Sense of the Mess
CandyLand Analysis & Spectrum Analysis
Communicating Insights
Samples Findings & Synthesis
Getting the Most Out of Research
Planning for the Future
Q&A
Today’s Plan
4. #PracticalResearch @dafark8
Section 1
History of Research & Good Questions
I don’t pretend we have all the
answers. But the questions are
certainly worth thinking about.
- Arthur C. Clark
“
9. #PracticalResearch @dafark8
Field Research is observational research
of a user’s tasks and behaviors, most
usually conducted on location or in
context where the behaviors usually take
place.
Field Research, Defined
10. #PracticalResearch @dafark8
A good question seeks to explore the
unknown in a targeted and guided way,
while putting the participant at ease to
open up and provide feedback you might
not have expected or anticipated.
Good Questions, Defined
13. #PracticalResearch @dafark8
Trademarks of a Question
Good Questions Are:
• Open-ended
• Balance specificity
• Flow naturally
Bad Questions Are:
• Leading
• Shallow
• Personal or
unconscious bias
14. #PracticalResearch @dafark8
When to Break the Rules
• Leading - Elicit Emotional
Responses
• Shallow - Warming Up
• Personal Bias - Devil’s
Advocate
16. #PracticalResearch @dafark8
Quantitative Research focusses on
objective measurements through
statistics and analysis most often collect
through surveys, polls, and data analytics.
Quantitative, Defined
17. #PracticalResearch @dafark8
What Quantitative Is…
• Measured and quantified
• Automatically collected or
gathered
• Based on trends & historical
data
• Defines the “what” and “how”
• Cannot define the “why”
18. #PracticalResearch @dafark8
What Quantitative Is Not…
• Why something is working
• How something is broken
• Trends. Only represent past
behaviors, not future actions
• Generalized. Specific to the
question and doesn’t uncover
hidden trends
25. #PracticalResearch @dafark8
Words of Quantitative Caution
• Lots of different methods
• Not all are always appropriate
• Understand when qualitative
produces more tangible
results
• Quant requires larger
population/sample sizes
34. #PracticalResearch @dafark8
Words of Qualitative Caution
• Stakeholder (often) require
cold, hard facts
• Answers are not binary
• Responses are from smaller
data sets
35. #PracticalResearch @dafark8
Activity - Method Selection
Supplies
• Blank Paper
Steps
• Reflect on a research problem
• Identify questions
• Identify participants
• Method selection
36. #PracticalResearch @dafark8
Discuss - Method Selection
• Did you gravitate to some
methods over another?
• Why?
• What were some challenges in
choosing methods?
37. #PracticalResearch @dafark8
Section 3
Choosing Methods
But choose wisely, for while the
true Grail will bring you life, the
false Grail will take it from you.
- Grail Knight,
Indiana Jones and the Last Crusade, 1989
“
39. #PracticalResearch @dafark8
Mixing & Matching Methods
• Use Quantitative to inform
Qualitative
• Examples
• Analytics inform Contextual
Inquiries
• Surveys support Customer
Feedback
40. #PracticalResearch @dafark8
Mixing & Matching Methods
• Map the method(s) to the
goals
• Workflow? Don’t use Card
Sorting
• Motivations? Don’t use
Analytics
41. #PracticalResearch @dafark8
Activity - Mind Map
Supplies
• Mind Map template
• Pen & pencil
Steps
• Reflect on previous activity
• Choose one question
• Identify & gauge methods &
opportunities
42. #PracticalResearch @dafark8
Discuss - Mind Map
• Did writing doesn’t the risks /
opportunities change your
perception of the methods?
• What is a risk you saw
throughout a number of your
methods?
44. #PracticalResearch @dafark8
Section 4
Making Sense of the Mess
Data doesn’t exist. Information
exists and it’s ephemeral. When
we capture it, it’s data.
- TED Radio Hour, Big Data Revolution
“
46. #PracticalResearch @dafark8
Value of Analysis
• Validates assumptions
(challenges hypothesis)
• Provides insights
• Informs requirements
• Based on data
54. #PracticalResearch @dafark8
Activity - Candyland Analysis
Supplies
• Candy!
Steps
• Organize the candy based on
some attribute or interest
• Pause
• Reflect
• Reorganize & repeat
55. #PracticalResearch @dafark8
Activity - Candyland Analysis
Supplies
• Candy!
• Paper
Steps
• Draw 5-6 horizontal lines on
the paper
• Label each line with two
extremes (e.g. salt/sweet)
• Place candies along the scales
56. #PracticalResearch @dafark8
Discuss - Candyland Analysis
• How did you first organize the
candies? Why?
• How did you organize the
candy the second time?
• What is one thing you saw
differently across your
organization methods?
59. #PracticalResearch @dafark8
Define Your Goals
• Validate assumptions
• Promote & sell product
• Gain buy-in
• Promote more research
• Explore business needs
61. #PracticalResearch @dafark8
Define Your Story
• What is your audience
interested in?
• What do you want to achieve?
• Business strategy
• Product roadmap
• Discoveries & opportunities
• Identify additional research
• Identify knowledge gaps
• What should you use to
communicate that goal?
https://www.flickr.com/photos/rosenfeldmedia/25672123376/in/album-
72157665621011091/
65. #PracticalResearch @dafark8
1
3
7
8
SHARE FACEBOOK
ICON
TWITTER
ICON
Aug. 14, 2012 Highlights
for recycling. (more)
in energy assistance. (more)
te quia vit (more)
Strum voluptas dolut quat estint quos re-
hent. (more)
Evendit isintio nseque pro bla consect
enienihil ipicient. (more)
(235xScalable)
Ameren and...Highlights
Go Paperless!
Turn off your paper
bill and receive...
(more)
LOGO
SEARCH GO
1
DID YOU KNOW
CONTENT AREA
UserID
Password
LOGIN
Create Account
Other Logins
Refrigerator
Free pick up...
(more)
2
Expiring law could
(more)
It is essential to how
we do business...
(more)
Lorem Ipsum
It is essential to how
we do business...
(more)
3
5 6
Ameren employees
volunteer...
(more)
4
Power statement
About Ameren
Social Media
My Account
PAYBILL VIEW BILL
VIEW PAYMENT
HISTORY
REPORT
OUTAGE
TEMPLATE 1A-1
CORPORATE WITH HIGHLIGHTS AREA
MY ACCOUNT
Size is mindful of the Banner dimensions to add to
consistency
ROTATING BANNER
Increased banner size
2.1 Banner progress status, number of shapes will indicate
how many banners (3 should be minimum) and will have an
indicator to show which banner is active
ADDITIONAL CALLOUTS
3.1 About Ameren
3.2
3.3 Social Media icons are repeated and more accessible
MY ACCOUNT
Above fold, more accessible, and larger
4.1 Report an Outage within the My Account section and
equal size to other callouts in this area
HIGHLIGHTS
Highlights only appear in the Corporate homepage template
CHICLET AREA
Newly designed chiclets
DID YOU KNOW
Anchored to bottom of page layout column. Will contain call
to action
FOOTER
Contains copyright information and links
7.1 Share and social media links repeated in footer
1.
2.
3.
4.
5.
6.
7.
AMEREN
HOMEPAGE WIREFRAME TEMPLATE
(196xScalable)
2
4
5 6
2.1
3.1
3.2
3.3
7.1
4.1
Product Requirements
66. #PracticalResearch @dafark8
Activity - Synthesize Research
Supplies
• Research notes
• Post-it notes
Steps
• Review research notes
• Collect data points
• Group data points
• Label groupings
67. #PracticalResearch @dafark8
Discuss - Synthesize Research
• How can you look at the data
points through different points
of view?
• How do you determine which
groupings take priority?
70. #PracticalResearch @dafark8
Research Starts with Questions
• Questions at any stage of a
project lifecycle
• No bad time for questions
• No bad time for research
• Research can be small or
large
72. #PracticalResearch @dafark8
Risks of Skipping Research
• Defining the wrong product
• Build the wrong features
• Feature creep
• Code debt
• Design debt
73. #PracticalResearch @dafark8
Activity - Plan for Tomorrow
Supplies
• Research findings
• Post-it notes
• Plan for Tomorrow worksheet
Steps
• Work through each column
• 5-minutes per column
• Discuss
74. The best time to plant a tree
do research was 20-years ago
last sprint.
The second best time is today.
-Chinese Proverb, Adapted