In this session, Jon talks about analytics in the agency business. He discussed best practices and some operational hacks to help leaders become successful in the world of analytics in the marketing domain(one of the early adopter of technology)

Timeline: 0:29 John's journey. 6:07 Use cases for the benchmark studies at L2. 7:16 The struggles and challenges in the digital industry. 11:30 How much data is good data. 14:55 Staying relevant during times of disruption. 20:18 Analysing data of various cultures for a global company. 24:30 Art of doing business and science of doing business. 27:22 Jon's current role. 30:06 How much of L2 in facing and out facing? 31:45 Qualifying a source/platform. 35:20 Integrating a new source into the existing algorithm. 38:16 Building classifiers. 40:00 Jon's leadership style. 43:00 Client facing a leadership. 45:12 Jon's magic data science hire. 47:28 Suggestion for starting a data practice in a dissimilar industry. 50:55 World without survey. 53:11 Future of data in the digital industry.

Podcast link: https://futureofdata.org/futureofdata-jon-gibs-chief-data-officer-l2-inc/

Bio- Jon Gibs is the Chief Data Officer and Chief Data Scientist at L2, a digital research, benchmarking, and advisory services company recently acquired by the Gartner Group. Prior to his time at L2, Jon founded and was the group vice president of data science and analytics at Huge, a digital agency in Brooklyn, and spent 10 years at Nielsen running its digital analytics practice.

Jon's graduate work has been in Geography and spatial statistics at The University at Buffalo.

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey to create the data-driven future.

Wanna Join? If you or any you know wants to join in, Register your interest @ http://play.analyticsweek.com/guest/

Want to sponsor? Email us @ [email protected]

Keywords:

FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

Develop your digital/online marketing skills and learn web analytics to understand the performance of websites and ad campaigns. Approaches covered will be immediately useful for business or nonprofit organizations. If you are completely new to Google Analytics and you want to learn the basics, this guide will introduce you to the content quickly. Web analytics is critical to online marketers as they seek to track return on investment and optimize their websites. Introduction to Google Analytics covers the basics of Google Analytics, starting with creating a blog, and monitoring the number of people who see the blog posts and where they come from. What You'll Learn Understand basic techniques to generate traffic for a blog or website Review the performance of a website or campaign Set up a Shopify account to track ROI Create and maximize AdWords to track conversion Discover opportunities offered by Google, including the Google Individual Qualification Who This Book Is For Those who need to get up to speed on Google Analytics tools and techniques for business or personal use. This book is also suitable as a student reference.

• ERRATA (As Reported by Peter: "The book Peter mentioned (at 46:20) by Stuart Russell, "Do the Right Thing", was published in 2003, and not recently"

In this session Peter Morgan, CEO Deep Learning Partnership sat with Vishal Kumar, CEO AnalyticsWeek and shared his thoughts around Deep Learning, Machine Learning and Artificial Intelligence. They've discussed some of the best practices when it comes to picking right solution, right vendor and what are some of the keyword means.

Here's Peter's Bio: Peter Morgan is a scientist-entrepreneur starting out in high energy physics enrolled in the PhD program at the University of Massachusetts at Amherst. After leaving UMass, and founding my own company, Peter has moved into computer networks, designing, implementing and troubleshooting global IP networks for companies such as Cisco, IBM and BT Labs. After getting an MBA and dabbling in financial trading algorithms. Peter has worked for three years on an experiment lead by Stanford University to measure the mass of the neutrino. Since 2012. He had been working in Data Science and Deep Learning, founding an AI Solutions company in Jan 2016.

As an entrepreneur Peter has founded companies in the AI, social media, and music industries. He has also served on the advisory board of technology startups. Peter is a popular speaker at conferences, meetups and webinars. He has cofounded and currently organize meetups in the deep learning space. Peter has business experience in the USA, UK and Europe.

Today, as CEO of Deep Learning Partnership, He leads the strategic direction and business development across product and services. This includes sales and marketing, lead generation, client engagement, recruitment, content creation and platform development. Deep Learning technologies used include computer vision and natural language processing and frameworks like TensorFlow, Keras and MXnet. Deep Learning Partnership design and implement AI solutions for our clients across all business domains.

Interested in sharing your thought leadership with our global listeners? Register your interest @ http://play.analyticsweek.com/guest/

In this session, Nathaniel discussed how NFPA uses data to empower fire stations worldwide with data-driven insights. We discussed the future of fire in this tech-driven world.

Timeline: 0:29 Nathaniel's journey. 3:50 What's NFPA? 6:12 Nathaniel's role in NFPA. 8:50 Nathaniel's book. 12:21 The data science team at NFPA. 15:01 Working with the government. 18:50 Interesting use cases of NFPA. 25:49 Fining tuning the data model at NFPA. 28:11 NFPA alliance with the Insurance industry. 31:33 Recruiting an idea concept or tool. 33:16 How to approach NFPA? 36:03 Nathaniel's role: in facing or outfacing? 40:41 Suggestions for Non-profits to build a data science practice. 43:49 Putting together a data science team. 46:34 Predicting the fire outcome. 48:11 Closing remarks.

Podcast link: https://futureofdata.org/futureofdata-nathaniel-lin-chief-data-scientist-nfpa/

Bio- Nathaniel Lin has an extensive background in business and marketing analytics with strategic roles in both start-ups and Fortune 500 companies. He offers the National Fire Protection Association (NFPA) agency and client perspective gleaned from his work at Fidelity Investments, OgilvyOne, Aspen Marketing, and IBM Worldwide. During his tenure with IBM Asia Pacific, he also built and led a marketing analytics group that won a DMA/NCDM Gold Award in B2B Marketing.

Lin served as an adjunct professor of business analytics at Boston College and Georgia Tech College of Management. He is also the founder of two LinkedIn groups related to big data analytics and is the 2014 author of Applied Business Analytics – Integrating Business Process, Big Data, and Advanced Analytics. Lin has an MBA in Management of Technology/Sloan Fellows from MIT Sloan School of Management and earned both a Ph.D. In Environmental Engineering and an Honors B.S from Birmingham University in England.

Founded in 1896, NFPA is a global, nonprofit organization devoted to eliminating death, injury, property, and economic loss due to fire, electrical and related hazards. The association delivers information and knowledge through more than 300 consensus codes and standards, research, training, education, outreach, and advocacy; and partner with others who share an interest in furthering the NFPA mission. For more information, visit www.nfpa.org.

The podcast is sponsored by: TAO.ai(https://tao.ai), Artificial Intelligence Driven Career Coach

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey to create the data-driven future.

Wanna Join? If you or any you know wants to join in, Register your interest @ http://play.analyticsweek.com/guest/

Want to sponsor? Email us @ [email protected]

Keywords:

FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

Over the past decade, data science has come out of the back office to become a force of change across the entire organization. At the forefront of this change is the open data science movement that advocates the use of open source tools in a powerful, connected ecosystem. This report explores how open data science can help your organization break free from the shackles of proprietary tools, embrace a more open and collaborative work style, and unleash new intelligent applications quickly. Authors Michele Chambers and Christine Doig explain how open source tools have helped bring about many facets of the data science evolution, including collaboration, self-service, and deployment. But you’ll discover that open data science is about more than tools; it’s about a new way of working as an organization. Learn how data science—particularly open data science—has become part of everyday business Understand how open data science engages people from other disciplines, not just statisticians Examine tools and practices that enable data science to be open across technical, operational, and organizational aspects Learn benefits of open data science, including rich resources, agility, transparency, and collective intelligence Explore case studies that demonstrate different ways to implement open data science Discover how open data science can help you break down department barriers and make bold market moves Michele Chambers, Chief Marketing Officer and VP Products at Continuum Analytics, is an entrepreneurial executive with over 25 years of industry experience. Prior to Continuum Analytics, Michele held executive leadership roles at several database and analytic companies, including Netezza, IBM, Revolution Analytics, MemSQL, and RapidMiner. Christine Doig is a senior data scientist at Continuum Analytics, where she's worked on several projects, including MEMEX, a DARPA-funded open data science project to help stop human trafficking. She has 5+ years of experience in analytics, operations research, and machine learning in a variety of industries.

The definitive reference book with real-world solutions you won't find anywhere else The Big Book of Dashboards presents a comprehensive reference for those tasked with building or overseeing the development of business dashboards. Comprising dozens of examples that address different industries and departments (healthcare, transportation, finance, human resources, marketing, customer service, sports, etc.) and different platforms (print, desktop, tablet, smartphone, and conference room display) The Big Book of Dashboards is the only book that matches great dashboards with real-world business scenarios. By organizing the book based on these scenarios and offering practical and effective visualization examples, The Big Book of Dashboards will be the trusted resource that you open when you need to build an effective business dashboard. In addition to the scenarios there's an entire section of the book that is devoted to addressing many practical and psychological factors you will encounter in your work. It's great to have theory and evidenced-based research at your disposal, but what will you do when somebody asks you to make your dashboard 'cooler' by adding packed bubbles and donut charts? The expert authors have a combined 30-plus years of hands-on experience helping people in hundreds of organizations build effective visualizations. They have fought many 'best practices' battles and having endured bring an uncommon empathy to help you, the reader of this book, survive and thrive in the data visualization world. A well-designed dashboard can point out risks, opportunities, and more; but common challenges and misconceptions can make your dashboard useless at best, and misleading at worst. The Big Book of Dashboards gives you the tools, guidance, and models you need to produce great dashboards that inform, enlighten, and engage.

Abstract Continuing its commitment to developing and delivering industry-leading storage technologies, IBM® introduces the IBM Storwize® V7000 solution powered by IBM Spectrum Virtualize™, which is an innovative storage offering that delivers essential storage efficiency technologies and exceptional ease of use and performance, all integrated into a compact, modular design that is offered at a competitive, midrange price. The IBM Storwize V7000 solution incorporates some of the top IBM technologies that are typically found only in enterprise-class storage systems, raising the standard for storage efficiency in midrange disk systems. This cutting-edge storage system extends the comprehensive storage portfolio from IBM and can help change the way organizations address the ongoing information explosion. This IBM Redbooks® publication introduces the features and functions of the IBM Storwize V7000 and IBM Spectrum Virtualize V7.8 system through several examples. This book is aimed at pre-sales and post-sales technical support and marketing and storage administrators. It helps you understand the architecture of the Storwize V7000, how to implement it, and how to take advantage of its industry-leading functions and features.

JMP 13 Consumer Research focuses on analyses that help users observe and predict subject's behavior, particularly those in the market research field. The Uplift platform predicts consumer behavior based on shifts in marketing efforts. Learn how to tabulate and summarize categorical responses with the Categorical platform. Factor Analysis rotates principal components to help identify which directions have the most variation among the variables. The book also covers Item Analysis, a method for identifying latent traits that might affect an individual's choices. And read about the Choice platform, which market researchers use to estimate probability in consumer spending.

If you burned all of the data created in just one day on DVDs, you could stack them on top of each other and reach the moon - twice. Today, we manage to solve the most complex issues. Yet, with that much data to handle, we sometimes forget to solve everyday concepts. Measuring the actual profitability of your digital marketing campaigns - in real time - is one of them.

Getting the most out of your optimization efforts means understanding the data you’re collecting, from analytics implementation, to report setup, to analysis techniques. Using that data to drive your Optimization, and eventually Personalization efforts, will help you to get the most out of your marketing dollars. In this session, Krista will give you several tips for using your analytics data to identify more opportunities, prioritize your efforts, and begin to personalize your user experience.

As the available marketing data and quality of it matures it's time to rethink the core metrics we use to quantify marketing performance. In this talk Kristoffer will focus on the gaps between what we know marketing should focus on and what is typical today, and look forward at the new data-driven key metrics in performance marketing.

At Superweek we all love data. Some of us analyze marketing campaigns and some analyze SEO; some play with statistics and some play with visual tools; some discuss ethical challenges and some ask technical questions. But we all love data and believe it can help other people. In this presentation I will discuss why and how we should use our love and skills to make an impact not just on businesses but also in our communities and society in general.

The world is an oyster. It's also a system. A complex system! Companies are components in that system, and they're systems unto themselves! And marketing departments, and digital marketing, and the data therein, are systems, too. As analysts, we're looking for pearls in these systems (and you were wondering where we were going with this)! Join Michael and Tim as they chat with Christopher Berry of the Canadian Broadcasting Corporation (CBC) about "systems thinking." You'll be smarter for it! As a special "feature" (not a bug!) for this episode, we've done a bit of a throwback to the earliest days of this podcast, in that Michael's audio sounds a little bit like he was chatting through a tin can with a string tied to it. We apologize for that! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Pop psychology is fun, if not that useful. Pop analytics can be dangerous! What IS pop analytics? It's a term coined (as far as we can tell) by analytics legend Kevin Hillstrom, and we managed to get him on the show to chat about it! The fact that it turned into a therapy session for Tim was just an added bonus. NOTE: We hit a glitch with Kevin's audio 45 minutes into the episode and have done our best to work around it. It was especially painful, in that he had some very nice things to say about the show, but, alas, the choppy audio means we won't be able to repurpose the clip for marketing purposes! We apologize for the glitch. It was something we didn't recognize for what it was when it happened, but now we know! See the show notes, links, and transcription at: http://www.analyticshour.io/2017/01/17/054-pop-analytics-with-kevin-hillstrom/.

2016 is almost in the books! In just over a week, we'll be ringing in the new year, and we have it on Very Good Authority that 2017 will be the Year of Mobile. But, this episode is as much about looking back as it is about looking forward -- looking back on how our industry has evolved, what product launches piqued our interest the most, and what Snoop Dogg-related stunt marketing occurred during the year. We even do a little navel gazing about the podcast itself: our favorite topics and guests (although we love ALL the topics and guests!), and a bit of news about what will be happening with the podcast in 2017. So kick back, bust open a few roasted chestnuts, spike your eggnog generously, and give it a listen! Technologies, services, and random items mentioned in this episode include: more past episodes than are worth linking to, RSiteCatalyst, Hidden Brain podcast: Can Social Science Help You Quit Smoking for Good?, SUPERWEEK, Matt Gershoff, Caleb Whitmore, Adobe Summit, eMetrics, MeasureCamp, Un-Summit, Digital Analytics Hub, Gary Angel / Digital Mortar, Paco Underhill / Why We Buy: The Science of Shopping, Jan Exner, Justin Cutroni, Kevin Hillstrom, Measure Slack, Lee Isensee, Tableau, Domo, the Domo stunt at the Tableau Conference, John Scalzi, Joe Haldeman, and Philip K. Dick.

Data scientists are faced with the need to conduct continual experiments, particularly regarding user interface and product marketing. Designing experiments is a cornerstone of the practice of statistics, with clear application to data science. In this lesson, you’ll learn about A-B testing and hypothesis, or significance tests—critical aspects of experimental design for data science. What you’ll learn—and how you can apply it You will learn the central concepts of A-B testing, understand its role in designing and conducting data science experiments, and the characteristics of a proper A-B test. Through a series of sample tests, you’ll learn how to interpret results, and apply that insight to your analysis of the data. Since A-B tests are typically constructed with a hypothesis in mind, you’ll also learn how to conduct various hypothesis, or significance tests, enabling you to avoid misinterpreting randomness. This lesson is for you because You are a data scientist or analyst working with data, and want to gain beginner-level knowledge of key statistical concepts to improve the design, and outcome of your experimental tests with data. Prerequisites: Basic familiarity with coding in R Materials or downloads needed: n/a

A non-technical guide to leveraging retail analytics for personal and competitive advantage Style & Statistics is a real-world guide to analytics in retail. Written specifically for the non-IT crowd, this book explains analytics in an approachable, understandable way, and provides examples of direct application to retail merchandise management, marketing, and operations. The discussion covers current industry trends and emerging-standard processes, and illustrates how analytics is providing new solutions to perennial retail problems. You'll learn how to leverage the benefits of analytics to boost your personal career, and how to interpret data in a way that's useful to the average end business user or shopper. Key concepts are detailed in easy-to-understand language, and numerous examples highlight the growing importance of understanding analytics in the retail environment. The power of analytics has become apparent across industries, but it's left an especially indelible mark on retail. It's a complex topic, but you don't need to be a data scientist to take advantage of the opportunities it brings. This book shows you what you need to know, and how to put analytics to work with retail-specific applications. Learn how analytics can help you be better at your job Dig deeper into the customer's needs, wants, and dreams Streamline merchandise management, pricing, marketing, and more Find solutions for inefficiencies and inaccuracies As the retail customer evolves, so must the retail industry. The retail landscape not only includes in-store but also website, mobile site, mobile apps, and social media . With more and more competition emerging on all sides, retailers need to use every tool at their disposal to create value and gain a competitive advantage. Analytics offers a number of ways to make your company stand out, whether it's through improved operations, customer experience, or any of the other myriad factors that build a great place to shop. Style & Statistics provides an analytics primer with a practical bent, specifically for the retail industry.

In this session, John Young, Chief Analytics Officer, Epsilon Data Management, sat with Vishal Kumar, CEO AnalyticsWeek and shared his journey to Chief Analytics Officer, life @ Epsilon, and discussed some challenges/opportunities faced by data-driven organizations, its executives and shared some best practices.

Timeline: 2:51 What's Epsilon? 5:12 John's journey. 9:24 The role of CAO in Epsilon. 12:12 How much John's role is in facing and out facing. 13:19 Best practices in data analytics at Epsilon. 16:15 Demarcating CDO and CAO. 19:52 Depth and breadth of decision making at Epsilon. 25:00 Dealing with clients of Epsilon. 28:48 Best data practices for businesses. 34:39 Build or buy data? 37:21 Creating a center of excellence with data. 40:01 Building a data team. 43:45 Tips for aspiring data analytics executives. 46:05 Art of doing business and science of doing business. 48:31 Closing remarks.

Podcast link: https://futureofdata.org/analyticsweek-leadership-podcast-with-john-young-epsilon-data-management/

Here's John's Bio: Mr. Young has general management responsibilities for the 150+ member Analytic Consulting Group at Epsilon. His responsibilities also include design and consultation on various database marketing analytic engagements, including predictive modeling, segmentation, measurement, and profiling. John also brings thought leadership on important marketing topics. John works with companies in numerous industries, including financial services, technology, retail, healthcare, and not-for-profit.

Before joining Epsilon in 1994, Mr. Young was a Marketing Research Manager at Digitas, a Market Research Manager at Citizens Bank, Research Manager at the AICPA, and an Assistant Economist at the Federal Reserve Bank of Kansas City.

Mr. Young has presented at numerous conferences, including NCDM Winter and Summer, DMA Annual, DMA Marketing Analytics, LIMRA Big Data Analytics, and Epsilon’s Client Symposiums. He has published in DM News, CRM Magazine’s Viewpoints, Chief Marketer, Loyalty 360, Colloquy, and serves on the advisory board of the DMA’s Analytics Community.

Mr. Young holds a B.S. and M.S. in Economics from Colorado State University, Fort Collins, Colorado.

The podcast is sponsored by: TAO.ai(https://tao.ai), Artificial Intelligence Driven Career Coach

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey to create the data-driven future.

Want to Join? If you or any you know wants to join in, Register your interest @ http://play.analyticsweek.com/guest/

Want to sponsor? Email us @ [email protected]

Keywords:

FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

Business executives today are well aware of the power of data, especially for gaining actionable insight into products and services. But how do you jump into the big data analytics game without spending millions on data warehouse solutions you don’t need? This 40-page report focuses on massively parallel processing (MPP) analytical databases that enable you to run queries and dashboards on a variety of business metrics at extreme speed and Exabyte scale. Because they leverage the full computational power of a cluster, MPP analytical databases can analyze massive volumes of data—both structured and semi-structured—at unprecedented speeds. This report presents five real-world case studies from Etsy, Cerner Corporation, Criteo and other global enterprises to focus on one big data analytics platform in particular, HPE Vertica. You’ll discover: How one prominent data storage company convinced both business and tech stakeholders to adopt an MPP analytical database Why performance marketing technology company Criteo used a Center of Excellence (CoE) model to ensure the success of its big data analytics endeavors How YPSM uses Vertica to speed up its Hadoop-based data processing environment Why Cerner adopted an analytical database to scale its highly successful health information technology platform How Etsy drives success with the company’s big data initiative by avoiding common technical and organizational mistakes

In this session, Dr. Nipa Basu, Chief Analytics Officer, Dun&Bradstreet, sat with Vishal Kumar, CEO AnalyticsWeek and shared her journey as Chief Analytics Officer, life @ D&B, Future of Credit Scoring, and some challenges/opportunities she's observing as an industry observer, executive, and practitioner.

Timeline: 0:29 Nipa's background. 4:14 What is D&B? 7:40 Depth and breadth of decision making at D&B. 9:36 Matching security with technological evolution. 13:42 Anticipatory analytics. 16:00 CAO's role in D&B: in facing or outfacing? 18:32 Future of credit scoring. 21:36 Challenges in dealing with clients. 24:08 Cultural challenges. 28:42 Good use cases in security data. 31:51 CDO, CAO, and CTO. 33:56 Optimistic trends data analytics businesses. 36:44 Social data monitoring. 39:18 Creating a holistic model for data monitoring. 41:02 Overused terms in data analytics. 42:10 Best practices for small businesses to get started with data analytics. 44:33 Indicators that indicate the need for analytics for businesses. 47:06 Advice for data-driven leaders. 49:30 Art of doing business and science of doing business.

Podcast link: https://futureofdata.org/analyticsweek-leadership-podcast-with-dr-nipa-basu-dun-bradstreet/

Here's Nipa's Bio: Dr. Nipa Basu is the Chief Analytics Officer at Dun & Bradstreet. Nipa is the main source of inspiration and leadership for Dun & Bradstreet’s extensive team of data modelers and scientists that partner with the world’s leading Fortune 500 companies to create innovative, analytic solutions to drive business growth and results. The team is highly skilled in solving a wide range of business challenges with unique, basic, and advanced analytic applications.

Nipa joined Dun & Bradstreet in 2000 and since then has held key leadership roles focused on driving the success of Dun & Bradstreet’s Analytics practice. In 2012, Nipa was named Leader, Analytic Development, and in March 2015, Nipa was named Chief Analytics Officer and appointed to Dun & Bradstreet’s executive team.

Nipa began her professional career as an Economist with the New York State Legislative Tax Study Commission. She then joined Sandia National Laboratories, a national defense laboratory where she built a Microsimulation Model of the U.S. Economy. Prior to joining Dun & Bradstreet, Nipa was a database marketing statistician for AT&T with responsibility for building predictive marketing models.

Nipa received her Ph. D. in Economics from the State University of New York at Albany, specializing in Econometrics.

Follow @nipabasu

The podcast is sponsored by: TAO.ai(https://tao.ai), Artificial Intelligence Driven Career Coach

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey to create the data-driven future.

Want to Join? If you or any you know wants to join in, Register your interest @ http://play.analyticsweek.com/guest/

Want to sponsor? Email us @ [email protected]

Keywords:

FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy