Stakeholders often miss key insights that can be provided by data to drive action forward, due to the way the data is presented and communicated to them. My guest today believes that data storytelling is key to resolving this common pain point. Kam Lee, a BI Data Storytelling Mastery alumn and graduate who has used our framework to surface over $100M for the fintech company he works with! Kam is the Chief Data Scientist at his company Finetooth Analytics (specializing in marketing analytics), working with top marketers like Russell Brunson from Clickfunnels! Our data masterclass with Kam today delves deep into how he used our BI Data Storytelling Methodology and framework to straddle data engineering, data science, and storytelling. Kam shares game-changing concepts from the course and how he has used them to connect to stakeholders, influence their actions, and overcoming what he calls 'emotional responses' to data. Tune in to this knowledge bomb-filled episode! In this episode, you'll learn: [0:12:20] Three buckets Kam uses to organize the data storytelling process. [0:14:56] The challenge of dealing with stakeholders who respond emotionally to data. [0:26:48] Whether to start with the storyboarding or the analytics data dictionary first. [0:28:19] The difference between KPIs, trends, and actions. For full show notes, and the links mentioned visit: https://bibrainz.com/podcast/76 Enjoyed the Show? Please leave us a review on iTunes.
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The Data Mirage: Why Companies Fail to Actually Use Their Data is a business book for executives and leaders who want to unlock more insights from their data and make better decisions. The importance of data doesn’t need an introduction or a fancy pitch deck. Data plays a critical role in helping companies to better understand their users, beat out their competitors, and breakthrough their growth targets. However, despite significant investments in their data, most organizations struggle to get much value from it. According to Forrester, only 38% of senior executives and decision-makers “have a high level of confidence in their customer insights and only 33% trust the analytics they generate from their business operations.” This reflects the real world that I have experienced. In this book, I will help readers formulate an analytics strategy that works in the real world, show them how to think about KPIs and help them tackle the problems they are bound to come across as they try to use data to make better decisions.
Send us a text Want to be featured as a guest on Making Data Simple? Reach out to us at [[email protected]] and tell us why you should be next.
Abstract Hosted by Al Martin, VP, Data and AI Expert Services and Learning at IBM, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts.
This week on Making Data Simple, we have Kathy Parks. Kathy worked at IBM and is now an owner and independent Angel Investor. Kathy started her career in publishing and then transitioned over to Kurzweil Computer Products working on reading machine for the blind. Kathy then moved to Interleaf working on publishing products, then to QA and Project Management. Kathy then moved to Rational Software and in 2003 Rational was bought by IBM.
Show Notes 2:10 - How do you go from publishing to project management? 3:15 - How did you transition to Angel Investing? 5:58 - What’s at the core 7:06 - What is Angel Investing 12:40 - Is there a formula that you use? 17:24 - How are pitches made? 18:37 - Other forms of capital 22:43 - How many investments have you made? 29:21 - What data to you use? 30:42 - Why are we not seeing the investments? 31:58 - What are the KPIs in a pitch? 33:20 - How long does the process take? Angel Capital Association KJParks - Linkedin Angel Investing - Venture Deals Age of Surveillance Capitalism
Connect with the Team Producer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter. Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.
This guide shows how to combine data science with social science to gain unprecedented insight into customer behavior, so you can change it. Joanne Rodrigues-Craig bridges the gap between predictive data science and statistical techniques that reveal why important things happen -- why customers buy more, or why they immediately leave your site -- so you can get more behaviors you want and less you don’t. Drawing on extensive enterprise experience and deep knowledge of demographics and sociology, Rodrigues-Craig shows how to create better theories and metrics, so you can accelerate the process of gaining insight, altering behavior, and earning business value. You’ll learn how to: Develop complex, testable theories for understanding individual and social behavior in web products Think like a social scientist and contextualize individual behavior in today’s social environments Build more effective metrics and KPIs for any web product or system Conduct more informative and actionable A/B tests Explore causal effects, reflecting a deeper understanding of the differences between correlation and causation Alter user behavior in a complex web product Understand how relevant human behaviors develop, and the prerequisites for changing them Choose the right statistical techniques for common tasks such as multistate and uplift modeling Use advanced statistical techniques to model multidimensional systems Do all of this in R (with sample code available in a separate code manual)
Identify issues in a fraction of the time and streamline root cause analysis for your DAGs. Airflow is the leading orchestration platform for data engineers. But when running Airflow at production scale, many teams have bigger needs for monitoring jobs, creating the right level of alerting, tracking problems in data, and finding the root cause of errors. In this talk we will cover our suggested approach to gaining Airflow observability so that you have the visibility you need to be productive. What is observability? The capability of monitoring and analyzing event logs, along with KPIs and other data, that yields actionable insights. In the data engineering context, observability is crucial for finding problems in jobs and data before those problems impact data consumers downstream. It’s a particularly difficult challenge because of the different platforms data engineers use (Airflow, Spark, Kubernetes, etc.) and the complicated life cycle of data pipeline CI/CD. In the session, we will do a deep dive into the visibility gaps your team might face running production-scale Airflow. We will walk through a typical day in the life of finding errors in DAGs, offer best practices, and discuss open source tools you can use to extend Airflow for observability and robust monitoring. We will use standard Airflow DAG examples to guide the presentation.
Deliver eye-catching and insightful business intelligence with Microsoft Power BI Desktop. This new edition has been updated to cover all the latest features of Microsoft’s continually evolving visualization product. New in this edition is help with storytelling—adapted to PCs, tablets, and smartphones—and the building of a data narrative. You will find coverage of templates and JSON style sheets, data model annotations, and the use of composite data sources. Also provided is an introduction to incorporating Python visuals and the much awaited Decomposition Tree visual. Pro Power BI Desktop shows you how to use source data to produce stunning dashboards and compelling reports that you mold into a data narrative to seize your audience’s attention. Slice and dice the data with remarkable ease and then add metrics and KPIs to project the insights that create your competitive advantage. Convert raw data into clear, accurate, and interactive information with Microsoft’s free self-service BI tool. This book shows you how to choose from a wide range of built-in and third-party visualization types so that your message is always enhanced. You will be able to deliver those results on PCs, tablets, and smartphones, as well as share results via the cloud. The book helps you save time by preparing the underlying data correctly without needing an IT department to prepare it for you. What You Will Learn Deliver attention-grabbing information, turning data into insight Find new insights as you chop and tweak your data as never before Build a data narrative through interactive reports with drill-through and cross-page slicing Mash up data from multiple sources into a cleansed and coherent data model Build interdependent charts, maps, and tables to deliver visually stunninginformation Create dashboards that help in monitoring key performance indicators of your business Adapt delivery to mobile devices such as phones and tablets Who This Book Is For Power users who are ready to step up to the big leagues by going beyond what Microsoft Excel by itself can offer. The book also is for line-of-business managers who are starved for actionable data needed to make decisions about their business. And the book is for BI analysts looking for an easy-to-use tool to analyze data and share results with C-suite colleagues they support.
Summary The landscape of data management and processing is rapidly changing and evolving. There are certain foundational elements that have remained steady, but as the industry matures new trends emerge and gain prominence. In this episode Astasia Myers of Redpoint Ventures shares her perspective as an investor on which categories she is paying particular attention to for the near to medium term. She discusses the work being done to address challenges in the areas of data quality, observability, discovery, and streaming. This is a useful conversation to gain a macro perspective on where businesses are looking to improve their capabilities to work with data.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar to get you up and running in no time. With simple pricing, fast networking, S3 compatible object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! You listen to this show because you love working with data and want to keep your skills up to date. Machine learning is finding its way into every aspect of the data landscape. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. The Data Engineering Podcast is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to dataengineeringpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll. Your host is Tobias Macey and today I’m interviewing Astasia Myers about the trends in the data industry that she sees as an investor at Redpoint Ventures
Interview
Introduction How did you get involved in the area of data management? Can you start by giving an overview of Redpoint Ventures and your role there? From an investor perspective, what is most appealing about the category of data-oriented businesses? What are the main sources of information that you rely on to keep up to date with what is happening in the data industry?
What is your personal heuristic for determining the relevance of any given piece of information to decide whether it is worthy of further investigation?
As someone who works closely with a variety of companies across different industry verticals and different areas of focus, what are some of the common trends that you have identified in the data ecosystem? In your article that covers the trends you are keeping an eye on for 2020 you call out 4 in particular, data quality, data catalogs, observability of what influences critical business indicators, and streaming data. Taking those in turn:
What are the driving factors that influence data quality, and what elements of that problem space are being addressed by the companies you are watching?
What are the unsolved areas that you see as being viable for newcomers?
What are the challenges faced by businesses in establishing and maintaining data catalogs?
What approaches are being taken by the companies who are trying to solve this problem?
What shortcomings do you see in the available products?
For gaining visibility into the forces that impact the key performance indicators (KPI) of businesses, what is lacking in the current approaches?
What additional information needs to be tracked to provide the needed context for making informed decisions about what actions to take to improve KPIs? What challenges do businesses in this observability space face to provide useful access and analysis to this collected data?
Streaming is an area that has been growing rapidly over the past few years, with many open source and commercial options. What are the major business opportunities that you see to make streaming more accessible and effective?
What are the main factors that you see as driving this growth in the need for access to streaming data?
With your focus on these trends, how does that influence your investment decisions and where you spend your time? What are the unaddressed markets or product categories that you see which would be lucrative for new businesses? In most areas of technology now there is a mix of open source and commercial solutions to any given problem, with varying levels of maturity and polish between them. What are your views on the balance of this relationship in the data ecosystem?
For data in particular, there is a strong potential for vendor lock-in which can cause potential customers to avoid adoption of commercial solutions. What has been your experience in that regard with the companies that you work with?
Contact Info
@AstasiaMyers on Twitter @astasia on Medium LinkedIn
Parting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, Podcast.init to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
Links
Redpoint Ventures 4 Data Trends To Watch in 2020 Seagate Western Digital Pure Storage Cisco Cohesity Looker
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Thoughspot Tibco Elastic Splunk Informatica Data Council DataCoral Mattermost Bitwarden Snowplow
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CHAOSSEARCH
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Kafka Streams Pulsar
Podcast Interview Followup Podcast Interview
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LDAP == Lightweight Directory Access Protocol Anodot Databricks Flink
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Free Data Storytelling Training Attend our FREE 'How to be the Chief Datastoryteller in your Org - Part 2 using our Analytics Design Guide' training at webinars.bidatastorytelling.com and download the FREE 50-page Guide!
In this episode, you'll learn:
[12:10] Programming Powers: Liza got into tech to be a winner and take things apart. [24:45] Why did Liza decide to sign-up for BIDF course? Learn from mistakes made when projects failed. [41:28] Step 3 - KPIs: Clearly define and develop skills to capture data metrics. For full show notes, and the links mentioned visit: https://bibrainz.com/podcast/49
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"DAX Cookbook: Over 120 recipes to enhance your business with analytics, reporting, and business intelligence" is the ultimate guidebook for mastering DAX (Data Analysis Expressions) in business intelligence, Power BI, and SQL Server Analysis Services. With hands-on examples and extensive recipes, it enables professionals to solve real-world data challenges effectively. What this Book will help me do Understand how to create tailored calculations for dates, time, and duration to enhance data insights. Develop key performance indicators (KPIs) and advanced business metrics for strategic decision-making. Master text and numerical data transformations to construct dynamic dashboards and reports. Optimize data models and DAX queries for improved performance and analytics accuracy. Learn to handle and debug calculations, and implement complex statistical and mathematical measures. Author(s) Greg Deckler is a seasoned business intelligence professional with extensive experience in using DAX and Power BI to provide actionable insights. As a recognized expert in the field, Greg brings practical knowledge of developing scalable BI solutions. His teaching approach is rooted in clarity and real-world application, making complex topics accessible to learners of all levels. Who is it for? This book is perfect for business professionals, BI developers, and data analysts with basic knowledge of the DAX language and associated tools. If you are looking to enhance your DAX skills and solve tough analytical challenges, this book is tailored for you. It's highly relevant for those aiming to optimize business intelligence workflows and improve data-driven decisions.
What problems do you have with key performance indicators (KPIs)? Are they actionable or not? How do you measure them?
Today, I'm sharing a session that I did that was rated #1 at the Real Business Intelligence event at MIT back in 2017. It's called "Secrets to building actionable KPIs". Specifically, I discuss three of the more well-known and commonly used methods: Specific, Measurable, Achievable, Relevant, Timebound (SMART) Goals, Wildly Important Goals (WIGs), and Objectives and Key Results (OKRs).
In this episode, you'll learn: [02:19] Are KPIs and metrics the same? Do you track KPIs? Metrics? Core competencies? [02:44] Key Quote: Living in KPI land, there's a lot of measurements, but you discover that not all measurements are created equal. - Mico Yuk [04:02] KPI Problem #1: Too many KPIs; focus on tracking 3-5, no more than 10. For full show notes, his book give away, and the links mentioned visit: https://bibrainz.com/podcast/37 Sponsor This exciting season of AoF is sponsored by our BI Data Storytelling Mastery Accelerator 3-Day Live workshop. Our next workshop on January 28-30, 2020 is now open for registration at a special early bird rate! Consider upgrading to the VIP option for extras including access to our online course. Many BI teams are still struggling to deliver consistent, high-engaging analytics their users love. At the end of three days, you'll leave with a clear BI delivery action plan. Register today! Enjoyed the Show? Please leave us a review on iTunes.
What did you think of my very first solo podcast? If you haven't listened to it yet, go to Analytics on Fire - 31: My Top BI User Adoption Hacks (Part 1). Today is Part 2, where I wrap up my list of top BI User Adoption hacks to help you be successful. Use adoption is the only KPI that BI teams should be focused on IMO. Enjoy and I'd love your feedback!
Sponsor
This exciting season of AOF is sponsored by our BI Data Storytelling Mastery Accelerator 3-Day Live workshop. Our first workshop coming up on Sept 17-19 almost full and VIP is sold out! Many BI teams are still struggling to deliver consistent, high-engaging analytics their users love. At the end of three days, you'll leave with the tools and techniques to truly engage your users. Register today
Enjoyed the Show? Please leave us a review on iTunes. For all links and resources mentioned visit: https://bibrainz.com/podcast/32
We kick off season 2 with a true business intelligence success story! Jonathan Sharr is the kind of story that keeps us going! Using our free BIDF KPI Blueprint template, he put all questions around KPI definitions to an end. Since then he went from being an analyst to now Manager of Business Intelligence & Analytics at Middlesex Health. He describes the exact moment as an analyst when he knew something had to change, how he currently moves his team forward, and how you too can use some of his techniques to stop the KPI madness!
Sponsor
This exciting season of AOF is sponsored by our BI Data Storytelling Mastery Accelerator 2-Day Live workshops. Many BI teams are still struggling to deliver consistent, high-engaging analytics their users love. At the end of two days, you'll leave with a clear BI delivery action plan for your BI team. Join us!
Enjoyed the Show? Please leave us a review on iTunes.
For the show notes and all the free resources mentioned visit: https://bibrainz.com/podcast/19
The book "Hands-On Data Science for Marketing" equips readers with the tools and insights to optimize their marketing campaigns using data science and machine learning techniques. Using practical examples in Python and R, you will learn how to analyze data, predict customer behavior, and implement effective strategies for better customer engagement and retention. What this Book will help me do Understand marketing KPIs and learn to compute and visualize them in Python and R. Develop the ability to analyze customer behavior and predict potential high-value customers. Master machine learning concepts for customer segmentation and personalized marketing strategies. Improve your skills to forecast customer engagement and lifetime value for more effective planning. Learn the techniques of A/B testing and their application in refining marketing decisions. Author(s) Yoon Hyup Hwang is a seasoned data scientist with a deep interest in the intersection of marketing and technology. With years of expertise in implementing machine learning algorithms in marketing analytics, Yoon brings a unique perspective by blending technical insights with business strategy. As an educator and practitioner, Yoon's approachable style and clear explanations make complex topics accessible for all learners. Who is it for? This book is tailored for marketing professionals looking to enhance their strategies using data science, data enthusiasts eager to apply their skills in marketing, and students or engineers seeking to expand their knowledge in this domain. A basic understanding of Python or R is beneficial, but the book is structured to welcome beginners by covering foundational to advanced concepts in a practical way.
In this podcast, John Busby(@johnmbusby), Chief Analytics Officer @CenterfieldUSA, talks about his journey leading the data analytics practice of a digital marketing agency. He sheds light on some methodologies for building a sound data science practice. He sheds light on the future of digital marketing and shared some big opportunities ripe for disruption in the digital space.
Timeline: 0:28 John's journey. 4:26 Introduction to Centerfield. 6:00 John's role. 6:50 Designing a common platform for customers. 9:15 Analytics in Amazon. 11:02 Data science and marketing. 18:02 Importance of understanding the product for marketing. 21:44 AI in the marketing business. 25:26 Making sense of customer behavior. 27:50 End to end consumer behavior. 31:05 Editing and calibrating KPIs. 32:53 Creating an inside driven organization. 35:35 Recipe for a successful chief analytic officer. 37:46 On data bias. 39:12 Hiring the right people. 41:33 Big opportunities in digital marketing. 44:15 Future of digital marketing. 45:27 John's recipe for success. 48:52 John's favorite reads. 50:35 Key takeaways.
John's Recommended Read: Secrets of Professional Tournament Poker (D&B Poker) by Jonathan Little amzn.to/2MNKjN3
Podcast Link: https://futureofdata.org/data-today-shaping-digital-marketing-of-tomorrow-johnmbusby-centerfieldusa/
John's BIO: John Busby serves as Centerfield’s Chief Analytics Officer. A seasoned digital marketing executive, John leads the company’s data science, analytics and insights teams. Before joining Centerfield, John was Head of Analytics for Amazon’s grocery delivery service and responsible for business intelligence, data science and automated reporting. Prior to Amazon, John was Senior Vice President of Analytics and Marketing at Marchex. John began his career in product management for InfoSpace, Go2net and IQ Chart. He holds a Bachelor of Science from Northwestern University. Outside of work, John coaches youth hockey, and enjoys sports, poker and hanging out with his wife and two children.
About #Podcast:
FutureOfData podcast is a conversation starter to bring leaders, influencers and lead practitioners to come on show and discuss their journey in creating the data driven future.
Wanna Join? If you or any you know wants to join in, Register your interest by mailing us @ [email protected]
Want to sponsor? Email us @ [email protected]
Keywords: FutureOfData,
DataAnalytics,
Leadership,
Futurist,
Podcast,
BigData,
Strategy
Prepare for Microsoft Exam 70-779 and help demonstrate your real-world mastery of Microsoft Excel data analysis and visualization. Designed for BI professionals, data analysts, and others who analyze business data with Excel, this Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the MCSA level. Focus on the expertise measured by these objectives: Consume and transform data by using Microsoft Excel Model data, from building and optimizing data models through creating performance KPIs, actual and target calculations, and hierarchies Visualize data, including creating and managing PivotTables and PivotCharts, and interacting with PowerBI This Microsoft Exam Ref: Organizes its coverage by exam objectives Features strategic, what-if scenarios to challenge you Assumes you have a strong understanding of how to use Microsoft Excel to perform data analysis
Raise your hand if you work for a company that sells exclusively low-consideration products and only sells them online. Anyone? Anyone? We only see a couple of hands out there. For all the rest of you, this episode might be of interest. We sat down with Amy Sample — Senior Director of Consumer Insights and Strategy at PBS by day, president of the DAA board by night — to discuss approaches for effective digital measurement in the absence of a clear online conversion. That challenge doesn't get much bigger than in the mission-driven, not-for-profit world of public television! After listening to this episode, you may actually feel like you have it easy! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.
In this podcast, Chuck Rehberg from Trigent Software sat with Vishal to discuss how, as a technologist, leaders should think about connecting technology to help solve real business pains. Chuck also shared some of the best practices technologists could adopt to built successful integrity-filled bias-free teams and solutions.
Timeline 0:29 Chuck's journey. 8:45 Chuck's role in Trigent. 14:18 Trigent's niche clients. 16:26 Semantics and Trigent model. 18:42 What is semantics? 22:00 The state of semantics today. 28:00 Best practices for businesses to use technology optimally. 33:13 Tips for businesses to remain stable in the time of disruptive technology. 36:18 App technology vis a vis enterprise stack. 39:43 Perspectives on the bias. 43:40 Measuring KPIs for success. 48:16 Ingredients of a good technology team. 50:56 Creating a technology team from scratch. 54:42 Things to be done in semantics. 58:52 Chuck's success mantra. 1:02:24 Chuck's favorite reads. 1:07:05 Closing remarks.
Chuck's Recommended Read: World Hypotheses: A Study in Evidence - by Stephen C. Pepper http://amzn.to/2GXGYVV Women, Fire and Dangerous Things: What Categories Reveal About the Mind - by George Lakoff http://amzn.to/2GWIQOA How to Solve It: A New Aspect of Mathematical Method (Princeton Science Library) - by G. Polya (Author), John H. Conway (Foreword, Contributor) http://amzn.to/2BLECtw The Better Angels of Our Nature: Why Violence Has Declined - by Steven Pinker http://amzn.to/2EaLQZI Finite and Infinite Games – by James Carse (Author) http://amzn.to/2BLfIdx Being Mortal: Medicine and What Matters in the End - by Atul Gawande http://amzn.to/2BhgBtp
Podcast Link: https://futureofdata.org/chuckrehberg-trigentsoftware-translating-technology-solve-business-problems-futureofdata/
Here is Chuck's Bio: As CTO at Trigent Software and Chief Scientist at Semantic Insights, Chuck Rehberg has developed patented high-performance rules engine technology and advanced natural language understanding technologies that empower a new generation of semantic research solutions.
Chuck has more than thirty years in the high-tech industry, developing leading-edge solutions in the areas of Artificial Intelligence, Semantic Technologies, analytics, and product configuration software.
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
In this podcast, Venu Vasudevan(@ProcterGamble) talks about the best practices of creating a research-led data-driven data science team. He walked through his journey of creating a robust and sustained data science team, spoke about bias in data science, and some practices leaders and data science practitioners could adopt to create an impactful data science team. This podcast is great for future data science leaders and practitioners leading organizations to put together a data science practice.
Timeline: 0:29 Venu's jouney. 11:18 Venu's current role in PNG. 13:11 Standardization of technology and IoT. 17:18 The state of AI. 19:46 Running an AI and data practice for a company. 22:30 Building a data science practice in a startup in comparison to a transnational company. 24:05 Dealing with bias. 27:32 Culture: a block or an opportunity. 30:05 Dealing with data we've never dealt with before. 32:32 Sustainable vs. disruption. 36:17 Starting a data science team. 38:34 Data science as an art of doing and science of doing business. 41:37 Tips to improve storytelling for a data practitioner. 43:30 Challenges in Venu's journey. 44:55 Tenets of a good data scientist. 47:27 Diversity in hiring. 50:50 KPI's to look out for if you are running an AI practice. 51:37 Venu's favorite read.
Venu's Recommended Read: Isaac Newton: The Last Sorcerer - Michael White http://amzn.to/2FzGV0N Against the Gods: The Remarkable Story of Risk - Peter L. Bernstein http://amzn.to/2DRPveU
Podcast Link: https://futureofdata.org/venu-vasudevan-venuv62-proctergamble-on-creating-a-rockstar-data-science-team-futureofdata/
Venu's BIO: Venu Vasudevan is Research Director, Data Science & AI at Procter & Gamble, where he directs the Data Science & AI organization at Procter & Gamble research. He is a technology leader with a track record of successful consumer & enterprise innovation at the intersection of AI, Machine Learning, Big Data, and IoT. Previously he was VP of Data Science at an IoT startup, a founding member of the Motorola team that created the Zigbee IoT standard, worked to create an industry-first zero-click interface for mobile with Dag Kittlaus (co-creator of Apple Siri), created an industry-first Google Glass experience for TV, an ARRIS video analytics and big data platform recently acquired by Comcast, and a social analytics platform leveraging Twitter that was featured in Wired Magazine and BBC. Venu held a Ph.D. (Databases & AI) from Ohio State University and was a Motorola’s Science Advisory Board (top 2% of Motorola technologists). He is an Adjunct Professor at Rice University’s Electrical and Computer Engineering department and was a mentor at Chicago’s 1871 startup incubator.
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
With the abundance of data available in analytics we are no longer competing on the volume of data we have access to, but the quality of the questions we ask. The challenge is that we have never been taught to ask or taught other people to ask the right questions from analytics. So we spend our lives answering questions like "what is our bounce rate?" or "how much did the last campaign convert?" when we could be asking important and business critical question. This means that we in analytics have to consider putting the KPQ (Key Performance Question) before the KPI (Key Performance Indication) to start asking better questions.
In this podcast, Paul Ballew(@Ford) talks about best practices when running a data science organization spanned across multiple continents. He shared the importance of being Smart, Nice, and Inquisitive in creating tomorrow's workforce today. He sheds some light on the importance of appreciating culture when defining forward-looking policies. He also builds a case for a non-native group and discusses ways to implement data science as a central organization(with no hub-spoke model). This podcast is great for future data science leaders leading organizations with a broad consumer base and multiple geo-political silos.
Timeline: 0:29 Paul's journey. 5:10 Paul's current role. 8:10 Insurance and data analytics. 13:00 Who will own the insurance in the time of automation. 18:22 Recruiting models in technologies. 21:54 Embracing technological change. 25:03 Will we have more analytics in Ford cars? 28:25 How does Ford stay competitive from a technology perspective. 30:30 Challenges for Analytics officer in Ford. 32:36 Ingredients of a good hire. 34:12 How is the data science team structured in Ford. 36:15 Dealing with shadow groups. 39:00 Successful KPIs. 40:33 Who owns data? 42:27 Who should own the security of data assets. 44:05 Examples of successful data science groups. 46:30 Practises for remaining bias-free. 48:55 Getting started running a global data science team. 52:45 How does Paul's keep himself updated. 54:18 Paul's favorite read. 55:45 Closing remarks.
Paul's Recommended Read: The Outsiders Paperback – S. E. Hinton http://amzn.to/2Ai84Gl
Podcast Link: https://futureofdata.org/paul-ballewford-running-global-data-science-group-futureofdata-podcast/
Paul's BIO: Paul Ballew is vice president and Global Chief Data and Analytics officer, Ford Motor Company, effective June 1, 2017. At the same time, he also was elected a Ford Motor Company officer. In this role, he leads Ford’s global data and analytics teams for the enterprise. Previously, Ballew was Global Chief Data and Analytics Officer, a position to which he was named in December 2014. In this role, he has been responsible for establishing and growing the company’s industry-leading data and analytics operations that are driving significant business value throughout the enterprise. Prior to joining Ford, he was Chief Data, Insight & Analytics Officer at Dun & Bradstreet. In this capacity, he was responsible for the company’s global data and analytic activities along with the company’s strategic consulting practice. Previously, Ballew served as Nationwide’s senior vice president for Customer Insight and Analytics. He directed customer analytics, market research, and information and data management functions, and supported the company’s marketing strategy. His responsibilities included the development of Nationwide’s customer analytics, data operations, and strategy. Ballew joined Nationwide in November 2007 and established the company’s Customer Insights and Analytics capabilities.
Ballew sits on the boards of Neustar, Inc. and Hyatt Hotels Corporation. He was born in 1964 and has a bachelor’s and master’s degree in Economics from the University of Detroit.
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