Growth marketing has been all the rage, but what is it? Is it new age optimizers looking for a fancy title? Marketers spinning their services in a new way? A buzzword for a game of bingo? User lifecycle marketing? In this session, Krista walks you through what Growth Marketing is, why it's important, how it's different, how we can all do it, and most importantly, how measurement works for growth.

The European digital marketing industry is inevitably shifting towards programmatic advertising, which poses difficult challenges for all involved parties – publishers, marketeers and analysts alike. As the technical infrastructure is getting more complex, it is increasingly difficult to properly collect, integrate and analyse the appropriate customer behavioral data sets, while those players who succeed first get a strong competitive advantage on the market. Luckily, it is not required anymore to employ a swarm of data engineers and data scientists to get started, as many of the critical business questions can be answered with relatively little effort using off-the-shelf tools.

The data available to marketers -- literally at their fingertips by way of a few mouse clicks -- has exploded over the last decade. Yet, while there is more data -- and it is more accessible -- than it has ever been, the way we think about and use data has hardly evolved at all. With the recent advances in cloud computing and processing power, the industry is abuzz with talk of machine learning and artificial intelligence. How, then, will we get from the world of Microsoft Excel (or Tableau) to a world where "the machines" are automatically and dynamically optimizing all aspects of our marketing?

In this podcast, Henry Eckerson and Stephen Smith discuss the movement to operationalize data science.

Smith is a well-respected expert in the fields of data science, predictive analytics and their application in the education, pharmaceutical, healthcare, telecom and finance industries. He co-founded and served as CEO of G7 Research LLC and the Optas Corporation which provided the leading CRM / Marketing Automation solution in the pharmaceutical and healthcare industries.

Smith has published journal articles in the fields of data mining, machine learning, parallel supercomputing, text understanding, and simulated evolution. He has published two books through McGraw-Hill on big data and analytics and holds several patents in the fields of educational technology, big data analytics, and machine learning. He holds a BS in Electrical Engineering from MIT and an MS in Applied Sciences from Harvard University. He is currently the research director of data science at Eckerson Group.

Construct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If you're a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you'll increase your productivity exponentially. Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience. Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics. Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer. What You Need: You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems.

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.

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey in creating 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, Joel Comm from The Bad Crypto Podcast sat with Vishal Kumar, CEO AnalyticsWeek, and discuss the World of Crypto Currencies. The discussion sheds light on the nuances in the rapidly exploding world of Crypto Currencies and some of the thinking behind the currencies. The discussion also sheds light on the opportunities and risks in the industry. Joel sheds his insights about how to think about these currencies and the long term implications of the algos that run these currencies. The podcast is a great listen for anyone who wants to understand the world of cryptocurrencies.

Timeline: 0:29 Joel's journey. 5:45 Thinking behind "Bad Crypto". 7:50 Getting into the domain of cryptocurrency. 13:30 Underlying technology behind cryptocurrency. 17:00 On Bitcoin. 18:50 Tracing back a ledger. 20:36 The use of blockchain. 23:00 Every bitcoin is a country. 25:11 Parameters to invest in Cryptocurrency. 26:05 Some better known Cryptocurrency. 31:52 The security aspect of Cryptocurrency. 41:52 Security and regulations of tokens. 44:03 The consensus element of blockchain. 46:25 Alternatives to the blockchain. 49:30 Bitcoin as payment. 58:15 Manipulation of the Crypto market. 1:00:51 Joel's favorite reads.

Youtube: https://youtu.be/xJucEIDitas iTunes: http://apple.co/2ynxopz

Please note, this podcast and/or its content in no way advocate any investment advice and nor intended to generate any positive or negative influence. Crypto Currencies are highly volatile in nature, and any investor must use absolute caution and care while evaluating such currencies.

Joel's Recommended Read: Cryptocurrencies 101 By James Altucher http://bit.ly/2Bi5FMv

Podcast Link: https://futureofdata.org/discussing-world-crypto-joelcomm-badcrypto/

Joel's BIO: As a knowledgeable & inspirational speaker, Joel speaks on a variety of business and entrepreneurial topics. He presents a step-by-step playbook on how to use social media as a leveraging tool to expand the reach of your brand, increase your customer base, and create fierce brand loyalty for your business. Joel is also able to speak with authority on the various ways to harness the marketing power of technology to explode profits. He offers an inspiring yet down-to-earth call to action for those who dream of obtaining growth and financial success. As someone who went from having only 87 cents in his bank account to creating multiple successful businesses, Joel is uniquely poised to instruct and inspire when it comes to using the various forms of new media as avenues towards the greater goal of business success. He is a broadcast veteran with thousands of hours in radio, podcasting, television, and online video experience. Joel is the host of two popular yet completely different podcasts. FUN with Joel Comm features the lighter side of the top business and social leaders. The Bad Crypto Podcast makes cryptocurrency and bitcoin understandable to the masses.

Joel is the New York Times best-selling author of 14 books, including The AdSense Code, Click Here to Order: Stories from the World's Most Successful Entrepreneurs, KaChing: How to Run an Online Business that Pays and Paysm Twitter Power 3.0, and Self Employed: 50 Signs That You Might Be an Entrepreneur. He has also written over 40 ebooks. He has appeared in The New York Times, on Jon Stewart's The Daily Show, on CNN online, on Fox News, and many other places.

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, George Corugedo(@RedpointCTO) / @Redpoint talks about the ingredients of a technologist in a data-driven world. He sheds light on technology & technologist bias and how companies could work progressively to respond in an unbiased manner. He shared some insights on leading a data science product as a technologist and shared some takeaways for future technologists. This podcast is great for future technologists thinking of shaping their organization to take advantage of technological disruptions to stay competitive.

Timeline: 0:29 George's journey. 3:35 Challenges in George's journey. 7:22 The relevance of mathematics in this data-driven world. 13:02 Statistitians getting into the technology stack. 22:38 Data-driven customer engagement platform. 24:24 Challenges for a technologist to connect with various platforms and prospects. 28:52 Customer challenges for businesses. 31:55 What do businesses get about marketing? 34:04 Bridging the gap between data and analytics. 42:42 Hacks for mitigating bias. 46:18 Appification: a bane or an opportunity. 48:45 An candidate for a data analytics startup. 52:40 Important KPIs for a data-driven customer engagement company. 56:33 How does George keep himself updated? 57:58 What keeps George up at night? 59:15 George's favorite read. 1:01:05 Closing remarks.

Youtube: https://youtu.be/u6CtN-TYjXI iTunes: http://apple.co/2AJDnuz

Ed's Recommended Read: To Kill a Mockingbird by Harper Lee http://amzn.to/2hZnwwx Self-Reliance and Other Essays (Dover Thrift Editions) by Ralph Waldo Emerson http://amzn.to/2i0WcOx

Podcast Link: https://futureofdata.org/redpointcto-redpointglobal-on-becoming-an-unbiased-technologist-in-datadriven-world/

George's BIO: A former math professor and seasoned technology executive, RedPoint Chief Technology Officer and Co-Founder George Corugedo has more than two decades of business and technical experience. George is responsible for directing the development of the RedPoint Customer Engagement Hub, RedPoint’s leading enterprise customer engagement solution. George left academia in 1997 to co-found Accenture’s Customer Insights Practice, which specialized in strategic data utilization, analytics, and customer strategy. George’s previous positions include director of client delivery at ClarityBlue, Inc., a provider of hosted customer intelligence solutions, and COO/CIO of Riscuity, a receivables management company that specialized in using analytics to drive collections.

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

I sat down with Ali Ghodsi, CEO and found of Databricks, and John Chirapurath, GM for Data Platform Marketing at Microsoft related to the recent announcement of Azure Databricks. When I heard about the announcement, my first thoughts were two-fold.  First, the possibility of optimized integrations with existing Azure services.  This would be a big benefit to heavy Azure users who also want to use Spark.  Second, the benefits of active directory to control Databricks access for large enterprise. Hear Ali and JG's thoughts and comments on what makes Azure Databricks a novel offering.  

In this podcast, @CRGutowski from @GE_Digital talks about the importance of data and analytics in transforming sales organizations. She sheds light on challenges and opportunities with transforming the sales organization of a transnational enterprise using analytics and implement a growth mindset. Cate shared some of the tenets of the transformation mindset. This podcast is great for future leaders who are thinking of shaping their sales organization and empower them with the digital mindset.

Timeline: 0:29 Cate's journey. 7:40 Cate's typical day. 9:07 How does the sales cope up with disruption? 13:25 Data science in sales. 14:48 Planning a digital software for 25000 workforces. 18:00 The thin line between marketing and sales. 22:13 Safeguarding the workforce against tech disruption. 24:57 The culture of sales. 27:55 Designing a digitally connected strategy. 30:08 Designing customer experience. 33:48 Sales strategy for a startup. 36:43 Selling transformative sales strategies to executives. 40:55 How can organizations go digital? 43:25 Digital thread. 44:14 How can a sales organization deal with IT? 45:54 Pitfalls in the process of digitization. 48:44 Challenges for sales folks amid disruption. 50:30 How does Cate keep herself updated? 52:10 Cate's success mantra. 54:06 Closing remarks.

Youtube: https://youtu.be/3jcpYgvIli4 iTunes: http://apple.co/2hM9r5E

Cate's Recommended Read: Start with Why: How Great Leaders Inspire Everyone to Take Action by Simon Sinek http://amzn.to/2hGvc6w

Podcast Link: https://futureofdata.org/crgutowski-ge_digital-using-analytics-transform-sales/

Cate's BIO: Cate has 20 years of technical sales, marketing, and product leadership experience across various global divisions in GE. Cate is currently based in Boston, MA, and works as the VP – Commercial Digital Thread, leading the digital transformation of GE’s 25,000+ sales organization globally. Prior to relocating to Boston, Cate and her family lived in Budapest, Hungary, where she led product management, marketing, and commercial operations across EMEA for GE Current. Cate holds an M.B.A. from the University of South Florida and a Bachelor’s degree in Communications and Business Administration from the University of Illinois at Urbana-Champaign.

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, #Data, #Analytics, #Leadership Podcast, #Big Data, #Strategy

In this Podcast, Charlie Berger from Oracle discussed some of the challenges of data-driven enterprises.

Timeline: 0:29 Charlie's journey. 6:12 Charlie's current role. 8:55 Oracle's role in the future of data. 13:20 The evolution of ML. 20:41 The need for revaluating mathematical models that data science is based on. 27:50 On the concept of appification of analytics. 36:17 On enterprise IT landscape changing. 43:17 Geekifying analytics. 47:15 Charlie's favorite read. 50:21 Closing remarks.

Charlie's favorite read suggestions: 1. The Naked Future: What Happens in a World That Anticipates Your Every Move?

Podcast link: https://futureofdata.org/futureofdata-charliedatamine-oracle-discussing-running-analytics-enterprise/

Charlie's BIO: Passionate technical professional skilled in building entrepreneurial, start-up initiatives, and environments. Strong technical, product management, communication, marketing, and leadership skills.

• Experienced product management professional with over 30 years of experience in leading-edge technologies in large corporations and entrepreneurial start-ups. • During 15 years at Oracle Corporation, developed an innovative portfolio of “big data analytics” products developed as in-database SQL data mining functions and integrated "predictive analytics" applications. • Strong technical, product management, communication, and leadership skills. • Responsible for product management and direction for the Oracle Database data mining and predictive analytics technology, including Oracle Data Mining, text mining, and statistical functions. • Strong product champion, evangelist, and frequent speaker in the field of predictive analytics and data mining. • Leveraged relationships with customers, development, and sales to communicate product capabilities and value proposition.

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 Data Analytics Leadership Podcast Big Data Strategy

Is your organization customer-centric? Does your product team dive into the demographics of your customers to figure out what features will make them as happy as possible? If so, then you're doing it all wrong! Perhaps. On this episode, the gang chats with Dr. Peter Fader about putting customer lifetime value (CLV) front and center when it comes to developing and executing marketing strategies. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

The New Edition of a Business Classic This landmark work, the first to introduce business leaders to analytics, reveals how analytics are rewriting the rules of competition. Updated with fresh content, Competing on Analytics provides the road map for becoming an analytical competitor, showing readers how to create new strategies for their organizations based on sophisticated analytics. Introducing a five-stage model of analytical competition, Davenport and Harris describe the typical behaviors, capabilities, and challenges of each stage. They explain how to assess your company’s capabilities and guide it toward the highest level of competition. With equal emphasis on two key resources, human and technological, this book reveals how even the most highly analytical companies can up their game. With an emphasis on predictive, prescriptive, and autonomous analytics for marketing, supply chain, finance, M&A, operations, R&D, and HR, the book contains numerous new examples from different industries and business functions, such as Disney’s vacation experience, Google’s HR, UPS’s logistics, the Chicago Cubs’ training methods, and Firewire Surfboards’ customization. Additional new topics and research include: Data scientists and what they do Big data and the changes it has wrought Hadoop and other open-source software for managing and analyzing data Data products—new products and services based on data and analytics Machine learning and other AI technologies The Internet of Things and its implications New computing architectures, including cloud computing Embedding analytics within operational systems Visual analytics The business classic that turned a generation of leaders into analytical competitors, Competing on Analytics is the definitive guide for transforming your company’s fortunes in the age of analytics and big data.

"Matplotlib 2.x By Example" is your comprehensive guide to mastering data visualization in Python using the Matplotlib library. Through detailed explanations and hands-on examples, this book will teach you how to create stunning, insightful, and professional-looking visual representations of your data. You'll learn valuable skills tailored towards practical applications in science, marketing, and data analysis. What this Book will help me do Understand the core features of Matplotlib and how to use them effectively. Create professional 2D and 3D visualizations, such as scatter plots, line graphs, and more. Develop skills to transform raw data into meaningful insights through visualization. Enhance your data visualizations with interactive elements and animations. Leverage additional libraries such as Seaborn and Pandas to expand functionality. Author(s) Allen Yu, Claire Chung, and Aldrin Yim are seasoned data scientists and technical authors with extensive experience in Python and data visualization. Allen and his coauthors are dedicated to helping readers bridge the gap between their raw data and meaningful insights through visualization. With practical applications and real-world examples, their approachable writing makes complex libraries like Matplotlib accessible and production-ready. Who is it for? This book is perfect for data enthusiasts, analysts, and Python programmers looking to enhance their data visualization skills. Whether you're a professional aiming to create high-quality visual reports or a student eager to understand and present data effectively, this book provides practical and actionable insights. Basic Python knowledge is expected, while all Matplotlib-related aspects are thoroughly explained.

In this podcast, Rob Griffin from Almighty(X), a Connelly partner company, sat with Vishal Kumar to discuss running innovation in a media agency.

Timeline: 0:29 Rob's journey. 8:12 Marketing, the earliest adopter of analytics. 10:43 Defining transformational innovation. 22:05 Art of doing business and science of doing business. 26:12 Problems for an innovative analytics company. 31:00 Innovation in different size companies. 34:31 Common mistakes businesses make on innovation. 40:00 The future of the creative industry. 42:49 ML and AI impact on the digital market. 49:29 Don't be efficient, be creative. 57:39 Rob's recommended books.

Podcast link: https://futureofdata.org/futureofdata-robtelerob-connellyagency-running-innovation-agency/

Here's Rob's Bio: Driving transformational innovation in marketing and advertising. Pushing creative and media technology limits. Helping brands take ownership of their technology, data, and media for greater transparency and accountability. Putting the agent back in the agency. Been working in digital marketing and advertising since 1996. A Bostonian. A die-hard Celtics fan. Dad. Speaker. Writer. Advisor. Skier. Comic book fan. Lover of good eats.

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

A straightforward, non-technical guide to the next major marketing tool Artificial Intelligence for Marketing presents a tightly-focused introduction to machine learning, written specifically for marketing professionals. This book will not teach you to be a data scientist—but it does explain how Artificial Intelligence and Machine Learning will revolutionize your company's marketing strategy, and teach you how to use it most effectively. Data and analytics have become table stakes in modern marketing, but the field is ever-evolving with data scientists continually developing new algorithms—where does that leave you? How can marketers use the latest data science developments to their advantage? This book walks you through the "need-to-know" aspects of Artificial Intelligence, including natural language processing, speech recognition, and the power of Machine Learning to show you how to make the most of this technology in a practical, tactical way. Simple illustrations clarify complex concepts, and case studies show how real-world companies are taking the next leap forward. Straightforward, pragmatic, and with no math required, this book will help you: Speak intelligently about Artificial Intelligence and its advantages in marketing Understand how marketers without a Data Science degree can make use of machine learning technology Collaborate with data scientists as a subject matter expert to help develop focused-use applications Help your company gain a competitive advantage by leveraging leading-edge technology in marketing Marketing and data science are two fast-moving, turbulent spheres that often intersect; that intersection is where marketing professionals pick up the tools and methods to move their company forward. Artificial Intelligence and Machine Learning provide a data-driven basis for more robust and intensely-targeted marketing strategies—and companies that effectively utilize these latest tools will reap the benefit in the marketplace. Artificial Intelligence for Marketing provides a nontechnical crash course to help you stay ahead of the curve.

Summary

Building a data pipeline that is reliable and flexible is a difficult task, especially when you have a small team. Astronomer is a platform that lets you skip straight to processing your valuable business data. Ry Walker, the CEO of Astronomer, explains how the company got started, how the platform works, and their commitment to open source.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.dataengineeringpodcast.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers This is your host Tobias Macey and today I’m interviewing Ry Walker, CEO of Astronomer, the platform for data engineering.

Interview

Introduction How did you first get involved in the area of data management? What is Astronomer and how did it get started? Regulatory challenges of processing other people’s data What does your data pipelining architecture look like? What are the most challenging aspects of building a general purpose data management environment? What are some of the most significant sources of technical debt in your platform? Can you share some of the failures that you have encountered while architecting or building your platform and company and how you overcame them? There are certain areas of the overall data engineering workflow that are well defined and have numerous tools to choose from. What are some of the unsolved problems in data management? What are some of the most interesting or unexpected uses of your platform that you are aware of?

Contact Information

Email @rywalker on Twitter

Links

Astronomer Kiss Metrics Segment Marketing tools chart Clickstream HIPAA FERPA PCI Mesos Mesos DC/OS Airflow SSIS Marathon Prometheus Grafana Terraform Kafka Spark ELK Stack React GraphQL PostGreSQL MongoDB Ceph Druid Aries Vault Adapter Pattern Docker Kinesis API Gateway Kong AWS Lambda Flink Redshift NOAA Informatica SnapLogic Meteor

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Solve business problems involving time-to-event and resulting probabilities by following the modeling tutorials in Business Survival Analysis Using SAS®: An Introduction to Lifetime Probabilities, the first book to be published in the field of business survival analysis! Survival analysis is a challenge. Books applying to health sciences exist, but nothing about survival applications for business has been available until now. Written for analysts, forecasters, econometricians, and modelers who work in marketing or credit risk and have little SAS modeling experience, Business Survival Analysis Using SAS® builds on a foundation of SAS code that works in any survival model and features numerous annotated graphs, coefficients, and statistics linked to real business situations and data sets. This guide also helps recent graduates who know the statistics but do not necessarily know how to apply them get up and running in their jobs. By example, it teaches the techniques while avoiding advanced theoretical underpinnings so that busy professionals can rapidly deliver a survival model to meet common business needs.

From first principles, this book teaches survival analysis by highlighting its relevance to business cases. A pragmatic introduction to survival analysis models, it leads you through business examples that contextualize and motivate the statistical methods and SAS coding. Specifically, it illustrates how to build a time-to-next-purchase survival model in SAS® Enterprise Miner, and it relates each step to the underlying statistics and to Base SAS® and SAS/STAT® software. Following the many examples—from data preparation to validation to scoring new customers—you will learn to develop and apply survival analysis techniques to scenarios faced by companies in the financial services, insurance, telecommunication, and marketing industries, including the following scenarios:

Time-to-next-purchase for marketing

Employer turnover for human resources

Small business portfolio macroeconometric stress tests for banks

International Financial Reporting Standard (IFRS 9) lifetime probability of default for banks and building societies

"Churn," or attrition, models for the telecommunications and insurance industries

Dive into the world of predictive analytics with 'Practical Predictive Analytics.' This comprehensive guide walks you through analyzing current and historical data to predict future outcomes. Using tools like R and Spark, you will master practical skills, solve real-world challenges, and apply predictive analytics across domains like marketing, healthcare, and retail. What this Book will help me do Learn the six steps for successfully implementing predictive analytics projects. Acquire practical skills in data cleaning, input, and model deployment using tools like R and Spark. Understand core predictive analytics algorithms and their applications in various industries. Apply data analytics techniques to solve problems in fields such as healthcare and marketing. Master methods for handling big data analytics using Databricks and Spark for effective prediction. Author(s) The author, None Winters, is an experienced data scientist and technical educator. With extensive background in predictive analytics, Winters specializes in applying statistical methods and techniques to real-world consultation scenarios. Winters brings a practical and accessible approach to this text, ensuring that learners can follow along and apply their newfound expertise effectively. Who is it for? This book is ideal for statisticians and analysts with some programming background in languages like R, who want to master predictive analytics skills. It caters to intermediate learners who aim to enhance their ability to solve complex analytical problems. Whether you're looking to advance your career or improve your proficiency in data science, this book will serve as a valuable resource for learning and growth.