2017 was a big year for both the digital analytics industry and for the Digital Analytics Power Hour. Join us, won't you, as we (figuratively!) gaze upon our navels? From the traction the #womeninanalytics movement gained on multiple fronts, to the looming promise of machine learning and AI getting a real foothold in the field, to the podcast finally adding a co-host who is universally admired, we had a lot to talk about! We had a LOT to talk about. Trust us, we edited this episode down heavily! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Written for students in undergraduate and graduate statistics courses, as well as for the practitioner who wants to make better decisions from data and models, this updated and expanded second edition of Fundamentals of Predictive Analytics with JMP(R) bridges the gap between courses on basic statistics, which focus on univariate and bivariate analysis, and courses on data mining and predictive analytics. Going beyond the theoretical foundation, this book gives you the technical knowledge and problem-solving skills that you need to perform real-world multivariate data analysis. First, this book teaches you to recognize when it is appropriate to use a tool, what variables and data are required, and what the results might be. Second, it teaches you how to interpret the results and then, step-by-step, how and where to perform and evaluate the analysis in JMP . Using JMP 13 and JMP 13 Pro, this book offers the following new and enhanced features in an example-driven format: an add-in for Microsoft Excel Graph Builder dirty data visualization regression ANOVA logistic regression principal component analysis LASSO elastic net cluster analysis decision trees k-nearest neighbors neural networks bootstrap forests boosted trees text mining association rules model comparison With today’s emphasis on business intelligence, business analytics, and predictive analytics, this second edition is invaluable to anyone who needs to expand his or her knowledge of statistics and to apply real-world, problem-solving analysis. This book is part of the SAS Press program.

Use machine learning and Oracle Business Intelligence Enterprise Edition (OBIEE) as a comprehensive BI solution. This book follows a when-to, why-to, and how-to approach to explain the key steps involved in utilizing the artificial intelligence components now available for a successful OBIEE implementation. Oracle Business Intelligence with Machine Learning covers various technologies including using Oracle OBIEE, R Enterprise, Spatial Maps, and machine learning for advanced visualization and analytics. The machine learning material focuses on learning representations of input data suitable for a given prediction problem. This book focuses on the practical aspects of implementing machine learning solutions using the rich Oracle BI ecosystem. The primary objective of this book is to bridge the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to machine learning with OBIEE. What You Will Learn See machine learning in OBIEE Master the fundamentals of machine learning and how it pertains to BI and advanced analytics Gain an introduction to Oracle R Enterprise Discover the practical considerations of implementing machine learning with OBIEE Who This Book Is For Analytics managers, BI architects and developers, and data scientists.

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/

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Keywords:

FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

Abstract This IBM® Redbooks® publication describes the concepts, architecture, and implementation of the IBM DS8880 family. The book provides reference information to assist readers who need to plan for, install, and configure the DS8880 systems. The IBM DS8000® family is a high-performance, high-capacity, highly secure, and resilient series of disk storage systems. The DS8880 family is the latest and most advanced of the DS8000 offerings to date. The high availability, multiplatform support, including IBM Z, and simplified management tools help provide a cost-effective path to an on-demand and cloud-based infrastructures. The IBM DS8880 family now offers business-critical, all-flash, and hybrid data systems that span a wide range of price points: DS8884 -- Business Class DS8886 -- Enterprise Class DS8888 -- Analytics Class The DS8884 and DS8886 are available as either hybrid models, or can be configured as all-flash. Each model represents the most recent in this series of high-performance, high-capacity, flexible, and resilient storage systems. These systems are intended to address the needs of the most demanding clients. Two powerful IBM POWER8® processor-based servers manage the cache to streamline disk I/O, maximizing performance and throughput. These capabilities are further enhanced with the availability of the second generation of high-performance flash enclosures (HPFEs Gen-2) and newer flash drives. Like its predecessors, the DS8880 supports advanced disaster recovery (DR) solutions, business continuity solutions, and thin provisioning. All disk drives in the DS8880 storage system include the Full Disk Encryption (FDE) feature. The DS8880 can automatically optimize the use of each storage tier, particularly flash drives, by using the IBM Easy Tier® feature.

Discover how to leverage the power of SAS for big data analytics in 'Big Data Analytics with SAS.' This book helps you unlock key techniques for preparing, analyzing, and reporting on big data effectively using SAS. Whether you're exploring integration with Hadoop and Python or mastering SAS Studio, you'll advance your analytics capabilities. What this Book will help me do Set up a SAS environment for performing hands-on data analytics tasks efficiently. Master the fundamentals of SAS programming for data manipulation and analysis. Use SAS Studio and Jupyter Notebook to interface with SAS efficiently and effectively. Perform preparatory data workflows and advanced analytics, including predictive modeling and reporting. Integrate SAS with platforms like Hadoop, SAP HANA, and Cloud Foundry for scaling analytics processes. Author(s) None Pope is a seasoned data analytics expert with extensive experience in SAS and big data platforms. With a passion for demystifying complex data workflows, None teaches SAS techniques in an approachable way. Their expert insights and practical examples empower readers to confidently analyze and report on data. Who is it for? If you're a SAS professional or a data analyst looking to expand your skills in big data analysis, this book is for you. It suits readers aiming to integrate SAS into diverse tech ecosystems or seeking to learn predictive modeling and reporting with SAS. Both beginners and those familiar with SAS can benefit.

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/

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Keywords:

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

Step into the world of advanced data analysis with 'R Data Analysis Projects.' In this hands-on guide, you will learn to build efficient analytics systems and pipelines using R for practical applications in finance, social media, and more. By following real-world projects, you'll enhance your data analysis skills, from implementing recommender systems to performing time-series modeling. What this Book will help me do Develop end-to-end data analysis and visualization solutions using R. Create scalable predictive analytics systems with actionable insights. Leverage RShiny to build interactive dashboards for effective communication. Master popular R packages like dplyr, ggplot2, and recommenderlab. Tackle real-world data challenges in varied domains such as finance and social networks. Author(s) Gopi Subramanian is an experienced data scientist and educator with an extensive background in statistical modeling and analytics. With years of hands-on practice and teaching, Gopi specializes in making complex concepts accessible through practical examples. His passion for R programming and real-world applications shines in his approachable style, making learning empowering and engaging. Who is it for? This book is designed for readers with a foundational understanding of R and data analysis, aiming to advance their skills to a professional level. Ideal for data analysts, R programmers, and aspiring data scientists seeking practical experience in building analytics systems. Whether you're transitioning to or deepening your expertise in R, this guide offers actionable knowledge to enhance your projects.

In this podcast, @EdwardBoudrot from @Optum talks about how leaders could induce design thinking into product design and process engineering. Ed shares some of the ways organizations (small or big) could create lean processes that induce not only efficient people-centric products but also help future proof companies by bringing them closer to their customer. This podcast is great for future leaders who are thinking of shaping their organization around design thinking concepts.

Timeline: 0:29 Edward's journey. 4:55 Innovation in a culturally thick company. 10:46 Life cycle of design thinking. 15:45 Designing thinking's role in business strategy. 19:28 Attributes of design thinking in business strategy. 23:07 Edward's expansion strategy. 25:30 Favorite design thinking concepts. 29:40 How to move a product mindset to a design thinking mindset. 32:22 Lab atmosphere to execute design thinking ideas. 34:15 Tips for startups to get started with design thinking. 35:40 Steps for companies to adopt design thinking. 38:15 Collaboration in design thinking. 41:00 Getting started with a human-centered design. 43:42 Tenets of a successful design thinking executive. 46:30 KPIs to measure the success of your design. 48:58 Design thinking and disruption. 53:22 Businesses that are doing well at design thinking. 55:33 How can design thinking protect itself from market changes. 59:17 Edward's favorite reads. 1:00:33 Closing remarks.

Ed's Recommended Read: Ten Types of Innovation: The Discipline of Building Breakthroughs http://amzn.to/2ywxKLx 101 Design Methods: A Structured Approach for Driving Innovation in Your Organization http://amzn.to/2AFiWvE Tools of Titans: The Tactics, Routines, and Habits of Billionaires, Icons, and World-Class Performers http://amzn.to/2zAkwAJ

Podcast Link: https://futureofdata.org/edwardboudrot-optum-designthinking-data-driven-products/

Ed's BIO: Ed Boudrot is the Vice President of Fusion, an enterprise accelerator for Optum. Optum’s mission is to help people live healthier lives and to help make the health system work better for everyone. Boudrot has founded and has been a part of serval startups in the Boston area and Intuit innovation labs. He specializes in the convergence of Human-centered design, business strategy, and rapid development to optimize experiences and business outcomes.

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

"Practical Data Wrangling" provides a comprehensive guide to cleaning and preparing data for analysis, focusing on techniques in Python and R. As you progress through the book, you'll learn how to handle various datasets, reshape their formats, and prepare them for insights, empowering you to derive more value from your data. What this Book will help me do Understand the data wrangling process and its importance in the data analysis pipeline. Learn how to retrieve, parse, and shape raw data into structured formats. Master packages and tools in Python and R to efficiently clean and manipulate data. Gain proficiency in using regular expressions for text data preparation. Acquire skills to analyze, merge, and transform datasets to meet analytics needs. Author(s) None Visochek has years of experience working with data and analytics, with expertise in using Python and R for solving real-world data challenges. Their teaching approach emphasizes practical examples and accessible explanations, ensuring complex concepts are easy to understand. Who is it for? This book is for data scientists, analysts, or statisticians who work with real-world data and want to optimize their data preparation process. It is ideal for professionals with basic knowledge of Python and R looking to enhance their skills in data wrangling and data preparation techniques. If you're seeking to streamline your data analysis workflow through better wrangling techniques, this book is for you.

Businesses regardless of industry or company size increasingly rely on data analytics and visualization to gain competitive advantage. That’s why organizations today are racing to gather, store, and analyze data from many sources in a wide range of formats. In the spring of 2017, Zoomdata commissioned an O’Reilly survey to assess the state of data analytics and visualization technology adoption across several industries, including manufacturing, financial services, and healthcare. Roughly 875 respondents answered questions online about their industry, job role, company size, and reasons for using analytics, as well as technologies they use in analytics programs, the perceived value of analytics programs, and many other topics. This report reveals: The industries furthest along in adopting big data analytics and visualization technologies The most commonly analyzed sources of big data The most commonly used technologies for analyzing streaming data Which analytics skills are in most demand The most valued characteristic of big data across all industries The types of users big data analytics and visualization projects typically target If you’re a technology decision maker, a product manager looking to embed analytics, a business user relying on analytics, or a developer pursuing the most marketable skills, this report provides valuable details on today’s data analytics trends.

Summary

Buzzfeed needs to be able to understand how its users are interacting with the myriad articles, videos, etc. that they are posting. This lets them produce new content that will continue to be well-received. To surface the insights that they need to grow their business they need a robust data infrastructure to reliably capture all of those interactions. Walter Menendez is a data engineer on their infrastructure team and in this episode he describes how they manage data ingestion from a wide array of sources and create an interface for their data scientists to produce valuable conclusions.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode 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. Continuous delivery lets you get new features in front of your users as fast as possible without introducing bugs or breaking production and GoCD is the open source platform made by the people at Thoughtworks who wrote the book about it. Go to dataengineeringpodcast.com/gocd to download and launch it today. Enterprise add-ons and professional support are available for added peace of mind. 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 Your host is Tobias Macey and today I’m interviewing Walter Menendez about the data engineering platform at Buzzfeed

Interview

Introduction How did you get involved in the area of data management? How is the data engineering team at Buzzfeed structured and what kinds of projects are you responsible for? What are some of the types of data inputs and outputs that you work with at Buzzfeed? Is the core of your system using a real-time streaming approach or is it primarily batch-oriented and what are the business needs that drive that decision? What does the architecture of your data platform look like and what are some of the most significant areas of technical debt? Which platforms and languages are most widely leveraged in your team and what are some of the outliers? What are some of the most significant challenges that you face, both technically and organizationally? What are some of the dead ends that you have run into or failed projects that you have tried? What has been the most successful project that you have completed and how do you measure that success?

Contact Info

@hackwalter on Twitter walterm on GitHub

Links

Data Literacy MIT Media Lab Tumblr Data Capital Data Infrastructure Google Analytics Datadog Python Numpy SciPy NLTK Go Language NSQ Tornado PySpark AWS EMR Redshift Tracking Pixel Google Cloud Don’t try to be google Stop Hiring DevOps Engineers and Start Growing Them

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

The definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to program in R. Short on theory and long on actionable analytics, it provides readers with a detailed comparative introduction and overview of both languages and features concise tutorials with command-by-command translations—complete with sample code—of R to Python and Python to R. Following an introduction to both languages, the author cuts to the chase with step-by-step coverage of the full range of pertinent programming features and functions, including data input, data inspection/data quality, data analysis, and data visualization. Statistical modeling, machine learning, and data mining—including supervised and unsupervised data mining methods—are treated in detail, as are time series forecasting, text mining, and natural language processing. • Features a quick-learning format with concise tutorials and actionable analytics • Provides command-by-command translations of R to Python and vice versa • Incorporates Python and R code throughout to make it easier for readers to compare and contrast features in both languages • Offers numerous comparative examples and applications in both programming languages • Designed for use for practitioners and students that know one language and want to learn the other • Supplies slides useful for teaching and learning either software on a companion website Python for R Users: A Data Science Approach is a valuable working resource for computer scientists and data scientists that know R and would like to learn Python or are familiar with Python and want to learn R. It also functions as textbook for students of computer science and statistics. A. Ohri is the founder of Decisionstats.com and currently works as a senior data scientist. He has advised multiple startups in analytics off-shoring, analytics services, and analytics education, as well as using social media to enhance buzz for analytics products. Mr. Ohri's research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces for cloud computing, investigating climate change and knowledge flows. His other books include R for Business Analytics and R for Cloud Computing.

In This podcast, Brian Haugli from The Hanover Insurance Group sat with Vishal to talk about some of the security led leader's mindset. From discussing some of the leadership mindset to practitioner tactical guide to help future security leaders to understand how to secure their organization. This session is great for any security, passionate leader willing to create a security wrapped growth mindset.

Timeline: 0:28 Brian's journey. 3:45 Brian's current role. 7:43 CSO combining with physical security. 10:12 Physical security infrastructure. 11:55 Brian's journey from Military and corporate. 14:42 Common challenges for a CSO. 17:37 Do security certifications help professionals secure an organization? 22:14 Advice for those wanting to join the security industry. 27:14 Recommendations for a startup to stay secure. 34:32 CSO's necessity in understanding tech and business. 36:35 Hacks to cope with new company integrations and operations. 40:50 Security vs. business innovation. 44:13 Security is seen as professional janitors. 52:30 The role of government and regulations in providing security. 55:30 Brian's keys to success. 58:36 Closing remarks.

Brian's Read Recommendation: On The Road by Jack Kerouac http://amzn.to/2hMhOhG

Podcast Link: https://futureofdata.org/brianhaugli-the_hanover-%e2%80%8fon-building-leadership-security-mindset/

GooglePlay: http://math.im/gplay

Brian's BIO: Brian Haugli is a Certified Information Systems Security Professional (CISSP) and a Global Industrial Cyber Security Professional (GICSP). Brian previously served as a senior advisor on cybersecurity and information risk management for the Department of Defense, US Army ITA, and Pentagon. He has 20 years of professional experience and expertise in network topologies, design, implementation, architecture, and cybersecurity. He has extensive knowledge of and has implemented risk management frameworks, methodologies, and processes. He has been responsible for creating compliant and secure networks for multiple sites through his extensive background in intrusion detection and full network end-to-end testing. He has outstanding communication skills, a positive demeanor, and the ability to interface with all levels of an organization.

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 episode, Professor Michael Kearns from the University of Pennsylvania joins host Kyle Polich to talk about the computational complexity of machine learning, complexity in game theory, and algorithmic fairness. Michael's doctoral thesis gave an early broad overview of computational learning theory, in which he emphasizes the mathematical study of efficient learning algorithms by machines or computational systems. When we look at machine learning algorithms they are almost like meta-algorithms in some sense. For example, given a machine learning algorithm, it will look at some data and build some model, and it's going to behave presumably very differently under different inputs. But does that mean we need new analytical tools? Or is a machine learning algorithm just the same thing as any deterministic algorithm, but just a little bit more tricky to figure out anything complexity-wise? In other words, is there some overlap between the good old-fashioned analysis of algorithms with the analysis of machine learning algorithms from a complexity viewpoint? And what is the difference between strategies for determining the complexity bounds on samples versus algorithms? A big area of machine learning (and in the analysis of learning algorithms in general) Michael and Kyle discuss is the topic known as complexity regularization. Complexity regularization asks: How should one measure the goodness of fit and the complexity of a given model? And how should one balance those two, and how can one execute that in a scalable, efficient way algorithmically? From this, Michael and Kyle discuss the broader picture of why one should care whether a learning algorithm is efficiently learnable if it's learnable in polynomial time. Another interesting topic of discussion is the difference between sample complexity and computational complexity. An active area of research is how one should regularize their models so that they're balancing the complexity with the goodness of fit to fit their large training sample size. As mentioned, a good resource for getting started with correlated equilibria is: https://www.cs.cornell.edu/courses/cs684/2004sp/feb20.pdf Thanks to our sponsors: Mendoza College of Business - Get your Masters of Science in Business Analytics from Notre Dame. brilliant.org - A fun, affordable, online learning tool.  Check out their Computer Science Algorithms course.

In this podcast, Andrea Gallego, Principal & Global Technology Lead @ Boston Consulting Group, talks about her journey as a data science practitioner in the consulting space. She talks about some of the industry practices that up and rising data science professionals must deploy and talks about some operational hacks to help create a robust data science team. It is a must-listen conversation for practitioner folks in the industry trying to deploy a data science team and build solutions for a service industry.

Timeline: 0:29 Andrea's journey. 5:41 Andrea's current role. 8:02 Seasoned data professional to COO role. 11:27 The essentials for having analytics at scale. 14:56 First steps to creating an analytics practice. 18:33 Defining an engineering first company. 22:33 A different understanding of data engineering. 26:40 Mistakes businesses make in their data science practice. 30:21 Some good business problems that data science can solve. 36:42 Democratization of data vs. privacy in companies. 38:04 Tech to business challenges. 40:11 Important KPIs for building a data science practice. 43:47 Hacks to hiring good data science candidates. 49:07 Art of doing business and science of doing business. 52:16 Andrea's secret to success. 55:12 Andrea's favorite read. 58:35 Closing remarks.

Andrea's Recommended Read: Arrival by Ted Chiang http://amzn.to/2h6lJpv Build to Last by Jim Collins http://amzn.to/2yMCsam Designing Agentive Technology: AI That Works for People Paperback http://amzn.to/2ySDHGp

Podcast Link: https://futureofdata.org/andrea-gallego-bcg-managing-analytics-practice/

Andrea's BIO: Andrea is Principal & Global Technology Lead @ Boston Consulting Group. Prior to BCG, Andrea was COO of QuantumBlack’s Cloud platform. She also manages the cloud platform team and helps drive the vision and future of McKinsey Analytics’ digital capabilities. Andrea has broad expertise in computer science, cloud computing, digital transformation strategy, and analytics solutions architecture. Prior to joining the Firm, Andrea was a technologist at Booz Allen Hamilton. She holds a BS in Economics and MS in Analytics (with a concentration in computing methods for analytics).

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, Sid Probstein, CTO AIFoundry, talks about the mindset of technology transformist in a data-driven world. He discusses some of the challenges he faces as a technologist and provides some ways to mitigate them. Sid also talks about the mindset of technologists in a startup vs. a larger enterprise. It is a must-listen conversation for technology folks in the industry trying to navigate the technology and business divide.

Timeline: 0:28 Sid's journey. 7:02 Sid's current role. 15:26 Regulatory bottlenecks. 17:51 Efficiency of Banking Technologies of today. 20:22 Evolution of storage and processing. 23:30 How can legacy models upgrade themselves to newer ones. 27:40 Breaking the cultural mould and moving to big data. 32:56 Convincing the leadership for new technology. 35:55 CTO relation with CDO. 39:18 Difference in working style between a startup and an established company. 43:17 Quantifying and evaluating a product for enterprise projects. 46:02 How can the leadership pick the right software? 49:57 Team dynamics and hiring process. 51:55 Sid's success mantra. 53:47 Sid's favorite read. 54:50 Closing remarks.

Sid's Recommended Read: Arrival by Ted Chiang http://amzn.to/2h6lJpv Build to Last by Jim Collins http://amzn.to/2yMCsam Designing Agentive Technology: AI That Works for People Paperback http://amzn.to/2ySDHGp

Podcast Link: https://futureofdata.org/sidprobstein-aifoundry-becoming-technology-transformist-data-driven-world/

Sid's BIO: Sid Probstein is the CTO and VP of Solution Delivery for AI Foundry, the enterprise software arm, and the new face of Kodak Alaris. AI Foundry is disrupting the mortgage business by taking origination automation to the next level - enabling self-service, distributed capture, and the automatic classification and extraction of scanned & imaged documents into actionable intelligence. He was previously co-founder and CTO at Attivio and held executive positions at FAST Search & Transfer, Northern Light Technology, and John Hancock Financial Services.

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, John T Langton, Director of Applied Data Science, sat with Vishal, President AnalyticsWeek, and discussed his data analytics journey. He shared his insights, from his startup days to running a data science group within a big enterprise.

Timeline: 0:28 John's journey. 13:28 John's current role. 17:06 Succeeding as a data scientist in different organizations. 26:47 Challenges in putting together a data science company. 38:36 Hacks to selling innovative ideas to clients and customers. 47:20 Defining a good data science hire. 51:50 Maturity level of enterprise AI. 1:00:00 Closing remarks.

John's Recommended Read: Designing Agentive Technology: AI That Works for People Paperback http://amzn.to/2ySDHGp

Podcast Link: https://futureofdata.org/johntlangton-wolters_kluwer-discussed-ai-lead-startup-journey/

John's BIO: John Langton is Director of Applied Data Science at Wolters Kluwer. He was previously worked as Director of Data Science at athenahealth, CEO of VisiTrend, a visual analytics company that was acquired by Carbon Black in 2015. He has a Ph.D. in computer science and an extensive background in AI, machine learning, big data analytics, and visualization. Prior to founding VisiTrend, John was Principal Investigator (PI) on several DoD projects at Charles River Analytics (CRA). He has taught classes at Brandeis University and has several peer-reviewed publications.

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/

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Keywords: FutureOfData Data Analytics Leadership Podcast Big Data Strategy

Mohan and Oliver have been very fortunate to have intimate views into the data challenges that face the largest organizations and institutions across every possible industry—and what they have been hearing about for some time is how the business needs to use data and analytics to their advantage. They continually hear the same issues, such as: We're spending valuable meeting time wondering why everyone's data doesn't match up. We can't leverage our economies of scale while remaining agile with data. We need self-serve apps that let the enterprise experiment with data and accelerate the development process. We need to get on a more predictive curve to ensure long-term success. To really address the data concerns of today's enterprise, they wanted to find a way to help enterprises achieve the success they seek. Not as a prescriptive process—but a methodology to become agile and leverage data and analytics to drive a competitive advantage. You know, it's amazing what can happen when two people with very different perspectives get together to solve a big problem. This evolutionary guide resulted from the a-ha moment between these two influencers at the top of their fields—one, an academic researcher and consultant, and the other, a longtime analytics practitioner and chief product officer at Teradata. Together, they created a powerful framework every type of business can use to connect analytic power, business practices, and human dynamics in ways that can transform what is currently possible.

Nearly 7,000 new mobile applications appear every day, and a constant stream of data gives them life. Many organizations rely on a predictive analytics model to turn data into useful business information and ensure the predictions remain accurate as data changes. It can be a complex, time-consuming process. This book shows how to automate and accelerate that process using machine learning (ML) on a modern data warehouse that runs on any cloud. Product specialists from MemSQL explain how today’s modern data warehouses provide the foundations to implement ML algorithms that run efficiently. Through several real-time use cases, you’ll learn how to quickly identify the right metrics to make actionable business decisions. This book explores foundational ML and artificial intelligence concepts to help you understand: How data warehouses accelerate deployment and simplify manageability How companies make a choice between cloud and on-premises deployments for building data processing applications Ways to build analytics and visualizations for business intelligence on historical data The technologies and architecture for building and deploying real-time data pipelines This book demonstrates specific models and examples for building supervised and unsupervised real-time ML applications, and gives practical advice on how to make the choice between building an ML pipeline or buying an existing solution. If you need to use data accurately and efficiently, a real-time data warehouse is a critical business tool.