Dive right into the powerful world of Microsoft Power BI with this comprehensive guide. This book takes you through every step of mastering Power BI, from data modeling to creating actionable visualizations. You'll find clear explanations and practical steps to improve your data analytics and enhance business decision-making. What this Book will help me do Learn to connect and transform data using Power Query M Language to create clean, structured datasets. Understand how to design scalable and performance-optimized Power BI Data Models for effective analytics. Develop professional, visually appealing and interactive reports and dashboards to convey insights confidently. Implement best practices for managing Power BI solutions, including deployment, version control, and monitoring. Gain practical knowledge to administer Power BI across organizational structures, ensuring security and efficiency. Author(s) None Powell is a seasoned expert in business intelligence and a passionate educator in the field of data analytics. With extensive hands-on experience in Microsoft Power BI, None has supported many organizations in unlocking the potential of their data. The approachable writing style reflects a real-world yet proficient understanding of Power BI's capabilities. Who is it for? This book is ideal for business intelligence professionals looking to deepen their expertise in Microsoft Power BI. Readers already familiar with basic BI concepts and Power BI will gain significant technical depth. It suits professionals keen to enhance their data modeling, visualization, and analytics skills. If you're aiming to create impactful dashboards and benefit from advanced insights, this book is for you.

Mastering Qlik Sense is a comprehensive guide designed to empower you to utilize Qlik Sense for advanced data analytics and dynamic visualizations. This book provides detailed insights into creating seamless Business Intelligence solutions tailored to your needs. Whether you're building dashboards, optimizing data models, or exploring Qlik Cloud functionalities, this book has you covered. What this Book will help me do Build interactive and insightful dashboards using Qlik Sense's intuitive tools. Learn to model data efficiently and apply best practices for optimized performance. Master the Qlik Sense APIs and create advanced custom extensions. Understand enterprise security measures including role-based access controls. Gain expertise in migrating from QlikView to Qlik Sense effectively Author(s) Juan Ignacio Vitantonio is an experienced expert in Business Intelligence solutions and data analytics. With a profound understanding of Qlik technologies, Juan has developed and implemented impactful BI solutions across various industries. His writing reflects his practical knowledge and passion for empowering users with actionable insights into data. Who is it for? This book is perfect for BI professionals, data analysts, and organizations aiming to leverage Qlik Sense for advanced analytics. Ideal for those with a foundational grasp of Qlik Sense, it also provides comprehensive guidance for QlikView users transitioning to Qlik Sense. If you want to improve your BI solutions and data-driven decision-making skills, this book is for you.

"SQL Server 2017 Developer's Guide" provides a comprehensive approach to learning and utilizing the new features introduced in SQL Server 2017. From advanced Transact-SQL to integrating R and Python into your database projects, this book equips you with the knowledge to design and develop efficient database applications tailored to modern requirements. What this Book will help me do Master new features in SQL Server 2017 to enhance database application development. Implement In-Memory OLTP and columnstore indexes for optimal performance. Utilize JSON support in SQL Server to integrate modern data formats. Leverage R and Python integration to apply advanced data analytics and machine learning. Learn Linux and container deployment options to expand SQL Server usage scenarios. Author(s) The authors of "SQL Server 2017 Developer's Guide" are industry veterans with extensive experience in database design, business intelligence, and advanced analytics. They bring a practical, hands-on writing style that helps developers apply theoretical concepts effectively. Their commitment to teaching is evident in the clear and detailed guidance provided throughout the book. Who is it for? This book is ideal for database developers and solution architects aiming to build robust database applications with SQL Server 2017. It's a valuable resource for business intelligence developers or analysts seeking to harness SQL Server 2017's advanced features. Some familiarity with SQL Server and T-SQL is recommended to fully leverage the insights provided by this book.

Don't let a fear of numbers hold you back. Today's business environment brings with it an onslaught of data. Now more than ever, managers must know how to tease insight from data--to understand where the numbers come from, make sense of them, and use them to inform tough decisions. How do you get started? Whether you're working with data experts or running your own tests, you'll find answers in the HBR Guide to Data Analytics Basics for Managers. This book describes three key steps in the data analysis process, so you can get the information you need, study the data, and communicate your findings to others. You'll learn how to: Identify the metrics you need to measure Run experiments and A/B tests Ask the right questions of your data experts Understand statistical terms and concepts Create effective charts and visualizations Avoid common mistakes

In this episode, Wayne Eckerson and Lenin Gali discuss the past and future of the cloud and big data.

Gali is a data analytics practitioner who has always been on the leading edge of where business and technology intersect. He was one of the first to move data analytics to the cloud when he was BI director at ShareThis, a social media based services provider. He was instrumental in defining an enterprise analytics strategy, developing a data platform that brought games and business data together to enable thousands of data users to build better games and services by using Hadoop & Teradata while at Ubisoft. He is now spearheading the creation of a Hadoop-based data analytics platform at Quotient, a digital marketing technology firm in the retail industry.

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.

Practical Big Data Analytics is your ultimate guide to harnessing Big Data technologies for enterprise analytics and machine learning. By leveraging tools like Hadoop, Spark, NoSQL databases, and frameworks such as R, this book equips you with the skills to implement robust data solutions that drive impactful business insights. Gain practical expertise in handling data at scale and uncover the value behind the numbers. What this Book will help me do Master the fundamental concepts of Big Data storage, processing, and analytics. Gain practical skills in using tools like Hadoop, Spark, and NoSQL databases for large-scale data handling. Develop and deploy machine learning models and dashboards with R and R Shiny. Learn strategies for creating cost-efficient and scalable enterprise data analytics solutions. Understand and implement effective approaches to combining Big Data technologies for actionable insights. Author(s) None Dasgupta is an expert in Big Data analytics, statistical methodologies, and enterprise data solutions. With years of experience consulting on enterprise data platforms and working with leading industry technologies, Dasgupta brings a wealth of practical knowledge to help readers navigate and succeed in the field of Big Data. Through this book, Dasgupta shares an accessible and systematic way to learn and apply key Big Data concepts. Who is it for? This book is ideal for professionals eager to delve into Big Data analytics, regardless of their current level of expertise. It accommodates both aspiring analysts and seasoned IT professionals looking to enhance their knowledge in data-driven decision making. Individuals with a technical inclination and a drive to build Big Data architectures will find this book particularly beneficial. No prior knowledge of Big Data is required, although familiarity with programming concepts will enhance the learning experience.

Dewayne Washington is back this week for part II of his Secrets of Data Analytics Leaders podcast with Eckerson Group. In part I, Dewayne and I discussed the role of the CIO. In this episode we discuss the keys to IT success.

Washington is a senior consultant with 20+ years of experience in BI and Analytics in over two dozen verticals. He is the former BI manager at Dallas/Fortworth International Airport and the current CIO at The Business of Intelligence. He is also the author of the book Get In The Stream, the ultimate guide to customer adoption, and his Data Warehousing and Mobile Solutions implementations have been featured in CIO Magazine and the Wall Street Journal. Washington is also a sought-after speaker and mentor for organizations striving to leverage BI and Analytics to meet business goals, thus earning him the title, BI Pharaoh.

Learn how to leverage IBM SPSS Modeler for your data mining and predictive analytics needs in this comprehensive guide. With step-by-step instructions, you'll acquire the skills to import, clean, analyze, and model your data using this robust platform. By the end, you'll be equipped to uncover patterns and trends, enabling data-driven decision-making confidently. What this Book will help me do Understand the fundamentals of data mining and the visual programming interface of IBM SPSS Modeler. Prepare, clean, and preprocess data effectively for analysis and modeling. Build robust predictive models such as decision trees using best practices. Evaluate the performance of your analytical models to ensure accuracy and reliability. Export resulting analyses to apply insights to real-world data projects. Author(s) Keith McCormick and Jesus Salcedo are accomplished professionals in data analytics and statistical modeling. With extensive experience in consulting and teaching, they have guided many in mastering IBM SPSS Modeler through both hands-on workshops and written material. Their approachable teaching style and commitment to clarity ensure accessibility for learners. Who is it for? This book is designed for beginner users of IBM SPSS Modeler who wish to gain practical and actionable skills in data analytics. If you're a data enthusiast looking to explore predictive analytics or a professional eager to discover the insights hidden in your organizational data, this book is for you. A basic understanding of data mining concepts is advantageous but not required. This resource will set any novice on the path toward expert-level comprehension and application.

Learning Alteryx introduces you to using the powerful Alteryx platform for self-service analytics, helping you master key features like data preparation and predictive analytics without needing to code. With this book, you'll gain the skills to create workflows that generate actionable insights, empowering your business to make data-driven decisions. What this Book will help me do Master creating and optimizing workflows in Alteryx to address complex analytical problems. Learn how to clean, prepare, and blend data from various sources efficiently. Understand advanced Alteryx expressions for processing large datasets effectively. Develop meaningful reports and visualizations to communicate insights clearly. Leverage predictive analytics capabilities in Alteryx to make informed decisions. Author(s) The authors of Learning Alteryx collectively bring years of expertise in data analytics and business intelligence. Having worked on diverse projects across multiple industries, they understand the challenges faced by data professionals and are skilled in simplifying complex concepts. They focus on providing practical insights and step-by-step guides to empower learners. Who is it for? Learning Alteryx is ideal for professionals aspiring to enhance their data analytics capabilities or explore self-service analytics. It caters to beginners unfamiliar with analytics platforms, as well as intermediate users seeking to deepen their Alteryx knowledge. Readers should have a basic understanding of data analysis principles.

Learn how to harness the power of the Elastic Stack 6.0 to manage, analyze, and visualize data effectively. This book introduces you to Elasticsearch, Logstash, Kibana, and other components, helping you build scalable, real-time data processing solutions from scratch. By reading this guide, you'll gain practical insights into the platform's components, including tips for production deployment. What this Book will help me do Understand and utilize the core components of Elastic Stack 6.0, including Elasticsearch, Logstash, and Kibana. Set up scalable data pipelines for ingesting and processing vast amounts of data. Craft real-time data visualizations and analytics using Kibana. Secure and monitor Elastic Stack deployments with X-Pack and other related tools. Deploy Elastic Stack applications effectively in cloud or on-premise production environments. Author(s) Pranav Shukla and Sharath Kumar are experienced professionals with deep knowledge in distributed data systems and the Elastic Stack ecosystem. They are passionate about data analytics and visualization and bring their hands-on experience in building real-world Elastic Stack applications into this book. Their practical approach and explanatory style make complex concepts accessible to readers at all levels. Who is it for? This book is perfect for data professionals who want to analyze large datasets or create effective real-time visualizations. It is suited for those new to Elastic Stack or looking to understand its capabilities. Basic JSON knowledge is recommended, but no prior expertise with Elastic Stack is required to benefit from this practical guide.

If you're ready to untap the potential of data analytics in the cloud, 'Learning Google BigQuery' will take you from understanding foundational concepts to mastering advanced techniques of this powerful platform. Through hands-on examples, you'll learn how to query and analyze massive datasets efficiently, develop custom applications, and integrate your results seamlessly with other tools. What this Book will help me do Understand the fundamentals of Google Cloud Platform and how BigQuery operates within it. Migrate enterprise-scale data seamlessly into BigQuery for further analytics. Master SQL techniques for querying large-scale datasets in BigQuery. Enable real-time data analytics and visualization with tools like Tableau and Python. Learn to create dynamic datasets, manage partition tables and use BigQuery APIs effectively. Author(s) None Berlyant, None Haridass, and None Brown are specialists with years of experience in data science, big data platforms, and cloud technologies. They bring their expertise in data analytics and teaching to make advanced concepts accessible. Their hands-on approach and real-world examples ensure readers can directly apply the skills they acquire to practical scenarios. Who is it for? This book is tailored for developers, analysts, and data scientists eager to leverage cloud-based tools for handling and analyzing large-scale datasets. If you seek to gain hands-on proficiency in working with BigQuery or want to enhance your organization's data capabilities, this book is a fit. No prior BigQuery knowledge is needed, just a willingness to learn.

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, 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

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.

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.

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/

Want to sponsor? Email us @ [email protected]

Keywords: FutureOfData Data Analytics Leadership Podcast Big Data Strategy

Moore’s law has finally run out of steam for CPUs. The number of x86 cores that can be placed cost-effectively on a single chip has reached a practical limit, making higher densities prohibitively expensive for most applications. Fortunately, for big data analytics, machine learning, and database applications, a more capable and cost-effective alternative for scaling compute performance is already available: the graphics processing unit, or GPU. In this report, executives at Kinetica and Sierra Communications explain how incorporating GPUs is ideal for keeping pace with the relentless growth in streaming, complex, and large data confronting organizations today. Technology professionals, business analysts, and data scientists will learn how their organizations can begin implementing GPU-accelerated solutions either on premise or in the cloud. This report explores: How GPUs supplement CPUs to enable continued price/performance gains The many database and data analytics applications that can benefit from GPU acceleration Why GPU databases with user-defined functions (UDFs) can simplify and unify the machine learning/deep learning pipeline How GPU-accelerated databases can process streaming data from the Internet of Things and other sources in real time The performance advantage of GPU databases in demanding geospatial analytics applications How cognitive computing—the most compute-intensive application currently imaginable—is now within reach, using GPUs

The information technology revolutions over the past six decades have been astonishing, from mainframes to personal computers to smart and connected economies. But those changes pale in comparison to what’s about to happen. By 2020, seven billion people and roughly 50 billion devices will be connected to the internet, leaving the world awash in data. How do we make sense of it all? In this insightful book, Raghunath Nambiar from Cisco examines the role of analytics in enabling digital transformation within the enterprise, including challenges associated with the explosion of data. It embraces the need for analytics at the edge of the network with a local context and analytics at the data center core with a global context. He also explores the differences between the four types of analytics—descriptive, diagnostic, predictive, and prescriptive—including the driving factors behind the need for each of them, as well as the analytical systems required to process them to produce actionable insight. Raghu then takes a deep dive into how the explosion in internet connections affects key industries, and how applied analytics will impact our future. Learn how analytics can make a difference in: Smart cities to manage energy, the environment, traffic, parking, structures, waste, safety, and crowds Smart energy to enable sustainable and efficient offerings that provide substantial benefits for both providers and customers Healthcare to address the aging population, growing shortage of physicians, and rising costs through connected health Manufacturing for producing higher quality products, creating new lines of business, reducing time-to-market, and increasing revenue growth Transportation to address the increasing demand through collaborative consumption, connected cars, and the potential for autonomous vehicles

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