Websites come in many types, styles, and budgets. Each website, be it a news site, an online store or a personal blog, has its own performance indicators. These may include the number of unique visitors, average order value, conversion rate etc. Web analytics systems are aimed to help track those indicators, analyze them and make decisions. But how you can trust them if the data collection process is screwed and data is scattered. During this speech, you'll get a checklist for your project that covers all the main mistakes in the project settings and analysis. Save your time and learn from the mistakes other teams have already made.

ROI isn’t a static line in the sand. Analytics guides our constant strive for improvements. How can we change the conversation about the ROI of analytics for it to become a foundation for decision making.

If you work with a media agency (or are one) the first question to ask them is how many data scientists do you have? Do you prefer Amazon Web Services, Microsoft Azure, or the Google Cloud Platform? Come see examples from one of Canada's largest retailers of advertising spending that is wasted from poor targeting, access issues, and lack of big data understanding. We will also dive into examples of broken implementations of Analytics that cause even more issues. If you are not in-sourcing the core components of your Media and Analytics you are almost certainly at risk or already suffering from many of these problems. In this session, Martin and Charles Farina will show you what you need to find the right partner, but more importantly what you also have to provide.

With the abundance of data available in analytics we are no longer competing on the volume of data we have access to, but the quality of the questions we ask. The challenge is that we have never been taught to ask or taught other people to ask the right questions from analytics. So we spend our lives answering questions like "what is our bounce rate?" or "how much did the last campaign convert?" when we could be asking important and business critical question. This means that we in analytics have to consider putting the KPQ (Key Performance Question) before the KPI (Key Performance Indication) to start asking better questions.

You love analytics. Great. You even love your job (hopefully)! But, you're thinking about the future, and it looks like there is a fork in the road. Should you take the path that leads you down the people management path? Or, should you take the path that leads you deeper into the data itself, but as an individual contributor. Can you pursue both paths? As it turns out, Michael stumbled down the former path, while Tim has headed down the latter. So, Moe took a turn in the moderator chair to guide a discussion about the considerations and relative merits of each option. As well as how the culture and HR processes of different companies can influence the availability of alternate paths. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.  

In this podcast, 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.

In this episode, Mike Masciandaro and Wayne Eckerson discuss how to partner with the business and ensure high levels of customer satisfaction and adoption.

Mike is a veteran business intelligence practitioner who recently retired from an illustrious career at Dow Chemical. Mike has seen and done just about everything there is to do in the world of BI, data, and analytics. He is now intent on sharing his hard-won knowledge with others.

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.

Do you ever feel like you've got the analytics blues because you see what needs to happen, and it's something innovative, and all the signals say it's the right thing to do... but the realities of organizational life are a brick wall on the path to progress? Welcome to corporate life, buddy. That's just the way it is! Or...is it? On this episode, the gang sits down with Evan LaPointe and gets him to jam a bit -- literally at first, and then figuratively -- about organizational dynamics, the tradeoffs between personality types, and why it can be counterproductive to always try to cater to all of the different psychologies and mindsets in any given meeting. And round tables. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

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.

Summary

PostGreSQL has become one of the most popular and widely used databases, and for good reason. The level of extensibility that it supports has allowed it to be used in virtually every environment. At Citus Data they have built an extension to support running it in a distributed fashion across large volumes of data with parallelized queries for improved performance. In this episode Ozgun Erdogan, the CTO of Citus, and Craig Kerstiens, Citus Product Manager, discuss how the company got started, the work that they are doing to scale out PostGreSQL, and how you can start using it in your environment.

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 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 Ozgun Erdogan and Craig Kerstiens about Citus, worry free PostGreSQL

Interview

Introduction How did you get involved in the area of data management? Can you describe what Citus is and how the project got started? Why did you start with Postgres vs. building something from the ground up? What was the reasoning behind converting Citus from a fork of PostGres to being an extension and releasing an open source version? How well does Citus work with other Postgres extensions, such as PostGIS, PipelineDB, or Timescale? How does Citus compare to options such as PostGres-XL or the Postgres compatible Aurora service from Amazon? How does Citus operate under the covers to enable clustering and replication across multiple hosts? What are the failure modes of Citus and how does it handle loss of nodes in the cluster? For someone who is interested in migrating to Citus, what is involved in getting it deployed and moving the data out of an existing system? How do the different options for leveraging Citus compare to each other and how do you determine which features to release or withhold in the open source version? Are there any use cases that Citus enables which would be impractical to attempt in native Postgres? What have been some of the most challenging aspects of building the Citus extension? What are the situations where you would advise against using Citus? What are some of the most interesting or impressive uses of Citus that you have seen? What are some of the features that you have planned for future releases of Citus?

Contact Info

Citus Data

citusdata.com @citusdata on Twitter citusdata on GitHub

Craig

Email Website @craigkerstiens on Twitter

Ozgun

Email ozgune on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Citus Data PostGreSQL NoSQL Timescale SQL blog post PostGIS PostGreSQL Graph Database JSONB Data Type PipelineDB Timescale PostGres-XL Aurora PostGres Amazon RDS Streaming Replication CitusMX CTE (Common Table Expression) HipMunk Citus Sharding Blog Post Wal-e Wal-g Heap Analytics HyperLogLog C-Store

The intro and outro musi

In this podcast, Dewayne Washington speaks the unadulterated truth about the role of the CIO and discusses keys to success and common pitfalls. 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. 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.

Mike Masciandaro is a veteran business intelligence practitioner who recently retired from an illustrious career at Dow Chemical. Mike has seen and done just about everything there is to do in the world of BI, data, and analytics. He is now intent on sharing his hard-won knowledge with others. You will learn the definition and purpose of a BI program, the role of subject matter experts, how to hire and retain talent, keys to delivering value as a BI program, and more.

Perform Interactive Data Analysis with Smarter Visualizations and Support your Enterprise-wide Analytical Needs About This Book Get a practical demonstration of discovering data for sales, human resources, and more using Qlik Sense Create dynamic dashboards for business intelligence and predictive analytics Create and collaborate comprehensive analytical solutions using Rattle and Qlik Sense Who This Book Is For This course is for anyone who wishes to understand and utilize the various new approaches to business intelligence actively in their business practice. Knowing the basics of business intelligence concepts would be helpful when picking up this course, but is not mandatory. What You Will Learn Build simple visualization models with Rattle and Qlik Sense Desktop Get to grips with the life cycle and new visualization functions of a Qlik Sense application Discover simple ways to examine data and get it ready for analysis Visualize your data with Qlik Sense's engaging and informative graphs Build efficient and responsive Associative Models Optimize Qlik Sense for sales, human resources, and demographic data discovery Explore various tips and tricks of navigation for the Qlik Sense® front end Develop creative extensions for your Qlik Sense® dashboard In Detail Qlik Sense is powerful and creative visual analytics software that allows users to discover data, explore it, and dig out meaningful insights in order to make a profit and make decisions for your business. This course begins by introducing you to the features and functions of the most modern edition of Qlik Sense so you get to grips with the application. The course will teach you how to administer the data architecture in Qlik Sense, enabling you to customize your own Qlik Sense application for your business intelligence needs. It also contains numerous recipes to help you overcome challenging situations while creating fully featured desktop applications in Qlik Sense. It explains how to combine Rattle and Qlik Sense Desktop to apply predictive analytics to your data to develop real-world interactive data applications. The course includes premium content from three of our most popular books: Learning Qlik Sense: The Official Guide Second Edition Qlik Sense Cookbook Predictive Analytics using Rattle and Qlik Sense On completion of this course, you will be self-sufficient in improving your data analysis and will know how to apply predictive analytics to your datasets. Through this course, you will be able to create predictive models and data applications, allowing you to explore your data insights much deeper. Style and approach The course will follow a practical approach with rich set of examples through which it will demonstrate its concepts, features and its implementation. The course will also feature numerous solutions which will cover entire spectrum of BI use cases.

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