When it comes to effective presentations and engaging an audience, what questions should you ask to tell your story about data? It's time to take action and learn!

Nancy Duarte joins me as today's guest to discuss her new book, DataStory: Explain Data and Inspire Action Through Story. Some of you may already know Nancy from her 2012 TED Talk titled, The Secret Structure of Great Talks.

In this episode, you'll learn: [08:10] Inspiration behind DataStory: Normal people need help with data, too. [09:10] Key Quote: Data had been slowing us down, instead of speeding us up. We didn't have a mechanism to communicate data - Nancy Duarte [09:27] Key Quote: Data is becoming a very common communication language that didn't have a real clear wrapper around it about how to frame it.- Nancy Duarte For full show notes, his book give away, and the links mentioned visit: https://bibrainz.com/podcast/39 Sponsor The next BI Data Storytelling Mastery Accelerator 3-Day Live workshop will be held in January 2020. Many BI teams are still struggling to deliver consistent, high-engaging analytics their users love. At the end of three days, you'll leave with a clear BI delivery action plan. Register today!

Get a head-start on learning one of SQL Server 2019’s latest and most impactful features—Big Data Clusters—that combines large volumes of non-relational data for analysis along with data stored relationally inside a SQL Server database. This book provides a first look at Big Data Clusters based upon SQL Server 2019 Release Candidate 1. Start now and get a jump on your competition in learning this important new feature. Big Data Clusters is a feature set covering data virtualization, distributed computing, and relational databases and provides a complete AI platform across the entire cluster environment. This book shows you how to deploy, manage, and use Big Data Clusters. For example, you will learn how to combine data stored on the HDFS file system together with data stored inside the SQL Server instances that make up the Big Data Cluster. Filled with clear examples and use cases, this book provides everything necessary to get started working with Big Data Clusters in SQL Server 2019 using Release Candidate 1. You will learn about the architectural foundations that are made up from Kubernetes, Spark, HDFS, and SQL Server on Linux. You then are shown how to configure and deploy Big Data Clusters in on-premises environments or in the cloud. Next, you are taught about querying. You will learn to write queries in Transact-SQL—taking advantage of skills you have honed for years—and with those queries you will be able to examine and analyze data from a wide variety of sources such as Apache Spark. Through the theoretical foundation provided in this book and easy-to-follow example scripts and notebooks, you will be ready to use and unveil the full potential of SQL Server 2019: combining different types of data spread across widely disparate sources into a single view that is useful for business intelligence and machine learning analysis. What You Will Learn Install, manage, and troubleshoot Big Data Clusters in cloud or on-premise environments Analyze large volumes of data directly from SQL Server and/or Apache Spark Manage data stored in HDFS from SQL Server as if it were relational data Implement advanced analytics solutions through machine learning and AI Expose different data sources as a single logical source using data virtualization Who This Book Is For For data engineers, data scientists, data architects, and database administrators who want to employ data virtualization and big data analytics in their environment

Business decisions today are tactical and strategic at the same time. How do you respond to a competitor’s price change? Or to specific technology changes? What new products, markets, or businesses should you pursue? Decisions like these are based on information from only one source: data. With this practical report, technical and non-technical leaders alike will explore the fundamental elements necessary to embark on a data analytics initiative. Is your company planning or contemplating a data analytics initiative? Authors Brett Stupakevich, David Sweenor, and Shane Swiderek from TIBCO guide you through several analytics options. IT leaders, product developers, analytics leaders, data analysts, data scientists, and business professionals will learn how to deploy analytic components in streaming and embedded systems using one of five platforms. You’ll examine: Analytics platforms including embedded BI, reporting, data exploration & discovery, streaming BI, and data science & machine learning The business problems each option solves and the capabilities and requirements of each How to identify the right analytics type for your particular use case Key considerations and the level of investment for each analytics platform

Do you know what skill sets you're going to need in the next three years to stay relevant in the BI (business intelligence) industry? Don't wait for the BI bubble to pop! When I met Jen Underwood, she was one of Microsoft's first Power BI product managers and an undeniable voice of reason in the BI industry through her blogs and other insightful posts online. I always admired her technical aptitude and was not surprised that she went on to work at cool startups like DataRobot and now Aible. In this exciting episode, Jen breaks down what you need to know and do to be viable and relevant in the BI market in 2020 and beyond! Let's just say, it's definitely not what you're doing today. Tune in, and level up!

In this episode, you'll learn: [02:58] Jen's CV: Marketing degree and love for data leads to 20+ years in BI industry. [05:17] Key Quote: I knew that I fell in love with programming and all that. But I didn't have the patience to extend my college years. - Jen Underwood [06:41] Fear of Failure: Jen's only option was to succeed during her homeless period. For full show notes, his book give away, and the links mentioned visit: https://bibrainz.com/podcast/38 Sponsor This exciting season of AOF is sponsored by our BI Data Storytelling Mastery Accelerator 3-Day Live workshop. Our second one is coming up on Jan 28-30 and registration is open! Join us and consider upgrading to be a VIP (we have tons of bonuses planned). Many BI teams are still struggling to deliver consistent, high-engaging analytics their users love. At the end of three days, you'll leave with the tools, techniques, and resources you need to engage your users. Register today!   Enjoyed the Show?  Please leave us a review on iTunes.

Data is critical for learning about the needs of the market, product bugs and issues, competitive solutions, and many other things. As such, analytics plays an important role in the innovation process.

Originally published at https://www.eckerson.com/articles/how-can-analytics-support-business-innovation

READ ME!!! LISTEN!!! DO YOU KNOW WHY THIS IS IN ALL CAPS?! IS IT RAISING YOUR HEART RATE?! IS IT MAKING YOU A LITTLE IRRITATED?! IT MIGHT BE! IF IT IS, WE COULD MEASURE IT, AND MAYBE WE WOULD REALIZE THAT WE WERE INDUCING A SUBCONSCIOUS EMOTIONAL RESPONSE AND REALLY SHOULD TURN OFF THE CAPS LOCK! That's the topic of this episode: the brain. Specifically: neuroscience. Even more specifically: neurodesign and neuromarketing and the measurement and analytics therein. We're talking EEGs, eye tracking, predictive eye tracking, heart rate monitoring, and the like (and why it matters) with Diana Lucaci from True Impact. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

This business analytics (BA) text discusses the models based on fact-based data to measure past business performance to guide an organization in visualizing and predicting future business performance and outcomes. It provides a comprehensive overview of analytics in general with an emphasis on predictive analytics. Given the booming interest in analytics and data science, this book is timely and informative. It brings many terms, tools, and methods of analytics together. The first three chapters provide an introduction to BA, importance of analytics, types of BA-descriptive, predictive, and prescriptive-along with the tools and models. Business intelligence (BI) and a case on descriptive analytics are discussed. Additionally, the book discusses on the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics-machine learning, neural networks, and artificial intelligence. The concluding chapter discusses on the current state, job outlook, and certifications in analytics.

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R

In this episode, Wayne Eckerson and Matthew Schwartz discuss non-traditional uses of business intelligence tools. Although BI tools have been around for almost three decades, most companies just scratch the surface of what’s possible to do with those tools. Using web layers and APIs, a company can use their imagination to customize and leverage their exiting BI tool-set to monetize data, integrate tribal knowledge and build industry-specific proprietary products.

Matthew Schwartz is the chief technology officer of Sage Hospitality, one of the world's largest hotel operators. Although Matt is responsible for all aspects of Sage’s IT operations, he has a deep fondness for data and analytics, having served as a BI director for several companies, including PetSmart and Staples. Matt firmly believes in the power of BI tools to transform organizations.

What problems do you have with key performance indicators (KPIs)? Are they actionable or not? How do you measure them?

Today, I'm sharing a session that I did that was rated #1 at the Real Business Intelligence event at MIT back in 2017. It's called "Secrets to building actionable KPIs". Specifically, I discuss three of the more well-known and commonly used methods: Specific, Measurable, Achievable, Relevant, Timebound (SMART) Goals, Wildly Important Goals (WIGs), and Objectives and Key Results (OKRs).

In this episode, you'll learn: [02:19] Are KPIs and metrics the same? Do you track KPIs? Metrics? Core competencies? [02:44] Key Quote: Living in KPI land, there's a lot of measurements, but you discover that not all measurements are created equal. - Mico Yuk [04:02] KPI Problem #1: Too many KPIs; focus on tracking 3-5, no more than 10. For full show notes, his book give away, and the links mentioned visit: https://bibrainz.com/podcast/37 Sponsor This exciting season of AoF is sponsored by our BI Data Storytelling Mastery Accelerator 3-Day Live workshop. Our next workshop on January 28-30, 2020 is now open for registration at a special early bird rate! Consider upgrading to the VIP option for extras including access to our online course. Many BI teams are still struggling to deliver consistent, high-engaging analytics their users love. At the end of three days, you'll leave with a clear BI delivery action plan. Register today!   Enjoyed the Show?  Please leave us a review on iTunes.      

This audio blog probes the business value in analytics by examining the concept of Action Distance.

Originally published at https://www.eckerson.com/articles/finding-value-in-analytics-action-distance

One of the hardest parts of running a data analytics program inside a large organization is governing data and reports. It’s simply too easy for the definition of core data elements and metrics to get out of sync and reports to contain conflicting information.

Angie Davis has straddled both the business and IT worlds for more than 20 years. She served as a business analyst in several organizations before switching to the information technology side of the business where she ran analytics teams, first at JD Irving for six years and more recently at Brookfield Renewable where she is an IT director. Angie has a degree in mathematics and electrical engineering from Dalhousie University in Halifax, Nova Scotia.

Mastering pandas is the ultimate guide to harnessing the power of the pandas library for data analysis. Covering everything from installation to advanced techniques, this book provides comprehensive instructions and examples to help you perform efficient data manipulation and visualization. Explore key features of pandas, such as multi-indexing and time series analysis, and become proficient in actionable analytics. What this Book will help me do Master importing and managing datasets of various formats using pandas. Expertly handle missing data and clean datasets for robust analysis. Create powerful visualizations and reports using pandas and Jupyter notebooks. Leverage advanced indexing and grouping techniques to derive insights. Utilize pandas for time series analysis to analyze trends and patterns. Author(s) None Kumar is an experienced data scientist specializing in data analysis and visualization using Python. With a deep understanding of the pandas library, None has been helping professionals and enthusiasts alike to make data-driven decisions. Known for an example-driven teaching style, None bridges complex theoretical concepts with practical applications in data science. Who is it for? If you're a data scientist, analyst, or Python developer seeking to enhance your data analysis capabilities, this book is for you. Prior knowledge of Python is beneficial but not mandatory, as foundational concepts are explained. This guide spans beginner to advanced topics, accommodating users looking to deepen their skills and those aiming to start with pandas.

As your business tries to make sense of today’s staggering amount of structured and unstructured data, traditional analytics will take you only so far. The key to success over the next few years will depend on augmented analytics, a method that embeds machine learning and natural language processing (NLP) in the process. This report explains how augmented analytics can help you uncover hidden insights, predict results, and even prescribe solutions. Author Alice LaPlante provides best practices for deploying augmented analytics, along with real-world case studies that show you how to take full advantage of this method. IT professionals, business managers, and CFOs will learn ways to democratize data use among business users and executives, using a self-service model. The future belongs to those who can get more from their data. This report shows you how. Get a primer on the key components and learn how they work together Delve into the benefits of—and roadblocks to—adopting augmented analytics Learn how companies use this method in marketing, sales, finance, and human resources Examine case studies of companies including Accenture and Riverbed

Elasticsearch 7 Quick Start Guide introduces the core capabilities of Elasticsearch, one of the most powerful distributed search and analytics tools available. Through this concise and practical guide, you will learn how to install, configure, and effectively utilize Elasticsearch while exploring its powerful features, including real-time search and data aggregation. What this Book will help me do Install and configure Elasticsearch to create secure and scalable deployments. Understand and utilize analyzers, filters, and mappings to optimize search results. Perform data aggregations using advanced techniques in metric and bucket operations. Identify and troubleshoot common Elasticsearch performance issues for smooth operation. Leverage best practices to ensure effective deployment in production environments. Author(s) None Srivastava and None Miller are experienced writers and technologists who bring real-world expertise in search systems and analytics. With practical backgrounds in distributed systems and data management, the authors deliver a straightforward and hands-on approach in their writing. They aim to make Elasticsearch concepts approachable and practical for developers and administrators alike. Who is it for? This book is ideal for software developers, data engineers, and IT professionals who are seeking to implement Elasticsearch within their projects. It is particularly suited for those with basic to intermediate technical experience and a need for robust search and analytics solutions. If you're aiming to learn the fundamentals and acquire practical skills in Elasticsearch 7, this book will serve as an excellent resource for you.

Send us a text Want to be featured as a guest on Making Data Simple? Reach out to us at [[email protected]] and tell us why you should be next.  Abstract Our guest for this week is Carlvin Paris, IBM North America Data Science & AI Sales Leader. Host Al Martin helps curate the conversation towards decision optimization and analytics, while Carlvin offers his insight to the state of the data science industry. Tune in for a technical, yet approachable discussion. Connect with Carlvin LinkedIn Show Notes 05:37 - Take a look at this article which aims to explain the journey of a data scientist. 08:33 - Learn more about descriptive, predictive, and prescriptive analytics here. 26:00 - Check out the IBM DTE site and YouTube channel to increase your knowledge of other Data and A.I. concepts. 26:30 - Take a look at Informs website through this link.  Connect with the Team Producer Liam Seston - LinkedIn. Producer Lana Cosic - LinkedIn. Producer Meighann Helene - LinkedIn.  Host Al Martin - LinkedIn and Twitter. Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

This audio blog focuses on the importance of establishing a strong relationship between business and technical teams and describes various business engagement models that sit at the heart of all successful data analytics programs.

Summary The scale and complexity of the systems that we build to satisfy business requirements is increasing as the available tools become more sophisticated. In order to bridge the gap between legacy infrastructure and evolving use cases it is necessary to create a unifying set of components. In this episode Dipti Borkar explains how the emerging category of data orchestration tools fills this need, some of the existing projects that fit in this space, and some of the ways that they can work together to simplify projects such as cloud migration and hybrid cloud environments. It is always useful to get a broad view of new trends in the industry and this was a helpful perspective on the need to provide mechanisms to decouple physical storage from computing capacity.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! This week’s episode is also sponsored by Datacoral, an AWS-native, serverless, data infrastructure that installs in your VPC. Datacoral helps data engineers build and manage the flow of data pipelines without having to manage any infrastructure, meaning you can spend your time invested in data transformations and business needs, rather than pipeline maintenance. Raghu Murthy, founder and CEO of Datacoral built data infrastructures at Yahoo! and Facebook, scaling from terabytes to petabytes of analytic data. He started Datacoral with the goal to make SQL the universal data programming language. Visit dataengineeringpodcast.com/datacoral today to find out more. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Dipti Borkark about data orchestration and how it helps in migrating data workloads to the cloud

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what you mean by the term "Data Orchestration"?

How does it compare to the concept of "Data Virtualization"? What are some of the tools and platforms that fit under that umbrella?

What are some of the motivations for organizations to use the cloud for their data oriented workloads?

What are they giving up by using cloud resources in place of on-premises compute?

For businesses that have invested heavily in their own datacenters, what are some ways that they can begin to replicate some of the benefits of cloud environments? What are some of the common patterns for cloud migration projects and what challenges do they present?

Do you have advice on useful metrics to track for determining project completion or success criteria?

How do businesses approach employee education for designing and implementing effective systems for achieving their migration goals? Can you talk through some of the ways that different data orchestration tools can be composed together for a cloud migration effort?

What are some of the common pain points that organizations encounter when working on hybrid implementations?

What are some of the missing pieces in the data orchestration landscape?

Are there any efforts that you are aware of that are aiming to fill those gaps?

Where is the data orchestration market heading, and what are some industry trends that are driving it?

What projects are you most interested in or excited by?

For someone who wants to learn more about data orchestration and the benefits the technologies can provide, what are some resources that you would recommend?

Contact Info

LinkedIn @dborkar on Twitter

Parting Question

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

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.init to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

Alluxio

Podcast Episode

UC San Diego Couchbase Presto

Podcast Episode

Spark SQL Data Orchestration Data Virtualization PyTorch

Podcast.init Episode

Rook storage orchestration PySpark MinIO

Podcast Episode

Kubernetes Openstack Hadoop HDFS Parquet Files

Podcast Episode

ORC Files Hive Metastore Iceberg Table Format

Podcast Episode

Data Orchestration Summit Star Schema Snowflake Schema Data Warehouse Data Lake Teradata

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

Support Data Engineering Podcast

Become an expert who can use window functions to solve T-SQL query problems. Replace slow cursors and self-joins with queries that are easy to write and perform better. This new edition provides expanded examples, including a chapter from the world of sports, and covers the latest performance enhancements through SQL Server 2019. Window functions are useful in analytics and business intelligence reporting. They came into full blossom with SQL Server 2012, yet they are not as well known and used as often as they ought to be. This group of functions is one of the most notable developments in SQL, and this book shows how every developer and DBA can benefit from their expressive power in solving day-to-day business problems. Once you begin using window functions, such as ROW_NUMBER and LAG, you will discover many ways to use them. You will approach SQL Server queries in a different way, thinking about sets of data instead of individual rows. Your querieswill run faster, be easier to write, and easier to deconstruct, maintain, and enhance in the future. Just knowing and using these functions is not enough. You also need to understand how to tune the queries. Expert T-SQL Window Functions in SQL Server clearly explains how to get the best performance. The book also covers the rare cases when older techniques are the best bet. What You Will Learn Solve complex query problems without cumbersome self-joins that run slowly and are difficult to read Create sliding windows in a result set for computing such as running totals and moving averages Return aggregate and detail data simultaneously from the same SELECT statement Compute lag and lead and other values that access data from multiple rows in a result set Understand the OVER clause syntax and how to control the window Avoid framing errors that can lead to unexpected results Who This Book Is For Anyone who writes T-SQL queries, including database administrators, developers, business analysts, and data scientists. Before reading this book, you should understand how to join tables, write WHERE clauses, and build aggregate queries.

Make Better Decisions, Leverage New Opportunities, and Automate Decisioning at Scale Prescriptive analytics is more directly linked to successful decision-making than any other form of business analytics. It can help you systematically sort through your choices to optimize decisions, respond to new opportunities and risks with precision, and continually reflect new information into your decisioning process. In Prescriptive Analytics, analytics expert Dr. Dursun Delen illuminates the field’s state-of-the-art methods, offering holistic insight for both professionals and students. Delen’s end-to-end, all-inclusive approach covers optimization, simulation, multi-criteria decision-making methods, inference- and heuristic-based decisioning, and more. Balancing theory and practice, he presents intuitive conceptual illustrations, realistic example problems, and real-world case studies–all designed to deliver knowledge you can use. Discover where prescriptive analytics fits and how it improves decision-making Identify optimal solutions for achieving an objective within real-world constraints Analyze complex systems via Monte-Carlo, discrete, and continuous simulations Apply powerful multi-criteria decision-making and mature expert systems and case-based reasoning Preview emerging techniques based on deep learning and cognitive computing