A classic management practice dictates that a newly-appointed leader must accomplish certain things in their first 90 days. While some of this is general knowledge, there are specifics when it comes to Data Management and Analytics If you have been recently named to head any group that has to manage or facilitate the use of data, at any level in the organization, then this audio blog post is for you.

Originally published at https://www.eckerson.com/articles/what-do-you-do-first-after-being-hired-as-a-bi-analytics-data-engineering-director

Standardizes the definition and framework of analytics ABOK stands for Analytics Body of Knowledge. Based on the authors’ definition of analytics—which is “a process by which a team of people helps an organization make better decisions (the objective) through the analysis of data (the activity)”— this book from Institute for Operations Research and the Management Sciences (INFORMS) represents the perspectives of some of the most respected experts on analytics. The INFORMS ABOK documents the core concepts and skills with which an analytics professional should be familiar; establishes a dynamic resource that will be used by practitioners to increase their understanding of analytics; and, presents instructors with a framework for developing academic courses and programs in analytics. The INFORMS ABOK offers in-depth insight from peer-reviewed chapters that provide readers with a better understanding of the dynamic field of analytics. Chapters cover: Introduction to Analytics; Getting Started with Analytics; The Analytics Team; The Data; Solution Methodology; Model Building; Machine Learning; Deployment and Life Cycle Management; and The Blossoming Analytics Talent Pool: An Overview of the Analytics Ecosystem. Across industries and academia, readers with various backgrounds in analytics – from novices who are interested in learning more about the basics of analytics to experienced professionals who want a different perspective on some aspect of analytics – will benefit from reading about and implementing the concepts and methods covered by the INFORMS ABOK.

  This book looks at strategies to help companies become more intelligent, connected, and agile. It discusses how companies can define and measure high-impact outcomes and use effectively analytics technology to achieve them. It also looks at the technology needed to implement the analytics necessary to achieve high-impact outcomes—from both analytics tool and technical infrastructure perspective. Also discussed are ancillary, but critical, topics such as data security and governance that may not traditionally be a part of analytics discussions but are essential in helping companies maintain a secure environment for their analytics and access the quality data they need to gain critical insights and drive better decision-making.

Use data analytics to drive innovation and value throughout your network infrastructure Network and IT professionals capture immense amounts of data from their networks. Buried in this data are multiple opportunities to solve and avoid problems, strengthen security, and improve network performance. To achieve these goals, IT networking experts need a solid understanding of data science, and data scientists need a firm grasp of modern networking concepts. Data Analytics for IT Networks fills these knowledge gaps, allowing both groups to drive unprecedented value from telemetry, event analytics, network infrastructure metadata, and other network data sources. Drawing on his pioneering experience applying data science to large-scale Cisco networks, John Garrett introduces the specific data science methodologies and algorithms network and IT professionals need, and helps data scientists understand contemporary network technologies, applications, and data sources. After establishing this shared understanding, Garrett shows how to uncover innovative use cases that integrate data science algorithms with network data. He concludes with several hands-on, Python-based case studies reflecting Cisco Customer Experience (CX) engineers’ supporting its largest customers. These are designed to serve as templates for developing custom solutions ranging from advanced troubleshooting to service assurance. Understand the data analytics landscape and its opportunities in Networking See how elements of an analytics solution come together in the practical use cases Explore and access network data sources, and choose the right data for your problem Innovate more successfully by understanding mental models and cognitive biases Walk through common analytics use cases from many industries, and adapt them to your environment Uncover new data science use cases for optimizing large networks Master proven algorithms, models, and methodologies for solving network problems Adapt use cases built with traditional statistical methods Use data science to improve network infrastructure analysisAnalyze control and data planes with greater sophistication Fully leverage your existing Cisco tools to collect, analyze, and visualize data

In this podcast, John Busby(@johnmbusby), Chief Analytics Officer @CenterfieldUSA, talks about his journey leading the data analytics practice of a digital marketing agency. He sheds light on some methodologies for building a sound data science practice. He sheds light on the future of digital marketing and shared some big opportunities ripe for disruption in the digital space.

Timeline: 0:28 John's journey. 4:26 Introduction to Centerfield. 6:00 John's role. 6:50 Designing a common platform for customers. 9:15 Analytics in Amazon. 11:02 Data science and marketing. 18:02 Importance of understanding the product for marketing. 21:44 AI in the marketing business. 25:26 Making sense of customer behavior. 27:50 End to end consumer behavior. 31:05 Editing and calibrating KPIs. 32:53 Creating an inside driven organization. 35:35 Recipe for a successful chief analytic officer. 37:46 On data bias. 39:12 Hiring the right people. 41:33 Big opportunities in digital marketing. 44:15 Future of digital marketing. 45:27 John's recipe for success. 48:52 John's favorite reads. 50:35 Key takeaways.

John's Recommended Read: Secrets of Professional Tournament Poker (D&B Poker) by Jonathan Little amzn.to/2MNKjN3

Podcast Link: https://futureofdata.org/data-today-shaping-digital-marketing-of-tomorrow-johnmbusby-centerfieldusa/

John's BIO: John Busby serves as Centerfield’s Chief Analytics Officer. A seasoned digital marketing executive, John leads the company’s data science, analytics and insights teams. Before joining Centerfield, John was Head of Analytics for Amazon’s grocery delivery service and responsible for business intelligence, data science and automated reporting. Prior to Amazon, John was Senior Vice President of Analytics and Marketing at Marchex. John began his career in product management for InfoSpace, Go2net and IQ Chart. He holds a Bachelor of Science from Northwestern University. Outside of work, John coaches youth hockey, and enjoys sports, poker and hanging out with his wife and two children.

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers and lead practitioners to come on show and discuss their journey in creating the data driven future.

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In this episode, Wayne Eckerson and Rich Fox discuss what differentiates data science from analytics, why and how data science addresses business needs, why balance scorecards are relevant, and why Excel is a problem. Throughout the podcast, Fox shares many real-life examples and personal experiences.

Fox is vice president of Data Science and Analytics at Apex Parks Group, one of the largest entertainment center companies in the United States, which operates amusement parks, water parks, and family entertainment centers.

With all the hype and attention around big data and huge data platforms, there can sometimes be some data envy. There are still organizations and companies that don’t have big data: are they not poised for analytics too? Can they not get insights as well? The BI Pharaoh gives tips on how to work with your little data just like the big boys.

Originally published at https://www.eckerson.com/articles/little-data-needs-love-too

How can analytics scholars and healthcare professionals access the most exciting and important healthcare topics and tools for the 21st century? Editors Tinglong Dai and Sridhar Tayur, aided by a team of internationally acclaimed experts, have curated this timely volume to help newcomers and seasoned researchers alike to rapidly comprehend a diverse set of thrusts and tools in this rapidly growing cross-disciplinary field. The Handbook covers a wide range of macro-, meso- and micro-level thrusts—such as market design, competing interests, global health, personalized medicine, residential care and concierge medicine, among others—and structures what has been a highly fragmented research area into a coherent scientific discipline. The handbook also provides an easy-to-comprehend introduction to five essential research tools—Markov decision process, game theory and information economics, queueing games, econometric methods, and data science—by illustrating their uses and applicability on examples from diverse healthcare settings, thus connecting tools with thrusts. The primary audience of the Handbook includes analytics scholars interested in healthcare and healthcare practitioners interested in analytics. This Handbook: Instills analytics scholars with a way of thinking that incorporates behavioral, incentive, and policy considerations in various healthcare settings. This change in perspective—a shift in gaze away from narrow, local and one-off operational improvement efforts that do not replicate, scale or remain sustainable—can lead to new knowledge and innovative solutions that healthcare has been seeking so desperately. Facilitates collaboration between healthcare experts and analytics scholar to frame and tackle their pressing concerns through appropriate modern mathematical tools designed for this very purpose. The handbook is designed to be accessible to the independent reader, and it may be used in a variety of settings, from a short lecture series on specific topics to a semester-long course.

Enjoy reading interviews with more than two dozen data professionals to see a picture of what it’s like to work in the industry managing and analyzing data, helping you to know what it takes to move from your current expertise into one of the fastest growing areas of technology today. Data is the hottest word of the century, and data professionals are in high demand. You may already be a data professional such as a database administrator or business intelligence analyst. Or you may be one of the many people who want to work as a data professional, and are curious how to get there. Either way, this collection helps you understand how data professionals work, what makes them successful, and what they do to keep up. You’ll find interviews in this book with database administrators, database programmers, data architects, business intelligence professionals, and analytics professionals. Interviewees work across industry sectors ranging from healthcare and banking tofinance and transportation and beyond. Each chapter illuminates a successful professional at the top of their game, who shares what helped them get to the top, and what skills and attitudes combine to make them successful in their respective fields. Interviewees in the book include: Mindy Curnutt, Julie Smith, Kenneth Fisher, Andy Leonard, Jes Borland, Kevin Feasel, Ginger Grant, Vicky Harp, Kendra Little, Jason Brimhall, Tim Costello, Andy Mallon, Steph Locke, Jonathan Stewart, Joseph Sack, John Q. Martin, John Morehouse, Kathi Kellenberger, Argenis Fernandez, Kirsten Benzel, Tracy Boggiano, Dave Walden, Matt Gordon, Jimmy May, Drew Furgiuele, Marlon Ribunal, and Joseph Fleming. All of them have been successful in their careers, and share their perspectives on working and succeeding in the field as data and database professionals. What You'll Learn Stand out as an outstanding professional in your area of data work by developing the right set of skills and attitudes that lead to success Avoid common mistakes and pitfalls, and recover from operational failures and bad technology decisions Understand current trends and best practices, and stay out in front as the field evolves Break into working with data through database administration, business intelligence, or any of the other career paths represented in this book Manage stress and develop a healthy work-life balance no matter which career path you decide upon Choose a suitable path for yourself from among the different career paths in working with data Who This Book Is For Database administrators and developers, database and business intelligence architects, consultants, and analytic professionals, as well as those intent on moving into one of those career paths. Aspiring data professionals and those in related technical fields who want to make a move toward managing or analyzing data on a full-time basis will find the book useful. Existing data professionals who want to be outstanding and successful at what they do will also appreciate the book's advice and guidance.

Using Power Query, you can import, reshape, and cleanse any data from a simple interface, so you can mine that data for all of its hidden insights. Power Query is embedded in Excel, Power BI, and other Microsoft products, and leading Power Query expert Gil Raviv will help you make the most of it. Discover how to eliminate time-consuming manual data preparation, solve common problems, avoid pitfalls, and more. Then, walk through several complete analytics challenges, and integrate all your skills in a realistic chapter-length final project. By the time you're finished, you'll be ready to wrangle any data–and transform it into actionable knowledge. Prepare and analyze your data the easy way, with Power Query · Quickly prepare data for analysis with Power Query in Excel (also known as Get & Transform) and in Power BI · Solve common data preparation problems with a few mouse clicks and simple formula edits · Combine data from multiple sources, multiple queries, and mismatched tables · Master basic and advanced techniques for unpivoting tables · Customize transformations and build flexible data mashups with the M formula language · Address collaboration challenges with Power Query · Gain crucial insights into text feeds · Streamline complex social network analytics so you can do it yourself For all information workers, analysts, and any Excel user who wants to solve their own business intelligence problems.

We’re at the dawn of a new era in decision making made possible by the intersection of business intelligence and artificial intelligence. Rather than replace BI, AI will make BI more pervasive. AI-infused BI tools will be easier to use, generate more useful insights, and make business users more productive. Rather than replace human decision makers, AI will free them to focus on value-added activities and make decisions with data rather than rely solely on gut instinct.

Originally published at https://www.eckerson.com/articles/the-impact-of-ai-on-analytics-machine-generated-intelligence

Send us a text Making Data Simple host Al Martin has a chance to discuss all thing data with Laura Ellis, also known as Little Miss Data. Laura is an analytics architect for IBM Cloud as well as a frequent blogger. Together, they talk about how critical it is to understand your data in order create specific calls to action, and what it means to build a data democracy. Show Notes 00:00 - Follow @IBMAnalyticsSupport on Twitter. 00:22 - Check out our YouTube channel. We're posting full episodes weekly. 00:24 - Connect with Al Martin on LinkedIn and Twitter. 01:20 - Check out littlemissdata.com. 01:22 - Connect with Laura Ellis on Twitter, Instagram, and LinkedIn. 02:20 - Curious to know more about analytics architecture? Check out this IBM article on the topic. 03:52 - Check out the Little Miss Data article Al referenced here. 04:45 - Learn more about Data Democracy here in Laura's blog post. 05:31 - Understand more about the importance of data for your business in this article. 09:11 - Find out more about the challenges of being a data scientist here. 12:45 - Working with good quality data is crucial. Check out this article for more details. 16:12 - Simple data can provide the most effective returns. Learn more here. 21:15 - Choosing the right, supportive environment for your data science journey will make sure you don't get burnt out. This article examines your options. 21:35 - Data is a fundamental step when working with AI. But do you know the difference between data analytics, AI and machine learning? This Forbes article walks you through it. 22:42 - Need to brush up on what a data dashboard is? Learn more here. 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.

In this podcast, Maksim, CDO @ City of San Diago, discussed the nuances of running big data for big cities. He shares his perspectives on effectively building a central data office in a complex and extremely collaborative environment like a big city. He shared his thoughts on some ways to effectively prioritize which project to pursue. He shared how leadership and execution could blend to solve civic issues relating to big and small cities. A great practitioner podcast for folks seeking to build a robust data science practice across a large and collaborative ecosystem.

Timeline: 0:28 Maksim's journey. 6:45 Maksim's current role. 11:46 Collaboration process in creating a data inventory. 14:52 Working with the bureaucracy. 18:35 Dealing with unforeseen circumstances at work. 20:22 Prioritization at work. 22:58 Qualities of a good data leader. 26:15 Collaboration with other cities. 27:40 Cool data projects in other cities. 30:55 Shortcomings of other city representatives. 36:54 Use cases in AI 39:00 What would Maksim change about himself? 40:50 Future cities and data 43:55 Opportunities for private investors in the public sector. 45:53 Maksim's success mantra. 50:19 Closing remark.

Maksim's Book Recommendation: The Phoenix Project: A Novel about IT, DevOps, and Helping Your Business Win by Gene Kim, Kevin Behr, George Spafford amzn.to/2MAu5Xv

Podcast Link: https://futureofdata.org/understanding-bigdata-for-bigcities-with-maksim-mrmaksimize-cityofsandiego-futureofdata-podcast/

Maksim's BIO: Maksim Pecherskiy: As the CDO for the City of San Diego, working in the Performance & Analytics Department, Maksim strives to bring the necessary components together to allow the City's residents to benefit from a more efficient, agile government that is as innovative as the community around it. He has been solving complex problems with technology for nearly a decade. He spent 2014 working as a Code For America fellow in Puerto Rico, focusing on economic development. His team delivered a product called PrimerPeso that provides business owners and residents a tool to search, and apply for, government programs for which they may be eligible.

Before moving to California, Maksim was a Solutions Architect at Promet Source in Chicago, where he built large web applications and designed complex integrations. He shaped workflow, configuration management, and continuous integration processes while leading and training international development teams. Before his work at Promet, he was a software engineer at AllPlayers, who was instrumental in the design and architecture of its APIs and the development and documentation of supporting client libraries in various languages.

Maksim graduated from DePaul University with a bachelor of science degree in information systems and from Linköping University, Sweden, with a bachelor of science degree in international business. He is also certified as a Lean Six Sigma Green Belt.

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers and lead practitioners to come on show and discuss their journey in creating the data driven future.

Wanna Join? If you or any you know wants to join in, Register your interest by mailing us @ [email protected]

Want to sponsor? Email us @ [email protected]

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In this episode, Wayne Eckerson and Rich Galan discuss the obstacles to delivering timely analysis, the problems that large volumes of data create, solutions to those issues, and where BI is headed in the near future. Rich is a veteran data analytics leader with 20 years of experience in a variety of data-driven organizations.

Abstract This IBM® Redbooks® publication introduces the latest member of the IBM Z platform, the IBM z14 Model ZR1 (Machine Type 3907). It includes information about the Z environment and how it helps integrate data and transactions more securely, and provides insight for faster and more accurate business decisions. The z14 ZR1 is a state-of-the-art data and transaction system that delivers advanced capabilities, which are vital to any digital transformation. The z14 ZR1 is designed for enhanced modularity, which is in an industry standard footprint. This system excels at the following tasks: Securing data with pervasive encryption Transforming a transactional platform into a data powerhouse Getting more out of the platform with IT Operational Analytics Providing resilience towards zero downtime Accelerating digital transformation with agile service delivery Revolutionizing business processes Mixing open source and Z technologies This book explains how this system uses new innovations and traditional Z strengths to satisfy growing demand for cloud, analytics, and open source technologies. With the z14 ZR1 as the base, applications can run in a trusted, reliable, and secure environment that improves operations and lessens business risk.

Abstract This IBM® Redbooks® publication introduces the latest IBM z platform, the IBM z14™. It includes information about the Z environment and how it helps integrate data and transactions more securely, and can infuse insight for faster and more accurate business decisions. The z14 is a state-of-the-art data and transaction system that delivers advanced capabilities, which are vital to the digital era and the trust economy. This system includes the following functionality: Securing data with pervasive encryption Transforming a transactional platform into a data powerhouse Getting more out of the platform with IT Operational Analytics Providing resilience with key to zero downtime Accelerating digital transformation with agile service delivery Revolutionizing business processes Blending open source and Z technologies This book explains how this system uses both new innovations and traditional Z strengths to satisfy growing demand for cloud, analytics, and mobile applications. With the z14 as the base, applications can run in a trusted, reliable, and secure environment that both improves operations and lessens business risk.

Data analysts who sit in each business function (i.e., sales, marketing, finance) are critical to the success of a self-service analytics strategy. The problem is that most data analysts don’t receive the training and support they need to be proficient with self-service data and analytics tools. The easiest way to improve the skills and satisfaction of most data analysts is simple: bring them together into a power user network.

Originally published at https://www.eckerson.com/articles/power-user-networks-the-key-to-self-service-analytics

In this podcast, Jim Sterne shares how marketing has evolved through disruptive times. He shares some of the best practices in the marketing and digital analytics space. He sheds light on some opportunities in the marketing and analytics space and how machine learning is changing the face of digital and marketing. This is a great podcast for anyone looking to understand how AI is impacting marketing and what are some big opportunities in marketing and digital.

Timeline: 0:30 Jim's journey. 5:25 The evolution of marketing. 8:45 Breaking down the digital. 11:40 Marketing and analytics. 13:27 Misuse of analytics in marketing. 17:35 Resolving bad data and bias. 22:20 Good digital analyst vs. bad digital analyst. 28:06 Defining a well-oiled marketing machine. 30:33 Marketing industry's adoption of technology. 34:19 Technology adoption strategy. 38:23 Impact of machine learning and digital marketing. 42:19 Decision making, accountability, and AI. 47:08 Advice for start-ups. 48:52 Disruption opportunities in digital marketing. 55:57 Ethics and marketing. 58:52 What's next in digital marketing. 1:02:27 Jim's success mantra. 1:05:36 Jim's reading list. 1:07:30 Key takeaways.

Jim's Books: amzn.to/2KB1QCR

Jim's Current Read List: Shift: 19 Practical, Business-Driven Ideas for an Executive in Charge of Marketing but Not Trained for the Task by Sean Doyle amzn.to/2KG4K9d Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking by Foster Provost and Tom Fawcett amzn.to/2AWR3Dz

Podcast Link: https://futureofdata.org/future-of-data-in-marketing-digital-jimsterne/

Jim's BIO: Jim Sterne focused his thirty-five years in sales and marketing to create and strengthen customer relationships through digital communications. He sold business computers to companies that had never owned one in the 1980s, consulted and keynoted online marketing in the 1990s, and founded a conference and a professional association around digital analytics in the 2000s, following his humorous Devil's Data Dictionary. Sterne has just published his twelfth book Artificial Intelligence for Marketing: Practical Applications. Sterne produced the eMetrics Summit from 2002 - 2017 and now produces the Marketing Evolution Experience. He was co-founder and served for 17 years as the Board Chair of the Digital Analytics Association.

Jim was named one of the 50 most influential people in digital marketing by a top marketing magazine in the United Kingdom and identified as one of the top 25 Hot Speakers by the National Speakers Association.

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers and lead practitioners to come on show and discuss their journey in creating the data driven future.

Wanna Join? If you or any you know wants to join in, Register your interest by mailing us @ [email protected]

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Explore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis. You'll review scientific computing with NumPy, visualization with matplotlib, and machine learning with scikit-learn. This revision is fully updated with new content on social media data analysis, image analysis with OpenCV, and deep learning libraries. Each chapter includes multiple examples demonstrating how to work with each library. At its heart lies the coverage of pandas, for high-performance, easy-to-use data structures and tools for data manipulation Author Fabio Nelli expertly demonstrates using Python for data processing, management, and information retrieval. Later chapters apply what you've learned to handwriting recognition and extending graphical capabilities with the JavaScript D3 library. Whether you are dealing with sales data, investment data, medical data, web page usage, or other data sets, Python Data Analytics, Second Edition is an invaluable reference with its examples of storing, accessing, and analyzing data. What You'll Learn Understand the core concepts of data analysis and the Python ecosystem Go in depth with pandas for reading, writing, and processing data Use tools and techniques for data visualization and image analysis Examine popular deep learning libraries Keras, Theano,TensorFlow, and PyTorch Who This Book Is For Experienced Python developers who need to learn about Pythonic tools for data analysis

Transform your R programming into interactive web applications with "Web Application Development with R Using Shiny." This book takes you step-by-step through creating dynamic user interfaces and web solutions with the R Shiny package, empowering you to build impactful tools that showcase your data. What this Book will help me do Create interactive web applications using R Shiny. Apply JavaScript for added functionality and customization in Shiny apps. Effortlessly deploy Shiny apps online for accessibility. Understand Shiny UI functions to design effective user interfaces. Leverage data visualization techniques for insightful analytics in apps. Author(s) Chris Beeley and Shitalkumar R. Sukhdeve bring their profound expertise in R programming and Shiny development to this book. Chris is an experienced data scientist passionate about interactive data solutions, while Shitalkumar, with a strong computing background, shares his hands-on insights. Their collaborative and tutorial approach ensures learners grasp each concept smoothly. Who is it for? This book is ideal for R programmers eager to transition from static data evaluation to engaging, interactive web applications. It caters to professionals and enthusiasts seeking practical, hands-on coding guidance. Readers should have foundational R programming knowledge, ensuring a smooth transition into Shiny concepts.