Today's global organizations depend on being able to unlock business insights from massive volumes of data. Now, with IBM® FlashSystem 900 Model AE3 that is powered by IBM FlashCore® technology, they can make faster decisions that are based on real-time insights. They also can unleash the power of the most demanding applications, including online transaction processing (OLTP) and analytics databases, virtual desktop infrastructures (VDIs), technical computing applications, and cloud environments. This IBM Redbooks® publication introduces clients to the IBM FlashSystem® 900 Model AE3. It provides in-depth knowledge of the product architecture, software and hardware, implementation, and hints and tips. Also presented are use cases that show real-world solutions for tiering, flash-only, and preferred-read. Examples of the benefits that are gained by integrating the FlashSystem storage into business environments also are described. This book is intended for pre-sales and post-sales technical support professionals and storage administrators, and anyone who wants to understand how to implement this new and exciting technology.

Data Science for Business and Decision Making covers both statistics and operations research while most competing textbooks focus on one or the other. As a result, the book more clearly defines the principles of business analytics for those who want to apply quantitative methods in their work. Its emphasis reflects the importance of regression, optimization and simulation for practitioners of business analytics. Each chapter uses a didactic format that is followed by exercises and answers. Freely-accessible datasets enable students and professionals to work with Excel, Stata Statistical Software®, and IBM SPSS Statistics Software®. Combines statistics and operations research modeling to teach the principles of business analytics Written for students who want to apply statistics, optimization and multivariate modeling to gain competitive advantages in business Shows how powerful software packages, such as SPSS and Stata, can create graphical and numerical outputs

Get started with Apache Flink, the open source framework that powers some of the world’s largest stream processing applications. With this practical book, you’ll explore the fundamental concepts of parallel stream processing and discover how this technology differs from traditional batch data processing. Longtime Apache Flink committers Fabian Hueske and Vasia Kalavri show you how to implement scalable streaming applications with Flink’s DataStream API and continuously run and maintain these applications in operational environments. Stream processing is ideal for many use cases, including low-latency ETL, streaming analytics, and real-time dashboards as well as fraud detection, anomaly detection, and alerting. You can process continuous data of any kind, including user interactions, financial transactions, and IoT data, as soon as you generate them. Learn concepts and challenges of distributed stateful stream processing Explore Flink’s system architecture, including its event-time processing mode and fault-tolerance model Understand the fundamentals and building blocks of the DataStream API, including its time-based and statefuloperators Read data from and write data to external systems with exactly-once consistency Deploy and configure Flink clusters Operate continuously running streaming applications

Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist “the hottest job in America.” Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining. Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets.

Summary How much time do you spend maintaining your data pipeline? How much end user value does that provide? Raghu Murthy founded DataCoral as a way to abstract the low level details of ETL so that you can focus on the actual problem that you are trying to solve. In this episode he explains his motivation for building the DataCoral platform, how it is leveraging serverless computing, the challenges of delivering software as a service to customer environments, and the architecture that he has designed to make batch data management easier to work with. This was a fascinating conversation with someone who has spent his entire career working on simplifying complex data problems.

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! Managing and auditing access to your servers and databases is a problem that grows in difficulty alongside the growth of your teams. If you are tired of wasting your time cobbling together scripts and workarounds to give your developers, data scientists, and managers the permissions that they need then it’s time to talk to our friends at strongDM. They have built an easy to use platform that lets you leverage your company’s single sign on for your data platform. Go to dataengineeringpodcast.com/strongdm today to find out how you can simplify your systems. Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support. 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, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. 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 Your host is Tobias Macey and today I’m interviewing Raghu Murthy about DataCoral, a platform that offers a fully managed and secure stack in your own cloud that delivers data to where you need it

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what DataCoral is and your motivation for founding it? How does the data-centric approa

Data virtualization has been around for decades and has always been controversial. In the 1990s, it was called virtual data warehousing or VDW-- or as some skeptics liked to say, "voodoo and witchcraft”. It’s also been known as query federation and more recently, data services. The idea is that business users don't need to know the location of the data; they merely need to log into the data service and all data appears as if it’s local to their server, modeled in a fashion that makes sense to them.

Andrew Sohn is the Global Head of Data and Analytics at Crawford & Company, a $1B+ service provider to the insurance and risk management industry, where he designed and leads its data and digital transformation strategy and program. With more than 25 years in the industry, Andrew has managed a broad range of infrastructure and application technologies. He’s a strong advocate of data virtualization technology and believes it is an integral part of a modern, agile data ecosystem.

Use Adobe Analytics as a marketer —not a programmer! If you're a marketer in need of a non-technical, beginner's reference to using Adobe Analytics, this book is the perfect place to start. Adobe Analytics For Dummies arms you with a basic knowledge of the key features so that you can start using it quickly and effectively. Even if you're a digital marketer who doesn't have their hands in data day in and day out, this easy-to-follow reference makes it simple to utilize Adobe Analytics. With the help of this book, you'll better understand how your marketing efforts are performing, converting, being engaged with, and being shared in the digital space. Evaluate your marketing strategies and campaigns Explore implementation fundamentals and report architecture Apply Adobe Analytics to multiple sources Succeed in the workplace and expand your marketing skillset The marketing world is continually growing and evolving, and Adobe Analytics For Dummies will help you stay ahead of the curve.

Summary Analytics projects fail all the time, resulting in lost opportunities and wasted resources. There are a number of factors that contribute to that failure and not all of them are under our control. However, many of them are and as data engineers we can help to keep our projects on the path to success. Eugene Khazin is the CEO of PrimeTSR where he is tasked with rescuing floundering analytics efforts and ensuring that they provide value to the business. In this episode he reflects on the ways that data projects can be structured to provide a higher probability of success and utility, how data engineers can get throughout the project lifecycle, and how to salvage a failed project so that some value can be gained from the effort.

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! Managing and auditing access to your servers and databases is a problem that grows in difficulty alongside the growth of your teams. If you are tired of wasting your time cobbling together scripts and workarounds to give your developers, data scientists, and managers the permissions that they need then it’s time to talk to our friends at strongDM. They have built an easy to use platform that lets you leverage your company’s single sign on for your data platform. Go to dataengineeringpodcast.com/strongdm today to find out how you can simplify your systems. Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support. 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, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Eugene Khazin about the leading causes for failure in analytics projects

Interview

Introduction How did you get involved in the area of data management? The term "analytics" has grown to mean many different things to different people, so can you start by sharing your definition of what is in scope for an "analytics project" for the purposes of this discussion?

Wh

Data Science for Marketing Analytics introduces you to leveraging state-of-the-art data science techniques to optimize marketing outcomes. You'll learn how to manipulate and analyze data using Python, create customer segments, and apply machine learning algorithms to predict customer behavior. This book provides a comprehensive, hands-on approach to marketing analytics. What this Book will help me do Learn to use Python libraries like pandas & Matplotlib for data analysis. Understand clustering techniques to create meaningful customer segments. Implement linear regression for predicting customer lifetime value. Explore classification algorithms to model customer preferences. Develop skills to build interactive dashboards for marketing reports. Author(s) None Blanchard, Nona Behera, and Pranshu Bhatnagar are experienced professionals in data science and marketing analytics, with extensive backgrounds in applying machine learning to real-world business applications. They bring a wealth of knowledge and an approachable teaching style to this book, focusing on practical, industry-relevant applications for learners. Who is it for? This book is for developers and marketing professionals looking to advance their analytics skills. It is ideal for individuals with a basic understanding of Python and mathematics who want to explore predictive modeling and segmentation strategies. Readers should have a curiosity for data-driven problem-solving in marketing contexts to benefit most from the content.

Dive into the exciting world of big data analytics with 'Hands-On Big Data Analytics with PySpark'. This practical guide offers you the tools and knowledge to tackle massive datasets using PySpark. By exploring real-world examples, you'll learn to unleash the power of distributed systems to analyze and manipulate data at scale. What this Book will help me do Master using PySpark to handle large and complex datasets efficiently and effectively. Develop skills to optimize Spark programs using best practices like reducing shuffle operations. Learn to set up a PySpark environment, process data from platforms like HDFS, Hive, and S3. Enhance your data analytics capabilities by implementing powerful SQL queries and data visualizations. Understand testing and debugging techniques to build reliable, production-quality data pipelines. Author(s) Authored by Rudy Lai and Bartłomiej Potaczek, both seasoned data engineers and authors in the big data field. Rudy and Bartłomiej bring their extensive experience working with distributed systems and scalable data architectures into this book. Their approach is hands-on, focusing on real-world applications and best practices. Who is it for? This book is tailored for data scientists, engineers, and developers eager to advance their big data analytics capabilities. Whether you're new to big data or experienced with other analytics frameworks, this book will equip you with practical knowledge to utilize PySpark for scalable data solutions.

The book "Hands-On Data Science for Marketing" equips readers with the tools and insights to optimize their marketing campaigns using data science and machine learning techniques. Using practical examples in Python and R, you will learn how to analyze data, predict customer behavior, and implement effective strategies for better customer engagement and retention. What this Book will help me do Understand marketing KPIs and learn to compute and visualize them in Python and R. Develop the ability to analyze customer behavior and predict potential high-value customers. Master machine learning concepts for customer segmentation and personalized marketing strategies. Improve your skills to forecast customer engagement and lifetime value for more effective planning. Learn the techniques of A/B testing and their application in refining marketing decisions. Author(s) Yoon Hyup Hwang is a seasoned data scientist with a deep interest in the intersection of marketing and technology. With years of expertise in implementing machine learning algorithms in marketing analytics, Yoon brings a unique perspective by blending technical insights with business strategy. As an educator and practitioner, Yoon's approachable style and clear explanations make complex topics accessible for all learners. Who is it for? This book is tailored for marketing professionals looking to enhance their strategies using data science, data enthusiasts eager to apply their skills in marketing, and students or engineers seeking to expand their knowledge in this domain. A basic understanding of Python or R is beneficial, but the book is structured to welcome beginners by covering foundational to advanced concepts in a practical way.

Machine Learning with R Quick Start Guide takes you through the foundations of machine learning using the R programming language. Starting with the basics, this book introduces key algorithms and methodologies, offering hands-on examples and applicable machine learning solutions that allow you to extract insights and create predictive models. What this Book will help me do Understand the basics of machine learning and apply them using R 3.5. Learn to clean, prepare, and visualize data with R to ensure robust data analysis. Develop and work with predictive models using various machine learning techniques. Discover advanced topics like Natural Language Processing and neural network training. Implement end-to-end pipeline solutions, from data collection to predictive analytics, in R. Author(s) None Sanz, the author of Machine Learning with R Quick Start Guide, is an expert in data science with years of experience in the field of machine learning and R programming. Known for their accessible and detailed teaching style, the author focuses on providing practical knowledge to empower readers in the real world. Who is it for? This book is ideal for graduate students and professionals, including aspiring data scientists and data analysts, looking to start their journey in machine learning. Readers are expected to have some familiarity with the R programming language but no prior machine learning experience is necessary. With this book, the audience will gain the ability to confidently navigate machine learning concepts and practices.

Extract actionable insights from text and unstructured data. Information extraction is the task of automatically extracting structured information from unstructured or semi-structured text. SAS Text Analytics for Business Applications: Concept Rules for Information Extraction Models focuses on this key element of natural language processing (NLP) and provides real-world guidance on the effective application of text analytics. Using scenarios and data based on business cases across many different domains and industries, the book includes many helpful tips and best practices from SAS text analytics experts to ensure fast, valuable insight from your textual data. Written for a broad audience of beginning, intermediate, and advanced users of SAS text analytics products, including SAS Visual Text Analytics, SAS Contextual Analysis, and SAS Enterprise Content Categorization, this book provides a solid technical reference. You will learn the SAS information extraction toolkit, broaden your knowledge of rule-based methods, and answer new business questions. As your practical experience grows, this book will serve as a reference to deepen your expertise.

Discover how to harness the power of Tableau 2019 to transform raw data into insightful, actionable business intelligence. This book serves as a comprehensive guide to mastering Tableau's features-from creating stunning visualizations to managing complex datasets with Tableau Prep. By the end, you'll be well-equipped to use Tableau for informed decision-making. What this Book will help me do Master the essential features of Tableau 2019 to become proficient in data visualization. Learn to prepare and integrate data effectively using Tableau Prep. Develop advanced visual analytics skills, including calculations and table calculations. Understand how to craft compelling dashboards and data stories for impactful communication. Leverage new Tableau features like set actions and transparent views for enhanced analytics. Author(s) Joshua N. Milligan is a Tableau-certified professional and Tableau Zen Master with extensive industry experience in data analytics. Known for his clarity in teaching, Joshua takes a practical and comprehensive approach to help users navigate Tableau effectively. His passion for empowering data-driven decisions is evident in his writing. Who is it for? This book is ideal for data professionals, analysts, or anyone new to Tableau who seeks to gain proficiency in data visualization and analysis. It is suitable for beginners, as it walks the reader through foundational concepts before introducing complex topics. Readers looking to enhance their skills in advanced Tableau techniques will also find value here. Familiarity with databases is helpful but not mandatory.

We thought we deserved a break from the podcast, so we went looking for some AI to take over the episode. Amazon Polly wasn't quite up to the task, unfortunately, so we wound up sitting down as humans with another human -- Erik Driessen from Greenhouse -- to chat about the different ways that automation can be put to use in the service of analytics: from pixel deployment to automated alerts to daily reports, there are both opportunities and pitfalls! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Summary Data integration is one of the most challenging aspects of any data platform, especially as the variety of data sources and formats grow. Enterprise organizations feel this acutely due to the silos that occur naturally across business units. The CluedIn team experienced this issue first-hand in their previous roles, leading them to build a business aimed at building a managed data fabric for the enterprise. In this episode Tim Ward, CEO of CluedIn, joins me to explain how their platform is architected, how they manage the task of integrating with third-party platforms, automating entity extraction and master data management, and the work of providing multiple views of the same data for different use cases. I highly recommend listening closely to his explanation of how they manage consistency of the data that they process across different storage backends.

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! Managing and auditing access to your servers and databases is a problem that grows in difficulty alongside the growth of your teams. If you are tired of wasting your time cobbling together scripts and workarounds to give your developers, data scientists, and managers the permissions that they need then it’s time to talk to our friends at strongDM. They have built an easy to use platform that lets you leverage your company’s single sign on for your data platform. Go to dataengineeringpodcast.com/strongdm today to find out how you can simplify your systems. Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support. 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, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. 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 Your host is Tobias Macey and today I’m interviewing Tim Ward about CluedIn, an integration platform for implementing your companies data fabric

Interview

Introduction

How did you get involved in t

Data is changing everything. Many industries today are being fundamentally transformed through the accumulation and analysis of large quantities of data, stored in diversified but flexible repositories known as data lakes. Whether your company has just begun to think about big data or has already initiated a strategy for handling it, this practical ebook shows you how to plan a successful data lake migration. You’ll learn the value of data lakes, their structure, and the problems they attempt to solve. Using Zaloni’s data lake maturity model, you’ll then explore your organization’s readiness for putting a data lake into action. Do you have the tools and data architectures to support big data analysis? Are your people and processes prepared? The data lake maturity model will help you rate your organization’s readiness. This report includes: The structure and purpose of a data lake Descriptive, predictive, and prescriptive analytics Data lake curation, self-service, and the use of data lake zones How to rate your organization using the data lake maturity model A complete checklist to help you determine your strategic path forward

Abstract As big data becomes more ubiquitous, businesses are wondering how they can best leverage it to gain insight into their most important business questions. Using machine learning (ML) and deep learning (DL) in big data environments can identify historical patterns and build artificial intelligence (AI) models that can help businesses to improve customer experience, add services and offerings, identify new revenue streams or lines of business (LOBs), and optimize business or manufacturing operations. The power of AI for predictive analytics is being harnessed across all industries, so it is important that businesses familiarize themselves with all of the tools and techniques that are available for integration with their data lake environments. In this IBM® Redbooks® publication, we cover the best practices for deploying and integrating some of the best AI solutions on the market, including: IBM Watson Machine Learning Accelerator (see note for product naming) IBM Watson Studio Local IBM Power Systems™ IBM Spectrum™ Scale IBM Data Science Experience (IBM DSX) IBM Elastic Storage™ Server Hortonworks Data Platform (HDP) Hortonworks DataFlow (HDF) H2O Driverless AI We map out all the integrations that are possible with our different AI solutions and how they can integrate with your existing or new data lake. We also walk you through some of our client use cases and show you how some of the industry leaders are using Hortonworks, IBM PowerAI, and IBM Watson Studio Local to drive decision making. We also advise you on your deployment options, when to use a GPU, and why you should use the IBM Elastic Storage Server (IBM ESS) to improve storage management. Lastly, we describe how to integrate IBM Watson Machine Learning Accelerator and Hortonworks with or without IBM Watson Studio Local, how to access real-time data, and security. Note: IBM Watson Machine Learning Accelerator is the new product name for IBM PowerAI Enterprise. Note: Hortonworks merged with Cloudera in January 2019. The new company is called Cloudera. References to Hortonworks as a business entity in this publication are now referring to the merged company. Product names beginning with Hortonworks continue to be marketed and sold under their original names.

Send us a text In part 2 of this two-part series, host Al Martin welcomes back Rob Thomas, GM of IBM Data and AI. The conversation ranges across the latest trends in enterprise analytics, how to tell the truth from the hype, and Rob’s own strategies for staying current, connected, and engaged in this fast-changing space.

Show Notes

00:00 - Missed part 1? Catch up with the conversation here.

00:05 - Check us out on YouTube and SoundCloud.  00:10 - Connect with Producer Steve Moore on LinkedIn and Twitter.  00:15 - Connect with Producer Liam Seston on LinkedIn and Twitter.  00:20 - Connect with Producer Rachit Sharma on LinkedIn.  00:25 - Connect with Host Al Martin on LinkedIn and Twitter.  00:52 - Connect with Rob Thomas on LinkedIn and Twitter. 01:02 - Check out Rob's article on Watson Anywhere here.  02:03 - There is now proof that quantum computing can now boost machine learning! Read more here. 05:17 - Your team needs a compass, not a map. 10:00 - Warren Buffet and Mark Cuban agree, reading is like compound interest. 14:24 - Check out Wooden on Leadership here. 14:35 - Check out Mindset 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.

Maximize performance with better data Developing a successful workforce requires more than a gut check. Data can help guide your decisions on everything from where to seat a team to optimizing production processes to engaging with your employees in ways that ring true to them. People analytics is the study of your number one business asset—your people—and this book shows you how to collect data, analyze that data, and then apply your findings to create a happier and more engaged workforce. Start a people analytics project Work with qualitative data Collect data via communications Find the right tools and approach for analyzing data If your organization is ready to better understand why high performers leave, why one department has more personnel issues than another, and why employees violate, People Analytics For Dummies makes it easier.