Data Analytics for IT Networks: Developing Innovative Use Cases

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

Table of Contents:
    Foreword xvii     Introduction: Your future is in your hands! xviii Chapter 1 Getting Started with Analytics 1     What This Chapter Covers 1         Data: You as the SME 2         Use-Case Development with Bias and Mental Models 2         Data Science: Algorithms and Their Purposes 3     What This Book Does Not Cover 4         Building a Big Data Architecture 4         Microservices Architectures and Open Source Software 5         R Versus Python Versus SAS Versus Stata 6         Databases and Data Storage 6         Cisco Products in Detail 6     Analytics and Literary Perspectives 7         Analytics Maturity 7         Knowledge Management 8         Gartner Analytics 8         Strategic Thinking 9         Striving for “Up and to the Right” 9         Moving Your Perspective 10         Hot Topics in the Literature 11     Summary 12 Chapter 2 Approaches for Analytics and Data Science 13     Model Building and Model Deployment 14     Analytics Methodology and Approach 15         Common Approach Walkthrough 16     Distinction Between the Use Case and the Solution 18     Logical Models for Data Science and Data 19         Analytics as an Overlay 20         Analytics Infrastructure Model 22     Summary 33 Chapter 3 Understanding Networking Data Sources 35     Planes of Operation on IT Networks 36         Review of the Planes 40     Data and the Planes of Operation 42         Planes Data Examples 44         A Wider Rabbit Hole 49         A Deeper Rabbit Hole 51     Summary 53 Chapter 4 Accessing Data from Network Components 55     Methods of Networking Data Access 55         Pull Data Availability 57         Push Data Availability 61         Control Plane Data 67         Data Plane Traffic Capture 68         Packet Data 70         Other Data Access Methods 74     Data Types and Measurement Considerations 76         Numbers and Text 77         Data Structure 82         Data Manipulation 84         Other Data Considerations 87         External Data for Context 89     Data Transport Methods 89         Transport Considerations for Network Data Sources 90     Summary 96 Chapter 5 Mental Models and Cognitive Bias 97     Changing How You Think 98     Domain Expertise, Mental Models, and Intuition 99         Mental Models 99         Daniel Kahneman’s System 1 and System 2 102         Intuition 103     Opening Your Mind to Cognitive Bias 104         Changing Perspective, Using Bias for Good 105         Your Bias and Your Solutions 106         How You Think: Anchoring, Focalism, Narrative Fallacy, Framing, and Priming 107         How Others Think: Mirroring 110         What Just Happened? Availability, Recency, Correlation, Clustering, and Illusion of Truth 111         Enter the Boss: HIPPO and Authority Bias 113         What You Know: Confirmation, Expectation, Ambiguity, Context, and Frequency Illusion 114         What You Don’t Know: Base Rates, Small Numbers, Group Attribution, and Survivorship 117         Your Skills and Expertise: Curse of Knowledge, Group Bias, and Dunning-Kruger 119         We Don’t Need a New System: IKEA, Not Invented Here, Pro-Innovation, Endowment, Status Quo, Sunk Cost, Zero Price, and Empathy 121         I Knew It Would Happen: Hindsight, Halo Effect, and Outcome Bias 123     Summary 124 Chapter 6 Innovative Thinking Techniques 127     Acting Like an Innovator and Mindfulness 128         Innovation Tips and Techniques 129     Developing Analytics for Your Company 140         Defocusing, Breaking Anchors, and Unpriming 140         Lean Thinking 142         Cognitive Trickery 143         Quick Innovation Wins 143     Summary 144 Chapter 7 Analytics Use Cases and the Intuition Behind Them 147     Analytics Definitions 150     How to Use the Information from This Chapter 151         Priming and Framing Effects 151         Analytics Rube Goldberg Machines 151     Popular Analytics Use Cases 152         Machine Learning and Statistics Use Cases 153         Common IT Analytics Use Cases 170         Broadly Applicable Use Cases 199         Some Final Notes on Use Cases 214     Summary 214 Chapter 8 Analytics Algorithms and the Intuition Behind Them 217     About the Algorithms 217         Algorithms and Assumptions 218         Additional Background 219     Data and Statistics 221         Statistics 221         Correlation 224         Longitudinal Data 225         ANOVA 227         Probability 228         Bayes’ Theorem 228         Feature Selection 230         Data-Encoding Methods 232         Dimensionality Reduction 233     Unsupervised Learning 234         Clustering 234         Association Rules 240         Sequential Pattern Mining 243         Collaborative Filtering 244     Supervised Learning 246         Regression Analysis 246         Classification Algorithms 248         Decision Trees 249         Random Forest 250         Gradient Boosting Methods 251         Neural Networks 252         Support Vector Machines 258         Time Series Analysis 259     Text and Document Analysis 262         Natural Language Processing (NLP) 262         Information Retrieval 263         Topic Modeling 265         Sentiment Analysis 266     Other Analytics Concepts 267         Artificial Intelligence 267         Confusion Matrix and Contingency Tables 267         Cumulative Gains and Lift 269         Simulation 271     Summary 271 Chapter 9 Building Analytics Use Cases 273     Designing Your Analytics Solutions 274     Using the Analytics Infrastructure Model 275     About the Upcoming Use Cases 276         The Data 276         The Data Science 278         The Code 280     Operationalizing Solutions as Use Cases 281         Understanding and Designing Workflows 282     Tips for Setting Up an Environment to Do Your Own Analysis 282     Summary 284 Chapter 10 Developing Real Use Cases: The Power of Statistics 285     Loading and Exploring Data 286     Base Rate Statistics for Platform Crashes 288     Base Rate Statistics for Software Crashes 299     ANOVA 305     Data Transformation 310         Tests for Normality 311         Examining Variance 313     Statistical Anomaly Detection 318     Summary 321 Chapter 11 Developing Real Use Cases: Network Infrastructure Analytics 323     Human DNA and Fingerprinting 324     Building Search Capability 325         Loading Data and Setting Up the Environment 325         Encoding Data for Algorithmic Use 328         Search Challenges and Solutions 331     Other Uses of Encoded Data 336     Dimensionality Reduction 337     Data Visualization 340     K-Means Clustering 344     Machine Learning Guided Troubleshooting 350     Summary 353 Chapter 12 Developing Real Use Cases: Control Plane Analytics Using Syslog Telemetry 355     Data for This Chapter 356     OSPF Routing Protocols 357     Non-Machine Learning Log Analysis Using pandas 357         Noise Reduction 360         Finding the Hotspots 362     Machine Learning—Based Log Evaluation 366         Data Visualization 367         Cleaning and Encoding Data 369         Clustering 373         More Data Visualization 375         Transaction Analysis 379     Task List 386     Summary 387 Chapter 13 Developing Real Use Cases: Data Plane Analytics 389     The Data 390     SME Analysis 394     SME Port Clustering 407     Machine Learning: Creating Full Port Profiles 413     Machine Learning: Creating Source Port Profiles 419     Asset Discovery 422     Investigation Task List 423     Summary 424 Chapter 14 Cisco Analytics 425     Architecture and Advisory Services for Analytics 426     Stealthwatch 427     Digital Network Architecture (DNA) 428     AppDynamics 428     Tetration 430     Crosswork Automation 431     IoT Analytics 432     Analytics Platforms and Partnerships 433     Cisco Open Source Platform 433     Summary 434 Chapter 15 Book Summary 435     Analytics Introduction and Methodology 436     All About Networking Data 438     Using Bias and Innovation to Discover Solutions 439     Analytics Use Cases and Algorithms 439     Building Real Analytics Use Cases 440     Cisco Services and Solutions 442     In Closing 442 Appendix A Function for Parsing Packets from pcap Files 443 9781587145131, TOC, 9/19/18