Achieve best-in-class metrics and get more from your data with JMP JMP Connections is the small- and medium-sized business owner's guide to exceeding customer expectations by getting more out of your data using JMP. Uniquely bifunctional, this book is divided into two parts: the first half of the book shows you what JMP can do for you. You'll discover how to wring every last drop of insight out of your data, and let JMP parse reams of raw numbers into actionable insight that leads to better strategic decisions. You'll also discover why it works so well; clear explanations break down the Connectivity platform and metrics in business terms to demystify data analysis and JMP while giving you a macro view of the benefits that come from optimal implementation. The second half of the book is for your technical team, demonstrating how to implement specific solutions relating to data set development and data virtualization. In the end, your organization reduces Full Time Equivalents while increasing productivity and competitiveness. JMP is a powerful tool for business, but many organizations aren't even scratching the surface of what their data can do for them. This book provides the information and technical guidance your business needs to achieve more. * Learn what a JMP Connectivity Platform can do for your business * Understand Metrics-on-Demand, Real-Time Metrics, and their implementation * Delve into technical implementation with information on configuration and management, version control, data visualization, and more * Make better business decisions by getting more and better information from your data Business leadership relies on good information to make good business decisions--but what if you could increase the quality of the information you receive, while getting more of what you want to know and less of what you don't need to know? How would that affect strategy, operations, customer experience, and other critical areas? JMP can help with that, and JMP Connections provides real, actionable guidance on getting more out of JMP.
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List of Figures
1 Generalized Context for Decision Process Improvement
1.1 SITUATIONAL ASSESSMENT (CURRENT STATE)
1.2 PROBLEM STATEMENT
1.3 VISUALIZING STATE TRANSITION
1.4 METRICS ON-DEMAND
2 Real-Time Metrics Business Case
2.1 PROJECT DESCRIPTION AND OBJECTIVES—A CASE STUDY
2.2 SOLUTION DESCRIPTION
2.3 COST AND BENEFIT ANALYSIS
2.4 FINANCIAL ASSESSMENT
2.5 IMPLEMENTATION TIMELINE
2.6 CRITICAL ASSUMPTIONS AND RISK ASSESSMENT
2.7 RECOMMENDATIONS: TRANSMIGRATE THE ENTERPRISE
3 Technical Details and Practical Implementation
3.1 HARDWARE FOUNDATIONS
3.2 SOLUTION STACK
3.3 INTEGRATION OF HARDWARE AND SOFTWARE INFRASTRUCTURE
3.4 BUILD OUT
3.5 THE CONSTRUCTION OF A METRIC
3.6 METRIC CASE STUDY
4 Harvesting Benefits and Extensibility
4.1 BENEFITS EXAMPLE
4.3 CONFIGURATION MANAGEMENT VERSION CONTROL
5 So What About a Bad Economy?
5.1 OVERACHIEVEMENT—DATA VIRTUALIZATION
CONNECTION AS THE UNIVERSAL SERVER
5.3 WELL-FORMED DATA
5.4 LINKED DATA
6 Decision Streams
7 Delivery and Presentations
7.1 PUSH VERSUS PULL DELIVERY
7.3 DIY, BUT LEAVE THE POOR BI PERSON ALONE!
7.4 ADVANCED PRESENTATION METHOD
8 In Closing (As-Built)
APPENDIX A: Server-Side PHP Code
APPENDIX B: JMP
JSL Time Constant Learning Curve Script
APPENDIX C: JMP
GUI User Interface Code Example
APPENDIX D: Resource Description Framework File Example
APPENDIX E: Sample Hardware Requirements
APPENDIX F: Early Warning Deliverable
APPENDIX G: JMP
PRO Connections: The Transversality of the Capability Maturity Model
G.1 TANGENTIAL CONCEPT
G.2 TRANSVERSAL CONCEPT
G.3 UNIVARIATE TO MULTIVARIATE PROCESS CONTROL
G.5 TRANSVERSAL MATURITY SPACE IN RELATION TO JMP
End User License Agreement
Figure 1.1 JMP
CONNECTIONS Capability Maturity Model Levels 0 and 1
Figure 1.2 JMP
CONNECTIONS Capability Maturity Model Levels 2 and 3
Figure 1.3 JMP
CONNECTIONS Capability Maturity Reference Model
Figure 1.4 Maturity through Level State Transitions
Figure 1.5 Common Cycle Time to Build and Publish a Weekly Dashboard
Figure 1.6 Transition from Level 1 to Level 2
Figure 1.7 Achieving On-Demand Capability
Figure 2.1 Business Case Basics
Figure 2.2 Dashboard 
Figure 2.3 Balanced Scorecard
Figure 2.4 Graphical Ontology Example: Human Resources
Figure 2.5 Micro-warehouse Value Tool
Figure 2.6 Generating Value—Leveraged Knowledge Asset
Figure 2.7 KPI Development Timeline
Figure 2.8 BRF Risk Assessment
Figure 2.9 Example—Large Application Spreadsheet Type
Figure 2.10 Financial Regulation Documents  
Figure 3.1 Conceptual Illustrations of the JMP
Figure 3.2 Disk Operating System (DOS) Command Prompt
Figure 3.3 Definition of the Time-Constant Learning Curve Function
Figure 3.4 Total Productive Maintenance Conceptual Diagram
Figure 3.5 Sequence Column and OEE Values Column in a JMP
Figure 3.6 Learning Curve Formula Specification
Figure 3.7 Configuration for Gauss-Newton Iteration
Figure 3.8 Nonlinear Fit Program
Figure 3.9 Nonlinear Fit Output
Figure 3.10 Nonlinear Fit Results
Figure 3.11 Learning Curve Formula for Estimating OEE
Figure 3.12 Estimating OEE Formula
Figure 3.13 Setting Up to Display the Control Chart
Figure 3.14 Control Chart with Forecasting Errors Out of Criteria
Figure 5.1 Data Virtualization Server
Figure 5.2 Federated Virtualization Engine
Figure 5.3 Federated Concept 
Figure 5.4 Small-Scale Cookie Batch
Figure 5.5 Recipe Ingredient Metrics
Figure 5.6 Hierarchical Data Format
Figure 5.7 Cookie Data in a Table Format
Figure 7.1 Senders push and receivers pull
Figure 7.2 Junctured KPI Thought Process for Connecting the Dots
Figure 7.3 Information Linkage between Parent–Child KPIs
Figure 7.4 Quick Response Code
Figure 7.5 JMP
GUI Window with JMP
Figure 7.6 Office Temperature Study
Figure 7.7 Chocolate velvet anyone?
Figure 8.1 Public Domain: William Thomson Oil Painting
Figure G.1 Tangential View
Figure G.2 Transversal Maturity Model View
Figure G.3 Office Temperature Study
Table of Contents
The Wiley & SAS Business Series presents books that help senior-level managers with their critical management decisions.
Titles in the Wiley & SAS Business Series include:
Analytics: The Agile Way
by Phil Simon
Analytics in a Big Data World: The Essential Guide to Data Science and Its Applications by
A Practical Guide to Analytics for Governments: Using Big Data for Good
by Marie Lowman
Bank Fraud: Using Technology to Combat Losses
by Revathi Subramanian
Big Data Analytics: Turning Big Data into Big Money
by Frank Ohlhorst
Big Data, Big Innovation: Enabling Competitive Differentiation through Business Analytics
by Evan Stubbs
Business Analytics for Customer Intelligence
by Gert Laursen
Business Intelligence Applied: Implementing an Effective Information and Communications Technology Infrastructure
by Michael Gendron
Business Intelligence and the Cloud: Strategic Implementation Guide
by Michael S. Gendron
Business Transformation: A Roadmap for Maximizing Organizational Insights
by Aiman Zeid
Connecting Organizational Silos: Taking Knowledge Flow Management to the Next Level with Social Media
by Frank Leistner
Data-Driven Healthcare: How Analytics and BI Are Transforming the Industry
by Laura Madsen
Delivering Business Analytics: Practical Guidelines for Best Practice
by Evan Stubbs
Demand-Driven Forecasting: A Structured Approach to Forecasting, Second Edition
by Charles Chase
Demand-Driven Inventory Optimization and Replenishment: Creating a More Efficient Supply Chain
by Robert A. Davis
Developing Human Capital: Using Analytics to Plan and Optimize Your Learning and Development Investments
by Gene Pease, Barbara Beresford, and Lew Walker
The Executive’s Guide to Enterprise Social Media Strategy: How Social Networks Are Radically Transforming Your Business
by David Thomas and Mike Barlow
Economic and Business Forecasting: Analyzing and Interpreting Econometric Results
by John Silvia, Azhar Iqbal, Kaylyn Swankoski, Sarah Watt, and Sam Bullard
Economic Modeling in the Post Great Recession Era: Incomplete Data, Imperfect Markets
by John Silvia, Azhar Iqbal, and Sarah Watt House
Enhance Oil & Gas Exploration with Data Driven Geophysical and Petrophysical Models
by Keith Holdaway and Duncan Irving
Foreign Currency Financial Reporting from Euros to Yen to Yuan: A Guide to Fundamental Concepts and Practical Applications
by Robert Rowan
Harness Oil and Gas Big Data with Analytics: Optimize Exploration and Production with Data Driven Models
by Keith Holdaway
Health Analytics: Gaining the Insights to Transform Health Care
by Jason Burke
Heuristics in Analytics: A Practical Perspective of What Influences Our Analytical World
by Carlos Andre Reis Pinheiro and Fiona McNeill
Human Capital Analytics: How to Harness the Potential of Your Organization’s Greatest Asset
by Gene Pease, Boyce Byerly, and Jac Fitz-enz
Implement, Improve and Expand Your Statewide Longitudinal Data System: Creating a Culture of Data in Education
by Jamie McQuiggan and Armistead Sapp
Intelligent Credit Scoring: Building and Implementing Better Credit Risk Scorecards, Second Edition,
by Naeem Siddiqi
by John Wubbel
Killer Analytics: Top 20 Metrics Missing from your Balance Sheet
by Mark Brown
Machine Learning for Marketers: Hold the Math
by Jim Sterne
On-Camera Coach: Tools and Techniques for Business Professionals in a Video-Driven World
by Karin Reed
Predictive Analytics for Human Resources
by Jac Fitz-enz and John Mattox II
Predictive Business Analytics: Forward-Looking Capabilities to Improve Business Performance
by Lawrence Maisel and Gary Cokins
Profit Driven Business Analytics: A Practitioner’s Guide to Transforming Big Data into Added Value
by Wouter Verbeke, Cristian Bravo, and Bart Baesens
Retail Analytics: The Secret Weapon
by Emmett Cox
Social Network Analysis in Telecommunications
by Carlos Andre Reis Pinheiro
Statistical Thinking: Improving Business Performance, Second Edition
by Roger W. Hoerl and Ronald D. Snee
Strategies in Biomedical Data Science: Driving Force for Innovation
by Jay Etchings
Style & Statistic: The Art of Retail Analytics
by Brittany Bullard
Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics
by Bill Franks
The Analytic Hospitality Executive
by Kelly A. McGuire
The Value of Business Analytics: Identifying the Path to Profitability
by Evan Stubbs
The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions
by Phil Simon
Too Big to Ignore: The Business Case for Big Data
by Phil Simon
Using Big Data Analytics: Turning Big Data into Big Money
by Jared Dean
Win with Advanced Business Analytics: Creating Business Value from Your Data
by Jean Paul Isson and Jesse Harriott
For more information on any of the above titles, please visit www.wiley.com.
Copyright © 2018 by John Wiley & Sons, Inc. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750–8400, fax (978) 646–8600, or on the Web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748–6011, fax (201) 748–6008, or online at www.wiley.com/go/permissions.
Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
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Library of Congress Cataloging-in-Publication Data is available:
ISBN 9781119447757 (Hardcover)ISBN 9781119453741 (ePDF)ISBN 9781119453727 (ePub)
Cover Design: WileyCover Image: © Thirteen/Shutterstock
I dedicate this book to my mother and father, my wife Rosmary Wubbel, and son Leslie Wubbel for all their love and support. I especially want to thank three strong ladies, sisters to me for their guidance, care, and constructive comments. Thanks to my best friend and son Leslie for his reviews and the many conversations and topic discussions that helped make this book possible. Thanks to my nieces, especially Catherine Mintmire, for producing the graphic art work. And finally, too numerous to mention the many business friends and mentors in software engineering and data science fields including Philip Douglas Brown for his insights and perspectives on making the connections in data for advancing analytical capabilities.
CONNECTIONS Capability Maturity Model Levels 0 and 1
CONNECTIONS Capability Maturity Model Levels 2 and 3
CONNECTIONS Capability Maturity Reference Model
Maturity through Level State Transitions
Common Cycle Time to Build and Publish a Weekly Dashboard
Transition from Level 1 to Level 2
Achieving On-Demand Capability
Business Case Basics
Graphical Ontology Example: Human Resources
Micro-warehouse Value Tool
Generating Value—Leveraged Knowledge Asset
KPI Development Timeline
BRF Risk Assessment
Example—Large Application Spreadsheet Type
Financial Regulation Documents
Conceptual Illustrations of the JMP
Disk Operating System (DOS) Command Prompt
Definition of the Time-Constant Learning Curve Function
Total Productive Maintenance Conceptual Diagram
Sequence Column and OEE Values Column in a JMP
Learning Curve Formula Specification
Configuration for Gauss-Newton Iteration
Nonlinear Fit Program
Nonlinear Fit Output
Nonlinear Fit Results
Learning Curve Formula for Estimating OEE
Estimating OEE Formula
Setting Up to Display the Control Chart
Control Chart with Forecasting Errors Out of Criteria
Data Virtualization Server
Federated Virtualization Engine
Small-Scale Cookie Batch
Recipe Ingredient Metrics
Hierarchical Data Format
Cookie Data in a Table Format
Senders push and receivers pull
Junctured KPI Thought Process for Connecting the Dots
Information Linkage between Parent–Child KPIs
Quick Response Code
GUI Window with JMP
Office Temperature Study
Chocolate velvet anyone?
Public Domain: William Thomson Oil Painting
Transversal Maturity Model View
Office Temperature Study
JMP® Pro® is the centerpiece software that is capable of saving your business in difficult economic times. JMP® CONNECTIONS (herein referred to as “the Model Platform”)1 illustrates the technical means and financial variables that will leverage peak productivity. JMP® CONNECTIONS provides a clear pathway toward quickly generating actionable intelligence from your organization's raw data for optimal decision‐making purposes. The prime reason for describing a CONNECTIONS platform is the fact that JMP® Pro® enables computational in‐memory statistical analytical capability second to none in the business, engineering, and scientific world. When a person is able to make a connection, what most often happens is a decision and this fact should generate broad discussion as well as potentially collective performance improvements for groups, teams, or large organizations.
More than ever before, metrics are playing the most important role in the conduct of a business on the competitive stage today. In typical fashion, software comes with a wealth of features, functions, and extensibility. In many cases several software packages may be required to satisfy or facilitate common business functions in support of the operation. Office suites come to mind as an example.
When business conditions are challenging or when strategic goals continually set the bar higher for better performance, innovation is a key factor toward contributing to results that exceed expectations. Consequently, the task of producing metrics must become an innovation as well. As a result, one must visualize a model of capability when it comes to designing, developing, generating, and reporting within your own company, division, or all the way down to the department level. Given the nature of today's office suites, metrics tend to be produced once a week, once a month, or quarterly with each having a cycle time to completion. JMP® CONNECTIONS suggests a model, or innovation, that eliminates cycle time so that there is a reduction in full‐time equivalents (FTEs) for metric production purposes whereby the metrics produced are real‐time or, in other words, “metrics on‐demand.”
The key to understanding how this type of innovation can lessen tough economic times is through improved business decision making. It is innovative by differentiating between cycle time methods versus metrics that are available with either the latest available data or real‐time aggregate raw data material, transformed into usable knowledge.
JMP® Pro® is the central hub and can become your command and control center for managing and executing a business operating system on many varied scales. The journey in building a real‐time metric production system is simplified through a series of capability maturity steps. Pooling the data from disparate silos starts with data aggregation and integration forming a repository. Mining the repository for conducting statistical analysis, the journey transitions through three levels leading to a final maturity level of predictive modeling and analytic goals. The goals are supportive of the key performance indicators required by the strategic objectives set forth for proper performance management. This book will not only discuss the model but help an organization implement the model with their own people.
The Model Platform describes a Capability Maturity Model supporting the development of Business Intelligence Competency Center for yielding knowledge from data for making optimal decisions in a business enterprise.
Situational Assessment (current state)
Visualizing State Transition
DECISION PROCESS IMPROVEMENT FOR CORPORATE PERFORMANCE MANAGEMENT
Business is making clear that to stay competitive in the market we need to make decisions quickly and often with disparate data sets. JMP® CONNECTIONS should be viewed as a business-oriented data discovery tool and is not an information technology (IT) or enterprise SAP®1 Centric model because as is so often the case, data sets are not under the control of the IT department. Data may reside in silos, dozens of spreadsheets, or proprietary database applications. Thus, we can best describe this exercise as the “decision process improvement.” If we can improve on the way metrics are produced, it can directly improve the timely implementation of actual decisions for corporate performance management.
The Holy Grail of the Information Age particularly in the information technology (IT) shop is the notion of data integration and interoperability. The Institute of Electrical and Electronics Engineers defines interoperability as:
The ability of two or more systems or components to exchange information and to use the information that has been exchanged.
Unfortunately, interoperability has never been entirely achieved across a large enterprise before.
However, in support of staying competitive, the popular business press and IT periodicals have been pushing “business intelligence” (BI). Business intelligence is a broad category of applications and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions.
As postulated in the Preface, a tough economy implies a propensity to cut back on expenditures across a wide cross section of the enterprise that may also include BI software acquisitions. Utilizing JMP® Pro®, the following pages will show precisely how the development of state-of-the-art metrics can be facilitated without the need for a major capital expenditure (CAPEX) project.
ADVANCEMENT IN METRICS FOR BUSINESS AUGMENTATION
Before describing the common state of affairs that may be typical from small to large businesses, a framework for visualizing capability maturity with regard to the development of metrics and their use is outlined in Figure 1.1.
Figure 1.1 JMP® CONNECTIONS Capability Maturity Model Levels 0 and 1
The lowest level of capability maturity (Level 0) would be a business or organization that may not have an IT department. Most of the management and reporting of business data is done using spreadsheets and perhaps the facilities of software office suites/applications for presentations. Reporting may be ad hoc or sporadic due to such factors as data that is not readily in a form for use in conducting statistical analysis when required. Companies often have so much data that they realize knowledge is locked up; however, they have no practical, inexpensive way to develop and utilize it.
The first level of maturity (Level 1) is where companies produce dashboards, scorecards, and KPIs on a regular basis. Perhaps on an annual basis, metrics are reviewed for relevance as needs change over time. Metrics retained may be refined and presentation and timely delivery mechanisms are
depending on who is to be receiving them and at what levels of the enterprise they are to be receiving and using them. Publishing BI tools like dashboards (DBs) and scorecards (SCs) have measurable cycle times.
The second level of maturity (Level 2) for an organization would be a realization that some subset of deliverable metrics could be converted to metrics “on-demand.” In identifying these on-demand metrics, the cycle time to generate or refresh a set of deliverable dashboards would be completely eliminated. (See
Figure 1.2 JMP® CONNECTIONS Capability Maturity Model Levels 2 and 3
The third and highest level of maturity (Level 3) is a two-part configuration. (See
Level 3, Part 1
Eliminate cycle time to create on-demand metrics resulting in reduction in FTEs.
Level 3, Part 2
Human capital resource reallocation for:
Performing advanced statistical analysis
Predictive analytics and modeling
Level 3, Part 1, maturity level, focuses on reducing the time it takes (cycle time) to produce the metrics on a scheduled basis, thus in turn reducing the number of FTEs required to produce those metrics. One FTE required to update a dashboard every week does not leave enough time for any other production tasks for metrics. The amount of time for an FTE is finite. As hours are freed up, other knowledge within the data sets can be developed and utilized. Achieving the second level of maturity leads into Level 3, Part 2 because now predictive analytics and the full power of JMP® Pro can be leveraged perhaps without the addition of more FTEs. The graphic view in Figure 1.3 summarizes the reference model for maturity capability for business intelligence metrics.
Figure 1.3 JMP® CONNECTIONS Capability Maturity Reference Model
The development of JMP® CONNECTIONS is applicable to literally every type of business. All examples cited in this book are totally fictional and for illustrative purposes, which can be adapted to any business. The examples are generic in the sense that the common fuel crucial to business execution is the enterprise data, mature knowledge assets, and performance indicators across the spectrum of organizations that desire optimal results. In many circumstances, particularly in larger firms, one expects to find whatever data they need on the large enterprise database applications. In fact, the information is out there but its access is less than ideal. It may in no way be in a format to provide any statistical analysis capability. It lacks a certain agility for manipulative processes for generating BI tools or data. It is a “what you see is what you get” due to the hard-coded requirements built into the application. Consequently, a query returned is often a table of data or records that do not necessarily communicate or impart knowledge to the recipient. Something extra needs to be done.
Additionally, one would think that, especially within technology firms or scientific and engineering firms, data management would be state of the art. For many and perhaps for a majority, business is conducted using spreadsheets, small desktop database applications, web applications, text files, and sticky notes. In fact, the proliferation of spreadsheets from one year to the next with no sense of version control is prevalent where many sheets act as placeholders for data rather than actually doing any computations or analysis.
Given the standard corporate desktop environment, when a set of metrics are required, they are likely prepared using a combination of the office suite applications. These may include the word processor, spreadsheet, and presentation software applications. A chart or graph may be present with some annotation explaining the meaning of the numbers and is the bare minimum or Level 0 of maturity for making metrics. Thus, it is useful to point out here exactly what types of BI solutions exist.
Executive scorecards and dashboards
Online Analytical Processing (OLAP) analysis
Ad hoc reporting
Each of the BI solutions has a data analysis ingredient or function that derives the reported out metric for a particular BI solution. While features and functions may be alike, what sets these apart is how they are applied to support decision making.
To be more precise in thinking about analytic metrics, there are three areas of data analysis derived from data science, information technology, and business applications that can be categorized as follows:
* PREDICTIVE (Forecasting)
* DESCRIPTIVE (Business Intelligence and Data Mining)
* PRESCRIPTIVE (Modeling, Optimization, and Simulation)
Without efficient sharing of operational business intelligence, a company is going to suffer breakdowns from small to large, be unable to properly grow, and could even be flirting with massive disaster. A small issue, for example, can escalate into something very large very quickly if there's not good sharing of business intelligence. No operational intelligence, or incorrect intelligence, means that a company will create and execute strategies and plans (i.e., make decisions) that could inadvertently be bad for the company.
Beyond the Level 3 capability maturity, one may begin to get a sense about the concept of a BI Competency Center. A competency center is inclusive of all three areas when it comes to data analysis—PREDICTIVE, DESCRIPTIVE, and PRESCRIPTIVE—with respect to applying BI solutions to support various decision-making units within the enterprise. The competency center concept also can act as a facilitator for efficiently sharing operational business intelligence.
Even though the word predict is embedded in the notion of predictive analytics with inference toward trending and forecasting, the proper application of predictive analytics can also provide an essential competitive edge with regard to what is going to happen in the next minute, hour, or day. Consequently, our concepts of on-demand and real-time metrics are synonymous. An example of metrics on-demand is in Section 3.6, on page 80, Metric Case Study. A preview here of the case study illustrates how predictive analytics can be used by the maintenance department for real-time business service management (BSM). What is BSM? Simply, it means one department that provides services to another within a corporate enterprise. Business service management is an approach used in information technology departments to manage business aligned with IT services. In the case study, BSM is the relationship the maintenance department has with supporting manufacturing in a factory. On the shop floor of a factory, maintenance services are essential for operational efficiency. Alignment for one department makes sense when maintenance is interested in predicting machine part failures or determining the optimal time to do preventative maintenance that should be conducted for minimizing downtime in the factory.
In a Focus Factory3 manufacturing model, real-time metrics are essentially operational business intelligence where information is used on a daily basis to run production. Thus, the word descriptive implies it is the knowledge of the factory's contiguity (i.e., the “state of being”). To determine the cooperation of entities (e.g., persons) with equipment or to monitor the usage of equipment, whether good, bad, or steady state, for example, in a focus factory model, is to know the operational condition or situation for a given period of time.
Performance issues will be directly related to the type of metrics used to support specific areas of the factory. Measures will typically be at a more granular level. Current dashboard and scorecard metrics will reflect the higher level results and aggregate productivity measures.
The prescriptive kinds of metric examples are the types conducted prior to the introduction of a new process—the design of experiment statistical modeling to establish, for example, critical parameters and limits.
While having enumerated the types of BI above, none of these are inexpensive for companies finding themselves in uncertain economic situations. With commercial applications, no one size fits all and few at the retail level in the marketplace for commercial applications understand the level of data integration required. Data integration and its implementation is not generic or available off-the-shelf. Data integration enables interoperability across the enterprise, which is directly related to capability. In other words, the more integrated the data, the better the operability, which translates into a higher level of capability when utilized. With JMP® CONNECTIONS, a lower granularity of data aggregation and subsequent integration is possible. (Granularity means a finely detailed but not necessarily more voluminous amount of data.) Granularity may be more desirable to go from a generalized metric to something more specific. Together, the data integration, data definition/description language (i.e., may use a declarative syntax to define fields and data types), and selection steps using the structured query language (i.e., using a collection of imperative verbs) for the codified business logic, collectively, should strive to produce only the minimal data set required to perform any analysis and the final metric presentation.
At the lowest level of maturity (Level 0), many key performance metrics are produced with a great deal of effort. Effort in effect means the non-automated tasks required from data gathering to finished presentations. From start to finish the cycle time for such efforts is associated with a (Level 0) maturity capability. The non-automated process of making a metric is the manual work done by hand and, when performed repeatedly, the business logic that goes into it is unconsciously executed by the analyst. The manual effort and repeatable business logic required is measured as cycle time in the generation of metrics. In order to reduce the cycle time, the manual work has to be off-loaded to the computer. To eliminate the manual work, the business logic is codified as Structured Query Language statements for retrieving data from the Data Virtualization Server (DVS) so that it can be acted upon by JMP®. The business logic can also be written into JMP® scripts for execution within JMP® prerequisite for any analysis. If the metric is not a one of a kind or ad hoc production and is required periodically by management, the codification thereby reduces or eliminates the cycle time required to produce the metric by doing away with mundane manual tasks. More details regarding the technical aspects will follow in Chapter 2—Real-Time Metrics Business Case.
Even with the best visual presentations, graphical layout and designs offered by the office suites appear acceptable, but they are less than optimal compared to JMP® visualization metrics. Third-party BI applications may require extensive programming to achieve even a partial analytic capacity. Unlike with JMP®
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