Discover the Blueprint for Next Generation AnalyticsJim ParnitzkeAAJ Technologies

November, 2014

Introduction

Jim ParnitzkeBusiness Intelligence and Enterprise Architecture Advisor, Expert, Trusted Partner, and PublisherOver his career he has served in executive, technical, publisher (commercial software), and practice management roles across a wide range of industries.

3

What is new about the next generation?

The five (5) keys for success you need

Strategy and approach (road map development)

• Understanding your current state

• End State - Where do you want to go?

• Gap analysis – uncovering the actionable activities you will need to accomplish success

How to get started

Discussion Topics

4Confidential and Proprietary

What is new about the next generation?

Visual Discovery Tools Quickly and easily visualize and interact with data to gain new insights and make better decisions

High-Performance Analytical Platforms Massively parallel (MPP) databases, ETL work streams

Integrated Master Data Management Clean, high quality reference data

Customer, Product, Contract-Agreement, Location

Mature federated environments

Robust data acquisition and profiling

Closed loop processing Analytic products feed back to OLTP

(continuous, stream-oriented query processing)

Business Services Support (SOA)

5Confidential and Proprietary

Visual Discovery

6Confidential and Proprietary

Visual Discovery - Structured

Source: http://finviz.com/

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Visual Discovery - Unstructured

Source: Wikipedia, National Visualization and Analytics Center, Pacific Northwest Laboratory

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High-Performance – Large Data Analytics

• Large Data – Map/ReduceIntroduced by Google (based on Skeleton Programming Models, proposed by Murray Cole in 1989) to support distributed computing on large data sets

• Large distributed computations as a sequence of distributed operations on data sets

• Harnesses cluster of machines and executes user defined Map/Reduce jobs across the nodes in the cluster

• Computation in two phases:

• map phase

• reduce phase

• Amazon (AWS) EC2 Elastic Map/ReduceAster Data SQL+Map/Reduce

• Open Source Apache Hadoop

• Used for:• Text tokenization, indexing, and search

• Creation of other kinds of data structures (e.g., graphs)

• Data mining and machine learning (clustering, segmentation, association)

• Predictive Model Markup Language (PMML)

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High-Performance – Execution

map(String key, String value):// key: document name// value: document contentsfor each word w in value:EmitIntermediate(w, "1");

reduce(String key, Iterator values):// key: a word// values: a list of countsint result = 0;for each v in values:result += ParseInt(v);Emit(AsString(result));

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High-Performance – Usage

Source: MapReduce: Simplified Data Processing on Large ClustersJeffrey Dean and Sanjay Ghemawat, Google, Inc.

11Confidential and Proprietary

High-Performance Methods

• Decision Trees - Starting point for data exploration. It is primarily a classification algorithm, and works well for predictive modeling of both discrete and continuous attributes. Evaluate how each input attribute in a dataset affects the result of thepredicted attribute.

• Use to find a combination of input attributes and their states to predict the outcome of the predicted attribute.

• Naïve Bayes - Build models used for classification and prediction. Calculate probabilities for each possible state of the input attribute given each state of the predictable attribute. Algorithm supports only discrete (non-continuous) attributes and considers all the input attributes to be independent given the predictable attribute.

• Use the Naïve Bayes algorithm during the initial data exploration phase, and for classification and prediction problems.

• Clustering - Iterative techniques to group records from the dataset into clusters containing similar characteristics.

• Use to explore the data to find relationships and create predictions from the clustering model.

• Association - Based on the a priori algorithm, provides an efficient method for finding N-way correlations within large datasets.

• Use to cycle through the transactions in the database to find which items are most likely to appear together in the transactions of a single user, most often used for market basket analysis.

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High-Performance Methods

• Sequence Clustering - Combine sequence analysis and clustering for data exploration and prediction. The model is sensitive to the order in which events occur, can correlate sequential and non-sequential information.

• Used to perform click stream analysis to analyze the flow of Web site traffic, to identify which pages are most closely related to the sale of a particular product, and to predict which pages are going to be visited next.

• Time Series - Create models that can be used to predict one or more continuous variables (e.g. an equity price). The algorithm bases its prediction solely on the trends derived from the training data used during the creation of the model. Time Series usesan AutoRegression Trees technique, is very easy to use, and generates highly accurate models.

• Use for statistical analysis devoted to time series. Many data mining products now provide techniques such as ARMA, ARIMA, and Box-Jenkins, determine the model's best fit.

• Neural Net - Similar to Decision Trees and Naïve Bayes, is used primarily for data exploration, classification and prediction.

• Use this artificial intelligence technique to explore all possible data relationships. Because it is such a thorough technique, it is the slowest of the three classification algorithms.

13Confidential and Proprietary

Usage Scenarios

Analytical problem Examples Typical algorithms used

Classification

Assign cases to predefined

classes such as "Good" vs "Bad"

for example.

• Credit risk analysis

• Churn analysis

• Customer retention

• Decision Trees

• Naïve Bayes

• Neural Nets

Segmentation

Develop a taxonomy for

grouping similar cases

• Customer profile analysis

• Mailing campaign

• Clustering

• Sequence Clustering

Association

Advanced counting for

correlations

• Market basket analysis

• Advanced data exploration

• Decision Trees

• Association Rules

Time Series Forecasting

Predict the future

• Forecast sales

• Predict stock prices

• Time Series

Prediction

Predict a value for a new case

(such as a new customer) based

on values for similar cases (such

as existing customers)

• Quote insurance rates

• Predict customer income

• Predict temperature

• All

Deviation analysis

Discover how a case or segment

differs from others

• Credit card fraud detection

• Network infusion analysis

• All

14Confidential and Proprietary

Integrated Master Data Management

Customer

Table (0)Table

(0)Table

(1) Table

(2)

Entity

(1)Entity (0)

Product

Table (0)Table

(0)Table

(1) Table

(2)

Entity

(1)Entity (0)

Supplier

Table (0)Table

(0)Table

(1) Table

(2)

Entity

(1)Entity (0)

Contract

Table (0)Table

(0)Table

(1) Table

(2)

Entity

(1)Entity (0)

Location

Table (0)Table

(0)Table

(1) Table

(2)

Entity

(1)Entity (0)

Right Party (Customer, Suppler) Right Product (Authorized) Right Terms and Conditions (Contract) Right Product Bundle (Components) Right Price Right Cross Reference Right Hierarchy Right Location (variant)

Sales Transaction

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Robust Data Acquisition and Profiling

Migration Staging

Table Scan

Attribute Scan

Statistical Analysis

Reporting

Integrated Data Store

Common Data Model

Detailed Data

80/20 Rule Applied

Cumulative RC Build

Data Profiling

Metadata Management

Data Integration

Table (0) Table (0)Table (1)

Table (2)

Entity (1)Entity (0)

Table (0) Table (0)Table (1)

Table (2)

Entity (1)Entity (0)

Transformation

Execution

Data Re-engineering

Production Target

Entity (1)Entity (0)

Test Release Candidate

Entity (1)Entity (0)

Reference

Non-Sourced

Data

Release

Candidate

Promotion

Test Results

and quality

index

Data Sources

Current-State

16Confidential and Proprietary

Analytic Platform – Closed Loop Processing

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Services can be Java, C++, .Net

Content-Based Routing

Shared state to enable stateful services

In Memory Data Grid

Object Space Computational Model

Capture Manage ActAnalyze

Conceptual Design

Replication

Distribution

Mo

bil

e W

ork

Fo

rce

Reporting Services

oneDOT Operational Data

Store

Access and Analysis

Event Notification

Management Reports

Closed Loop Processing Signal

Authorization Authentication Encryption and Masking

Activity Report Cubes

Subscription Registry

Systems Monitoring and Service Management

Executive Dashboard

oneDOT Data Warehouse

Extract, Transform and Load to Analytic

Publish and Distribute

Enterprise Security Framework

Data shipped to external Decision Support system

Database and Network Administration

A handheld application

captures and transmits dots

(GPS coordinates) to collect

the fundamental building blocks

for information products.

Applications auto-generate

alerts, messages,

checklists, events, and more

to the field force member’s

smart phone which results in

a truly proactive, location

intelligent mobile field force.

Operations Center

provides professional

reliable value-added

data base and

network management.

Work force takes

action on up to date,

reliable information.

Optimize the information

about the dots. Deliver

analytic products to define,

build, and operate an

effective organization.

Event Broker

Raw Data Capture

Location and Mapping Framework

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Analytic Platform – Example Use

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Analytic Platform – Results

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The five (5) keys for success

People, not Technology

Process, not Project

Value, not Cost

Insight, not Data (Data ≠ Information)

Think out of the box

1

2

3

4

5

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The five (5) keys for successUnderstanding what counts

People - Insight and Visibility

Customer support rep

“I need better access to information to make better decisions on cross-sell and up-sell opportunities.”

“I need to have the right demographic information so I can better target my opportunity prospecting.”

Sales rep

Chief executive officer (CEO)

“I need to know that the people in my organization have the right goals in place to understand and execute on the strategic initiatives of the company.”

VP, operations

“I need better visibility into my cost of operations so I can target specific cost reduction opportunities that won’t have a negative impact.”

Chief financial officer (CFO)

“I need to improve our analytics capabilities so we can understand our current business performance and do a better job of planning for the future.”

Source: “Creating the Office of Strategy Management” by Robert Kaplan and David P. Norton, Harvard Business School, April 2005

VP, sales and marketing

“I need better visibility into our pipeline performance so I can focus on deals that help me grow business with my most profitable customers.”

Gross Margin?Profit Margin (EBIT, EBITDA, NOPAT)?Operating Expenses?Asset Turnover?Working Capital Management?GMROI?Actual Employee Turnover Costs?

Debt to Asset Ratios?Debt to Capitalization?Interest Coverage?Burden Coverage?Cash Flow? Cash Flow / Share?Return on Total Net Worth?Return on Common Equity?

Earnings Per Share?Share Prices Appreciation?Dividends Per Share?Price to Earning Ratio?Market Book Value?Value Drivers?

Our Market Share?Effectiveness of Campaigns?Selling Together?Not Selling Well?Selling in What Market?

My Customers?Their Competition?My Next Sales Opportunity?What is Selling Now?

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People – What Is Needed

• Address the human-side systematically

• Obtain top level support

• Involve every layer

• Clearly communicate the business case

• Create ownership, install change agents

• Clearly and continually communicate the message

• Assess the cultural impacts and issues

• Prepare for the unexpected

• Define WIIFM (What's in it for me?) for each role or individual

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Distinguish casual users from power users1

Source: The Data Warehousing Institute, Wayne Erickson, February 2009

Monitor, Analyze, and Drill to detail

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Match Profiles to Tool Categories

Analytic Profile % Users Users Type of Activity Type of User Optimal Tool Category Important Functionality

Miners 3% 3 Creating Statistical

Models

Statistician Data Mining Tools (neural networks,

decision trees, statistical analysis, etc.)

Workbench that supports model

development lifecycle: Database

integration

Developers 7% 7 Create reports,

queries, OLAP cubes,

applications

Programmer, Systems

Analyst, Technology

Savvy Business Analyst

Ad hoc query tools, reports writers, OLAP

tools, development tools

Query complexity, rapid development

and testing, source data access, report

broad- and select-casting

Explorers 10% 10 Analyze large

amounts of data or

data with lots of

attributes in an

interactive,

exploratory fashion

Business Analyst,

"Power Users"

Relational OLAP Performance scalability, analytical

breadth and depth

Planners 20% 20 Perform "what if"

analyses to create

budgets or planning

assumptions in order

to run a department

Managers Spreadsheets, Desktop OLAP, Multi-

Dimensional OLAP, Custom Applications

Read/write capability, collaboration,

integration with Excel

Reviewers 40% 40 Review a consistent

set of data on a

consistent basis, and

drill down to more

detail only when

something in awry in

the data

Managers, Executives,

Customers, and

Suppliers

Interactive reports (parameterized

formatted reports, pivot tables, or OLAP

views that users filter against)

Exception alerts, personalized delivery,

ease of use

Gatherers 20% 20 Retrieve a specific

piece of data in near

real-time to perform a

specific business

process

Customer Service

Representatives,

Administrative Workers

Custom applications, etc. Ease of use, sub-second response

times, reliability

1

26 Confidential and Proprietary

Think Process, Not Projects

Process, Role, and UML Modeling

AS-IS, TO-BE State Models

Optimize Key Processes

Capture Business Rules

Produce Consistent, Repeatable Results

Focus on Value-Added Activity

STRATEGIC INTENT

HISTORICALDATA

ANALYSIS

DEMANDCREATION

BUSINESSPLANNING

PORTFOLIO PLANNING

PROCESS OPTIMIZATION

DEPLOYMENTPLANNING

EXECUTION

RESULTS CAPTURED

Flow/State Maps

Process Simulation

Reverse Goal Seeking

Value Chain Analysis

Protoytpe Evaluation

Financial Results

Operating Performance Metrics

Capacity Utilization

Intangibiles - (HR, Workplace)

Incentive and Goal Compliance

Other Key Performance Indicators

Kaplan and Norton G3 Scorecard

Benchmark to Best Practices

Earned Value Analysis

Activity Based Costing

BEM (EFQM) Model Conformance

Expand the ability of the organization to execute

Add value to meet customer expectations

Improve organization’s ability to respond quickly

Ensure process quality

Attract / Retain Quality Employees

Improve Communications

Enhance the workplace environment

Process Automation

Workflow

Business Rules enforce Policy

Business Activity Monitoring

Managed Devices

Event Notification

Configuration Management

Product and Data Management

Staffing Utilization

Continuity of Business

Risk Management

Software Portfolio Management

Asset Consolidation and Retirement

ROI, IRR, Financial Measures

Cost Center, P&L Opportunities

Total Cost of Ownership

Define Perspectives, Ontologies

Financial

Customer or External Relations

Internal

Learning and Growth

Strategy Maps, KPIs, Org. Models

Managed Costs Optimization

Capital and Operating Budgets

Service Assortment Planning

Investment Criteria

Clarity of strategy links to execution will help

focus the organization to meet objectives and

align with strategic intent...

2

27Confidential and Proprietary

Think Process, Not Projects2

Design Chain Operations Reference

(DCOR) v1.0

Research IntegrateDesign Source DeliverMake

Product Design Chain Supply Chain

Program Design Chain Cycle Time Order Fulfillment Cycle Time

Customer Requirements

Bill of Materials, Specification

Product

Program and Operational

Business Plan

Supply Chain Operations Reference

(SCOR) v9.0

Product Development Global Operations

Product Lifecycle Management

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Reduce Action Latency

Value

Time

Text

Business Event

Data Stored

Information Delivered

Action Taken

Action Lag

Data Latency

Analysis Latency

Decision Latency

Opportunity

Costs

Planning, analysis, decision and execution cycles are accelerating

3

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Deliver value to meet business needs

Source: The Data Warehousing Institute, Wayne Erickson, February 2009

3

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Right platform for the right job

One size does not fit all…

3

31Confidential and Proprietary

Right tools for the right job

Purchase Analytic tools that are not too complex for casual users but are sophisticated enough for power users.

Source: The Data Warehousing Institute, Wayne Erickson, February 2009

3

32

33%

23%

77%

40%

36%

65%

8%

23%

Have significant decision-

support/analytical capabilities

Value Analytical insights to a very

large extent

Have above average analytical

capability within industry

Use analytics across their entire

organization

Source: Competing on Analytics, Thomas Davenport

Insight – The Competitive Edge

Low Performers

High Performers

High performing companies are more likely to use analytic information strategically

4

Web Analytics Standards: http://www.webanalyticsassociation.org/

33

Think out of the box5

Source: FMS Advanced Systems Group, Sentinel Visualizer

• Find hidden relationships • Identify clusters and patterns quickly • Perform ad-hoc analysis, and test theories and scenarios • Organize complex networks into manageable groups• Geospatial Visualizations

34

Think out of the box5

35

Strategy and approachRoad map development

36

IDEAL Method

Initiating

Diagnosing Establishing

Acting

Learning

ProposeFutureActions

AnalyzeandValidate

Pilot/TestSolution

CreateSolution

Develop Approach

Set Priorities

DevelopRecommendations

Characterize Current and Desired

States

CharterInfrastructure

BuildSponsorship

Stimulus for Change

Set Context

ImplementSolution

RefineSolution

Plan Actions

Roadmap for

Management

Improvement

Initiative

Assessment

Roadmap

Initiative

Projects

The IDEAL(SM) Model

The IDEAL model is an organizational

improvement model that serves as a

roadmap for initiating, planning, and

implementing improvement actions.

37

Creating the Road Map2. Future State Definition 3. Initiatives Definition 4. Prioritization

Business

Objectives

Functional

Needs

High Impact

Business

Processes

Org and

Process

Improvement

Cost and

Complexity

Drivers

Functional

Initiatives

Performance

Targets

Guiding

Principals

Architectural

Imperatives

Organization

Initiatives

Organizational

Initiatives

Process

Initiatives

Roadmap

The process will focus on

placing business objectives,

initiatives, and projects on

the roadmap.

1.

2.

3.

4.

5.

5. Roadmap Development

1.

2.

3.

4.

5.

Business and

Technical

Artifacts

Recommendations

Gap Analysis Optimization Planning ProductsDraft FindingsOutputs From Current

State Field Work

1. Current State

Review

Road Map Development – The Pattern

38

Road Map – Program View

2003 2004 2005 - 2006 2007 - 2008

2003 2004 2005 - 2006 2007 - 2008

v46 04.25.2003

Roadmap

QUALITY OF INFORMATION

A-20 | Define testing framework for assessing

ongoing testing needs of tools/products/platforms/

services

Business

Objectives

Objective

Date

Tactical

Initiatives

Strategic

Initiatives

Initiatives

Completed

Process

and

Organization

Objective Groups Functional Initiative Groups Status

INFORMATION ACCESS & SELF SERVICE

TACTICAL INITIATIVES

DATA

PROCESS AND QA

MAKING SYSTEM CHANGES

F-3b | Automate lower-priority

reconciliation processes to reduce

latency 6-12m

F-2c | Enhance DW to improve accuracy and reliability (Enchance support for all downstream

reporting) 12+ m

F-5a | Move reporting to data warehouse out of mainframe 12+ m

F-2b | Implement H-Routing 1-5m

F-3a | Improve high priority reconciliation capabilities (e.g., labor code

exceptions, claim info for rptg) [PROGRAM] 6-12m

A-2 | Create infrastructure for message-based processing 12m

A-17 | Define and pilot new standard development

environment 3-9m

A-9 | Create relational data store for operational data 12-18m

GMAC-MIC TACTICAL

INITIATIVESBUSINESS GOALS

ARCHITECTURAL IMPERATIVES

B-12 | Decrease time it takes to make

complex plan changes - Currently: 6-7

Months

ST Goal:

2 months

(1y)

LT Goal:

1 month (2y)

B-10 | Decrease time it takes to make simple

plan changes - Currently: 2-3 months ST Goal:

2 weeks (1y)

LT Goal:

On Demand

(2y)

F-2d | Web-enabling claims for non-GM

dealers 6-12m

B-15 | Decreasing % of

policy rejects. Current: ? % ST Goal:

5% (6 mos)

LT Goal:

1% (18 mos)

LT Goal:

90% reduction (1

yr)

B-1 | Decreasing claim payment

leakage ($). Current: 2% ST Goal: 1.5% (< 1 yr)

1 year from implementation LT Goal:: 1% ( > 1 yr)

LT Goal:: 20%

reduction (1 yr from roll-

out)

B-4 | Enhancement costs [25-40% are

repetitive] - Current: 200 req & 3.5 $M / yr

PLATFORM & ENTERPRISE APP INTEGRATION

A-23 | Create relational data store for

informational/reporting data 12m

A-21 | Incorporate GMAC security solution into future state arch 6-12m

A-22 | Incorporate GMAC systems management into

future state arch 6-12m

GM INITIATIVES

ST Goal: 25%

reduction (1y AI) LT Goal:: 40% reduction (?)

B-14 | Accuracy of

Coverage. Currently ? LT Goal:

100%

B-13 | Accuracy of Rates

Currently 100% Ongoing Goal: 100%

F-1a" | Enhance front end for

MES to improve quality of info and

reduce back-end errors 1-5 m

F-1a' | Enhance front end for

CDR to improve quality of info

and reduce back-end errors 1-

5m

A-7 | Establish

standard ETL tools

F-2a | Implement process standardization 6-12m

B-8 | Increase the percentage of transactions completed

online & via phone - Current 100 CSR cancellations/day ST Goal: 40%

reduction (?)

B-2 | Reduce training time

Current: 60 days ST Goal: ? (?)

Biz case being defined

F-1b | IVR Expansion -

Implement self-service policy

cancellation , endorsements, and

agreement transfers 1-5 m

B-7 | Receive more timely field reports / real-time field reports - Current:

Data available 30 dy after quarter end (45 dy after tx for Reinsurance) ST Goal Dependant

upon H-Routing LT Goal:: real-time

DRAFT

Consolidation of reporting

functions: Downstream

Reporting Systems

G-5 | DominoDoc Document

Management System

OTHER GMAC/MIC INITIATIVES

G-15 | (MIC) Claims

assignment

replacement

G-16 |(GMCL) eCM

Target 2004G-11 | (MIC) CTI

G-13 | (MIC) QuickRater(Canada)

G-6 | (MIC) Menu-selling

G-9 | (GMAC) eDealer Target 2003

G-11a | (MIC)

CARES/IVR Upgrade Target 2003 / Early

2004

G-2 | (GM) GMDID Replacement

GM INITIATIVES

G-17 | (GM) WINS ? Target ?

G-12 | (GM) GMVIS

G-5 | (GM) Dealer facing applications web-based by 2006

G-7 | (GM) Retail process vision

G-2 | (GM) GMDID Extended

G-10 | (GM) DSP Common Interface

Target

2003

B-11 | Decrease time it takes to make rate

changes

Currently: 3-4 months, up to a year

ST Goal:

2 weeks (1y)

LT Goal:

On Demand (2y)

Define message

layer testing

standards

Define app

monitoring

standards

F-7 | Implement new reinsurance

system 6-12m

F-17

Choose

solution

approaches

1-3m

STRATEGIC INITIATIVES

F-2" | Implement new claims management

system 12+ m

F-3" | Implement new accounting system 9-14m

F-1" | Implement new policy administration system

9-14m

F-5 | Implement Enterprise-Wide Reporting

System for Operational Data 4-10m

F-10 | Implement new accounting feed to General

Ledger (FMS) 6-12m

F-9 | Implement web self-service 6-12m

F-19 | Baseline EDS support for current

mechanical suite of applications (ongoing)

F-16

Define critical

user, functional,

and tech reqs 3m

F-18 | Define deployment

and retirement strategy 1-5m

Design & Build

7-11m

Deployment and

Migration 2m

Design & Build

7-11 m

Deployment and

Migration 2m

Design & Build

10+ m

Deploy

2m

Deploy

1-4m

Design & Build

3-6m

F-3' | Assess

new accounting

system reqs

(3-4 mos)

...Primary Systems Assessed:

GEAC, FMS, Mechanical

Primary Systems Assessed:

MES, CDR, Mechanical, DCS Canada...

Retirement of systems:

GEAC, FMS

Migration of policy functionality off of systems:

Mechanical, DCS Canada, CDR

Retirement of systems:

MES, Possible retirement of DCS Canada

F-1' | Assess

new policy system

requirements

3-4m

...

...

ENTERPRISE REPORTING SYSTEM

CLAIMS MANAGEMENT SYSTEM

POLICY MANAGEMENT SYSTEM

ACCOUNTING SYSTEM

... F-2' |

Assess new

claims sys

reqs 4-5m

Retirement of system:

Likely retirement of Mechanical, DCS Canada

Primary Systems Assessed:

Mechanical, DCS Canada...

F-5' |

Assess

reporting sys

reqs 1-2m

... ...Primary Systems Assessed:

Data Warehouse, Downstream Reporting Systems

PHASES OF GMAC-MIC STRATEGIC INITIATIVES AND PROGRAMS

Ramp-down support

for GEAC/FMS

Ramp-up support for

accounting system assessment

Ramp-up support for policy

system assessment

Key to Phases

PHASE II

PHASE III

PHASE IV

PHASE V

PHASE I RED

ORANGE

YELLOW

GREEN

BLUE

GMAC/MIC INITIATIVES

MIC PROCESS AND ORGANIZATIONAL INITIATIVES

P-1 | Establish a PMO to manage the

execution of the MIC roadmap 6m

P-2 | Create a framework for

program & project level collaboration

across MIC < 6m

P-3 | Improve IT supplier estimate visibility and quality 12m

P-4 | Improve quality & completeness of systems documentation

<12m

P-5 | Impvove business process documentation >12 m

Ramp-down claims

systems support

Ramp-down policy

systems support

Ramp-up support for

claims system assessment

Reporting

system reqs

Define dev

environment testing

standards

2. Future State Definition 3. Initiatives Definition 4. Prioritization

Business

Objectives

Functional

Needs

High Impact

Business

Processes

Org and

Process

Improvement

Cost and

Complexity

Drivers

Functional

Initiatives

Performance

Targets

Guiding

Principals

Architectural

Imperatives

Organization

Initiatives

Organizational

Initiatives

Process

Initiatives

Roadmap

The process will focus on

placing business objectives,

initiatives, and projects on

the roadmap.

1.

2.

3.

4.

5.

5. Roadmap Development

1.

2.

3.

4.

5.

Business and

Technical

Artifacts

Recommendations

Gap Analysis Optimization Planning ProductsDraft FindingsOutputs From Current

State Field Work

1. Current State

Review

39

Road Map – Details

People and Organization Processes Technology and Tools

2007

Q3 Q2 Q3

2008

Legend:

Q1Q4

Environmental Data Management Roadmap (DRAFT) March 31, 2007

External Dependency

Data Warehouse Roadmap – Proprietary and Confidential June 8, 2005

Assemble Development Teams

Iterative Project Planning

Detail Design

Implementation (Pilot)

Transition to Production

Iterative Project Planning

Detail Design

Implementation

Transition to Production

Iterative Project Planning

Detail Design

Initiate Project

Business Case Development

Complete Business Requirements

Pilot Production Begins

LIMS Subject Area Requirements Defined

Initiate KWS Function

Define, Plan, and Align Process Improvements with Business Requirements

Introduce (IRM) function

Extract, Transform, Load Design Processes

Data Staging Process Design and Build

Detail Process

Design Complete

Configure Development/Test

Environment

Configure Production Environment

Deploy warehouse ETL systems processes

Construct and Test

Core Data Warehouse Foundation (Infrastructure)

Deploy

Iterative

Performance Tuning

Completed

Performance Engineering and Tuning

Tool Evaluation and Selection

Complete detailed design

Initiate Service Management functions

Collaborative Application Prototyping BeginsProtyping Iteration

One Ends

For Exposition Only

DRAFT

Initiate Requirements Change Management

Complete Architecture Design

Query Optimization, Schema Development, Performance tuning

Macro Invertebrate

Data Mart

Completed

Macroinvertebrate Reporting Data Mart Staged

--- Control and Manage Project Iterations ---

40

How to get started

41

How to get started

• Evaluate incremental improvements to existing architecture and enabling technology

• Leverage what you already have

• Ensure you have the capability to deliver• Aligned with agreed strategy and goals

• Supporting organization is well defined

• Processes (vertical through planning and budgeting, or horizontal through lateral relationships (matrix) exist or can be adopted to meet the new platform

• Leverage key people and core competencies

• Performance measures and rewards match intent

• Supporting business case is sound and defensible

• Mitigate or minimize technical and design debt in the design and adoption of the new platform (http://c2.com/cgi/wiki?TechnicalDebt)

42

Leverage what you have

Database Platform, Network, Middleware, Security, Naming Services

Reporting Ad hoc Query Visualization Dashboards Microsoft Office

Integration

Scorecards OLAP Visualization Predictive modeling Data Mining

Workflow and Collaboration Development Metadata Management

Integration

Analysis

Information Delivery

Database Servers

OLAP Servers

Application Servers

Availability (Caching and Failover (HA))

Publishing and Distribution Services

Portal Services

Security Providers

Audit Services

Administration and Monitoring Services

Job Scheduling and Production Controls

Infrastructure

43

Ensure you have the capability to deliver

Enhance Organizational Readiness – identify baseline adoption management capability, create executive consensus, highlight missing operational capabilities

Stage the Transformation - consciously choose maturity jumps, understand the expected change in process consistency and complexity, articulate associated operational impacts

Develop Capability-Based Plans – account for internal deployment bandwidth, factor in time to stabilize the foundations, articulate critical dependencies, secure the participation of critical players

Right-Fit Software Solution

Build Organizational

Capability

Drive Organizational Commitment

Market the Compelling Vision – quantify and repeatedly communicate the value to the organization, develop “what’s in it for me” messaging for critical stakeholders

Proactively Manage Stakeholder Buy-In – create opportunities for stakeholder involvement, design usage metrics and incentives to align behavior

Maintain Strong Governance – execute active executive sponsor involvement, define performance outcomes to direct and track success, hold managers accountable for progress

Refine the Operating Model – balance the trade-offs between structure and process, formally assign decision rights, define the new roles

Enhance Change Leadership – develop manager’s communication, expectation and capacity management skills, assign dedicated transition management resources

Develop User Skills – enhance domain specific skills, increase decision management competency

Do not try to build a system whose complexity

exceeds the organization's capabilities

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Develop a supporting business case

Table of contents

1. Executive summary

Background

2. Current business

Description/economics

3. Proposed project

Description/strategic fit

4. Options evaluation

5. Timescale and investment analysis

6. Standard management practices

7. Appendices

Supporting material

• Analysis of benefits

• Analysis of costs

• Financial spreadsheet

• Metrics – ROI, NPV, etc.

• Risk analysis and mitigation

• Alignment

• Project change mgmt

• Quality assurance

• Project finance

• Reporting

• Governance

45

Plan and design carefully

Business Need

Logical Specification

Gap Analysis

Current

State

End

StateBusiness

Requirements

Work Breakdown

Structure

Functional Specification – (Statement of Work)

Logical View

Physical View

Process View

Development View

Use Case View (+1)

Construction

Deployment

Concurrency

Operational

Functional

Information

Stakeholder Requirements

Reference ArchitectureSupporting Schedules (micro - schedules)

Detailed Planning Products

Product

Breakdown

Structure

Organization

Breakdown

Structure

SEI-CARR Risk

Taxonomy

System

Component

Taxonomy

Predecessor

Successor

Mapping

Project Plan

Supporting Schedules (micro - schedules)

Supporting Schedules (micro - schedules)

Program Alternatives – Contingency Plans

Approach

Strategy

Measures of

Success

Project Plan Project Plan

System (Non-Functional) Requriements

Business Owners View

Sol

utio

n S

peci

ficat

ion

Request for Quotation

Business Case

Common Information

Model

Canonical Model

Operating Model

Modeling the Analytic Blueprint

46

A word about technical debt…

• Metaphor developed by Ward Cunningham…

• Doing things the quick and dirty way sets us up with a technical debt, which is similar to a financial debt

• Like financial debt, technical debt incurs interest payments in the form of the extra effort in future development due to the quick and dirty design choices.

• We can:

– Choose to continue paying the interest, or

– Pay down the principal by refactoring the quick and dirty design into a better design. Although it costs to pay down the principal, we gain by reduced interest payments in the future.

Technical Debt (Ward Cunningham) http://c2.com/cgi/wiki?TechnicalDebt

Questions and reference links

• Beyond Reporting - Delivering Insights with Next-Generation AnalyticsTDWI 2009, Wayne W. Eckerson

• Applied Enterprise ArchitectureJames Parnitzkehttp://www.pragmaticarchitect.wordpress.com

• Analytic Bridge http://www.analyticbridge.com/

• Emerging Standards

The Predictive Model Markup Language (PMML) http://www.dmg.org/

Web Analytics Standards: http://www.webanalyticsassociation.org/

Example Conformance (Google) http://cutroni.com/blog/2008/09/21/google-analytics-compliance-with-waa-standard-metrics/

Summary

Thank You!

What is new about the next generation?

The five (5) keys for success you need

Strategy and approach (road map development)

• Understanding your current state

• End State - Where do you want to go?

• Gap analysis – uncovering the actionable activities you will need to accomplish success

How to get started

AAJ’s Vision

Consultative Services from Experts

Full-Service One-Stop Shop

Latest Technology

SolutionFrameworks

AAJ’s Mission