1. September 10, 2020
Roadmap Refresh Workshop

2. Director of Strategy, CCG
Spirited, entrepreneurial leader bridging technical understanding,
deep analytical prowess, and a product-oriented mentality to drive
strategic growth for data-driven organizations. Architecting
solutions to complex client problems in retail, e-commerce,
marketing, finance, supply chain, and consumer packaged goods
(CPG). Vigilant in, and insistent upon, being ethical and client-
centric in all consulting practices.
Learn more by clicking on the links below:
https://ccganalytics.com/solutions/analytics-strategy-and-
roadmap
https://www.lexykassan.com
https://www.linkedin.com/in/lexykassan/
https://www.datascienceethics.com
Lexy Kassan

3. What do you hope to get out of today’s workshop?
Take a few minutes to comment in the chat
Virtual Introductions

4. Tools & Templates for Today
Join the discussion board:
• FunRetro Discussion Board
Open the roadmap assessment form – We’ll be using this for most of the workshop:
• Roadmap Refresh Workshop Assessment Form
Please open these in a new tab or window if you are viewing this workshop in the browser!
4
Initial Setup

5. We’ve
Gathered Here
Today to…
Build and
Refresh Our
Data and
Analytics
Roadmaps…
So That We
Can Plan for
Future Efforts
& Investments

6. The Modern Intelligent Enterprise
Intelligent Enterprise noun
in·​tel·​li·​gent · en·​ter·​prise | in-ˈte-lə-jənt · ˈen-tər-ˌprīz
Definition of Intelligent Enterprise
1 : a culture which enables and encourages the use of trusted, governed data and analytics to inform
decisions and respond nimbly to changing circumstances
2 : an organization that takes a holistic view of value across all stakeholders both internally and
externally
3 : an outlook that change is inevitable and continuous innovation to automate and augment the
intelligence of your organization is necessary to compete

7. 7
The Realization Of The Intelligent Enterprise
What If You Could……
Leverage the analytics from a fully integrated
value chain to understand the holistic
relationship between your business and
customers to react accordingly in real-time
Utilize IoT to monitor, automate, and prescribe
optimal conditions at the location level
Predict expected store traffic to optimize
operations
Trace and properly attribute your new customers
to the marketing source that encouraged them to
buy from you?
Value-based Outcomes
Intelligent Supply Chain
Omnichannel Customer
Automate vendor management using ML
Real-time inventory
Predictive maintenance and replacement for in-store capital
assets
Determine optimal store layout and product placements
Workforce optimization
Automate Inventory management and replenishment
Location analysis for new store placement
Multi-touch attribution
Real-time campaign effectiveness
Real-time product offerings

8. 2.8x
more likely to report
double-digit year over
year growth with
advanced insight-driven
capabilities
91%
of Global Executives say
effective data and analytics
strategies are essential for
business transformation
6%
average initial increase in
profits from investments in
data and analytics. That
number increases to 9%
for investments > 5 years
Data Drives Results
Data, Analytics, And Insights Investments Produce
Tangible Benefits — Yes, They Do, 2020
Understanding Why Analytics Strategies Fall Short
for Some, but Not for Others, Harvard Business
Review, 2019
Data Driven
Companies Are
Seeing The Lift
Enterprise Data and
Analytics Strategy is
Critical For growth
Analytics Investments
Show Consistent Profit
Increase
Big data: Getting a better read on performance, McKinsey
2018
46%
of enterprises are relying
on analytics to identify and
create new revenue
streams
Analytics Are
Fundamental to
Transformative Innovation
The Global State Of Enterprise Analytics, Forbes, 2019

9. 1 - 2020, Harvard Business Review, "The New Decision Makers: Equipping Frontline Workers for Success.“
2- 2019, Deloitte Survey: Analytics and Data-driven Culture Help Companies Outperform Business Goals in the 'Age of With’
3- 2019, Companies Are Failing in Their Efforts to Become Data-Driven
Yet…..
Only 20% of organizations are giving their
employees both the authority and the tools to make
decisions based on analytics1
67% of executives surveyed are not comfortable
accessing or using data from their existing tools and
resources2
53% state that they are not yet treating data as a
business asset3
Organizations are still struggling
with successfully implementing
the meaningful transformation
necessary to become truly data-
driven.

10. Culture, Strategy and Governance Are Most Critical to the
Success of Data & Analytics Teams
Percentage of Respondents
Activities most critical to D & A teams’ success
n = 292 All Respondents, excluding “unsure”
Q. Which of these activities, if any, are critical to your Data and Analytics team’s success?
Source: Gartner’s Fifth Annual CDO Survey (2019)
2%
2%
3%
4%
8%
8%
9%
9%
12%
13%
16%
17%
19%
20%
20%
22%
23%
25%
27%
41%
1%
0.34%
2%
2%
1%
1%
4%
4%
4%
4%
5%
4%
5%
7%
8%
7%
7%
13%
21%
0% 20% 40%
Other
Benchmarking D&A Maturity
System Adoption/Usage Metrics
Sharing Data Externally
Operational Intelligence/Real-Time Decision Automation
Metadata Management
AI Program
Data Acquisition
Master Data Management Program (MDM)
Sharing Data Internally
Data Science Program
Data Quality Program
Data Literacy Program/Data Skills Training
Data Integration
Enterprise Information Management Program (EIM)
Architect D&A Platform
Advanced Analytics Capability
Information/Data Governance Program
D&A Strategy Development/Implementation
Data-driven Culture
Sum of Top 3 1st choice

11. Two features underpin the full derivation of value from data and analytics
A clear strategy for how to use data and analytics to compete
The deployment of the right technology architecture and capabilities
Lead with Strategy
McKinsey, Harvard Business Review, 2013, Three keys to building a data-driven strategy
“Defining D&A strategy is the top responsibility of 86% of CDOs, up from 64% in 2016”
~Gartner CDO Survey Oct. 2019

12. We’ve
Gathered Here
Today to…
Build and
Refresh Our
Data and
Analytics
Roadmaps…
So That We
Can Plan for
Future Efforts
& Investments

13. Enable PROCESSES that
supports analytics at the
speed of
business
Take advantage of the
latest TECHNOLOGY
to support the
volume, variety, and
velocity of your
industry
Treat DATA as an
enterprise asset
throughout its lifecycle to
maximize its utility across
your organization
Unlock
BUSINESS
VALUE
GOVERN data to
ensure veracity and
compliance in a
changing world
Invest in developing
PEOPLE to support analytic
adoption & create a data-
driven culture
The Gears of Data-Driven Progress

14. To the Discussion Board!

15. Strategy & Governance
• Rapid Data Governance Solution
• Strategic Roadmap Solution
Services
• Strategic Roadmaps
• Data & Analytics Leadership
• Data Health Assessments
• Platform Assessments
• Master Data Management
• Meta Data Management
• Data Governance
Information Management
• Platform Modernization Solution
• Cloud Migration Solution
Services
• Data Integration
• Data Architecture
• Data Warehouses and Lakes
• PowerApps
• Cloud Management
• Cloud Migration
• DR/BC through Azure
• Azure Governance/Security
Analytics
• Leadership Development
• Customer Analytics
Services
• Dashboards and Visualizations
• Operational Reporting
• Self-Service
• Training
• Data Exploration
• Location Intelligence (GIS)
Data Science and AI
• RapidInsight with Machine Learning
Prototype Solution
Services
• Model as a Service
• Data Science as a Service
• Predictive Analytics
• Natural Language Processing
Machine Learning
• Artificial Intelligence
• Machine Learning Ops
CCG Solutions and Services
Take a Quick
Break
Back in 10
Minutes

16. CCG Strategic Roadmap Framework
Framework
Gears
People
Process
TechnologyData
Governance
Data Enablement
Organizational Structure
Project Management & Ownership
Data & Analytic Literacy
Data & Analytic Skills Inventory
Professional Development Programs
Executive Leadership Support
Use Case Management
Project Methodology
Development Methodology
Testing Process
Operational Support
Deployment Process
Analytics Integration
Adoption Process
Data Quality
Metadata Management
Data Privacy & Compliance
Data Security
Governance Program Management
Data Asset Lifecycle
Data Architecture
Data Source Ownership
Derived Data Management
Platform Infrastructure
Orchestration Capabilities
Integration Capabilities
Disaster Recovery & Resiliency
Platform Elasticity

17. Discover
•Identifying the needs
of the organization
•Determining how
this could impact
business results
Design
•Architecting a plan
for addressing the
need
•Evaluating options
for moving forward
Plan
•Developing the
implementation plan
•Gaining alignment
and buy-in on the
design
Execute
•Implement the plan
and any associated
dependencies
•Gain initial adoption
Optimize
•Iterate on the design
and execution
•Ongoing adoption
and refinement
17
Ranking Your Organizational Maturity

18. People
1
8

19. Data Enablement
• To what degree do the teams around your enterprise have the data, tools, reporting, and insights to make
informed decisions in their daily tasks?
• How empowered are they to act upon the data and insights they see?
• Points to consider:
• Consistency across departments
• Degree of access
• Pockets of insights or deeper analysis
19
People
People

20. Organizational Structure
• Do you have an intentional analytic center of excellence or a distributed network of analysts?
• How long a backlog do your analytic resources have of business requests?
• Points to consider:
• Subject matter expertise
• Capacity to meet demand
• Guidance and growth opportunities
20
People
People

21. Project Management & Ownership
• Who owns the data and analytics backlog?
• Which business leaders sponsor data and analytics projects?
• Are project managers or Scrum Masters available to accelerate team velocity?
• Points to consider:
• Consistent product ownership
• Dedicated project management
• Allocated time for organizing projects
21
People
People

22. Data & Analytic Literacy
• What proportion of your organization can interpret available reporting, dashboards, or analytic
presentations and distill insights from them?
• How many routinely consume these data and analytics as part of their daily jobs?
• Points to consider:
• Multiple levels of experience
• Field vs headquarters
• Level of literacy
22
People
People

23. Data & Analytic Skills Inventory
• What skills are most common among your data and analytics staff?
• What skills are missing or underdeveloped?
• Points to consider:
• Data understanding & engineering
• Business intelligence & data visualization
• Data analytics & data science
23
People
People

24. Professional Development Programs
• What career paths are now opened to those seeking to be more data-oriented?
• What training is required and to which teams to achieve the desired level of data and analytic literacy?
• Points to consider:
• Data literacy requirements by role
• Consumption vs creation needs
• Training programs and options
24
People
People

25. Executive Leadership Support
• How often do executives require data for decision making rather than relying on gut feel?
• Are executives actively transforming the culture from the top to encourage data use and analytics?
• Points to consider:
• Insistence upon data-backed evidence
• Socializing and evangelizing metrics
• Transparently messaging data centricity
25
People
People

26. Process
26

27. Use Case Management
• How do you identify high value use cases for your analytic backlog?
• How are these use cases prioritized for completion against other initiatives?
• Points to consider:
• Focus on specific business units or enterprise wide
• Evaluation criteria for return
• Meeting cadence and constituents
27
Process
Process

28. Project Methodology
• What project methodology suits your data and analytics workflow?
• How do you manage the complexities and unknowns within these projects?
• Points to consider:
• Alignment to other project methods
• Comfort of the business with process difference or change
• Up-front specification capability
28
Process
Process

29. Development Methodology
• What process is followed for your SDLC or analytics development lifecycle?
• What controls do you have in place to maintain code integrity?
• Points to consider:
• Alignment to other development methods
• Formal vs ad-hoc process
• Existing technology for process enforcement
29
Process
Process

30. Testing Process
• What process is followed for testing and validating data?
• How many levels of testing are needed for the business to be confident in the results?
• Points to consider:
• System integration through user testing
• Groups needed to test and availability
• Automated testing software or methods
30
Process
Process

31. Deployment Process
• How do new data sources, reports, dashboards, and analytics get to production?
• What gates must these deployments pass to be considered production candidates?
• Points to consider:
• Certification of deployments
• Continuous vs point deployment
• Documentation requirements
31
Process
Process

32. Operational Support
• What process is needed around maintaining data and analytics in production environments?
• How are new analytic applications monitored and any issues resolved?
• Points to consider:
• Data sources and integration
• Machine learning scoring (inference) endpoints
• Data that “looks off” in BI or reports
32
Process
Process

33. Analytics Integration
• How are new analytics integrated into existing processes, applications, and dashboards?
• How are changes communicated to stakeholders and users?
• Points to consider:
• Changes to business meaning
• Downstream usage identification
• Coordinated roll-out across applications
33
Process
Process

34. Adoption Process
• How is organizational change management communicated and supported for initiatives?
• What metrics are used to gauge adoption in key business units?
• Points to consider:
• Assessment method
• Measurement method
• Continual reinforcement process
34
Process
Process

35. Technology
35

36. Platform Infrastructure
• How well does your current technology platform support the initiatives in your roadmap?
• How well can it support the changing needs of multiple types of users while maintaining security?
• Points to consider:
• Volume, Velocity, Variety, Veracity, Value
• Processing location (e.g. central, distributed, edge)
• Power users, data consumers, executives, third parties
36
Technology
Technology

37. Orchestration Capabilities
• How easy is it to automate reports, dashboards, and machine learning scoring for ongoing use?
• Which teams are enabled to orchestrate their workflows?
• Points to consider:
• Scheduling routine jobs
• Establishing notifications for completion or outage
• Triggered, scheduled, or both
37
Technology
Technology

38. Integration Capabilities
• What types of data processing does your platform enable?
• Can the platform support an integrated, real-time experience across channels and business units?
• Points to consider:
• Batch and incremental processing
• Microservice architecture
• Messaging infrastructure
38
Technology
Technology

39. Disaster Recovery & Resiliency
• How quickly can you be back up and running in the event of a main system failure?
• What SLAs are needed to support business as usual despite outages?
• Points to consider:
• Replication and failover
• Data center contention
• Managed support
39
Technology
Technology

40. Elasticity
• How easily can you expand the capabilities of your technology backbone to unlock new use cases?
• Can the platform scale to serve the growing needs of the intelligent enterprise?
• Points to consider:
• Ease of incorporating new data and systems
• Enabling an increasing user population
• Volume and velocity scaling, both increasing and decreasing
40
Technology
Technology

41. Data
41

42. Data Asset Lifecycle
• To what degree is data treated as an enterprise asset with consideration for its procurement and use?
• How is data handled during and at the end of its useful life?
• Points to consider:
• Evaluation criteria for new data acquisition
• Integration and maintenance of data with existing sources
• Retention policy adherence and data destruction
42
Data
Data

43. Data Architecture
• Is data architected and optimized in such a way as to enable maximum value?
• Is data accessible to all in the organization who have a use for it?
• Points to consider:
• Storage and organization methods
• Data access and retrieval capabilities
• Optimization for use and collation
43
Data
Data

44. Data Source Ownership
• Does each data source have a clear owner (or owning business unit)?
• Who maintains the data and has accountability for its quality and availability?
• Points to consider:
• Data vendor management
• SME on common quality or processing issues
• Follows throughout the data asset lifecycle
44
Data
Data

45. Derived data
• Where does your derived data come from and how well is it managed?
• Who ensures that the usage of derived data is appropriate?
• Points to consider:
• KPIs, business metrics, advanced analytic calculations
• Process to arrive at derived calculations including interim logic
• Accessibility of definitions
45
Data
Data

46. Governance
46

47. Metadata management
• What metadata is captured and how is it stored?
• How is metadata accessible and updated within the organization?
• Points to consider:
• Data source information and descriptions
• Business usage metadata
• Audit process for metadata
47
Governance
Governance

48. Data quality
• How clean and trustworthy is your data?
• Are there sources of truth in the data on which your business can make decisions?
• Points to consider:
• Measuring data quality issues
• Strategies for data alignment
• Ongoing data science model veracity
48
Governance
Governance

49. Data privacy & compliance
• How prepared is your organization to meet the changing demands of data privacy?
• Are your audit and compliance processes automated for ongoing use?
• Points to consider:
• Classification of protected data
• Reporting capabilities
• Automated processes for consumer data requests
49
Governance
Governance

50. Data security
• How well regulated is internal access to data?
• What measures do you have to secure data both in flight and at rest?
• Points to consider:
• Automated adaptive threat identification
• Data access logging and anomaly detection
• Third party data sharing capabilities
50
Governance
Governance

51. Data governance program
• Do you have a strategic program for governing your data?
• Is the program empowered to enforce the policies required for success?
• Points to consider:
• Established governance councils
• Ensure organizational alignment on processes
• Delegate and assign responsibilities for governance
51
Governance
Governance

52. PARTNERSHIP SPOTLIGHT: MICROSOFT
A premier Microsoft partner, CCG uses leading cloud
platforms to develop solutions and provide analytics that help
customers advance their digital strategies.
5
2
Certifications
Gold Partner
Independent System Vendor (ISV)
and Co-Seller
AI Inner Circle Partner
Technologies
Azure Data Services
Azure Data Factory
Azure Data Lake Store
Azure Databricks
Azure Cognitive Services
Azure Machine Learning
Azure Stream Analytics
Azure Analysis Services
Power BI Platform
Take a Quick
Break
Back in 10
Minutes

53. We’ve
Gathered Here
Today to…
Build and
Refresh Our
Data and
Analytics
Roadmaps…
So That We
Can Plan for
Future Efforts
& Investments

54. Where can you be in 12 months?
54
Setting Up the Plan
•Whether coming from the top or developed for only Data & Analytics, define the finish line for the next 12 months
Establish Goals
•Not all markers must, or even should, be a 5 for your initiatives to succeed
Make It Plausible
•Determine how prepared your organization is for new patterns and processes in addition to the cost of any investments
Gauge the Appetite for Change

55. 55
Prioritize Use Cases
Set some starting projects to prove value incrementally
Engage stakeholders and those who will need to support the changes
Establish a value ladder to showcase the impact of these projects
Already have some analytic projects in mind?

56. Opportunities
New lines of
business or
revenue
Enhanced
experiences and
markets
Risks
Mitigate external
risks
Minimize internal
disruptions
Efficiencies
Automate or
augment
Reduce data
integrity problems
and goose chases
Objectives
Alignment to
strategic initiatives
Ranking against
competitors
56
Value Areas

57. Order of Operations Can Matter
57
Map Dependencies
• Some projects will be foundational to more use cases and therefore more value
• Natural synchronization can be found within a line of business
• Data sources may be usable by a subset of organizational areas that all benefit from their accessibility
• Start governance early as it often takes longer to get running and will decrease future rework
• Involve organizational change management at inception to encourage faster adoption

58. Take the Next Step

59. Strategic Roadmap
Methodology
› Know Where You’re Going – Energize and align your
organization behind a unified vision for data and analytics to
meet current and future business needs
› Know Where You Are – Assess the current-state of your people,
processes, technology, data and governance to understand the
starting point for your analytics journey
› Know How to Get There – Deliver a pragmatic and actionable
strategic roadmap and modern data architecture
recommendations to make the vision a reality

60. Vision
Strategic
Business
Goals
Stakeholder
Outcomes
Value
Propositions
Capabilities
› Elicit and document your Strategic Business Goals to ensure D&A
program alignment
› Uncover Stakeholder Outcomes that contribute to achievement
of your strategic business goals
› Define the Value Propositions the position D&A as a utility,
enabler or driver for the organization
› Determine the Capabilities that are required to deliver the
desired stakeholder outcomes

61. Assessing the Organization
Framework
Gears
People
Process
TechnologyData
Governance
› Identify key Use Cases containing Business Value that can be
unlocked through data and analytics
› Evaluate 29 high-level and over 120 Low-Level Markers within
the five framework gears
› Light touch to most stakeholders to Minimize Overhead or
Disruption during the assessment process
› Establish the needs and priorities of for Achieving the Vision

62. Roadmap to Success
› Prioritized use case delivery to Maximize Incremental Value
› Map dependencies and interactions to Minimize Technical Debt
and rework incurred
› Plan for organizational change management and adoption to
Realize the Vision faster and more completely
› Six-month refreshes to Anticipate New Needs, trends, and
competitive threats in the industry

63. Vision
What You Get
Assessment Roadmap
An Enterprise Analytics
Vision that establishes
direction for the
organization
• Executive Summary
• Roadmap Strategy
• Vision Statement
Current State Assessment of
key lines of business, IT, and
data estate.
• Use Case Prioritization
Matrix with ROI Analysis
and Assessment
• Current State IT/Analytics
Architecture Diagram
• Survey and Workshop Notes
and Results
A Personalized Roadmap to
accelerate on the path to
analytics nirvana
• Full Report including roadmap
recommendations
• Execution Plan and Timeline
• Future State IT and Analytics
Architecture Diagrams
• Data Governance Program
Recommendations
• Executive presentation

64. About CCG

65. CCG At A Glance
DATA ANALYTICS SOLUTIONS 18
Years of
continued
growth
What we do
CCG helps organizations become more insights-driven, solve
complex challenges and accelerate growth through
industry-specific data and analytics solutions.
Case studies on our website:
https://ccganalytics.com/resources/case-studies
We are a team of strategists, technologists and business experts helping
forward-thinking organizations transform into intelligent enterprises guided
by analytics and insights. We empower optimized, real-time data driven
decisions and make data and analytics adoption pervasive so you can respond
quickly and intelligently to both crisis and opportunity alike.

66. 66
OFFERINGS OVERVIEW
Data and
Analytics Strategy
Advanced Analytics,
Machine Learning, and AI
Data Management
and Data Governance
Enterprise Business
Intelligence
Cloud Strategy, Migration,
And Management

67. Analytics strategy and roadmap
Analytics maturity assessment
Data literacy program design and
enablement
Analytics adoption and enablement
Operating model design and enablement
Center of excellence, competency centers
Data and analytics platform rationalization
Data management operations and process
improvement
67
DATA AND ANALYTICS STRATEGY
Solutions
Assessment, vision and
roadmap (AVR)
Accelerated AVR –
RapidRoadmap
RapidDash
Platform Modernization
Digital Transformation
Architecture Design Session

68. 68
ENTERPRISE BUSINESS
INTELLIGENCE (BI)
Business intelligence development
Adoption
Self-service
Reporting and dashboards
Our business intelligence experts can help
your organization implement reliable, secure
dashboards and scorecards that deliver real-
time, key performance indicators and visual
analytics on a single, consumable canvas.
RapidDash
Solutions

69. Data Management
Modern data warehouse /
data estate design and
implementation
Data Architecture
Metadata and master data
management
Data quality
Data and analytics
platform modernization
69
DATA GOVERNANCE AND
DATA MANAGEMENT
Data Governance
Program design and
implementation
Organization design
Policy and standards definition
Process and procedure creation
Platform selection and
implementation
Data privacy
Data classification
Regulatory reporting - CCPA,
GDPR, Compliance Support
CCGDG
RapidDG
Solutions

70. Advanced Analytics
Predictive Analytics
Prescriptive Analytics
70
ADVANCED ANALYTICS, MACHINE LEARNING,
AND ARTIFICIAL INTELLIGENCE
Artificial Intelligence
Azure Cognitive Services
Natural Language
processing/understanding
Computer vision/image
processing
Data Science and Machine
Learning Services
Model Development,
Deployment and Maintenance
ML Ops (Machine Learning
Operations)
Data Mining
Data Science Staffing
Data Science Enablement
Data Science Roadmap
Data Science Center of Excellence
RapidInsights
Model as a Service
Solutions

71. 71
CLOUD TRANSFORMATION
Cloud
Strategy
Cloud
Migration
Managed
Cloud Services
Solutions
Platform Modernization
Azure Cloud Service Provider

72. 72
INDUSTRY FOCUS
Retail – Restaurants,
Hospitality, + Leisure
Consumer + Industrial
Manufacturing
Banking + Wealth
Management
Professional
Services
Education

73. 73
STRATEGIC PARTNERSHIPS
Microsoft enables digital transformation for the
era of an intelligent cloud and an intelligent edge.
As the data governance company, Erwin provides
enterprise modeling, data cataloging and data
literacy software.
Profisee makes it easy and affordable for any size
organization to ensure a trusted data foundation.
Databricks unites big data and AI to help
organizations innovate faster and solve complex
challenges.

74. A Sampling of Thrilled Clients
Retail – Restaurants, Hospitality, and Leisure
Consumer + Industrial ManufacturingFinancial Institutions – Credit Unions, Banks, Wealth Management
Professional Services

75. THANK YOU
www.ccganalytics.com | (813) 968-3238