Dylan Dias. Neal Analytics and Dylan Dias bring a unique management consulting perspective to the world of advanced analytics. Utilizing technology effectively in concert with business yields superior results. Dylan will discuss Neal's approach to ensuring customer success, and explore this in relation to Neal's keystone customer: Coca-Cola. Go to https://channel9.msdn.com/ to find the recording of this session.

4. Creating the fabric of an enlightened organization
Data Infrastructure
• EDW/modern EDW
• OLTP workloads
• Big data platform
Info Strategy
• Reference architecture (cloud/on prem/
hybrid)
• Vendor/workload
• Data strategies selection
Business Expertise
• Self-directed problem solving
• Management consulting techniques
• Compelling exec story telling
Data Science
• Statistical consulting
• Data mining(With Azure machine learning)
• Predictive & advanced modelling
• Deployment & operationalization
Visualization
• Dashboards/Reports
• Exploration/Discovery
• Tools (Excel, Power BI)
Data Pipeline
• ETL/data in motion
• Transform/enhancement services
• Real time/streaming

5. Cloud Platform/
Big Data
Machine Learning/
Analytics
Visualization
SQL DB Blobs &
tables
HDInsight SQL Server
VM
Data Science/
Predictive Analytics
Data Engineering/
Big Data
Industry
Experience

6. Demo
Scenario Planning
Scorecard
Skillsets & RolesApproach
Engagement
Methodology
Challenges
Key Use Cases
Analytics Maturity
Assessment
Meet the
customer
Engage
Correctly
Solve the
Problem
Create a
Roadmap

7. CompetitiveAdvantage
Analytics
Machine
Learning
Optimization What’s the best that can happen?
Predictive Modeling What will happen next?
Forecasting What if these trends continue?
Statistical Analysis Why is this happening?
Alerts What action is needed?
Query & Drill-Down Where exactly is the problem?
Ad Hoc Reports How many? How often? Where?
Standard Reports What happened?
Access &
Reporting
Degree of intelligence

8. Solutions, pronto!I’m drowning. Help! Let’s build together!
• What has been attempted
previously?
• What unfilled promises from
previous efforts?
• What needs to be built within
versus outsourced?
• What is a core competence?
• What is the long-term
program aspiration?
• What kinds of pre-packaged
solutions?
• Are there ready insights from
our data?

9. • New account risk screens
• Fraud prevention
• Trading risk
• Maximize deposit spread
• Insurance underwriting
• Accelerate loan processing
• 360° view of the customer
• Analyze brand sentiment
• Localized, personalized
promotions
• Website optimization
• Optimal store layout
• Call detail records (CDRs)
• Infrastructure investment
• Next product to buy (NPTB)
• Real-time bandwidth allocation
• New product development
• Supplier consolidation
• Supply chain and logistics
• Assembly line quality
assurance
• Proactive maintenance
• Crowd source quality
assurance
Telecom ManufacturingRetailFinancial Services
• Genomic data for medical trials
• Monitor patient vitals
• Reduce re-admittance rates
• Store medical research data
• Recruit cohorts for
pharmaceutical trials
• Smart meter stream analysis
• Slow oil well decline curves
• Optimize lease bidding
• Compliance reporting
• Proactive equipment repair
• Seismic mage processing
• Analyze public sentiment
• Protect critical networks
• Prevent fraud and waste
• Crowd source reporting for
repairs to infrastructure
• Fulfill open records requests
• Consumer Goods & Identify
hidden revenue opportunities
• See and predict changes in
supply or demand Market
price volatility and production
planning Promotional demand
Suggested product engines
Public Sector Goods and ManufacturingUtilities & EnergyHealthcare

10. • Managers are challenged to see through all of the rapidly growing
volumes of data in order to understand what’s really happening
• Opportunities are missed or never even realized
• Too many vendors/tools/platforms to choose from
• Economic considerations – grow profits or cut costs
Decision Makers
are drowning in
data
Machine
Learning and
Predictive
Analytics
• Closer relationship with customers by understanding behaviour
• Targeted advertising and promotions
• Balance inventory with demand
• Charge exactly the price that customers are willing to pay at that moment
• Determine the best, most profitable use of marketing investments

11. Mismatched Expectations
• Too much, too quick
• Incorrect funding levels
• Misaligned semantics
Incorrect Team/Capabilities
• Beware the ‘schmexpert’
• Mismatched capabilities
• Complex coordination across
workstreams
Improper Transition
• Insufficient design for hand-
off (POC to Prod, between
teams)
• Stopping at the model /
analytics (instead of landing
the business go-do’s)

12. • Infrastructure
considerations
• Big Data, Models,
Predictive
Analytics
• Cloud
• Canonical
Scenarios
• Closed-form
hypotheses
• Definition of
success
• Rigorous
advanced
analytics process
• Work *with*
Customer
• Quick wins
• In-market testing
• Closed feedback
loops
• Analytical
Roadmap
1
3
4
2

13. The game-changing opportunities made possible by Azure Machine Learning are creating immense
interest among companies of all sizes and across all industries. As the barriers of entry and costs of
admission are eliminated by the advantages of cloud computing, specifically Microsoft Azure, there are
many organizations beginning to look for ways to leverage the power of machine learning and
predictive analytics to address a variety of business challenges and opportunities.
Challenges
• Marrying machine learning to
business value can be difficult
• Business stakeholders may not
understand how machine learning
can help them
• Lack of experience and in-house skills
can lead to uncertainty and confusion
as to how and where to start?
NEAL
Approach
Proof of
Profit
• Meet with business
stakeholders to
understand
challenges
• Customer-led or
NEAL assisted
Identify Scenarios Prioritize Production
• Assess business value
vs. feasibility
• Prioritize and select
scenarios
• Build & deploy
complete model
• Operationalize

14. Considerations
• Business Management is the key skill to
keep the endeavor laser-focused and
aligned with business outcomes/value
• Change Management and dealing with
Organizational inertia are critical
• Data Science is domain-aware and brings
analytical robustness (Subject Matter
Expertise)
• Works closely with Data Engineering to
iterate ideas for steady progress
• Data Engineering prepares two-way data
pipeline to enable development and
consumption of decision insights
Advanced Analytics Success:
A fine balance of three skill groups
Business Management
Data Engineering Data Science
Advanced Analytics Success

15. • Data Profiling/testing
• Data Visualization
• Model train/test/score/validation
• Model Deployment
• Regular Monitoring
• Model refresh/retirement
• Problem Framing
• Prioritization of Scenarios
• Hypothesis Generation
• Business Justification
• Model Specification
• Feature/Variable selection
• Adoption and Change
Management
• Data Sourcing
• Data Cleansing
• Data Transformation
• Data Staging
• Data Publish/catalog refresh
• Refresh updated/transfer
• Retirement & renewal
Advanced
Analytics
Projects
Data Science
Data
Pipeline

16. 4
5
Finalize budget timeline for
selected scenario
START
ENTER INTO
EXECUTION
1
Gather, Speak minds
2
Complete
questionnaire
3
Combine/synthesize
thoughts into scenarios
Write specification

17. Knowledge transfer
Prioritized Scenarios
(Canonical, Scored)
Right phasing/stages
(crawl-walk-run)
On-time, on-budget
Semantics Aligned
Roadmap/Program
Value (ROI, ROMI, Revenue
↑, Cost ↓)
Operationalized

19. Dylan Dias
CEO, Managing Consultant
Neal Analytics
847 942 0310
Dylan@NealAnalyitcs.com

21. David Brown
Solution Sales Director
Neal Analytics
425 283 6842
DavidB@NealAnalyitcs.com