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Cortana Analytics Workshop: Ensuring Customer Success with Advanced Analytics


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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 to find the recording of this session.

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Cortana Analytics Workshop: Ensuring Customer Success with Advanced Analytics

  1. 1. 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
  2. 2. 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
  3. 3. 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
  4. 4. 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
  5. 5. 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?
  6. 6. • 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
  7. 7. • 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
  8. 8. 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)
  9. 9. • 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
  10. 10. 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
  11. 11. 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
  12. 12. • 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
  13. 13. 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
  14. 14. 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
  15. 15. Dylan Dias CEO, Managing Consultant Neal Analytics 847 942 0310
  16. 16. David Brown Solution Sales Director Neal Analytics 425 283 6842