Mark Rittman presented at the UKOUG Tech'17 Conference in December 2017. He discussed how analytics has changed business models and driven disruption twice already. The first wave focused on using analytics for operational efficiency. The second wave saw companies like Amazon, Netflix, and Uber build entirely new data-driven business models. Now, a third wave is underway where analytics and machine learning are being embedded into all business applications and fueling new personalized, data-enriched offerings.

1. timeline
Analytics is Taking Over the World (Again)
Mark Rittman, Oracle ACE Director (and Product Manager)
UKOUG Tech’17 Conference,
Birmingham UK, December 2017
@markrittman

2. Mark Rittman, Oracle ACE Director
● Oracle ACE Director
● Past UKOUG Oracle Scene Editor
● Author of two books on Oracle BI
● 15+ Years in Oracle BI, DW, ETL + Big Data
● Now Independent Analyst & Product Manager
● Host of Drill to Detail Podcast, including:
○ Paul Sonderegger from Oracle on data capital
○ Timo Elliott from SAP on analytics and innovation
○ Donald Farmer the original PM behind Microsoft OLAP
○ Maxime Beauchemin on Airflow and data engineering
○ Cindi Hewson on Modern BI and *that* Gartner MQ
○ Special UKOUG Tech’17 Episode with Oracle’s Mike Durran
About The Presenter

3. My Career Was Built on Analytics
● Data Warehousing
● Reporting & Dashboards
● OLAP & reporting tools
● Turned that interest into a blog
● Turned that blog into a company
● Consulting, training and advising on analytics
● 20 years to-date

4. Analytics Changed the World
● The “first coming” of Analyitcs
● Thomas Davenport’s “Competing on Analytics” was the seminal textbook
○ Focus was more on operational efficiency of existing business models
● Analytics for cost-reduction, process optimization
● Analytics for manufacturing, retailing, healthcare
● Used by smart businesses (sometimes) to optimize their operations

5. “By analytics we mean the extensive use of
data, statistical and quantitative analysis,
explanatory and predictive models, and fact-
based management to drive decisions and
actions. Analytics are a subset of what is now
known as business intelligence.”

6. More Recently, I’ve Been Interested in “Big Data”
● Big Data, Hadoop, IoT, Cloud, Spark et al
● Termed “Analytics 2.0” by Thomas Davenport
● New disruptive businesses coming out of this world, driven
by analytics and data
● Using internal and externally-sourced data to create new
products and services
○ Exploited first by tech-driven
○ with significant first-mover / platform advantages

7. Then Big Data and Analytics Changed it Again
● Amazon with its logistical engine and ability to move product to where demand is, and where it’s
predicted to be
● Netflix with its film recommendations, and commissioning of new films and TV content based on
what it knows viewers like
● Uber, a big data tech company who happen to run taxis right now
● and Deliveroo, who now open pop-up restaurant kitchens in places where there’s lots of demand for
specific restaurants outside catchment area

10. “Frank”, Deliveroo’s AI-Powered Dispatch Engine
● Data is used in three ways at Deliveroo
○ Experimentation to understand effect of product changes
○ Supporting general algorithmic decisions
○ To power “Frank”, their AI-powered dispatch engine
■ Constantly recalculating best combination of riders to orders
■ Uses predictions for rider travel time,
food preparation time
■ Machine learning algorithms and vast quantities
of data to make predictions and decisions
about drivers in real-time
■ Then stack orders based on these decisions

11. How Analytics and Data Power Uber’s Business
● Supply Positioning
○ Stores (big) data for every trip taken
○ Gathers data on drivers’ speed and acceleration
○ Whether driver also works for competitor e.g. Lyft
○ Predict the customer’s wait time
○ Recommends where drivers should place themselves
for best fares and most passengers
■ Delivers 5% uplift in revenue of $3.4bn (2016)
■ 97% chance of being picked-up in <10 mins
● Bridging the Gap Between Demand and Supply
○ A/B testing, Clustering
● Management interviewees screened with analytics test
○ Basic Excel knowledge, stats and probability
○ Knowledge of key metrics, scenario analysis

12. Disrupting via Data, Analytics and Platforms
● Google, Airbnb, Ebay and other platforms
● Multi-sided markets where winner-takes-all
○ Aggregation Theory (Stratechery)
● Leverage data and analytics at-scale
● Using data as a form of economic capital
○ Data Capital Theory (MIT Technology Review)
○ Principle #1: Data Comes from Activity
○ Principle #2: Data Tends to Make More Data
○ Principle #3: Platforms Tend to Win

13. And I’ve Also Been Interested By Startups
● The culture
● New channels for delivery, new markets being served
● And because they're typically built around product
○ Because they're typically VC-funded
○ All about getting 10x return on the winners
■ (1/3 1/3 1/3 strategy for early-stage investors)
● I worked for a while at Gluent with Tanel Poder & Paul Bridger
○ Product Marketing and maker of bacon sandwiches
● And then I joined Qubit, a MarTech startup as Analytics PM
○ And so I entered a parallel world
■ The world of eCommerce and marketing analytics
■ Personalization and *real* big data analytics
■ (and startup SaaS vendors)

17. eCommerce and Marketing *live* by Analytics
● In our Oracle world of ERP, Manufacturing we think we do analytics,
but a lot of it is really reporting
○ BI Apps, Reports and ApEx, Dashboarding apps such as
OBIEE
● But in the eCommerce world, analytics are everywhere
○ Measuring the uplift from a campaign
○ Dividing customers into segments, and measuring and working
to improve the value of that customer segment
○ Ability to electronically define an offer, a new store, a new
business line, track its effectiveness and quickly pivot to a new
offer if needed

18. But it was like a parallel universe
● Instead of GL and Inventory, we have Conversions and Merchandising
● Instead of saying most BI projects fail because they don't solve a business problem, they say most
analytics projects fail because of not being actionable
● And the de-facto standard analytics tool in this world is .... Google Analytics
○ Which we think is just graphs of visitors and page views
○ But it actually a world of segments, metrics, goals, attribution and A/B testing
■ Quickly spot opportunities and present actionable information.
■ With credit assigned to every touchpoint in the customer journey,
■ Get granular in your analysis and create highly accurate models
■ Google Analytics 360 Suite – Google (link)

21. CRO, A/B Testing, Attribution and Uplift
● It's all about stats and conversion optimization
○ Try everything once, see what works
○ Very stats and probability-heavy
○ Attribution
○ Drives a whole industry
■ CRO
■ Monetate
■ Optimizely
○ Everything is measured, statistically modelled,
revenue tracked and digital
■ Typically drives ~ 1% - 3% uplift

25. SaaS Businesses Have Data and Analytics in DNA
● These new SaaS business define and drive themselves
through analytics
○ Churn
○ Metrics and Geckoboard
○ Metabase
○ Product analytics
● Analytics vendors you’ve never heard of serve these
high-growth SaaS businesses
○ Mixpanel
○ Amplitude Analytics
● Part of a wider move towards what Thomas Davenport
calls Analytics 3.0—the era of data-enriched offerings.

26. Discovering New Music in the Past
(Or Not)

28. Discovering New Music Today

30. Data + Analytics Disrupting Existing Industries
● Spotify replaced the local HMV through data, analytics and personalization
○ Data: Powerful By-product of Streaming Music
○ Every user gets a personalized playlist every week
○ Music that they have not heard before on the service,
but that will be something the listener is expected to enjoy
■ Discovery without the record store snobbery
○ Spotify for Artists app
■ Democratization of audience insight

31. 2 Years Ago at Tech’15 - Marketing Data Lake Concept
● Two years ago at UKOUG Tech’15 I talked about data reservoirs and Customer 360 analysis
○ Use of big data, analytics and ML to enable 1:1 marketing and personalization

32. Today

33. Proprietary 33
Identify
Onsite
behavioral
data
Onsite preference
data
Imported data
Segment and
experience data
Customer Data
Store
SegmentationEngine
ExperienceUpsell
ExperienceChurn Risk
VIP Experience
Influence
Programmatic
Experiences
Social Proof
Abandonment Recovery
Product Recommendations
Directed Experiences
Custom or templated
experiences aimed at
specific segments
Understand
Qubit Data Pipeline | Real-Time Data Ingestion and Processing of Raw Customer Events

35. AI and Data-Enriched Analytic Apps for Everyone
● And ML and AI are making their way into regular LOB applications
○ Salesforce Einstein
○ Google Docs
○ IBM Watson
○ And Oracle’s Adaptive Intelligent Apps

39. Analytics is Taking Over the World (Again)
https://medium.com/mark-rittman

40. timeline
Analytics is Taking Over the World (Again)
Mark Rittman, Oracle ACE Director (and Product Manager)
UKOUG Tech’17 Conference,
Birmingham UK, December 2017