DATA ANALYTICS ROADMAP FOR THE ENERGY INDUSTRY · data analytics roadmap for the energy industry...

Booz Allen Innovation Center, Washington DC

Kirk Borne

Principal Data Scientist

@KirkDBorne

DATA ANALYTICS ROADMAP FOR THE ENERGY INDUSTRY

JUNE 14, 2017

AGENDA FROM BIG DATA TO SMART DATA

TAMING BIG DATA WITH DATA SCIENCE & ANALYTICS

THE INTERNET OF THINGS (INTERNET OF CONTEXT)

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THE MATHEMATICAL CORPORATION

Booz Allen Hamilton Internal 1




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EVER SINCE WE FIRST EXPLORED OUR WORLD…

3Booz Allen Hamilton Internal

http://www.livescience.com/27663-seven-seas.html

http://www.livescience.com/27663-seven-seas.html

…WE HAVE ASKED QUESTIONS ABOUT THE WORLD…


https://atillakingthehun.wordpress.com/2014/08/07/atlantis-not-lost/

https://atillakingthehun.wordpress.com/2014/08/07/atlantis-not-lost/

…AND WHERE HAS THAT LED US?WE HAVE COLLECTED EVIDENCE (DATA) TO ANSWER OUR QUESTIONS, WHICH

LEADS TO MORE QUESTIONS, WHICH LEADS TO MORE DATA COLLECTION,

WHICH LEADS TO MORE QUESTIONS, WHICH LEADS TO BIG DATA!


y ~ 2 * x (linear growth)

y ~ 2 ^ x (exponential growth)

https://www.linkedin.com/pulse/exponential-growth-isnt-cool-combinatorial-tor-bair

y ~ x! ≈ x ^ x→ Combinatorial Growth!(all possible interconnections,linkages, and interactions:high variety for discovery!)

3+1 V’s of Big Data:Volume = most annoying VVelocity = most challenging VVariety = most rich V for discoveryValue = most important V

https://www.linkedin.com/pulse/exponential-growth-isnt-cool-combinatorial-tor-bair

ALL THOSE COMBINATIONS = SMART DATA !MAKING SENSE OF THE WORLD WITH SMART DATA:


Semantic, Meaning-filled Data:

• Ontologies (formal)

• Folksonomies (informal)

• Tagging / Annotation– Automated (Machine Learning)

– Crowdsourced

– “Breadcrumbs” (user trails)

Broad, Enriched Data:

• Linked Data (RDF)

– All of those combinations!

• Graph Databases

• Machine Learning

• Cognitive Analytics

• The 360o view

• Context

The Human Connectome Project:mapping and linking the major pathways in the brain.http://www.humanconnectomeproject.org/

http://www.humanconnectomeproject.org/

“ALL THE WORLD IS A GRAPH” – SHAKESPEARE?


(Graphic by Cray, for Cray Graph Engine CGE)

http://www.cray.com/products/analytics/cray-graph-engine

http://www.cray.com/products/analytics/cray-graph-engine

“ALL THE WORLD IS A GRAPH”


EXAMPLES

Connecting the nodes in the graph and labeling the edges (with context and relationship information) can lead to deeper insights than simple transactional databases.

• Customer Journey modeling

• Safety Incident Causal Factor Analysis

• Medical Research Discoveries across disconnected journals, through linked semantic

assertions

• Marketing Attribution Analysis

• Fraud networks, Illegal goods trafficking networks, Money-Laundering networks

The connection between black hat entities {1} and {3} never appears explicitly within a transactional database.

{1} {3}{2}




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THE WORLD OF BIG DATA IS ON FIRETame the flames with Data Science and Advanced Analytics, through Machine Learning (= mathematical algorithms that learn from experience)

(Is that why we are meeting at the Houston Heights Fire Station?)


1) Class Discovery: Find the categories of objects

(population segments), events, and behaviors in your data. + Learn the rules that constrain the class boundaries (that uniquely distinguish them).

2) Correlation (Predictive and Prescriptive Power) Discovery: Find trends, patterns, and

dependencies in data, which reveal new governing principles or behavioral patterns (the “DNA”).

3) Novelty (Surprise!) Discovery: Find new,

rare, one-in-a-[million / billion / trillion] objects, events, and behaviors.

4) Association (or Link) Discovery: (Graph and

Network Analytics) – Find the unusual (interesting) co-occurring associations / links / connections.

THE 5 LEVELS OF ANALYTICS MATURITYData Science can be applied at any level, depending on the level of analytics maturity required.


1) Descriptive Analytics

– Hindsight (What happened?)

2) Diagnostic Analytics

– Oversight (real-time / What is happening?

Why did it happen?)

3) Predictive Analytics

– Foresight (What will happen?)

4) Prescriptive Analytics

– Insight (How can we optimize what happens?)

(Follow the dots / connections in the graph!)

5) Cognitive Analytics– Right Sight (the 360 view , what is the right

question to ask for this set of data in this

context = Game of Jeopardy)

– Finds the right insight, the right action, the

right decision,… right now! = Next Best Action!

– Moves beyond simply providing answers, to

generating new questions and hypotheses.

PREDICTIVE VS PRESCRIPTIVE : WHAT’S THE DIFFERENCE?


PREDICTIVE

Analytics

Find a function (i.e., the model) f(d,t) that

predicts the value of some predictive

variable y = f(d,t) at a future time t, given

the set of conditions found in the training

data {d}.

=> Given {d}, find y.

PRESCRIPTIVE

Analytics

Find the conditions {d’} that will produce a

prescribed (desired, optimum) value y at a

future time t, using the previously learned

conditional dependencies among the

variables in the predictive function f(d,t).

=> Given y, find {d’}.



PREDICTIVE

Analytics





data {d}.


PRESCRIPTIVE

Analytics







Confucius says…

“Study your past to know

your future”



PREDICTIVE

Analytics





data {d}.


PRESCRIPTIVE

Analytics







Confucius says…

“Study your past to know

your future”

Baseball philosopher Yogi Berra says…

“The future ain’t what it

used to be.”




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THE INTERNET OF THINGSThe (Industrial) Internet of Things (IIoT / IoT) consists of an interconnected (Graph!) universe of Dynamic Data-Driven Application Systems (dddas.org) =>

Combinatorial Explosive Growth of Smart Data!

IoT / IIoT / M2M demands we deploy intelligence at the point of data collection! (= Machine Learning at the edge of the network) =>

Edge Analytics!


https://www.nsf.gov/news/news_images.jsp?cntn_id=122028

https://www.linkedin.com/pulse/can-elephant-bigdata-dance-iot-internet-things-tune-lambba

Internet of Everything

https://www.nsf.gov/news/news_images.jsp?cntn_id=122028

https://www.linkedin.com/pulse/can-elephant-bigdata-dance-iot-internet-things-tune-lambba

SMART THINGS IN THE IOT (THE INTERNET OF CONTEXT):INTELLIGENCE AT THE EDGE OF THE NETWORK(AT THE POINT OF DATA COLLECTION = EDGE ANALYTICS)


http://www.smarttechforyou.com/2015/03/smart-manufacturing-iot-fourth-industrial-revolution.html

• Smart business ops (context-aware)

• Smart maintenance

• Smart operations & controls

• Smart workforce deployment

• Smart manufacturing & inventory

• Smart supply chain & pricing

• Smart logistics (people, things)

• Smart customer engagement

Embedded IoT-driven Streaming Analytics = a top focus of Machine Learning projects: http://searchdatamanagement.techtarget.com/feature/Embedded-analytics-to-feel-widest-impact-of-machine-learning-projects

From IoT-enabled products to the IoT-enabled organization = multi-use data will dominate future IoT implementations:http://internetofthingsagenda.techtarget.com/feature/Multiuse-data-dominates-Future-of-IoT-implementation

CONTEXT IS KING!“You can see a lot just by looking.” – Yogi Berra


• Context is “other data” about your data = i.e., Metadata!

• The 3 most important things in your data are: Metadata, Metadata,

Metadata!

• Metadata are…

– Data that describes Data

– Other Data that describes Your Data

– Your Data that describes Other Data

• e.g., Connected “Smart” Cars = that car that is braking 3 vehicles

ahead of you = informs your vehicle to brake now!

• The Smart Enterprise = predictive / prescriptive maintenance

algorithm alerts the corresponding asset, the right skilled technician,

the right part, & the right tool to be at right place at the right time!

• IoT sensors + Open Data provide a lot of context data (metadata!):

The Interconnected Graph of data, information, and knowledge!

• Contextual data empowers both Prescriptive and Cognitive Analytics.

HOT APPLICATION (THE KILLER APP) FOR IOT : THE DIGITAL TWINREAD THIS: “DIGITALIZATION, DIGITAL TWINS AND CYBER -PHYSICAL SYSTEMS…”

http://blogs.dnvgl.com/software/2016/04/digital-twins-structural-engineering/


The digital twin is a virtual image of your asset, maintained throughout the lifecycle and easily accessible at any time, able to replay (simulate) any physical scenario.

http://www.gartner.com/smarterwithgartner/gartners-top-10-technology-trends-2017/

http://www.gartner.com/smarterwithgartner/7-technologies-underpin-the-hype-cycle-for-the-internet-of-things-2016/

“INDUSTRIES TURN TO DIGITAL TWINS AND IOT TO BOOST PROFITS…”

http://searchmanufacturingerp.techtarget.com/tip/Industries-turn-to-digital-twin-technology-IoT-to-boost-profit


Some key players (capitalizing on the IoT):

• NASA• GE• DNV-GL• Siemens• TWI Ltd• ANSYS

HOT APPLICATION (THE KILLER APP) FOR IOT : THE DIGITAL TWIN



http://www.gereports.com/amazon-turns-ge-wind-turbines-power-business/




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DATA SCIENCE AND ANALYTICS IMPROVE YOUR ODDS IN THE FUNDAMENTAL BUSINESS GAMBIT:

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http://www.telegraph.co.uk/news/worldnews/europe/russia/10061780/Russian-convicts-beat-Americans-in-cyber-chess-battle.html

SMART DATA ANALYTICS –-- DATA-TO-ACTION TRIAGEGENERAL EXAMPLE -- IOT EVENT MINING IN BIG DATA COLLECTIONS FOR ACTIONABLE INTELLIGENCE:

Listen: Behavior modeling (anomaly & trend detection), ad hoc inquiry for Discovery!

Learn: Identifying, characterizing, & understanding events for data-driven Decisions!

Act: Deciding which events need immediate investigation and/or intervention = Action!

… producing rewarding outcomes for people, products, and processes!


MANY OTHER EXAMPLES:

Customer churn early warning (from 360-view customer data)

Predictive Maintenance alerts (from ubiquitous machine / engine sensors)

Prescriptive efficiency modeling and implementation (from process mining)

Supply chain monitoring (from manufacturing & shipping sensors)

Predictive personnel & asset logistics (trajectory modeling from tracking data)

Cybersecurity alerts (from network logs)

Preventive Fraud alerts (from financial applications)

Health alerts (from electronic health records and national health systems)

Tsunami alerts (from geo sensors everywhere)

Social event alerts or early warnings (from social media)

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INFUSING ANALYTICS CAPABILITY INTO YOUR ORGANIZATIONDRIVING COMPETITIVE ADVANTAGE THROUGH DATA-TO-ACTION:

• AI (AUGMENTED INTELLIGENCE = MACHINE-ASSISTED HUMAN, AND VICE VERSA)

• MACHINE INTELLIGENCE

• THE MATHEMATICAL CORPORATION!


Activities• Enrich• Integrate

and Transform Data

Methods

• Descriptive Statistics

• Filtering• Aggregation

Activities• Reveal trends• Identify

Correlations• Learn

Patterns

Methods

• Unsupervised Learning

• Clustering• Outlier

Detection

Activities• Classify

Signals

• Predict Risks• Forecast

Resources

Methods

• Random Forest

• Neural Networks

• Bayesian Analysis

• Collaborative

Filtering

Activities•Optimize Resources

•Simulate Decision Outcomes

Methods

•Genetic Algorithms

•Integer Programming

•Non-Linear Programming

•Discrete Event

Simulation

Acquisition, aggregation and enrichment of information from multiple entry points will help create a holistic view that can enhance operations, reduce risk, provide powerful insight, and create value.

… Enables Effective Operations and Decision-Making

• Allows for accurate analysis of trends across the organization against defined KPI’s

• Supports strategic C-Suite decision making

• Reveals operational risks and potential bottlenecks in real-time

• Supports critical information infrastructure protection efforts by early detection of vulnerabilities

Products

Reports | Dashboards | Mitigations

360o Data Acquisition …Business Operations and

Performance Data

Logs: Systems, Customers,…

Reports, e-Docs, and Manuals

Open Data

The foundation is Data!



• Design Patterns for Streaming Data Analytics:– Detecting POI (Person of Interest, or Pattern of Interest, or any Point of Interest)

– Detecting BOI (Behavior of Interest from any “dynamic actor”)

– Precomputed scenarios and their responses (to speed up “next best action”)

– Design Thinking : DX, UX, CX, EX (Digital / User / Customer / Employee eXperience)

• Edge Analytics (what else is happening now at the point of data collection?)

– Locality in Time

• Near-field Analytics (who / what is local to this person / place / thing?)

– Locality in Geospace

• Related-entity Analytics (what else is similar to this entity / event?)

– Locality in Feature Space

• Agile Analytics: DataOps, Fail-fast, Iterative, MVP, Learning Energy Systems

Minimum Viable Product= your POV (Proof of Value)

http://www.agilebuddha.com/agile/demystifying-devops/




DevOps for Data Analytics = DataOps

Minimum Viable Product= your POV (Proof of Value)



THANK YOU!


@KirkDBorne

@BoozDataScience

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DATA ANALYTICS ROADMAP FOR THE ENERGY INDUSTRY · data analytics roadmap for the energy industry...

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