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Modern Analytics in Business - Michele Chambers

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Modern Analytics in Business presentation from Continuum Analytics CMO Michele Chambers at the Crunch Conference in Budapest, October 2015.

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Modern Analytics in Business - Michele Chambers

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  2. 2. O 5 MODERN ANALYTICS IN BUSINESS By Michele Chambers PPPPPPPPPP ucts @ Contmuum Analytics
  3. 3. ABOUT MllHlll Bockgound @mcAnolytics CMO & VP Products @ Continuum Anolytics President & COO @ RopidMiner CSO & VP Product Monogement @ Revolution Anolytics VP & GM Big Doto Anolytics @ IBM / Netezzo Educotion it - —-. t— ANALYTICS ANALYTICS _ . _ I MElHl]| ]Ul. UElES METHUUULUWES BIG DATA BS Computer Engineering Novo University r / zfx °'°. f"lAT. 'f35
  4. 4. %% if ANALYTICS SUPERCHARGE STRATEGIES MARKET EEADER STRATEGIES TABEE STAKES VS. COMPETITIVE ADVANTAGE - Customer intimocy Toble Stokes - Product innovotion - Keeping up with competitors ond morketploce - Operotionol excellence Competitive Advantage - Top line revenue - lnnovotive new discoveries - Operotionol cost sovings vio efficiencies
  5. 5. ' KJQGFOIIOHOI COSI SOVIFIQS VIO GTTICIGHCIGS Why odvonced onolytics? Example Pharmaceuticals I Drug Discovery I Supply Chain Optimization I HR Analytics I Customer Churn LI IBM 2f3|l“E
  6. 6. Whot guides onolytic roodmop? Deliver Business Volue ond lmpoct Focus on the Lost Mile Leveroge Koizen Accelerote Leorning ond Execution Differentiote Your Anolytics Embed Anolytics Estoblish Modern Anolytics Architecture Build on Humon Foctors Copitolize on Consumerizotion
  7. 7. Steps to creote roodmop 7. Establish roadmap 6. Evaluate potential opportunities 5. Create decision model 4. Define analytic solutions 3. Brainstorm opportunities 2. Define value chain 1. Set business goals
  8. 8. Applying the breadth of onolytics % Descriptive Anolytics Simulotion Whot hoppened’? Whot else could hoppen? Predictive Anolytics Prescriptive Anolytics "‘ Whot’s likely to hoppen? Whot is the best/ worst thot con hoppen?
  9. 9. What guides analytic roadmap? Business area How can we apply analytics to a new business area or problem? Data What data can we invent or enrich our analytical insights’? Approach How can we employ a combination of analytical approaches in on innovative way to discover new patterns and value? Precision What additional value would we realize if we could identify individuals (people, transaction, or resources) rather than groups? Algorithms Can we create or use new groundbreaking approaches that give us an advantage’? Embedding How can we systemize our insights by inserting analytics into operational processes? Speed How can we accelerate our pace of business to stay ahead of the competition?
  10. 10. Jumpstart the brainstorming IS THERE SOMETHING IN YOUR BUSINESS THAT YOU How (OULD YOU [USER INNOVATION? WISHED YOU KNEW TODAY RATHER THAN IN THE FUTURE? HOW IOUID YOU INCREASE PROEITARIIITYI WHAT IF YOU lOUlD. ..l
  11. 11. Imagine the possibilities <9” SET PRIIING DETERMINE IIKEIY RIGHT PEOPIE STRATEGIES lOMPETTTORS' MOVES
  12. 12. , ‘I i 3), 1- vrrI—il ju—I—IIl - , ,- an--nun-nil‘ in‘. : '.3&. lIii£l. m. i4;- -«-----A-——~--we I I . .. .. -n, .—_. ..: .,——. .. - I>. ~:-I Mir 7:! ’ -er . iI: 'u'nI: IuI. u-‘in — VP: ‘I -, ———. ._«, -an, -:4 -~
  13. 13. Market Forces Driving Constant Disruption BIG DATA INTERNET OE THINGS - Exploration is Central to Data Discovery " - Sensors - Analytics is Key to Unlocking Value - Consumer Applications - Shift from Data-at-Rest to Data—in-Motion ‘ - Industrial Applications I l'i'. 'ill I‘llIiill EDNSUMERIZAIIDN MASSPERSDNAIIZAIIDN - Empower Front Line - Shift from Aggregations to Individualized - Open Source & Open Data Behavior - Crowdsourcing - Right Sized for Contextual Relevancy - Patterns & Habits in Plain Sight
  14. 14. Analytics are Mainstream OUIIK TIME-TD-VAIUE - Innovating to Drive New Value - Move from Explore to Produce - Infuse Analytics into Front Line iIlli‘; I‘. lI‘i1lII. iiiil GET TO VAIUE SIMPIY - Empower Next Generation Data Scientists - Analytics Factories to Gain Repeatability - Streamline Ideation to Product Cycle f'f'I : lf'lI I'| ' IfV' MAYIMIZ E VAIUE - Business Requirement - Analytics Roadmop - Move Table Stakes to Business IONNEETING TOR VALUE - Breakdown Data Silos to Discover Patterns - Collaboration Across Teams to Drive Value - Leveraging Rich New Data Sources
  15. 15. Technology Evolution is Accelerating BIG DATA Niw EDMPUIE ENGINES - Leverage All Your Data " ’ - Move Compute to Data - A Need for Speed - Leverage All Your Horsepower - Richer Data for New Insight & Action - Divide & Conquer ‘“i; ‘ FIT i ‘ S‘tj: H . I.<. ,. M-. I lUlI GIDUD OPEN SDURIE - Elastic Compute - Quantity of Open Source Projects Accelerating - In-Place Leveraging of Cloud App Data - Faster Open Source Adoption in Enterprises - Streaming | oT Data - Innovation Accelerates through Open Source
  16. 16. The Traditional Proprietary Software Open Data Science Software Decreaslrlg U5‘: -' Accelerating adoption - Vendor lock in - Avoids vendor lock in - High costs - Reduced cost - Lack of integration - Open APIs and connectors - Inability to easily deploy - Eliminates chasm between - Skills gap build & deploy - Accessible to tomorrow’s talent
  17. 17. Evolvin Proprietary Software Decreasing use - Limited Data Sources - Legacy Compute Engines - On—premise Open Data Science Software Accelerating adoption - Big Data - Cloud - Streaming - Advanced Analytics - High Performance Computing
  18. 18. A brief c story of data sc enre by Caogemini cohsultant ‘/ omatno lpadnyaya, g ven cl the B g Data and Arrmyiirs Stirnrnil POI/ I n rlyderar, -ad. I"C? iO oles Proprietary Software Open Data Science Software Decreasing use Accelerating adoption - Programmer - Data Science Teams - Statistician - Data & Computational Scientists - Business Analysts - Software Engineer - Dev Ops - Tools Developers
  19. 19. Computer Science ' Twins machines ' - Inlotfvialioil rneory - ' We-nei 6- Cvbevneiws - son 8 Search Algorithms - Von Neuniariri Aicriiieciuie g, ,k5,, , . (,, ,sk, ,i she" 50,1‘ - - Heuiislics Sirnulaled Annealing ‘ L'‘’'’"'1 9‘’‘“‘Y L°9'° - Babbage Lovelace - Boolean Algebra _ Punch was - ompii Algorithms - Mulligrid mnlhoos - Firsx IBM Tioo oasoo methods compuiors Data Techno! ' DBMS ' Removable Disk drives I Relational DBMS Caliogiapliy I Wll Pl - Asiionoinicaiciians “am Ma" - Charles Minald - Fioioiice Nightingale Jonn Tukey Jacques Benin Taxi. suing seaicii I974 Pele! Naur ‘Concise Survey oi cornpuiei Meinoos‘ Dal: Science. Dalalogy Knuiii Ari oi compuiei Piogiamrning Desktop. iioppy SOL OOP i-iigri level Iangua . - Database Marketing - Daia Mining Knowledge Discovery - ‘Data science ciassiiicaiion and reiaieo meinous ‘ - 1959 riisi KDD Workshop . ciegary PiaieIsky—Shapiio I William Cleveland Daia Science - Leo iaieinionn Slaiisbcal Modeling 2 cuiiuies_ 0 I Edward Tulle Visualization - Word Cloud . Cloud — . _. - opiimizaiion Methods , A , I ‘ M“ ‘ --' . '- . a, iimi. ,.. i.i, m.m m*; *;; °,: ;;’; ,;; “W - issigimeiii ' - Matrix 5. Generalizations commumCaho, _5 ' Auiomai-0" y - i _ . Calculus - Nan-euclidean geomelnes Sche ’j»——-—( rm’! TS‘ . - Loganiiims ‘ ' N°"’T°"‘R39"5°“ mg - I962 Johriw Tukey, Fuiuie o . . )2—{-(_ D: IlaAnzIsis . ... . Mamemaflcs/63 . I976 — SAS insiiiuie ' D°°'S'°" 509"” 1' — Paiiein iecogniiion x = ~ - Theovelical Faundalioris oi Modern Si - Hypolhesis. DOE I Maihematical Slzlislics - Bayesian Methods - Time Series Molnods iaox Cox Survival oic ) - siocriasiic Methods - Plubzibillly ' Correlation - Bayes Tneoioiii - negiession. i.easi sqiiaios . TIMCSDHUS Statistics Pre 181105 - i977 The international Associaiion ioi Siaiisiicai Comnuiing (IAsCi - Machine learning - simuiaiion Maikov - Computational siaiisiics W1 A brief history of data science by Capgemini consultant Mamatha Upadhyaya, given at the Big Data and Analytics Summit ZOI4 in Hyderabad. India
  20. 20. Modern Analytic Platforms Empower Data Science Teams Developer Data Engineer Biz Analyst R Programmer Data Scientist Viz Expert Statistician Computer Science - Turing machines - Toxu slim seam - Information Theory - 197i I Wlainnuli Cihmmpiirl . e-. . - §; —A»»—k AI~»»IL. —«» Ma
  21. 21. - Interact with data using a fluid workflow - Build sophisticated high performance models - Visualize results - iterate on the analysis - Produce interactive plots, dashboards. and applications easily - Share discoveries with the team and across the organization - Play well with legacy data systems and open source libraries - Leverage many languages: R, Matlab, SAS, SPSS. Excel, Java, C++, C#/ .NET. Python, FORTRAN and more
  22. 22. AD HOC USAGE , POC& BI-REP Exploration DATAM| N|NG/ Expirimemoiion MODELLING PRODUCTION DEPLOYMENT A
  23. 23. Modern Agile Anolytics - Converging octivities - No longer chosm - Ropid ogile opp development
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  25. 25. Modern Agile onolytics stock APP3 HARDWARE '
  26. 26. ‘ Modern onolytics stock APP VIZ STORYBOARD ANALYTICS DATA HARDWARE / ‘I--2-_ - TI- -__-I-. L-
  27. 27. Modern businesses need Modern Anolytics Unique roodmops chort o course for high volue impoctful opplied odvonced onolytics Freedom ond ogility ore key to powering Modern Anolytics with modern orchitectures ond technology

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