Your SlideShare is downloading. ×
0
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014

978

Published on

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
978
On Slideshare
0
From Embeds
0
Number of Embeds
6
Actions
Shares
0
Downloads
17
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Sastry Chilukuri Partner, McKinsey & Company  Strategy and technology for Pharma, Med Device and Government  BS, Indian Institute of Technology (IIT); MBA, Kellogg
  • 2. 1
  • 3. 2
  • 4. 3
  • 5. 4 295 exabytes If you stacked a pile of CD-ROMs on top of one another until you’d reached the current global storage capacity for digital information … … it would stretch 80,000 km beyond the moon
  • 6. 5 7,ooo,ooo14,ooo,ooo21,ooo,ooo28,ooo,ooo35,ooo,ooo42,ooo,ooo49,ooo,ooo56,ooo,ooo63,ooo,ooo 84o,56o m Every hour, enough information is consumed by Internet traffic to fill 7 million DVDs Side by side, they’d scale Mount Everest 95 times
  • 7. 6 … to fill 5,955 football fields 5oo,ooo+ data centers are large enough ... The world’s
  • 8. 7 USD 3,2oo,ooo,ooo USD 16,9oo,ooo,ooo 2010 2015 The Big Data market is set to increase from to
  • 9. 8 35,ooo,ooo,ooo,ooo,ooo,ooo,ooo By 2020 one third of all data will be stored or have passed through the cloud and we will have created bytes of data
  • 10. 9 10x more servers 75x more files 50x more data By 2020 IT departments will be looking after
  • 11. 10 … than in all of history prior to thatBig Data and Advanced Analytics 3,5oo,ooo,ooo,ooo,ooo,ooo,ooo 2010 2012 o Bang We have produced more data in the last two years …
  • 12. 11 Lessons from the leaders Transformation journey1 Open data2 Organization and talent3 Frontline adoption4
  • 13. 12 Achieving transformational impact is a long journey 011998 99 2000 02 08 0906 201105 1003 0704 Net profit EUR mn Tesco personal finance launch Tesco.com launched Clubcard relaunched with key fobs Coupons@Till Roll out to int’l markets £1billion given back to customers Shopper panel launch Promotions cut by 1/4 Customer perception improves 100% data Lifestyles segmentation Assortment tool “Value index” integrates price and promotion indices Macro space optimisation All range reviews use substitutability analysis Promotions data Finest launched All promotions post-evaluated Standard reporting of customer insight KPIs and analytics SOURCE: Bloomberg; interviews; annual reports; analyst reports
  • 14. 13 ▪ Gender ▪ Age ▪ Household/billing address ▪ Number of people on plan Multiple stages along the journey – Telco Example Factual identity data Level 1 Level 2 Level 4 Level 5 Level 3 ▪ Billing preferences – paper, online, mobile ▪ Payment history and type (credit, debit, cash) ▪ Credit/history score Billing data ▪ POS data – location, time, amount, product ▪ Channel preferences and behaviour (through e.g., Clickfox) ▪ Online channel behaviour (sales and service) Usage data Network data Internal data sources ▪ Location ▪ Voice, SMS and data usage (CDR/ DDR)) ▪ Data consumption and type (e.g., online browsing, app types) ▪ VOD usage (amount, time, etc) and type (e.g., Tbox) ▪ Music consumption (e.g., Mog) Response data Marketing response data ▪ Contact history ▪ Campaign response ▪ Offered products ▪ Real time IMEI location ▪ Fixed connection location External data sources ▪ Housing lists ▪ Phone numbers ▪ Further demographic information External data Unstructured data ▪ Social media – e.g. LinkedIn data allows us to segment customers by occupation, recent promotion, etc… ▪ E-mail ▪ “Big data” insights ▪ E.g., through partnering with a major retailer ▪ Spending habits – average spend per month, preferred payment types, etc… Partner data
  • 15. 14 New capabilities and enablers are required to capture AD&A opportunities Analytics service providers Public and internal data sources Data partner- ships Techno- logy platform providers +
  • 16. 15 Lessons from the leaders Transformation journey1 Organization and talent3 Frontline adoption4 Open data2
  • 17. 16 Government increasingly providing Open Data Categories ▪ Product safety data (e.g., complaints, recalls) ▪ Census data ▪ Weather data ▪ Geo-spatial data ▪ 40+ countries with open data platforms ▪ Most of data not necessarily prioritized by value potential ▪ More sensitive information is typically withheld Govern- ment 1 ▪ Google search ▪ Social media (e.g., Twitter feeds, Facebook likes) ▪ Crowdsourcing (e.g., product features) ▪ Social media and Google search is mostly openly available data ▪ Companies have started leveraging crowd sourced feedback Consumer behavior/ pre- ferences 2 ▪ Product information offered, cost, terms, interest, etc. ▪ Statistics on damages/injuries incurred by customers ▪ Customer reviews of products ▪ Most of this data is proprietary and considered as competitive asset ▪ Some of this data may become open in the future Firms3 Current trendsExample data sources
  • 18. 17 Companies utilizing Open Data for impact Data sources Developed predictive models based on weather forecasts to improve inventory management Developed a proprietary methodology to rate healthcare providers based on publicly medicare data and data from healthcare bodies Developed an algorithm that assess credit worthiness of customers based on their social media profiles Created an online community to crowd- source product design inputs from customers Launched a contest for movie rating prediction to improve the accuracy of its existing movie recommendation Aggregated its transaction data to provide customer spending trends and patterns to other companies Govern- ment data Description Impact $50–100 incremental profit per account through optimized line assignment Increased inventory velocity and reduced revenue lost due to stock- outs Gathered cost and rating information on 5,000 hospitals and 16,000 nursing homes Revenues rose by 400% in a span of 8 years Increased recommendation quality by 10% Used big data analytic on proprietary data to provide relevant intelligence to customers Companies Consumer behavior data Firm data
  • 19. 18 Transformation journey1 Organization and talent3 Frontline adoption4 Open data2 Lessons from the leaders
  • 20. 19 Find the “translators”--people who can bridge different functional areas Business Owners Analytics IT Head of analytics Data Analysts Data Scientists Solution Architects ▪ Ensure best in class models and algorithms support internal customers ▪ Drive the design and execution of the overall data and analytic strategy ▪ Provide link across IT, analytics, and business ▪ Solid under- standing of statistics and analytics which can leverage into business decisions ▪ Ensure future data require- ments and delivery roadmap is robust and complete
  • 21. 20 Five ways companies are supercharging their talent Creating partnerships4 1 Making strategic hires Retaining advanced analytics talent 5 Sourcing globally2 Training and certification programs 3 Strategic hires from Yahoo Labs, Google and Amazon (~20) Sizable portion of the analytics team located in India(~40 out of 60) Set high expectations for all existing analytics staff (which is vast bulk of analytics talent) Constantly scan and collaborate with technology ecosystem players Dedicates significant senior management attention to promote/ retain new talent
  • 22. 21 Lessons from the leaders SOURCE: Source Transformation journey1 Organization and talent3 Frontline adoption4 Open data2
  • 23. 22 Typical building blocks for accelerating Advanced Analytics impact Transformed data model Modeling insights Data modeling “Black box” Heuristic insights “Smart box” Workflow integration Process redesign Tech-enablement Adoption Internal External Capability building Change management Source of value1 2 3 4 5 ▪ Advanced statistical analysis to drive business insights ▪ Codified heuristics dispersed in the organization to enhance analytics ▪ Management of large pools of internal and external data ▪ External strategic partnerships ▪ Developed frontline and management capabilities ▪ Proactive change management and tracking of adoption with performance indicators ▪ Easy-to-use user interface built in the platform ▪ Redesigned processes to embed rules in the workflow ▪ Clear articulation of the business needs for advanced analytics and assessment of expected impact Modeling insights Transformed data model Adoption Workflow integrationSource of value
  • 24. 23 Leading organizations adapt their operating model around analytics tools to fully take advantage of the insights generated Text Cutting edge technology ▪ Define the technology application(s) that will deliver analytics insights to the frontline (e.g., call center management platform) ▪ Identify innovative alternatives to the current state to deliver the same insights in new ways (e.g., mobile applications) Structured feedback loops ▪ Explicitly redesign front-line processes to incorporate analytics insights into decision making ▪ Structure future-state processes to explicitly facilitate capture decision outcomes and additional user feedback (e.g., subjective rationale and supporting facts)
  • 25. 24 Summary ▪ Understand priority use-cases from the business back – maintain a heatmap ▪ Operate portfolio of opportunities across multiple horizons ▪ Assess and align capabilities to support scale up and increased levels of sophistication ▪ Drive pilot projects to prove impact, secure funding, and work out operating model ▪ Focus on expanding talent over time, leveraging external partners where possible ▪ Focus beyond models to engage frontline users to achieve sustainable impact
  • 26. Iran Hutchinson, Product Manager and Software/Systems Architect, InterSystems Jon Pilkington, Vice President of Products, Datawatch Corporation @Datawatch Iran Hutchinson, Product Manager and Software/Systems Architect, InterSystems Bob Zurek, Senior Vice President, Products, Epsilon Marilyn Matz, Co-Founder and CEO, Paradigm4 @Paradigm4

×