From 'I think' to 'I know'


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Anil Kaul, CEO and Co-Founder, AbsolutData delivered a session on institutionalizing Big Data analytics for organizations, at the Big Data Innovation Summit, London on 1st May, 2013.

AbsolutData is a global leader in applying analytics to drive sales and increase profits for its customers. AbsolutData has built strong expertise and traction with Fortune 1000 companies across 40 countries. We specialize in big data, high end business analytics, predictive modeling, research, reporting, social media analytics and data management services. AbsolutData delivers world class analytics solutions by combining their expertise in industry domains, analytical techniques and sophisticated tools.

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    Don't make hype of Big Data analysis as a giant problem which cannot be handle -- General views observed all around the World

    So called Data of any Organization / Firm / Company / Unit / University / Researchers and in any type of formats such as - Quantitative, Qualitative, Logical, Electrical Signals or signs, Visual Form, Audio Form, Magnetic Form etc.

    But there is a definite way to overcome it. In my views following steps have taken , it definitely helps to solve the Moto, proper analysis, get required results & able to achieves the Goals / Aims desired by the Owner / Owners of any above mention Organization.

    1- First confirm the Goals, Aims, Required Results with proper time Schedule from the Owner / Owners of the Organization. Also confirm about the entire Procedures logistics & Budgets & Economical provisions ( if required ) by System Analyst for better achievements & Results in the on going Process of the Project.

    2- How many types of Data we are gathering / generating / creating / capturing as our Inputs for our Organization?
    Whether the present Inputs ( if any ) are sufficient for the Organizational Goals / Aims ?
    Whether any duplication / s found in the present Inputs? Can it erected & shorten the Inputs?
    What & where we should add in previous Inputs Formats to get the proper results?

    If entirely New Input Format, then think ten times along with the Owners as well as the different sections / Parts of the Organization and then prepare various Input level Formats which will be simple to understand, simple to fill by the different units / sections of Organization, mostly keep objective ( better for validation & other linguistic mistakes ).

    If Input is generates through the end users participation then the thinks for 100 times that how the end user will react to it by checking with any ten Lay-man / common man for the response in totality to get better idea for the Inputs.

    3- All input Data / Information should be first categorized in small Section / Units / Parts along with key Fields / Signals / Signs as far as possible in the Rows & Columns basis. Make same no. of present staff Teams / Units to handle the respective Sections of the Data. Give that unit's staff extra incentives to be a part of overall System Analysis. Hired a good System Analyst for a specific period basis OR by open sourcing from outside to help the present staff Teams / Units. If System Analyst requires more logistics / Equipments ( if any ) Allow them immediately.

    4- Keep the exact time schedule to everyone including Inputs, Analytical Procedures, Outputs and the co-operation required from the Owner / Owners of the Organization. For Actual analysis process use any Software / method which suits for ypur Organisation & so called Big Data convenience. I will prefer RDBMS for Analysis.

    5- Off course check the outputs with internationals / States Standards along with the similar type of work / Procedure done by another Organization nearby.

    6- After every completing cycle of the Organizational Procedure, think & ask to the Organizers happy / satisfy able in all respect. That’s the sloe of any System Analyst who is doing for so called Big Data.

    Sir if you like my views , Please keep it in the upcoming Summit of ' Big Data Analysis related' along with your Subject , because I am not attending the Summit. Otherwise sorry Sir I have wasted your valuable time.

    But any case reply me Sir
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  • From 'I think' to 'I know'

    1. 1. © Absolutdata 2014 Proprietary and Confidential Chicago New York London Dubai New Delhi Bangalore SingaporeSan Francisco April 30, 2014 From “I Think” to “I Know” Institutionalizing Big Data Analytics Anil Kaul, CEO - Absolutdata
    2. 2. © Absolutdata 2014 Proprietary and Confidential 2 Agenda Speaker Intro Absolutdata Overview The Real Presentation ?
    3. 3. © Absolutdata 2014 Proprietary and Confidential 3 Anil Bio  CEO & Co-Founder, Absolutdata  PhD and M.S. in Marketing from Cornell University  Experience with McKinsey and Co. deeply involved in marketing research and marketing science areas  Published articles on marketing in leading journals such as McKinsey Quarterly, Marketing Science, Journal of Marketing Research, and International Journal of Marketing  Invited speaker at conferences and premier business schools such as Cornell, Columbia, Dartmouth, NYU, and Yale  Presents at prestigious industry conferences like IIR, AMA, Esomar on analytics and research topics
    4. 4. © Absolutdata 2014 Proprietary and Confidential 4 About Us  Big Data and Analytics solutions provider  Founded in 2001  $20 million invested by Fidelity for expansion  Headquartered in San Francisco and additional offices in New Delhi, London, Dubai, and Singapore
    5. 5. © Absolutdata 2014 Proprietary and Confidential 5 Our Team  Senior management from McKinsey, CPG client, Pfizer, Mitsubishi, Nielsen, GE, HSBC, The Modellers and Citigroup  Over 320 professionals including Data Scientists, Modelers, Statisticians, and Business Analysts
    6. 6. © Absolutdata 2014 Proprietary and Confidential 6 Analytics Data Visualization & Reporting Market Research Big Data Service Offerings
    8. 8. © Absolutdata 2014 Proprietary and Confidential 8 What Is Big Data Generating, storing, processing, analyzing data that was previously ignored due to technological limitations
    9. 9. © Absolutdata 2014 Proprietary and Confidential 9 Why Institutionalize Big Data Analytics? 1 Based on Analysis of 31 companies with advanced analytical capabilities across BFSI, Telecom, Hi-Tech, CPG, Retail, E-Commerce, Pharma, Industrial Products, Logistics and Media/Entertainment sectors Source: Web search, Absolutdata Analysis 0% 50% 100% 150% 200% 250% 300% 350% 400% 450% Jun/04 Jun/05 Jun/06 Jun/07 Jun/08 Jun/09 Jun/10 Jun/11 Jun/12 Analytics Shakers S&P 500 Analytics Shakers1 vs. S&P 500 Companies that invested heavily in advanced analytical capabilities outperformed the S&P 500 and recovered quicker from economic downturns Information & insight advantage is the new way to win
    10. 10. © Absolutdata 2014 Proprietary and Confidential 10 Scope of Analytics  Analytics is implemented piecemeal across business units  Analytics coverage across all strategic functions & business units like Marketing, Finance, Recruitment, Supply Chain, Operations, Call Centres, Products etc Strategic Alignment  Localized analytics with no real strategic alignment  Embed analytics in the decision making fibre of the organization  Strong governance & change management Today Institutionalized Analytics Benefits Of Institutionalizing Big Data Analytics Source: Absolutdata Analysis
    11. 11. © Absolutdata 2014 Proprietary and Confidential 11 Results Oriented  Limited RoI uplift  Limited understanding in how to utilize data for business decision making & impact  Strong commercial & business impact focus  Demonstrated capability in delivering high Return on Analytics Investment Efficiency & Effectiveness  Low efficiency due to high localization  Low effectiveness due to lack of highly skilled domain + analytics experts  Cost effective with an engagement model to suit your needs (Onshore/Offshore/ hybrid)  Highly skilled workforce with a combination of analytics & industry expertise Today Institutionalized Analytics Benefits Of Institutionalizing Big Data Analytics Source: Absolutdata Analysis
    12. 12. © Absolutdata 2014 Proprietary and Confidential 12 What Does Institutionalizing Big Data Analytics Mean?  Analytics Trigger New/Different Actions Across The Organization – Long-Term Strategic Decisions – Day-to-Day Operational Decisions  Analytics are used by majority of decision makers – Senior Levels – Mid and Junior Levels  Analytics are used in a timely manner – Before the decision is made not after – Accelerate Time-To-Answer  Analytics provide a complete view
    13. 13. © Absolutdata 2014 Proprietary and Confidential 13 Big Data Information InsightsUnderstanding Enabled Intuition Actions Big Data Information InsightsActions Enabling Direct Business Actions Enabling Managerial Decision Making Two Ways Big Data Is Used To Drive Actions
    14. 14. © Absolutdata 2014 Proprietary and Confidential 14 How to Institutionalize Big Data Analytics? Easy Question – Difficult and Long Answer
    15. 15. © Absolutdata 2014 Proprietary and Confidential 15 Building Blocks Of Institutionalizing Analytics Source: Absolutdata Insights Hub Data Customer + Product + Transaction Data Mobile Data Primary and 3rd party data A Technology Data Loading & Transformation Data Storage Tools & Techniques Data / Insights Dissemination B People Capabilities People Model Training E Products Customer Analytics Social Media Analytics MIS + Ad-hoc Reporting Market Research D Marketing Effectiveness Data Visualization Web Analytics Partnerships People: Training Partners Data: Demographics, Industry Data Technology: Big Data Tools F Methodologies Diagnostic framework Analytics framework Quality G Governance Program Management Change Management Knowledge Management H Engagement Model Functions Finance Marketing SalesOperations C HR Products Supply ChainStrategy
    16. 16. © Absolutdata 2014 Proprietary and Confidential 16 Case Study Global Food Multinational with presence in more than 80 countries Analytics well entrenched in the US but not outside of the US
    17. 17. © Absolutdata 2014 Proprietary and Confidential 17 PEOPLE PROCESS DATA Challenges Institutionalizing Big Data Analytics
    18. 18. © Absolutdata 2014 Proprietary and Confidential 18 Absolutdata Answer To These Challenges People Challenges  Strong belief in gut instincts  Pre-assumption of analytics as “statistics; far from business reality”  Lack of understanding of the results & implications; can’t apply…. Confidence Building in the Power of Analytics  Definition of project team; key roles  On-job training of process, with clear view of expected output  Engagement of teams at key milestones – collect inputs vs. give updates  Working with Business Teams as partners in the application of results that drive business impact Absolutdata Response
    19. 19. © Absolutdata 2014 Proprietary and Confidential 19 Absolutdata Answer To These Challenges People Challenges  Limited budget and high expense  Complex, “black box “solutions  Standard, off the Shelf Solutions  Lack of standardized metrics for comparisons Adaptable, Practical & Affordable Solutions  Transparency of process, tool & techniques.  “Learn & Teach” not “Teach & Learn ”  Customizing of solution: “No two countries are same & no two brands are same”  Innovative, cost effective Excel and/or web-based tools that evolve with users  Standard Global Benchmark database to capture key outputs Absolutdata Response
    20. 20. © Absolutdata 2014 Proprietary and Confidential 20 Absolutdata Answer To These Challenges Data Challenges  Unstructured Capture & Measurement of data  Imperfect & missing data  Lack of Faith in data  Constantly Changing Environment, Dynamic Markets Building of Data & Causal factors  Alignment of different data streams (Shipment / Retail Audit/ etc.) to arrive at best representation of “Market Reality”  Break down of Aggregate data into weighted weekly data  Discussion-led creation of data flags to capture events and key spending time-periods  Creative application of surrogate variables Absolutdata Response
    21. 21. © Absolutdata 2014 Proprietary and Confidential 21 What We’ve Learned All hands in ownership and activation  Work sessions with simulation tools engage all players and lead to effective and efficient decision making  Cross country knowledge and support of benchmark database fosters shared learning and cross fertilization of ideas  Flexible working arrangements support region and country needs - Onsite, Offshore & Hybrid engagement models  Support from Corporate/CI Regional managers’ is critical  Ongoing dialogue leads to fast turnaround on projects and accelerates forward movement – Ownership, learning and decision making – Weekly meetings to check in and review priorities, timelines, next steps  The end game is not a single project or projects. Rather, it is an ongoing evolution to a more strategic, more data-based approach to decision making on the local level
    22. 22. © Absolutdata 2014 Proprietary and Confidential 22 Key Changes Lead To Higher Quality Deliverables  Dedicated ongoing teams: – Senior Delivery Manager – Internal team alignment towards building a regional focus  Scaling up of Management and Delivery Teams – Increase in Delivery Team size – Ad hoc resources available for ad hoc needs  Creation of a Process Book: A detailed user guide-line with extensive details/checklist across every stage of the project  Implementation of “Know your Brand/Category” initiative – Developing overviews on brands before project kick-start call  Development of client focused Feedback Form for ongoing project improvement
    23. 23. © Absolutdata 2014 Proprietary and Confidential 23 What & How Did Our Clients Gain?  Simple Yet Effective Solutions: – Quick & Practical analytically driven answers to critical business problems – Complex analyses delivered in an easy & business friendly format – Consistency in data approach for cross market benchmarking  Analytics Evangelists – Engagement with the business teams on an ongoing basis through the entire project and not just at the end – Joint workshops with the business team on application of analysis – Increasing trust with the process, output & team  Cost Efficient World Class Talent – Onsite as well as offshore In 2012  Top-line/bottom-line impact due to our projects – Increase in revenue by 50 Mn in just 5 countries  Large-scale strategic business decisions based on our presentations to the Board/ CXO level executives  Increase in the client’s own analytics team size (i.e. evidence of increasing traction with the whole concept of analytics in the client organization)
    24. 24. © Absolutdata 2014 Proprietary and Confidential 24 “Biggest Growth Impact” Award in 2011 Two 2012 Amazing Analytics Awards Success Stories: Identified opportunities to grow margin by $50 Million without any additional investment” - Central Insights Director, Global We finally ended up at +1.1% vs. previous year! - Marketing Director, Poland Absolutdata has won numerous Client awards, including: ‘this is the richest analysis I have ever seen at our company ’, - Director, Analytics & Foresights Western Europe Achievements
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