How to Quickly Leverage Big Data Analytics May 23, 2013

Transcription

1 How to Quickly Leverage Big Data Analytics May 23, 2013

2 Agenda 3V s of Big Data Value of External leading indicators Buy vs. Build Dilemma Case Study - Dow Chemical Company Case Study - Advanced Drainage Systems Methods to Identify Leading Indicators Summary 2

3 What is Big Data Big data is high-volume, -velocity and -variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. -Gartner

4 Clearly, knowledge is power It s why we know our internal data -- down to the decimal Profit Margin EBITDA Revenue trends by product line, division, geography 4

5 But according to experts, external data is even more important Companies who consistently review industry indicators outperform peers by 220 % IBM & MIT 85 % of a company s performance is the result of external factors Harvard Business Review 5

6 Problems to solve Global economic changes impact company results more frequently and with greater depth than ever before Companies scramble to find external data for upcoming board or leadership presentations Companies have redundant sources and spend for external data External data is not systematically analyzed against internal results Forecast accuracy suffers lowering profits Results suffer due to staying in existing markets to long or entering new markets to late 6

7 The problem is not a lack of data, and it s certainly not a lack of will It s a lack of systems to consistently collect, verify and interpret information effectively 7

8 8

9 Big Data Projects Build in house expertise on existing tools Build in house expertise on new tools Buy a focused product from a service provider 9

10 Case Study DOW Presentation Overview Goal was to leverage external leading drivers to improve forecast ability for several layers of business Within 5% very good Within 10% Good 10%-15% useable >20% needs work Decided to build with internal team, internal SAS and BI tools and external consulting In 6 months they were able to forecast total net sales with 98.47% accuracy 10

11 Case Study -Forecasting with 15,000 variables and 7,000 models Alli Soule May 3,

12 Dow Presentation Highlights -Forecasting with 15,000 variables and 7,000 models Alli Soule May 3,

13 Key Numbers 13

14 Case Study Overview Goal was to leverage external leading drivers to improve forecast ability at region and product level Within 5% very good Within 10% Good >10% needs work Decided to partner with one provider In 6 weeks they were able to forecast at product levels with 97.86% forecast accuracy 14

15 Case Study Global Manufacturer 126,000+ External Data Series Company Internal Results Customers & Competitors Economic Data Demographics $ales Volume EBIDTA Prevedére Patented- automated leading indicator identification using internal and external data 15 15

16 High Level Activities 8 Week Project Data Gathering and Structuring Leading Driver Identification Model Building and Tuning Implementation and Training Internal Data Business Attributes & Hierarchy External Data Sources Suppliers Competitors Customers Statistical Analysis Regression Lead/Lag ProCyclic CounterCyclic ROCET Methodology Engage business leaders Billing Days adjustments Variable Reduction Variable Selection Cross Correlation Co-integration Similarity and Back testing Finalize accounts and Permissions Deploy workbench and models Update Calendar Set Alerts Training and handover Ongoing Support 16

17 Case Study Highlights Overview 3 Internal FTE 11,000 Data Series from one provider 34 Models produced by experts in economics and statistics Forecast down to state and product levels No internal hardware or IT system support Data discovery Systematic Indicator analysis Interface for automation 4 Training and documentation 17

18 Key Numbers 18

19 Big Data - Build or Buy Build Buy Pros Own the IP of the finished product Build deep technical in house expertise Cons Long time to create High internal cost May still need experts FTE time away from current duties May not be best of breed Expertise of provider Can select best of breed products Shorter time to implement Internal staff stay focused on core business Acquisition Cost Integration Cost 19

20 Identifying Leading Indicators Key Steps Detective Work Format Data Chart and Graph to identify correlation Make it a repeatable Process 20

21 Thorough analysis Proprietary algorithms parse data, analyze influences and forecast future financials 17+ patents pending Certified in Time Series Analysis and Forecasting by NABE Deep knowledge of using economic data to craft actionable insight 20+ years enterprise app and Fortune 500 experience 21

22 Accurate and insightful outputs Clear picture of business cycle Comprehensive outlook Consistent, model-based forecast Higher returns on equity and assets 22

23 Actionable insights Performance Dashboard Monthly Reports Leading Indicator Analysis Our CFO and finance organization has to do detailed analysis of economic indicators vs. our business results. The amount of time and effort is immense. Prevedére eliminates the manual part of this analysis and makes it repeatable. Further, our private equity owners are extremely interested in our performance vs. economic indicators. Using Prevedére, we will be able to complete a standard, comprehensive analysis in less than a day. Additional custom requests can be done in hours -Kevin McGuire EVP, Business Process and IT, Momentive Specialty Chemicals 23

24 Better predictions Better actions Better results What would 94+% forecast accuracy mean to your bottom line? 85 % Without external data forecast accuracy 94 % With external data Companies that build and verify a set of leading indicators earn a 2.95% higher return on assets and a 5.14% higher return on equity. Wharton 24

25 Start conservatively See exactly what reports and insights you would get for your specific company, indicators and priorities A paid trial can be completed in just 2-3 weeks with almost nothing to lose 25