1. How To Manage Your Data Winery Perspective Jordan Ferrier Hogue Cellars WAWGG Conference Feb 4, 2009

2. Wineries are in the middle Vineyards 3-5 year planning cycle Wineries 1-3 year planning cycle Marketing / Distribution What’s for lunch? Plant grapes, deliver annually Receive wine, turn when out

3. Grower Corporate or Ownership Winery Stats Costing Pricing Information Pricing Info Costing Info Overall Efficiency Sorbanes-Oxley? Pricing Info Agricultural Expectations Crop Harvest date Cultural practices Quality Expected Desired wine Resulting quality Stats Maturity Treatments Crop Estimates Government Fruit Sources Pricing Info Homeland Security? Sorbanes-Oxley? Stats Pricing Pesticide use Water use Labor Outside Services Analytical results Proactive recs Corrective recs

4. Winery Makes the RIGHT Wine Hit volume targets –Not enough - out of stock / capacity not used –Too much – distributor backed up / high costs Meet quality expectations –Proper profile and consistency –Production goals and origins (labeling) –Lack of flaws – vs. – complexity Balancing capital and operating costs

5. PART I: A Plan HOW MANY CASES OF WHAT WINE? –Evaluate sales plan before harvest –May have to do this YOURSELF Your conversion factor (tons-to-cases) –‘Hangtime’ factors (dehydration / water) –Plans for lees loss/recovery –Plans for press wines –Filtration & evaporation losses

6. Production plan for YUMMY!

7. Look At Capacity Realistically

9. PART II: Manage The Grapes History is better than theory Early crop estimates require experience Many ‘yields’ to consider –Contract maximum –Expected yield – what you think will arrive –Target yield – in contract or annual target –CROP ESTIMATES

10. Crop Estimation Can use historical data –Visual estimate –Cluster counts LAG PHASE cluster weights –Can be very accurate –Harvest whole vines: clusters/vine, #/vine –Need vines/acre, % stand, GPS acres –PREDICT GROWTH FACTOR Historical data helps predictions

12. TIMING IS IMPORTANT

13. Good Sample Data Management Sample Schedule –allows winemaking and laboratory to plan Sample Log –Tracks maturity progress –Compare w/ previous years –Communicate results to growers –Use to SCHEDULE HARVEST!!!

14. Example – 2007 Sample Data No blank lines Consistent block codes Able to break out many ways

15. Harvest Scheduling Communication

16. PART III: Make The Wine Weigh tags –Winetracking software –Grape Payments Execute the plan

17. Winetracking Regulatory Compliance Lifesaver Inventory maintenance –Volume in tanks and barrels –Composition moves with wines –Analysis (SO2, pH, TA, EtOH, etc…) –Additions (yeast, nutrients, etc…) –Operations (pressing, racking, filtering, etc…) –Costing (grapes, overhead, bottling, etc…

18. There’s a lot missing All systems lack a qualitative component –Ferments aren’t ‘special’ and tracked –Quality parameters are missing No ‘treatment’ activity –Type, rate, and duration –Oak, punchdown/pumpover, MOx, etc… Can’t split lots to multiple products –Needs to be visual, simple, and persistent

19. DATA WORK AROUNDS Long Range Planning Spreadsheet(s) Capacity Calculation Spreadsheet(s) Harvest Scheduling Spreadsheet Red Wine Inventory Spreadsheet White Wine Inventory Spreadsheet Red Ferments Spreadsheet Tank Tracking Database Winemakers’ Tasting Database

20. Tracking Red Ferments All red ferments tracked externally Used to calculate nutrients, water, etc… Links to other worksheets by lot name –YAN, Brix, and Phenolics spreadsheets –Pressing characteristics (yield)

21. Phenolics Tracking

22. Pivot Table from Data Table Consistent lot names! No breaks in data Pivot table also references 2 other tables Converts dates for XL to use easily

23. XL Tools Build a PIVOT TABLE – trial and error –Select desired data table –Select how you want to slice it: LAYOUT –Row subtotals and sorting –Re-format data output –Reorganize Pivot w/ ‘format report’ XL Lookups –Use INDEX, MATCH, VLOOKUP –Make sure data’s set up right, catch errors Filters and data entry aids

24. Good Data Management ORGANIZES INFORMATION For easy sorting and filtering Easy aggregation of results –means, min, max,… Easy referencing and recombination Easy communication, export, sharing