Data Analytics
Powering
the Wine Industry
Delivering Best Practices for Data-Driven Inventory Analytics
The ability to manage real-time and accurate inventory and automating key business processes - once considered a revolutionary development for achieving speed and accuracy in ecommerce - is key to remaining competitive and meeting consumers' on-demand expectations, whatever the industry.
Why Wine Inventory matters
In order to track what has been sold or used, product data must be defined consistently. What products sell well together, which region is using this more, and who sells the same thing the best - all hold the key to powering business insights that can help you make data-driven decisions for increased productivity and profitability.
Industry: Retail & Leisure
Challenges
Data Solution
Data Clean: Accents, Abbreviations and Punctuation
Considering wine's global nature, there are a variety of diacritical marks. Our data cleaning process also accounts for the many (many) ways that data providers encode the information (sometimes inconsistently).
So if we see “ç” we know what it is.
The New World loves abbreviations, “Cabernet [Sauvignon]”; “Sauvignon” [Blanc]; GSM; S Australia.
Old World traditions presume you know so much about a Chateauneuf and in practical terms a Chateauneuf is the original Rhone “GSM ”.
There is a huge difference between hyphens, apostrophes, non-breaking spaces and full stops when it comes to wines. Stag’s Leap versus Stags Leap [Winery]; Latour versus La Tour anyone?
Business Rules Engine
Wine naming has its own rules and conventions, though surprisingly few are absolute. Such rules are useful to identify the important data attributes.
So we know Chablis means a Chardonnay, and a Chianti Classico will be mostly Sangiovese but not necessarily all Sangiovese.
Our rules engine can also spot inconsistencies so we can identify cases where retailers have “Chapel Down NV Vintage Reserve” and say a non-vintage vintage does not make sense.
An automated, consistent rules based process to clean, extract and standardise product data
We take wine product data, either just the product name or adding the available attributes (if they can be trusted).
We clean and normalise the data, identifying the things that matter: grape, colour, producer, region, classification whilst isolating SKU specific information such as unit size, retailer or add ons (decanters, boxes and teddy bears). These attributes are then used to define the uniqueness of product lines.
This uniqueness, combined with our experience of data matching, allows us to identify duplicates and consolidate them.
Importantly, the matching process identifies mismatches that matter: ie those that have contradictory data, and mismatches that are solely due to a difference in the depth of data.
