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Inventory Data

Data Analytics


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


Different Sales & Purchase Data Sources

Data formats will differ from source to source. In retail, barcodes and EANs are not always available. Data nightmare begins there, as conventional desktop applications, like Excel, are designed to match exact matches and aren't able to handle the subtleties and complexities of scores or hundreds of rules.

Inconsistent Inventory Data: SKU

Identifying and consolidating the same SKUs from different merchants and distributors is a classic inventory issue. I.e. Cab Sauv versus Cabernet; Mclaren Vale versus S Australia or Nuits-St-Georges versus Nuit St Georges?

Poor Data Quality

Different versions of the data, and the amount of data provided can differ for the same product. I.e. “2002 Hunting Moon, Shiraz, Margaret River, Western Australia 750 ml” may be the same as “2002 Hunting Moon”

Messy Data

Accents, Abbreviations & Punctuation: Due to wine's global nature, it has a wide range of diacritical variations. Providers often encode information in multiple, and often inconsistent, ways.

A fragmented technology landscape

Disparate data sources that were difficult to combine coherently.

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.


With Calimere Point's inventory analytics, a leading retailer gained data-driven insights that lead to more efficient and profitable business decisions. 

Scalable and repeatable process

It is possible to update the tables that tag the product data without having to change the wine data engine itself as product universes evolve.

Data automation

An automated, consistent rules based process to clean, extract and standardise product data based on description and attributes.

Conversion of data into insight

One of the biggest advantages of having a wealth of trustworthy data is the opportunity to obtain insights that allow for smarter and more profitable decision-making. We provided an accurate understanding of products being sold by whom, how, where and when.

Data analytics can enhance inventory management by reducing costs, improving operational efficiency, maximising sales, and increasing customer satisfaction. 

Related Data Solutions

Calimere Point have helped our clients gain value from the data revolution.