We have all heard the saying “Garbage In – Garbage Out”, for some of us maybe a frustratingly number of times. Information Technology has developed great solutions to automate all sorts of activity but in most cases the solutions require that the source data is clean and accurate.
Nowhere have I encountered data as disparate, messy or indicative of the ‘Garbage In’ adage as found in Direct Store Delivery data. In retail there are many products that for various reasons are shipped directly to the store (often direct to shelf) rather than semi-trailers delivered to distribution centers.
With this model every store delivery generates it’s own invoice. Add to this the same product is often delivered by a different distributor based on geography. This means you have an invoice for every store every week or maybe several times a week and the invoices are coming from multiple distributors whose invoicing systems and product identifiers are different. All of a sudden you can’t connect any of your invoices together much less connect your invoices to your sales data because none of the data uses the same product keys. This is a text book case creating ‘Garbage In – Garbage Out’.
Because of this retailers have inaccurate data in their price book which means they don’t know what they are paying for a product. If you don’t have accurate cost information how can you accurately price the product on your shelf to your customers?
If you can’t connect your invoice data to your sales data it becomes impossible to inform inventory positions. Do you have enough product on the shelf? Too much product on the shelf? How many units should the next delivery be?
These are just a couple of examples of data issues that are costing retailers millions of dollars every day.
All of this creates the perfect opportunity for companies to invest in solutions that deliver input data clean of high quality. Solutions that deploy artificial intelligence and machine learning to enhance the data across the various systems to make sure there is a single Master Data Element that can be used seamlessly across the enterprise.
Doing this will help companies recover the millions of dollars falling through their fingers because they have great systems but those systems can’t solve the ‘Garbage In – Insights Out’ requirement that businesses today so desperately need.