Big Data vs Small and Wide Data under a Master Data Lens

One of the 10 trends in data and analytics in 2021 identified by Gartner, the analyst firm, is a shift from big data to small and wide data.

A press release from yesterday elaborates on this topic outside the paywall. Here Gartner Says 70% of Organizations Will Shift Their Focus from Big to Small and Wide Data By 2025.

As said in there: “Potential areas where small and wide data can be used are demand forecasting in retail, real-time behavioural and emotional intelligence in customer service applied to hyper-personalization, and customer experience improvement.”

This is a topic close to me and something I wrote about, still using the term big data, last year in a Reltio whitepaper as mentioned in the post How to Use Connected Master Data to Enable New Revenue Models.

Small data is in my eyes very much equivalent to master data besides the meaning promoted by Gartner, which is approaches involving “certain time-series analysis techniques or few-shot learning, synthetic data, or self-supervised learning”.

The concrete wide data to be used and connected in the retail scenario is customer data and product data. There is a current trend of mastering wide customer data in a Customer Data Platform (CDP). Wide product data are best handled in a Product Information Management (PIM) platform with a collaborative Product Data Syndication (PDS) add-on.  

In the quest of providing hyper-personalization, you need to connect well identified customer data with product information elements aimed for customization and personalization by applying Artificial Intelligence (AI) methodologies.

So, is the term “small and wide data” better than “big data”?

I think it, besides the narrow analytic purpose forwarded by Gartner, can help unlocking the opportunities in master data underpinned big data that have existed the past decade but that have- by far – not been utilized as much as it could.   

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