The sushi principle is grounded in the idea that raw data is better than cooked data, just as raw fish is better than cooked fish in sushi. With raw data, the data is the ingredient, which means that you can cook it in as many different ways as you like, as many times as you like.

This is different to processed or aggregated data. You can't go back once the data has been “cooked” into a report! Imagine drilling through a category to find the source of a sales issue; you may discover the sub-category or SKU, but what is the impact at the customer or store level, and how does this vary over time?

The datasapiens Advanced Insights Platform is grounded in the sushi principle. 100% raw data, delivering instant, actionable insights for retailers.

Sushi principle in action

Let's say a retailer wants to understand the sales performance of a particular product category. They have access to a large dataset that includes information on individual transactions, including the product purchased, customer purchasing, store location and the date and time of the sale.

This analysis would involve drilling down to the individual transaction level and exploring the data in its most granular form, potentially from multiple datasets.

They might discover that sales of a particular product are highest on weekends or during specific times. Or that sales vary between different store locations or profiles of customers.

With this level of detail, the retailer can make critical data-driven decisions on behalf of their customers; including assortment; price; promotion; personalization; and inventory activities. This drives efficiency, shortening the cycle from insight to action and the resulting performance improvement.

Practical Examples

Plenty of well-known companies are analysing their data in the sushi principle. You may have come across it without even noticing.

Netflix

The company uses raw data to personalise its recommendations for each user. By analysing each user's viewing history, search queries, and ratings. Netflix can suggest movies and TV shows tailored to their unique preferences.

Uber

In real-time data analysis, Uber optimises its pricing, route planning, and driver dispatch. Uber can analyse each drive request & then make decisions optimised for its business goals.

Airbnb

Airbnb can use search history, booking behaviour, and reviews. Then thanks to this data, they can suggest listings that fit the user's unique preferences.

What are the key benefits of an analytical platform advocating the sushi principle?

1) Flexibility

100% Disaggregated Data. One of the main benefits of the sushi principle is that it provides 100% disaggregated data. This approach to data analysis can be beneficial for retailers or pharmacies. It gives them a more detailed understanding of their customers, products, and sales.

2) Speed

Design your insights on the fly. Analysts can extract insights without the need for time-consuming data preparation or cleaning. Adjust your analysis as you go.

For retailers or pharmacies, this is highly valuable. It allows them to respond promptly to changing market conditions or product trends. Get the insights in seconds rather than in days.

3) Iteration

The sushi principle also encourages iteration. You can explore the data and test different approaches until you reach the root cause. An example. It allows retailers to identify the underlying causes of problems, such as low sales or customer churn.

4)Accuracy

Working with raw, disaggregated data can also improve the accuracy of insights. Pre-processed or summarised data can sometimes obscure essential details or mask outliers. Organisations can avoid these issues by analysing raw data and ensuring accurate insights.

In conclusion, the sushi principle enables real-time analytics, shortening the cycle from insights to action. It has allowed us to build what is probably the fastest, real-time, insights platform, leveraging truly disaggregated customer data.

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The Sushi Principle