The secret to competitive retail
pricing is all in the data.
16 june 2022 – written by snowflake: click here to read original blog
Where retail product master data was once gathered manually in physical stores, Daltix’s product data aggregation platform blends multiple datasets to give its clients up-to-date regional and competitor insights through a single dashboard. Simon Esprit, CTO at Daltix, explained to us how it works using Snowflake’s Data Cloud.
Discover how Daltix makes it happen with Snowflake’s data cloud.
In the highly competitive retail world where brands are constantly fighting for customer loyalty, getting products right—across everything from price and promotions to unit size and nutritional information—can be critical to success. And it’s not just retailers that need to keep a close eye on industry data. Increasingly, fast-moving consumer goods (FMCG) manufacturers and even market research agencies need up-to-date product master data at their fingertips to ensure customers get exactly what they want.
That’s where Daltix comes in. Its technologies actively scrape a wide range of product data from across brands’ websites. This information is then blended with trusted third-party data—and even field data—to help its clients more accurately strategize product pricing and positioning against their competitors and the wider market across multiple locations.
Keen to disrupt and lead the e-commerce pricing market in Benelux, Daltix started with a simple PostgreSQL database. But as the company’s platform was scraping an increasing amount of data each day, Esprit and his team found it difficult to access data for different retailers. In addition, Daltix found it difficult to scale its workloads cost-effectively, access data for new use cases, change how it stored data, and maintain sufficient data analytics performance.
“After talking to various consultants, we built a data lake on AWS, but this proved costly and far too complex for our non-data engineering teams,” explained Esprit. “We also tried Amazon Redshift, but again, we needed something easier to use. Luckily, one of our data engineers suggested Snowflake’s Data Cloud. From day one it was far easier to scale and use, and had better data sharing capabilities while being secure. And unlike Redshift, Snowflake’s platform supports JSON data—making running queries far easier.”
An elastic single source of truth for data
With nearly 60TB of data stored across Snowflake’s platform, processing more than 10GB each day, Daltix’s data engineers and data scientists have noticed significant performance and efficiency improvements. In particular, the company’s analytics team no longer needs to manually clean its ingested data, instead, they use the automated data cleansing capabilities in Snowflake to maintain high-quality data quicker than ever before.
Esprit and his team also take advantage of nuanced data processing capabilities of Snowflake’s platform to reduce the risk of errors and enable them to experiment more with their datasets—which ultimately benefits their clients. “We use Time Travel to avoid making undetectable mistakes, which allows us to be more flexible, experiment more, and move faster,” explained Esprit. “We also use Zero-Copy Cloning to carry out fast testing without manipulating live databases. It gives us easy access to tables across our databases and allows us to share data with our clients for proof of concepts.”
Cost efficiency and performance in one solution
Since using Snowflake’s Data Cloud, Daltix’s analysts have doubled their productivity, boosting the team’s morale. And with no infrastructure to manage, the company has also avoided hiring unnecessary additional data engineers.
The way Daltix stores and processes its clients’ data is somewhat unique to the market, using the scale of Snowflake’s platform as an advantage over other players in the industry. Esprit explained: “Each of our clients benefits from their own isolated data warehouse in Snowflake. It gives us the flexibility to scale individual clients’ environments and create new ones with a single SQL command. The upshot is we can individualize costs per client with far greater oversight. And thanks to auto-suspend, we don’t pay for idle warehouses—cutting costs by half per customer versus an always-on solution. There’s also no maintenance on our end, which saves significant resources.”
Daltix’s clients are already feeling the performance benefits with faster response times and greater analytics team productivity, which translate to better quality insights.
For example, the company’s work with FMCG behemoth Unilever has helped the conglomerate achieve faster analysis for its go-to-market strategies. Using Daltix’s platform, Unilever can now track multiple data fields—such as product names and EANs—to bridge the gap between a new product being launched in stores and Unilever receiving critical performance insights.
Ambitious growth plans supported by near-limitless scalability
Spurred by the success its clients have already achieved using its platform’s data, Daltix is expanding its reach throughout Germany, with the view to enter other European markets in future. The company is also exploring giving all its customers access to their data in Snowflake’s Data Cloud, using an API to provide more up-to-date data—faster.
And while Snowflake was new to most of our data engineers, they all love using it. It’s removed a lot of manual data querying from their roles and everyone is happy. We can certainly forget about looking at other data cloud options—Snowflake has it all covered.”
Daltix introduces it’s Data Quality Indicators to ensure data transparency
If your data provider doesn’t give you a clear outline of how they test their own data for quality, you should get suspicious. In order to deliver data you can trust, we’ve developed the Daltix Data Quality Indicators. Read about what our DQIs are and why it’s good for your data here!
Download the Daltix Summary & Data Span Matrix
As we are expanding across Europe, here is a downloadable matrix on what data and features Daltix offers as well as what countries we collect in and for what retailers.
Daltix Data Architecture: Data Access
In the final blog post of the data architecture series, we’ll take a look at how Daltix can provide everyone who requires their data access in a way that suits them. To answer that, we’ll examine our two data setups and explain our experiences and how they fit into our data architecture.