Modern companies have long been generating large amounts of data. But before truly analyzing data, it first needs to be cleansed to generate truly actionable insights. Along with Orange Ventures, we were delighted to co-lead the $12m Series A investment round in YZR, which helps normalize data sets. We welcome Sébastien, Jean-Philippe, Yosr, Arjun and the entire YZR team to the Nauta Family!
Modern Data Stack
Within any organisation, various business processes, sensors, and other devices generate data. In the case of sensor or real-time, the data needs to be safely transported before it is prepped and stored. In other cases, it needs to be discovered and ingested into appropriate data stores. With the growing urgency to move applications to the cloud, enterprises have a hybrid legacy and cloud application mix, producing ever more data.
Add to this mix the complex and data-heavy machine learning applications and related operations and you get an evolving data environment. A now common part of the enterprise backdrop as they evolve and adapt to the modern data stack.
Quantity vs quality
Within the data value chain – generating data, transporting it, storing, transforming and analysing for insights – the insights are truly valuable only if the data is clean. Business intelligence and analytics that run on messy data are entirely useless. There is a computer science phrase for this: “Garbage in, garbage out.” The cleansing of the data that is created within an organization is the crucial first step, which will result in valuable and actionable business insights.
Where does YZR fit in
YZR was founded by Sébastien Garcin and Jean-Philippe Poisson from a common pain point they were facing when working on various digitisation projects across L’Oréal’s 60+ sub-brands. They kept facing messy heterogeneous data sets which required data normalisation.
Data normalisation is the process of prepping any kind of (business) data to be ready, accurate, and well-organised for reports, machine learning, analysis, predictions, and business intelligence functions.
They quickly realised that without the right normalisation and preparation of the 1000s of SKUs across various L’Oréal brands, the actual business use cases of the digitisation projects and the resulting business intelligence, insights, and optimisations proposed were just a faraway dream. They scoured the market for a suitable solution but only encountered Master Data Management Solutions, which only solve part of the problem.
Hence, in 2019 they founded YZR (pronounced ‘wiser’) to solve this issue of semantic data normalization.
YZR ingests raw and messy data sets either directly from the various data sources or from the customer’s ETL/data lakes via its API connection or batch processes. Then, the API uses AI to do the following:
Upon normalizing the data sets, it then returns this cleaned up and normalised data set to your data lake where it can flow as usual into other systems that rely upon this data. Precious for any business which uses a large amount of semantic data, YZR’s first clients were retail and e-commerce leaders such as Monoprix or La Redoute. YZR accompanied them in automatically standardizing heterogeneous product data from their suppliers. This more qualitative and granular data is then used to create next-generation pricing or customer loyalty policies.
What we loved about this investment opportunity
The explosion of data from all processes within the enterprise is evident. Evolving machine learning use cases and their adoption in enterprises are picking up pace. Among other reasons, the stellar team at YZR and an attractive market were some of the reasons we decided to invest in YZR.
Other reasons include:
We are very happy to welcome the YZR team to the Nauta Family! And we are delighted that we were invited to join YZR on this journey. We are extremely bullish on the data infrastructure and tooling market.
More about YZR on their website here: https://www.yzr.ai/
See a demo here. https://www.youtube.com/watch?v=UHQ_vXi0RgI