A huge amount of statistical data is publicly available, from many sources. The main challenge is being able to find them, gather them in the most desired format, and fill them into data sets for business and research purposes.
Furthermore, without a strong technology to perform this task, data set preparation is a real conundrum. Adding on top of it many different formats, it quickly becomes an overwhelming task.
Indeed, manual data set preparation is error-prone, time consuming and mentally exhausting for users.

After having lost a considerable time in manual data set preparation, rejustify co-founders finally solved this endless issue. Relying on the common characteristics between how statistical data are stored in complex databases and how they are used in a typical data set, they started to classify, structure, and connect data from multiple publicly available statistical sources into a data catalog. Rejustify team developed a technology to build data sets automatically. The technology has been set up as a freemium SaaS in a high-availability 100% cloud serverless architecture that can scale elastically.

The main asset of rejustify’s service is in being the only data management company with no data but knowing how and where to query them in real-time, instantly.

The solution brings a lot of value since it is a time saver. By enabling automation, it facilitates decision-making process. For analysts, it cuts their data set preparation time ten times. For businesses, this brings a competitive edge in speed and accuracy for their data analysis. This innovation is reached by employing learning algorithms that read analysts’ empty tables and data sets to characterize their structure and format, find and suggest the best matching tables from primary sources, simultaneously query multiple sources worldwide via API, and populate the analysts’ table with data in appropriate formats, in real-time. Rejustify is a one-stop-shop to 600 million structured data series from more than 70 most trusted publicly available statistical data sources. There is a gigantic opportunity to feed quality data to HPC Meluxina in Luxembourg, to support science and research as well as to bring more active money managers into Luxembourg.

Additionally, rejustify can set up tailor-made cloud data lakes and feature stores for users and make it highly secured for a closed private workgroup or make it open to public.

Rejustify already started with pilot trials, for example, for Bruegel, the world’s leading economic think-tank. The start-up took their top-notch research results originally disseminated as an Excel sheet only available on a subpage of their website and turned it into a structured data set in a public cloud data repository so that anyone in the world can find, access and merge it with any other statistical data.

Similarly, multinational companies have plenty of in-house data stored in legacy systems which only few IT profiles can access and even fewer can query before supplying them to operation teams. It is a clear waste of time, wages and business opportunities. There is value in rapid and seamless merging and enrichment of internal data with external data.

In the end, the tool could allow for production forecasting in industry, enabling manufacturers to optimize, for instance, load across production plants using also external factors like rising average temperature in one country, a surprise decrease in unemployment rate in another one and above-average rain accumulation yet in another one.

All these opportunities are just a few clicks away from anybody using rejustify.

Les auteurs
Céline Tarraube
Conseillère digitalisation et innovation auprès de la FEDIL