Dataddo for Data Quality: A Message From Our CTO

Tomáš Sedláček has been with us since the beginning, making sure Dataddo runs smoothly and efficiently in the backend. In his newly-appointed role as Chief Technology Officer, he speaks out on his vision for Dataddo's present and future. This is his message: 

At Dataddo, the emphasis is on our customers' data and its quality. 

As there are many differences between how SaaS systems, Database engines etc. treat data in both input and output directions, we must be both flexible and perfectly stable so we don't break any of our customer's flows and their subsequent data operations.

Quality Data Requires Data Healing

We are really keen on our customer’s data, and we do all we can to transmit it from their sources to destinations in the best quality possible. Every week we release new features which, though they might not be visible to customers, affect how we treat their data in the background. Under the hood, we run many operations on every data transmission to help our customers solve incomplete or malformed data problems on the input side. This allows us to load corrected data to their destinations in the format which that destination requires.

This basic "data healing" is already happening now, but we plan to do much more in the near future to help our customers better understand their data. We will soon launch a new service called “Data Detector” which will scan the input data and inform the customer about possible malformed data, anomalies, missing parts of time series etc.

Blending, Writing, and More

We help our customers obtain data which is as clean as possible on a daily basis. We transform and convert data to unified data types so we can load it to the widest possible range of destinations later on. On the output side we provide several strategies on how to write the data (insert / truncate insert / upsert) to the destinations, which let our customers organize their data in a very flexible way.

Between these two Extract and Load sides there resides the Transformation. We’re able to combine multiple sources together and output it as a single combined source. This is not just about combining two sources of the same structure, but we can also perform operations you might already know from databases such as joins, filters, sorting etc. All of these transformation functions help you to consume cleaner and more useful data.

Our goal now is not just to be the reliable partner that transfers your data, but also to be the platform that tells you more about your own data than you know right now.