Make Sense Of Your Data

Make Sense Of Your Data

Wherever we turn data is a mess. Inaccurate, inconsistent, a lot of records that don’t deliver much insight. Worse again, it is typically stored in a way that keeps vital relationships, connections and similarities between entities hidden. We change that. Here’s how.

Building A Model

Most entities are best understood through their connections and relationships with others. And in most cases, real-world rules dictate what form those relationships take. To give one example - an order must have a customer associated with it, a date, and a product or service. DataChemist’s machine learning capabilities automatically derive those rules from your data and create a clear, understandable model of your world.

shape--left

Automatic Data Correction

We then import your existing data - even when that data comes from multiple inconsistent sources - into this model. DataChemist automatically identifies and rectifies common inconsistencies between existing data sets and our target data model. Machine learning means the platform continues to find new ways to correct and standardise data automatically.

shape-right

Machine Readable

Structured, meaningful, consistent data is semantic data. And there’s one big advantage that semantic data brings to the table: it is machine readable at scale. That's the reason DataChemist enables your data to power in-the-moment decision making and well as deep (really, really deep) analysis. Result: more insight, faster.

shape--left

Find Errors Fast

As DataChemist imports diverse and inconsistent data sets into our structured model, it automatically returns any errors found so that humans can get to work correcting them. We’ll also report in full where the real-world rules governing relationships have been broken so you can take a look and amend. The end result is clean, meaningful, machine readable data.

shape-right