As data is increasingly used to build models and predict customer behavior it plays a major role in business and companies. However, about 80% of the effort in data driven models and projects is wasted in the data cleansing process. We had this very same problem and hence we know first-hand how frustrating it is performing this low value-added task.

Quality emphasizes the importance to add data quality processes before bad data reaches its destination. This proactive philosophy is saving hundreds of data profiling and cleansing hours to Quality’s users.

What can you expect from Quality? With few clicks on a pleasant UI you will be able to apply:

1. Cell based rules: based on analyzing each single cell values, spotting nulls, regular expression rules, finding their completeness against a source of truth, and much more.
2. Unicity rules: focused on detecting duplicate data arriving at the system.
3. Timeliness rules: focused on sensing whether incoming data is valid in time and their lifespan is not beyond expiry date.
4. Static Value Distribution: rules based on detecting if the histogram or distribution of some key indicators of the data has variated more than some threshold detected
5. Dynamic Value Distribution: rules based on measure ad-hoc metrics at each arrival and comparison based on time-series analysis, in order to detect anomalies.
6. Aqtiva  allows creating rules at each quality dimension: veracity, validity, timeliness, unicity, completeness, timeliness and more.
7. Ad-hoc rules created by users in order to include novel quality rules.

Moreover, all your data quality process will be handled in Spark to make sure everything happens in real-time and there is no latency added to your work flow.

Finally, you will have a quality dashboard automatically set up to help you oversee all the quality processes.

If obtaining high quality data is appealing to you, go ahead and request a demo now.