The AI team is building models based on raw data coming mainly from digital channels (web sites, apps, …). The IT teams are responsible for preparing the data for the AI team. Data scientists monitor their own results using tools like MLFlow, or alike.
The IT teams are regularly asked to adapt the model of the ingested data, which often changes the model of the data provided to the AI team.
However, for IT teams it is not possible to anticipate efficiently those impacts, because of the lack of visibility of downstream users.
Hence, the AI team is either contacted too late, or not at all, provoking failures in their projects ⇒ many wrong decisions taken based on data, and ultimately hitting the customers satisfaction.
WITH KENSU (see how)
Kensu offers a holistic view of all data usages, including the quality information at all control points, such that
Therefore, impact analyses and root cause analyses are made easy thanks to the end-to-end visibility on data pipelines.
Time-saving and re-focus on business demands then increase the market shares thanks to the end-to-end monitoring.
In order to respond to such demands, all data intelligence work whether it happens in an ETL, ML/AI notebook, or a BI tool must be reported through documentation.
The time spent on manual documentation impacts the capacity for an enterprise to grow the data monetization without growing the team linearly, and manage the turnover rate due to the associated boredom of repeatedly updating the document.
WITH KENSU (see how)
Participating in the lifecycle of the applications through the generation of real-time logs about data usage allows:
In the retail market, it is crucial to know the customers better and conduct efficient marketing campaigns.
For this, new sources of data are now used: social network, external newsletters, etc.
CHALLENGESDue to a lack of control and visibility on the data transformations and learnings (ML/AI), the company has been exposed to a variety of misusages:
WITH KENSU (see how)Kensu controls and monitors in real-time to generate alerts in cases where:
In the banking sector, new competitors move incredibly fast. Due to new products being introduced in the market some segments are at risk.
Therefore, a bank company is changing its legacy CRM system to increase the accuracy of their understanding of their customers and their products.
WITH KENSU (see how)Kensu monitors the analytical lines introduced in the new CRM to
BENEFITSWith Kensu embedded in the analytical chains too, the company can
Based on 360° information of their customer base, the insurance company discovered a new segment (younger car drivers) they can address with a new car policy.
This segment is also exposed to other insurance companies, therefore, the new product needs to be launched and advertised before competitors.
CHALLENGESThe insurance company was on the verge of losing market shares due to a lack of responsiveness with a constantly postponed launch date of the new product.
However, little visibility was available to understand why the product couldn’t be launched on time
WITH KENSU (see how)Kensu monitors along the data tools chain to discover the root cause:
The owner of that data proposed to not use as it is based on old product information (parents‘ past policies) which won’t change any more.