Mean time to detection (MTTD): When it comes to building trust in the data and the data team, every data practitioner’s nightmare is when business stakeholders are the first to detect data quality issues. It can really hurt the team’s credibility and the company’s ability to truly become data-driven. As you start to document the incidents and classify their criticality, it is important to also keep track of how they were detected and the time it took for the data team to acknowledge them. This metric can be a hong kong whatsapp number data good indicator of the robustness of your incident management but also reducing it means you reduce the risk that the incident could cause more damage.
Mean time to resolution (MTTR): What happens once an incident is reported? spent between becoming aware of a data incident and resolving it. The resolution time is greatly influenced by the criticality of the incident and the complexity of the data platform, which is why we are considering the average for the purpose of this framework.
Mean time to production (MTTP) is the average time it takes to ship new data products or, in other words, the average time to market for data products. This could be the time spent by an analyst “cleaning” the data for a data science model. In fact, according to Forbes, data preparation accounts for about 80% of the work of data scientists. In a world where we want to treat data as a product, improving data quality can have a direct impact on reducing the time to market.