At Costanoa, we believe that a new generation of companies will emerge to ease data operations between the data infrastructure layer and downstream data consumers. In order for this emerging ecosystem of technologies to exist, companies will have to be able to answer the question, “How do I know that I can trust my data?” As it turns out, this is a really hard problem related to data quality and observability. Both are foundational issues every data-driven business needs to address.
The impact of dirty data on real-time applications — from personalized product recommendations to fraud detection and alerting — translates to real downstream consequences in terms of cost, user impact, efficacy, revenue, business reputation, etc. Large organizations, for instance, report losing $15M annually to quality issues.
Data quality is also problematic for the in-house data engineers who are routinely pulled off more strategic work to put out these fires. In fact, it’s the number-one pain point among data scientists and engineers, who spend half their time on data cleaning and prep. This substantial investment of effort and energy comes at a high cost. Even if a company can afford to bring on a data engineer, it’s a misuse of valuable talent that would be better applied to higher-value work.
So while data is driving more decision making than ever before, reliability is still a huge challenge. There is a lack of purpose-built products and tools that will solve meaningful problems for data teams — especially data analysts and data engineers — at scale.
That’s why today we’re thrilled to announce our investment in Toro as they build an intuitive platform to automatically detect data quality problems and anomalies.
When Toro appeared on our radar, we were excited because the company’s experienced founders Kyle Kirwan and Egor Gryaznov had built a substantial amount of the data infrastructure stack at a company that faced unparalleled data volume and scaling challenges: Uber. They came equipped with a real roadmap for helping companies make data-driven business decisions with confidence, ultimately driving substantial enterprise value.
Toro has imagined that better way in a platform that can identify anomalies within specific slices of data, with explainable alerts that improve the speed of debugging, automatic recommendations and flexibility to define custom logic, engineering-friendly APIs, all optimized for performance on the most common data warehouses. Toro offers all this in an easy user interface that’s ready for product managers, analysts, and engineers to use.
At Costanoa, we see huge parallels between the push to “shift left” to modernize software development processes and the impending wave of adoption for data processes. We think companies will inevitably see what we see: a welcome answer to the data quality problem they’re addressing now with homegrown scripts, hastily applied and maintained, for applications and instances that can’t break. We think they’ll be delighted to give up an intensive, piecemeal, siloed process in favor of an automated solution that can deliver high-quality data that makes everything better, from machine learning models to applications.
In the near term, we’re excited to see Toro build out an incredible team to accelerate their progress on an achievable vision with real-world impact.