It’s time to believe the hype, every digital transformation needs a data transformation. Our world has been disrupted by the Coronavirus, and this has introduced a new urgency to reduce costs as well as discover and build digital revenue streams. The difference between thriving and surviving now hinges on our ability to make data actionable to more than a few technical “a-listers”.
The winners in this new race are working fewer weekends, and investing in modern data platforms that enable real-time decision-making.
If modern DataOps platforms are the key to every digital transformation, why do so many data projects fail to meet objectives? How many projects have we witnessed never make it to production, or worse yet, make it and crash and burn? Most estimates record data project failure rates as high as 87%*, because teams are up against the following challenges:*Venturebeat, 2019
What is DataOps? The practice of Data Operations takes the best bits from DevOps, removes the human bottlenecks from data projects, decouples business decisions from the infrastructure, and makes data more accessible to the right people.
This allows organizations to successfully deliver effective data experiences, and shift their focus to data-driven business outcomes and cross-team alignment.
DataOps practices are designed to:
What are the DataOps components you should consider?
“DataOps with Lenses.io has been critical in making Kafka production-ready. The productivity gains we have made have accelerated the delivery of new features and saved us approximately 2 million Euros per year.”– Ella Vidra, VP IT at PlaytikaRead Case Study →
“Kafka and Kafka Streams are powerful “black-boxes”. Limited visibility has led to unpredictable development times. DataOps, with Lenses.io, has created unparalleled visibility into our Kafka infrastructure, helping us reduce development times and build more confidence in R&D”– Maksym Schipka, CTO, VortexaRead Case Study →
“The more time our data scientists spend focused on building models and delivering products, the more money we generate to the business. Lenses is crucial for us in this area, helping make our data scientists more productive and valuable.”– Ryan Fergusson, CEO, RiskfuelRead Case Study →