Data is your business

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.

Why DataOps

Why DataOps?

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

Your top challenges:

  • “What data do we have and what’s in it?”
  • “Are my data flows GDPR compliant?”
  • “How do I get my data project to production?”
  • “How do I integrate the data platform into our corporate services?”
  • “How can I resource the skills to use this data technology?”
  • “How do I make developers more productive?”
  • “How do I give business users access to their data safely?”
  • “How do I share data ethically?”
  • “How can I deploy data products consistently?”
  • “Am I meeting my data quality SLAs?”
  • “How do I monitor the security posture of my data operations & respond to threats?”
Your top challenges

Dataops Definition

Bringing Data, Apps & People Together.

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 Outcomes

DataOps Outcomes

DataOps practices are designed to:

  • Increase productivity - More cool projects for engineering.
  • Fast track projects into production - Dev’s best friend.
  • Lower the need for niche technical skills - HR says thanks.
  • Improve collaboration - Can’t we all get along? Yes.
  • Eliminate tech lock-in - Happy CFOs? It’s possible.
  • Fewer lost weekends - Happy developers, more time for gaming.
  • Happier bosses - Keep the C-suite sweet.

DataOps definition

Key Capabilities

What are the DataOps components you should consider?

  • Data governance - Application & data catalog for automated discovery
  • Data processing & integration - Wrangle & move data with SQL
  • Self-service- Decentralized operations with governance
  • Security operations - Data access protected with authentication and auditing
  • Visibility - App topology, data observability, monitoring & alerting
  • Data compliance - Data lineage, data masking & PII data discovery
A best practice


The ingredients for DataOps success.

  • Open communication between Development, IT and the business
  • Transparent business metrics and measurement
  • Access to any streaming and batch data
  • Self-service operations
  • Data observability
  • Partnership with security operations
  • Promotes data meshes
  • Easy integration and visualization of data pipelines
  • Integration with modern, data-intensive applications
  • Reconciled data governance
  • Transparency to promote data ethics
  • Democratized data access with privacy and ethics directives


Teams reaping the rewards

“DataOps with 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, 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 →

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