Mission: Make Data a Valuable product!

How? Making your data trustworthy.
Allowing business to focus on improving operations & finding efficiencies.

Why you need to start investing in data quality improvement?

Organisational needs

  • Data stack is getting more complex
  • Data team spends about 30% of time fixing bad data
  • Number of data consumers is growing
  • The company is moving to a self-service analytics model
  • Data is a key part of the customer value proposition
  • Requirements for data governance and regulation GDPR, SOC 2

Increasing the remediation cost

The further down the data stack bad data travels, the more expensive the remediation.
It is much easier and quicker to have a data engineer fix an ETL pipeline than for a data scientist to re-train a machine learning.

Data is moving down the stack

If bad data is caught by a data engineer, it is a hero but if bad data is caught by the clients there could be reputational or legal repercussions.

Failing to meet customer expectations

Data teams are creating sophisticated data products as customer offering unlocking new value for their companies. If your team is not producing actionable insights, you will quickly get outperformed by someone who is.

Step to Implement Fluent

We start by answer these questions:

  • Who will use the platform?
  • What data quality problems do you want to solve?
  • What will be the single source of truth?
  • What are the challenges of data discovery and lineage?
  • What are your data governance requirements?

Building data pipeline monitoring

  • Using machine learning to understand the way data pipelines behave and send alerts when anomalies occur in that behavior.
  • Implement business rules for data validation

Operationalizing data pipeline with data observability

  • Define KPI to measure the success over time (downtime)
  • Coverage for freshness, volume, schema in place across entire data environment.
  • Optimize incident triage and resolution response, setting up clear lines of ownership.
  • Custom monitors centered around specific data SLAs (governance requirements)
  • Operationalizing preventive maintenance, preventing data incidents before pipeline breaks