How a Major Manufacturer Creates Responsible Self-Service Data with Datameer

Self-service is a highly overused term in the data and analytics software sectors.  Self-service BI.  Self-service analytics.  Self-service data preparation.  Self-service gave analytics users greater freedom to get their jobs done with less outside assistance in all three cases.

Yet, as they adopted data self-service, many organizations encountered issues.  Data governance.  Regulatory compliance.  Performance.  Extra infrastructure costs.  These problems caused organizations to scale back their self-service programs for data and analytics.

Self-service Problems at a Major Manufacturer

A major manufacturer was very reluctant to do a major rollout of data self-service within their organization.  Their reluctance stemmed around:

• The fact that business users could create inefficient pipelines that cost a lot of money and slow down other jobs in the queue and data warehouse
• Giving scheduling capabilities to non-IT resources without an expiration strategy will lead to duplicate and unnecessary jobs, making troubleshooting nearly impossible, and inconsistency on how data is viewed and used
• Analysts lack the motivation and the know-how to follow engineering best practices for promoting workflows from dev to prod, resulting in costly mistakes and pipelines that are impossible to revert. In most cases, their work becomes throw-away code, suited only for ad-hoc use cases.

The manufacturer wanted to roll out data self-service, but wanted “responsible data self-service.”  They turned to Datameer to help them.

Many so-called self-service data and analytics tools create the problems mentioned above.  These tools create irresponsible self-service .  They have functionality gaps that create technical or process-related problems which cause data governance issues, performance problems, or drive up costs in unintended ways.

The Datameer no-code data engineering platform is allowing this major manufacturer to implement responsible self-service that delivers real business value.  It gives analytics users greater freedom to run projects on their own while at the same time giving data engineers control of the infrastructure to avoid the problems mentioned above.  Let’s explore how.

No-code Delivers Business Value Through Responsible Data Self-Service

Enabling self-service reduces the burden on central data teams and gives analytics teams greater speed and freedom, but as the major manufacturer realized, this must be done responsibly.  The Datameer no-code data engineering platform delivered value by:

• Eliminating inefficiencies – data engineering and analytics team members can collaborate and work together to create optimized data pipelines.  This keeps cloud data warehouse costs low and ensures other jobs and processes in the CDW have the resources they require.
• Reducing errors and mistakes – Better management, scheduling, and running of jobs eliminates duplicate, overlapping, or unnecessary jobs.  This also helps keep cloud data warehouse costs down and eliminates confusion over which datasets to use and which to trust.
• Promoting best practices – Analysts less familiar with engineering best practices can safely learn how to use them to promote and productionize data pipelines with guidance from data engineering teams.  This eliminates process breakdowns and errors in the transformed datasets.