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

  • John Morrell
  • January 24, 2022

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.

How Does Datameer Deliver Responsible Data Self-service

The Datameer data modeling and transformation platform supports responsible self-service through a process that enables collaboration and best practices across the entire team of data and analytics professionals.  To support such a process, Datameer offers:

  • A multi-persona UI – by supporting three different UI styles (SQL coding, Low-code, and No-code), Datameer allows all team members to best use their skills and knowledge for data transformation.
  • Collaboration – Datameer facilitates shared processes where analysts and data engineers can work together to develop and productionize data transformation models, each having well-defined roles (see below).
  • Responsible scheduling – Datameer gives analysts the freedom to define their own data transformation models but lets data engineers, who better understand the overall workloads on the CDW, optimize and schedule jobs.
  • Change management – Datameer tracks changes to and the history of data transformation models for compliance reasons, to support collaborative processes, and to give greater transparency into the models.
  • Well-defined roles – Datameer offers granular security and roles that support the unique tasks and responsibilities split between analysts and data engineers while also facilitating collaboration.

Datameer

Datameer’s SaaS data transformation platform focuses on the T – transformation – in your ELT stack.  Datameer is the industry’s first collaborative, multi-persona data transformation platform integrated into Snowflake.  With no-code, low-code, and code (SQL) tools, the multi-persona UI brings together your entire team – data engineers, analytics engineers, analysts, and data scientists – on a single platform to collaboratively transform and model data.  Catalog-like data documentation and knowledge sharing facilitate trust in the data and crowd-sourced data governance.  Direct integration into Snowflake keeps data secure and lowers costs by optimally leveraging Snowflake’s scalable compute and storage.

Are you interested in learning more about Datameer and how it can deliver agility and collaboration for the “T” in your modern ELT data stack?  Please visit our website or schedule a personalized preview with our team today.

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