Making Data Science Work with Dataops Practices · Whether referred to as data science, data...

Making Data Science Work with Dataops Practices

Agenda● Big Data is “dead”● Data Culture● Data Management● Agile Data Management

Big Data is dead.

Making analytics products is a teamwork now.

Data Democratization requires Data Culture

3 types of data culture

Helps making decisions

Data & Context are starting points for making a decision

Data helps strategic goals

Data culture is about

Data culture is about

Data culture is about

We need to manage the data

Why Data Management● Data Quality management => no “garbage in”

● Data Stewards institute

● DWH and infrastructure development

● Collaboration with business users => data becomes an asset

● Creates and develops standards of interacting with data

DAMA DMBOK & Classic methodology

Agile Data Management

DataOps Manifesto

http://dataopsmanifesto.org

Whether referred to as data science, data engineering, data management, big data, business intelligence, or the like, through our

work we have come to value in analytics:

Individuals and interactions over processes and tools

Working analytics over comprehensive documentation

Customer collaboration over contract negotiation

Experimentation, iteration, and feedback over extensive upfront design

Cross-functional ownership of operations over siloed responsibilities

Dataops New Roles

Data Librarian

Data Journalist

Data Analyst

Data Engineer

Data Steward

Data Architect

DataOps practices

● Data Platform unifies the data

● Data Governance

● Self-service

● Testing & Code Review

● CI/CD & Containerization

● Different Environments & Git

● Collaboration & Knowledge Sharing

About meLead Big Data architect in MTS

● HSE Big Data programme grad● 5+ years in DWH dev&ops● 2 years in Big Data

madhape@gmail.com

Thank you!