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DataOps 101 Content

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What is DataOps?

People, process, technology. DataOps draws inspiration from lean manufacturing and the agile nature of DevOps. It is a method of data management focused on optimizing the end-to-end data supply chain.

DataOps is

  • Agile and extensible, bringing together 1st and 3rd party data sources and tools
  • A cycle that scales and improves over time through automation and ML
  • A “single pane of glass” that connects people, processes, and platforms through one collaborative view
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Learn more about these resources

Getting Started with DataOps – A Webinar
Getting Started with DataOps – A Webinar
Defining DataOps
Defining DataOps
Getting Started with DataOps – A Blog
Getting Started with DataOps - A Blog
DataOps Deep Dive
DataOps Deep Dive
What is DataOps? What is it not?
What is DataOps? What is it not?
eLearning Course: What is modern DataOps?(coming Soon)
eLearning Course: What is modern DataOps?

DataOps Maturity Model

Where does your organization land on the maturity model?

DataOps Maturity Model

Learn more in the blog, DataOps Maturity Model: The Journey to DataOps Success

Key Benefits of a modern DataOps platform

Zaloni Arena data mastering training configuration

Control data at every stage in its lifecycle See how TMX achieved this ➤

Zaloni Arena Data Control

Streamline data processes and gain deeper insights with automated data profiling

Zaloni Arena Product Cataloging

Discover trusted data quickly and easily in a self-service data marketplace See how a Top 10 Global Bank achieved this ➤

Zaloni Arena Self Service Data Marketplace

Collaborate with other users by sharing, tagging, and updating data entities

Zaloni arena collaboration across teams

Zaloni Arena data control

Visualize the entire data supply chain to quickly identify and resolve bottlenecks and inefficiencies

Zaloni Arena data mastering

Control data at every stage in its lifecycle See how TMX achieved this ➤

Zaloni Arena Data Control

Streamline data processes and gain deeper insights with automated data profiling

Zaloni Arena Product Cataloging

Discover trusted data quickly and easily in a self-service data marketplace See how a Top 10 Global Bank achieved this ➤

Zaloni Arena Self Service Data Marketplace

Collaborate with other users by sharing, tagging, and updating data entities

Zaloni arena collaboration across teams
Zaloni Arena data control
Get Started

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Learn from industry leaders as they share how modern, collaborative DataOps (with enterprise-wide governance) enables transformative business initiatives and drives success

Common DataOps Terms and Definitions

Big data refers to an extraordinarily large amount of structured and unstructured data that is processed and used for analytics.

A 360 degree view of a single customer’s data which may be used to improve marketing and sales effectiveness and customer experience.

The ability for users to access data in a self-service manner, typically in an authorized, secure way with the proper governance controls in place.

The process of generating or bringing data into an organization from an external source.

Automates data management processes for storing and processing data.

Provides an inventory of an organization’s data sets that are available for analytics, reporting and data science. Data catalogs provide detailed metadata and tools that help users search, find and understand data sets.

A process for organizing data into defined categories. Data classification may also be used to flag sensitive data to improve data security and privacy.

The act of cleaning data to identify and correct any inaccurate or incomplete records within a data set, table or database.

Ensures that sensitive data is appropriately managed and governed in a way that enables compliance with an organization’s legal requirements and governmental regulations.

The process of transforming data from one formation to another.

When data is collected for multiple sources, the data curation process takes place to integrate and organize that data in the given environment while maintaining and preserving data quality.

The process of controlling the integrity, security, and availability of data across an entire enterprise to ensure accuracy and proper usage.

Data is moved from one or multiple sources to another environment where it can be stored and analyzed.

The practice of collecting data from disparate sources and consolidating it into a single, accessible data set.

A centralized repository that stores structured and unstructured raw data in its native format.

From creation to consumption, data lineage tracks and records actions taken upon data throughout its life cycle.

A person who can interpret, analyze, create and communicate the information stored with a data point or set.

Referred to the administrative processes of preserving, authenticating, storing, processing, and collecting data to ensure accessible and reliable data for consumers.

Serving as a subset of a data warehouse, data marts house a narrow subject of data to provide relevant data to consumers quickly.

Also known as data obfuscation, hides original data with modified content to protect classified or personal information.

The process of matching and merging internal and external data sources to create a single master record.

Follows the ingestion of raw data and the process of transferring that data to a single source where it is stored and analyzed.

The act of collecting, manipulating, and extracting meaningful information from data.

Relates to the overall state and usability of a dataset.

The means of securing and protecting collected data from unauthorized access or uses.

Refers to the practice of making data accessible to authorized users.

Data set that is housed in a fixed repository that is isolated from the organization’s primary data environment.

The process of substituting sensitive data with another ID element. The new ID element is known as a token, and allows a user to safely access or retrieve sensitive data.

Converting data from its original format to another, often this process is used during data integration process and other data management tasks. data wrangling: Also known as Data Munging, this is a process that transforms and maps data from its “raw” form into a more usable format.

The common practice of copying data from one or multiple sources and refining that data for a new destination

EU government regulation to protect data privacy, setting guidelines on how to process and collect personal and private information (PPI).

Serving as a single source of truth, a golden record is the most accurate and complete data asset.

This discipline involves creating single master records for all critical business data to minimize error and accelerate business decisions.

Unstructured data is qualitative data in its native format that is not organized by a predefined data model