I’ve spent more than two decades leading enterprise-wide transformations for Fortune 200 companies. During this time, I’ve developed a deep understanding of the benefits that come from optimizing the data experience to produce successful outcomes.
John G. Schmidt, my longtime business partner, and Kirit Basu are two renowned leaders in the data space. They wrote an innovative book exploring the topic. In DataOps: The Authoritative Edition, they define the capabilities, methodologies, practices, and technologies needed to address data operations and management.
In their own words, “DataOps is the application of DevOps practices to data infrastructures with one caveat; it has to accommodate change at a much faster pace than what was capable in yesterday years.”
Back to Basics
Let’s start by focusing on a few important concepts:
- Applications support and automate functions and activities. They create, use, and exchange information and data. In isolation, data has no value. Data becomes information as reference. To give it and meaning is provided to it adding , you need context, applicability, circumstance, and conditions to define its creation, use, safety, governance, and retirement.
- A love letter, a job description, and a police report are examples of information created by humans. When we transform these documents into a structured repository, the data can be searched, modified, stored, secured, encrypted, duplicated, governed, and disposed of. Computers perform this subset of actions.
- As we analyze this data, we can assess relevant behaviors, trends, and patterns. They provide new insights that can help us understand the relationship between data and information.
The Role of DataOps
DataOps loads and maintains data elements like context and relationships so applications can support information processing logic. This includes:
- Archiving, subsetting, retaining, and purging data
- Managing all data movements and exchanges between application systems and datastores
- Identifying and coordinating the impact to cross-systems
- Executing exceptions for data delivery operations, quality, and security
Why This Matters
As organizations embark on digital transformation initiatives, they need to identify business processes and related services that will perform better and be more consumable on a digital platform. Maintaining the information model and data model—and keeping them as optimized as possible—becomes paramount. This is where DataOps comes in. It performs automated procedures to make sense of the data. It assesses raw data for insights and trends while implementing the necessary function to support processing logic.
The Key to Digital Transformation Success
Throughout my years supporting enterprises going through major digital transformations, I’ve witnessed different degrees of maturity in addressing the data challenges these transformations present. At the end of the day, it’s precisely this maturity that will determine success or failure.
Digital transformation has many layers, including:
- Identifying the transformational initiatives,
- Marking in scope the functions affected by the initiatives
- Analyzing the information subjects that will be created and used by the functions in scope
- Constructing the data model
Lastly, the organization will need to confront the complexities in maintaining a healthy data model after transitioning to the new operating model and digital platform. It becomes exponentially more difficult when information exchanges need to occur between application landscapes that reside in different environments. This complexity creates the need for a hybrid solution.
In subsequent blog posts, we’ll take a closer look at those complexities. We’ll also explore the important role DataOps plays in maintaining the health of the data model. And the roles needed in the organization to perform it.
We’ll also look at how technology automation can transform responsibilities typically performed by humans and how Pure Storage® technology can help you address these complexities.
