DataOps: Optimizing the Data Experience 

This blog was authored by Monica Kimbrough with support from Gina Noel D’Ambrosio

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 and 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. 

“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 yesteryears.” 

Back to Basics 

Let’s start by focusing on a few important concepts: 

• Applications play a crucial role in supporting and automating various functions and activities. They facilitate the creation, utilization, and exchange of information and data. Data, when considered on its own, lacks inherent value. However, it transforms into meaningful information when placed in a context, defined by its applicability, circumstances, conditions, and the guidelines surrounding its creation, usage, security, governance, and disposal. 
• Human-generated documents, such as love letters, job descriptions, and police reports, serve as examples of information. By converting these documents into a structured repository, we enable a range of actions: The data can be searched, edited, stored, secured, encrypted, duplicated, governed, and ultimately disposed of. This set of operations is easily managed by computers. 
• Through data analysis, we can uncover important behaviors, trends, and patterns. These insights deepen our understanding of how data interrelates with information. 

The Role of DataOps 

DataOps loads and maintains data elements like context and relationships, so applications can support information processing logic. This encompasses several key functions, including: 

• Archiving, subsetting, retaining, and purging data as needed 
• Overseeing the movement and exchange of data between application systems and datastores 
• Identifying and coordinating the impact of changes across interconnected systems 
• Executing exception handling for data delivery operations, quality assurance, and security measures 

Why This Matters 

As organizations embark on digital transformation initiatives, it is crucial to pinpoint business processes and associated services that can enhance performance and user experience on digital platforms. Ensuring that the information and data models are well-maintained and continuously optimized is essential. This is where DataOps plays a vital role. It automates processes that help clarify and interpret the data, evaluating raw data for insights and trends while also implementing the necessary functions 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 involves multiple layers, including: 

• Identifying key initiatives 
• Defining which functions are affected by the initiatives and included in scope 
• Analyzing the information subjects that will be generated and utilized by the functions in scope 
• Developing the data model to support these initiatives 

Finally, the organization must tackle the challenges of sustaining a robust data model following the shift to the new operating model and digital platform. This task becomes exponentially more difficult when information exchanges are required between application landscapes situated in diverse 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 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. We’ll discuss how evolving your infrastructure toward a data management platform, or even an Enterprise Data Cloud, is a critical step in gaining holistic control over your data and simplifying its management. 

Manage your data, not your storage.

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