Download the 2021 DataOps Vendor Landscape here. Read the complete blog below for a more detailed description of the vendors and their capabilities.

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DataOps is a hot topic in 2021. This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. Companies that implement DataOps find that they are able to reduce cycle times from weeks (or months) to days, virtually eliminate data errors, increase collaboration, and dramatically improve productivity.

As a result, vendors that market DataOps capabilities have grown in pace with the popularity of the practice. To…

Figure 1: DevOps is often depicted as an infinite loop, while DataOps is illustrated as intersecting Value and Innovation Pipelines

One common misconception about DataOps is that it is just DevOps applied to data analytics. While a little semantically misleading, the name “DataOps” has one positive attribute. It communicates that data analytics can achieve what software development attained with DevOps. That is to say, DataOps can yield an order of magnitude improvement in quality and cycle time when data teams utilize new tools and methodologies. The specific ways that DataOps achieves these gains reflect the unique people, processes, and tools characteristic of data teams (versus software development teams using DevOps). …

Industry analysts who follow the data and analytics industry tell DataKitchen that they are receiving inquiries about “data fabrics” from enterprise clients on a near-daily basis. Forrester relates that out of 25,000 reports published by the firm last year, the report on data fabrics and DataOps ranked in the top ten for downloads in 2020. Gartner included data fabrics in their top ten trends for data and analytics in 2019. From an industry perspective, the topic of data fabrics is on fire.

What is a Data Fabric?

Whenever a new technology or architecture gains momentum, vendors hijack it for their own marketing purposes. This is…

DataOps addresses a broad set of use cases because it applies workflow process automation to the end-to-end data-analytics lifecycle. DataOps reduces errors, shortens cycle time, eliminates unplanned work, increases innovation, improves teamwork, and more. Each of these improvements can be measured and iterated upon.

These benefits are hugely important for data professionals, but if you made a pitch like this to a typical executive, you probably wouldn’t generate much enthusiasm. Your data consumers are focused on business objectives. They need to grow sales, pursue new business opportunities, or reduce costs. They have very little understanding of what it means to…

As DataOps activity takes root within an enterprise, managers face the question of whether to build centralized or decentralized DataOps capabilities. Centralizing analytics brings it under control but granting analysts free reign is necessary to foster innovation and stay competitive. The beauty of DataOps is that you don’t have to choose between centralization and freedom . You can choose to do one or the other — or both. Below we’ll discuss some standard DataOps technical services that could be developed and supported by a centralized team. …

by James Royster

Savvy executives maximize the value of every budgeted dollar. Decisions to invest in new tools and methods must be backed up with a strong business case. As data professionals, we know the value and impact of DataOps: streamlining analytics workflows, reducing errors, and improving data operations transparency. Being able to quantify the value and impact helps leadership understand the return on past investments and supports alignment with future enterprise DataOps transformation initiatives. Below we discuss three approaches to articulating the return on investment of DataOps.

Resource Redeployment

In a recent Gartner survey (figure 1), data professionals spent 56% of…

Remote working has revealed the inconsistency and fragility of workflow processes in many data organizations. The data teams share a common objective; to create analytics for the (internal or external) customer. Execution of this mission requires the contribution of several groups: data center/IT, data engineering, data science, data visualization, and data governance.

Each of the roles mentioned above views the world through a preferred set of tools:

  • Data Center/IT — Servers, storage, software
  • Data Science Workflow — Kubeflow, Python, R
  • Data Engineering Workflow — Airflow, ETL
  • Data Visualization, Preparation — Self-service tools sucha as Tableau, Alteryx
  • Data Governance/Catalog (Metadata management)…

It is not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is most adaptable to change.

- Leon C. Megginson on Charles Darwin “Origin of Species”

Adapt or face decline. The agile alliance defines “ business agility “ as the ability of an organization to sense changes internally or externally and respond accordingly in order to deliver value to its customers. Responsiveness and flexibility can enable a business to survive disruptive change and thrive in uncertain times. Companies that move slowly get left behind.

The agile alliance definition of business…

By James Royster

DataOps revolutionizes how data-analytics work gets done. Like many other “big ideas,” it sometimes faces resistance from within the organization. For most organizations, data is a means to an end. The organization’s primary focus is on its mission, whether that is a product or a service. As data professionals, we communicate the value of data-driven insights. Although many of our colleagues appreciate the value of insight, they generally pay little attention to the process of uncovering that insight unless there is an issue or error.

If you are launching a DataOps initiative, executive sponsorship can give you…

Since the term was coined, DataOps has expanded the way that people think about data analytics teams and their potential. 2020 was a huge year in DataOps industry acceptance. Media mentions of DataOps are on track to increase 52% over the prior year. To date in 2020, DataKitchen has seen an additional 5K downloads of the DataOps Cookbook . Industrywide, searches on DataOps (and derivatives) are up 500% over the past three years. analyst inquiries related to DataOps are up over 1000%.

Looking ahead to 2021, we see signs of DataOps adoption in large enterprises, expansion of DataOps into new…


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