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Why DevOps Tools Fail at DataOps

Why DevOps Tools Fail at DataOps

Implementing DataOps requires a combination of new methods and automation that augment an enterprise’s existing toolchain. The fastest and most effective way to realize the benefits of DataOps is to adopt an off-the-shelf DataOps Platform.   Some organizations try to...

Improving Teamwork in Data Analytics with DataOps

Improving Teamwork in Data Analytics with DataOps

Without DataOps, a Bad System Overwhelms Good People When enterprises invite us in to talk to them about DataOps, we generally encounter dedicated and competent people struggling with conflicting goals/priorities, weak process design, insufficient resources, clashing...

Prove Your Team’s Awesomeness with DataOps Process Analytics

Prove Your Team’s Awesomeness with DataOps Process Analytics

Do you deserve a promotion? You may think to yourself that your work is exceptional. Could you prove it?As a Chief Data Officer (CDO) or Chief Analytics Officer (CAO), you serve as an advocate for the benefits of data-driven decision making. Yet, many CDO’s are...

Gartner: 3 Ways to Deliver Customer Value Faster with DataOps

Gartner: 3 Ways to Deliver Customer Value Faster with DataOps

Slow deployment is a challenge for many data organizations.  In fact, many organizations experience lengthy cycle times for creating analytic environments or deploying new analytics that run weeks and months. In their recent report, 3 Ways to Deliver Customer Value...

Deliver ML and AI Models at Scale with ModelOps

Deliver ML and AI Models at Scale with ModelOps

Data scientists work tirelessly to build and train a model then face the daunting challenge of deploying it into production. The model itself is only a fraction of the overall ML system. Moving a model from development into operations involves provisioning...

Continuous Governance with DataGovOps

Continuous Governance with DataGovOps

Data teams using inefficient, manual processes often find themselves working frantically to keep up with the endless stream of analytics updates and the exponential growth of data. If the organization also expects busy data scientists and analysts to implement data...

Govern Self-Service Analytics Without Stifling Innovation

Govern Self-Service Analytics Without Stifling Innovation

Enterprises have adopted self-service analytics in order to promote innovation – self-service tools are ubiquitous. While data democracy improves productivity, self-service analytics also bring a fair amount of chaos. Enterprises are searching for ways to control...

How DataOps Facilitates Your Cloud Migration

How DataOps Facilitates Your Cloud Migration

Cloud computing does NOT always deliver increased agility. Migrating from an on-prem database to a cloud database may produce cost, scalability, flexibility, and maintenance benefits. However, the cloud initiative will not deliver agility if the data scientists,...

Believe It Or Not, Your Tools are DataOps Compatible

Believe It Or Not, Your Tools are DataOps Compatible

 Almost every data analytic tool can be used in DataOps, but some don’t enable the full breadth of DataOps benefits. DataOps views data-analytics pipelines like a manufacturing process that can be represented by directed acyclic graphs. Each node in the graph...

What is a DataOps Kitchen?

What is a DataOps Kitchen?

In a previous blog, we talked about aligning technical environments to facilitate the migration of analytics from development to production. In this post, we will introduce the concept of “Kitchens” and illustrate how they simplify the deployment of data analytics. In...

Run Analytics Seamlessly Across Multi-Cloud Environments with DataOps

Run Analytics Seamlessly Across Multi-Cloud Environments with DataOps

Data analytics is performed using a seemingly infinite array of tools. In larger enterprises, different groups can choose different cloud platforms. Multi-cloud or multi-data-center integration can be one of the greatest challenges to an analytics organization....