Blog
2024 Gartner Market Guide To DataOps
We celebrate Datakitchen’s leadership in the 2024 Gartner Market Guide to DataOps. DataKitchen has the most complete, feature-rich, open, and modular DataOps product suite in the industry and the expertise to help you succeed
DataKitchen’s Data Quality TestGen Found 18 Potential Data Quality Issues In A Few Minutes!
Imagine a free tool that you can point at any dataset and find actionable data quality issues immediately! I took DataKitchen’s Data Quality TestGen for a test drive on ~600k rows of Boston City data and found 18 data quality hygiene issues in a few minutes.
What Is DataOps? Most Commonly Asked Questions
As DataOps continues to gain exposure, people are encountering the term for the first time. Below is our list of the most common questions that we hear about DataOps. What is DataOps? DataOps is a collection of technical practices, workflows, cultural norms, and...
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...
How Celgene Built a Billion-Dollar Product Launch Success 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
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
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
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
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...
Celgene – Meeting the Product Launch Challenge with DataOps
Evaluating Machine Learning Models with MLOps and the DataKitchen Platform
Data Science workflows traditionally follow the trajectory of the path shown in Figure 1. Most projects naively assume that most of the time and resources will be spent in the “black box,” building the machine learning (ML) model, whereas a majority of the project...
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
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,...
