DataKitchen’s DataOps Engineers Priyanjna Sharma & Chip Bloche discuss what DataOps Engineering entails, key skills required & when to add one to your data team
Accelerating Drug Discovery and Development with DataOps
A drug company tests 50,000 molecules and spends a billion dollars or more to find a single safe and effective medicine that addresses a substantial market. Figure 1 shows the 15-year cycle from screening to government agency approval and phase IV trials. Drug...
Addressing Data Mesh Technical Challenges with DataOps
Below is our third post (3 of 5) on combining data mesh with DataOps to foster greater innovation while addressing the challenges of a decentralized architecture. We’ve talked about data mesh in organizational terms (see our first post, “What is a Data Mesh?”) and how...
Use DataOps With Your Data Mesh to Prevent Data Mush
In our last post, we summarized the thinking behind the data mesh design pattern. In this post (2 of 5), we will review some of the ideas behind data mesh, take a functional look at data mesh and discuss some of the challenges of decentralized enterprise architectures...
What is a Data Mesh?
The data mesh design pattern breaks giant, monolithic enterprise data architectures into subsystems or domains, each managed by a dedicated team. With an architecture comprised of numerous domains, enterprises need to manage order-of-operations issues, inter-domain...
Using DataOps to Drive Agility & Business Value
“How do you make it so that your data and analytics team can get the ideas from their heads into production quicker? How do you create a process like a factory that produces Toyotas and not AMC Pacers? How can you get these teams who work all over the company to collaborate better? Those are the problems that DataOps solves.”
DataOps: The New Normal in Pharma
Learn how four pharma companies are quickly identifying new opportunities, improving research efficiency & accelerating new product adoption with DataOps.
What Is ‘Equity As Code,’ And How Can It Eliminate AI Bias?
DataOps “equity as code” provides the approach and methodological tools to impose equity controls on AI algorithms. A program of automated testing and continuous monitoring can help avoid deploying AI systems that instantiate and perpetuate inequities at scale.
Data Observability and Monitoring with DataOps
Data errors impact decision-making. When analytics and dashboards are inaccurate, business leaders may not be able to solve problems and pursue opportunities. Data errors infringe on work-life balance. They cause people to work long hours at the expense of personal...
Forrester – Chart Your Course To Insights-Driven Business Maturity
As organizations strive to become more data-driven, Forrester recommends five actions to take to move from one stage of insights-driven business maturity to another. After establishing a solid strategy, the second phase involves planning key processes and practices...
