Defining Your Data Universe with DataOps
The audacious, ambitious goal of teasing order out of the expanding chaos of modern data architectures requires a different approach to data integration. For decades, the automation of business analytics relied on predictable, well-defined data sets used in predictable, well-defined ways. But today’s data is messy. It’s unpredictable. Data producers and consumers find it painful to stay in sync. What is DataOps?
DataOps helps you tease order and discipline out of the chaos and solve the big challenges to turning data into business value. It is a set of practices and technologies that operationalize data management and integration to ensure resiliency and agility in the face of constant change.
Manage Data Drift with DataOps
The challenge to the provisioning of continuous data is the unexpected, unannounced, and unending changes to data that constantly disrupt dataflow. That’s data drift, and it’s the reason why, sometimes when you go fast, things break. But when you take your time, you fall behind.
To manage data drift, you need to ensure continuous data flows by automatically identifying and handling data drift. DataOps operationalizes data management and integration, turning data chaos into a continuous, reliable flow of data to the people and systems that turn it into value.
Why DataOps? Why Now?
You need data now, not later.
Modern analytics, data science, AI, machine learning…your analysts, data scientists and business innovators are ready to change the world. If you can’t deliver the data they need, faster and with confidence, they’ll find a way around you. (They probably already have.)
You depend on data that you can’t control.
When data came from carefully controlled systems, you could plan for change. Data fueling your business today comes from a wide range of internal and external sources. You have to detect and respond to change by design to keep up.
The next new technology just arrived.
Data warehouses held steady for 30 years. Hadoop had a decade-long run. The shift to the cloud and the rise of data science and machine learning has further accelerated new innovations in the data ecosystem. How do you enable your team to explore the possibilities of what’s next?