Data drives every business process now. But you can’t fully accelerate and optimize processes if you don’t automate data management workloads every step of the way.
Manual touchpoints and workflows throughout the data-management lifecycle impede many companies’ ability to ingest, aggregate, store, process, analyze, consume, and otherwise make the most of their data resources. Automating more data-pipeline processes can help your company execute transactions, make decisions, rethink strategies, and seize competitive opportunities better and faster.
More data professionals are adopting an industrial-grade work discipline focused on repeatable pipelining of patterned tasks. Their core focus is on creating an automation architecture in which repeatable pipeline artifacts–such as data-integration scripts and machine learning models—can be deployed rapidly into production environments. An architecture such as this can encapsulate best practices drawn from time-proven data-driven IT deployments.
Automating the data pipeline demands integration of industrial-grade DevOps practices into the working lives of data scientists, data engineers, business analysts, data administrators, IT operations, and other stakeholders. It also requires that these practices span your entire enterprise IT infrastructure, including your diverse data centers, mainframes, and private clouds, as well as any externally sourced “as a service” offerings being used by lines of business. Ideally, IT operations should have a single visual interface with which to develop repeatable scripts, run scheduled jobs, develop nuanced rules and orchestrations, and otherwise automate the scheduling, consumption, and administration of resources throughout your distributed data environment.
If you’re a big data, DevOps, or IT operations professional, you almost certainly have practical insights for automating data-driven business processes. Please join BMC and Wikibon on Monday, September 18 from 11am-12noon PDT/2-3pm EDT for a CrowdChat on Automating Data-Driven Business Processes. You can participate simply by clicking here, logging in with your Twitter handle, and posting your thoughts in an interactive, moderated Q&A format.
In this CrowdChat, experts, practitioners, and other interested parties will discuss issues, trends and opportunities in automating data management workloads across today’s increasingly complex enterprise architectures. BMC ’s Robby Dick, Basil Faruqui, Joe Goldberg, and Alon Lebenthal will be featured experts discussing BMC’s digital business automation solution, Control-M.
Here are the specific topics we’ll discuss in the CrowdChat:
- How does big data drive digital business processes and applications in your organization?
- How do you measure the business return from data-driven applications?
- Which, if any, data management workload processes are you currently automating across your environment?
- What are the principal platforms in your big-data analytics architecture?
- What platforms are you using to automate data workload management processes?
- How easy has it been to implement automated workload management processes that span your enterprise data architecture?
- To what extent have you aligned data workload automation capabilities with your organization’s DevOps architecture?
I’ll also be sharing my thoughts in the CrowdChat. Can we count on your participation? Please click here at the scheduled time and let’s chat.
