Q5. What 1-2 strategies do you recommend to overcome these challenges with data analytics?
A5: #DataOps is an intelligent approach that offers agility and automation of data management. Through collaboration focused on the outcome, we bring cross-functional data and IT teams together to deliver the right data to the right people at the right time with the right quality
A5. Evaluate #opensource and open algorithms. Find less complex tools. Allocate budget, people, and time. Start with proven use cases (#UBA, #AIOps). Plan to fail, plan to learn!
A5 : Data analytics stack must be designed in a flexible way to accommodate this data growth. This includes managed data platforms, integrated DevOps, and scalable and managed compute resources.
@vamsipaladugus1 A5:Re: talent issues, build training and certifications & encourage employee initiative, idea ownership, and innovation that has them thinking outside the box.
.@AndiMann Failing fast is key to learn from any data project
A5: Introducing DataOps is an enabler to successfully adopting enterprise-wide data analytics. Orgs that realize the benefit of analytics often employa multi-horizon approach, focusing on using DataOps for a small set of high value use cases before scaling across the company.
A5: Invest in data governance and data management platforms for data analytics. #eWEEKchat
A5: We often emphasize the tools aspect of #DataOps, but critical to remember the org aspect is just as critical. Intentional alignment and accountability of how individuals can engage in the #DataCloud makes a HUGE difference. It won't happen 'by accident'
@madhoop Exactly! I advise my clients and teams - fail fast, fail small, fail cheap, fail forward. Make space for #ContinuousLearning!
A5 (1/2): 1. Work with technology that puts flexibility and openness at the forefront. 2. Avoid lock in with proprietary vendor storage and compute — Your data services need to work for you as required on any cloud, not trap you.
Prioritize enablement and training, invest in data governance, select tools that reduce siloed data sets
@KatySalamati I wish I had more upvotes for this. Data governance is its own reward, not just in compliance or privacy, but the agility to reliably use the right data at the right time!
.@jeffhollan Organizational culture is front and center to any data problem. #DataOps is fundamentally a cross collaboration between #Data and #Ops teams.
A5: Know the relationships between data management, data science, and data analytics and commit to investing in those functions as a competitive advantage
A5 (2/2): 3. interoperability is essential. Regardless of what system you use, your tooling should enable streamlined and secure end-to-end workflows. 4. Avoid proprietary formats — If you're data is in an open usable format it gives your business more agility to stay ahead
@AndiMann, couldn't have said it better!
.@namessanti - #2023 will be the year of #DataOps
@madhoop You heard it here first!