If your title starts with the words "chief data," here are four areas where you can make a big impact in your job.
Enterprises are increasingly putting data leadership in the C-suite, tasking them with transforming data asset potential into data-driven action. Whether the role is labeled chief data officer (CDO), chief data and analytics officer (CDAO), or even head of analytics, the goals are similar. Placing the same leader at the helm of data pipelines to analytics and AI/ML creates a through line from raw data to business value. The strategy can yield a rinse-and-repeat cycle of data utilization -- at least for those data leaders who come into the role with the right plan.
In a new CDO or CDAO role -- and odds are you are the first-ever at your organization -- you arrive with expectations. The criteria for success includes implementing holistic data pipelines, enabling data sharing and analytics modernization that shows a clear impact. That's a lot.
Achieving quick wins is possible, and these successes begin with a smart modernization strategy that leverages the power of the cloud. As the newest change agent within your organization, here are four areas where you can make a rapid impact.
1. Embrace the cloud to shave months off modernization
New CDAOs and CDOs chasing data and analytics goals through traditional infrastructure strategies face headwinds. Gartner estimated the failure rate for big data projects at approximately 80 percent. Cobbling together DIY cloud-based data technologies for analytics can require six months to a year to get off the ground, and then seven-figure annual budgets to maintain. DevOps teams wielding both cloud and data skills, if you can find them, are expensive. Plan to spend about five times the cost on personnel than you do on the tech stack itself if you choose this approach. These numbers place hard limits on scalability, and make it clear why so many projects fail before yielding useful results.
As an incoming C-suite data leader with a mandate for change, introducing a modern cloud stack shifts the odds to your favor -- quickly. Cumbersome legacy platforms and processes can be ripped out and replaced with flexible, cloud-first tools designed to support the variety of ongoing use cases, data sets, and AI/ML applications you require. For example, cloud data lakes for analytics can be turnkey and production-ready without requiring internal DevOps, SecOps, or CloudOps personnel or overhead. Such strategies set a CDAO's foundation for project acceleration out of the gate. Cloud spending, per AWS, is still only 4 percent of the overall IT market. The opportunity for an incoming CDAO to be the data and analytics modernizer will be available for the taking for most.
2. Get the right people
Led by a strategic CDAO or CDO, a small team is all that's needed for data and analytics success.
