In an upcoming livestream on April 19, we’ll dig into how to build a foundation that supports AI and Machine Learning with industry experts and uncover what many companies are going through.
With so much news and focus on AI and machine learning, why is there such a big discrepancy between analytic organizations and their ability to evolve and embrace emerging technologies? In an upcoming livestream (Apr 19), we’ll dig into this topic with industry experts and uncover what many companies are going through.
But in the meantime, I have some initial thoughts on why so many enterprises have trouble evolving.
I think the reality is that a business can’t successfully make the shift to a whole new way of operating if they don’t have a solid foundation to build off. How are you supposed to build a solid foundation for a future we can’t fully imagine?
Before actively building AI and machine learning into your analytic initiatives, stop and assess your analytics maturity and scale. It’s important not to skip this part – if you go too far in your quest for AI and machine learning without pausing to understand where you started, you’ll overlook simple yet key areas that will help you achieve new analytics heights. And not just achieve them as small isolated projects, but at scale.
Perhaps your audit will reveal that your business is a well-run data and analytics machine. And perhaps you’ll find a few areas for improvement. Even if you come across problems you can’t immediately address, at least you know they exist and can work on finding solutions. Hopefully, though, you’ll find problems early and can start addressing them, creating a strong analytic foundation and culture throughout your business.
Once you have a current snapshot, you can rethink your analytic strategy to embrace AI and machine learning.
Often, analytic teams start from a weak position, attempting to innovate with legacy holdovers of analytics processes, technology, and team alignments. Instead, think of ways to create a true analytics ecosystem. Focus on integrating an end-to-end platform that breaks the traditional barrier between data scientists, IT, and citizen data workers, as well as the brittle framework of point solutions to perform key analytics tasks.
Create a collaborative environment using a platform that works for everyone, not just those proficient in R or SQL. According to a recent MIT Sloan review survey, 70% of highly digitally advanced companies use cross-functional teams to organize work and implement digital priorities.
The final strategy I’ll touch on regards your business’s most important assets – the analysts and data scientists. A huge part of building an analytic foundation strong enough to support AI and machine learning at scale is ensuring your analytic talent can be successful in the new environment.
Invest in solutions that free up time. For analysts, look for a platform that allows them to do basic data gathering, cleaning, and modeling without expert SQL knowledge. This strategy then allows data scientist to work on more complex tasks and flex their muscles like ever before. This shift may be uncomfortable for your data scientist teams at first, but establishing what citizen users can and can’t do, and providing guardrails along the way will pay off in dividends for your specialized analytic talent as they offload more fundamental data and modeling tasks.
Building for a future we only have vague notions about is a daunting task, but it isn’t impossible. In fact, it’s a doable and necessary task. Honestly assess where your organization is today, rethink your strategies to be collaborative, and make sure your brightest and most talented analysts and data scientists can do their best work.
The future is inevitable, AI and machine learning are already here, Join us on April 19 to hear how to embrace them confidently.