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Analytics Blog

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Chief Customer Officer
Chief Customer Officer

Build a Foundation that Supports AI and Machine Learning

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?

 

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. On the one hand, this principle sounds basic – don’t build your house on uneven ground! – but, on the other hand, it can be difficult to put into practice. How in the world are you supposed to build a solid foundation for a future we can’t fully imagine?

 

Honestly Assess Your Current Analytic Situation

Before turning your eyes towards that future and actively build AI and machine learning into your analytic initiatives, stop and fully assess your analytics maturity and scale now, right this moment. 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 probably overlook the simple yet key areas that will help you achieve these new analytics heights. And not just achieve them as small isolated projects but achieve them at scale. It is much easier to make important adjustments now than it will be to course correct in the future.

 

Now ask yourself, honestly, how well does your business currently use data, from big data to small data? Do you have a strong data culture, or are you still working to gain buy-in with certain people? How do your analysts and data scientist spend their days? Are they effective and efficient, or bogged down with too much tedious, mundane, and repetitive work?

 

Perhaps your audit will reveal that your business is a well-run data and analytics machine. And perhaps, more likely, 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.

 

Rethink Your Analytic Strategy

Once you have a current snapshot of your analytic situation and know what your strengths and weaknesses are, you can rethink your analytic strategy to be one that allows your business to fully embrace AI and machine learning.

 

How do you rethink processes that are ingrained in your organization? It’s hard, but to be successful you must move away from the traditional way of performing analytics.

 

As so perfectly quoted by Clayton Christensen, Author of The Innovator’s Solution and The Innovator’s Dilemma: “The reason why it is so difficult for existing firms to capitalize on disruptive innovations is that their processes and their business model that make them good at the existing business actually make them bad at competing for the disruption.”

 

More often than not, we find that 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.

 

Work towards creating a collaborative environment using a platform that works for everyone, not just those proficient in R or SQL. The more silos you break down and the more each stakeholder can have a piece of the analytic pie, the more innovative problem solving you’ll get – the results will speak for themselves.

 

This is often an area that analytic organizations tend to minimize, but the indisputable fact is that culture is a key underpinning to creating true business value and competitive advantage. In fact, according to a recent MIT Sloan review survey, 70% of highly digitally advanced companies use cross-functional teams to organize work and charge them with implementing digital priorities. Analytics is not different when reaching the next level of analytics maturity.

 

Free Your Analytic Talent

The final strategy I’ll touch on regards your business’s most important assets – the analysts and data scientists who do the grunt work, pulling enormous data sets, cleaning and prepping them, and digging up the insights that make your company successful. 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.

 

What’s the solution? Invest in solutions that can free up time and allow them to win back their time. For analysts, look for a platform that will allow them to do basic data gathering and cleaning and modeling without expert SQL knowledge, so they don’t have to wait for a data scientist to do that crucial task for them. This then allows data scientist to work on more complex tasks and really flex their muscles unlike ever before. You’ll have to establish and build a culture of player-coach relationships. This 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 will then be able to offload more fundamental data and modeling tasks.

 

You’ve already made a huge investment in hiring and retaining top analytic talent – make sure your business can unleash their full potential. You’ll need them and all their skills to succeed in this new world.

 

Confidently Embrace AI and Machine Learning

Building for a future we only have vague notions about is a daunting task – I’ll admit that. 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.

 

This is just the tip of the iceberg when it comes to preparing and building your analytics roadmap to support a future of AI and machine learning at scale.

 

Join us for a live streaming event as we cover this topic in more depth and detail with key industry thought leaders Tom Davenport, author of Competing on Analytics; Dr. Harsh W. Sharma, Managing Director, Data Business Group at Accenture; Heather Harris Solutions Architect & Data Scientist at Alaska Airlines; and Ashley Kramer VP of Product Management at Alteryx.

Olivia Duane Adams
Chief Customer Officer

Olivia Duane Adams is the Chief Customer Officer and a founding partner of Alteryx. In this role, Libby is responsible for overseeing and maximizing the complete Alteryx customer experience, from engagement to on-boarding, communications, performance, and retention. She has interacted with nearly every Alteryx customer, giving her a holistic perspective of the overall experience from implementation to adoption success.

Olivia Duane Adams is the Chief Customer Officer and a founding partner of Alteryx. In this role, Libby is responsible for overseeing and maximizing the complete Alteryx customer experience, from engagement to on-boarding, communications, performance, and retention. She has interacted with nearly every Alteryx customer, giving her a holistic perspective of the overall experience from implementation to adoption success.