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Reflections from within : Number 1 Gallery
They speak different languages. Unless resolved, the outcome will likely hurt the organization by limiting its BI evolution. To do their jobs, data scientists will wander off and implement stovepipe systems, floating in the clouds and free from IT involvement. And, business analysts will disown any responsibility or knowledge about those AI systems. Data analysts will continue to dabble in visual charts describing historical data.
Executives will be frustrated with managing two IT groups who do not communicate and with maintaining incompatible systems. And, no one will perform the hard work of conceptualizing the business use cases for analytic modules. Everyone will be unhappy! Over the history of BI in the enterprise, strong executive leadership is most needed now to navigate the entire organization through this next period of BI evolution. The balance between governance and agility has never been more delicate. Change in IT roles and responsibilities never more dramatic. And, the rapidity of technology change has never been greater.
The data science team must focus on the bits, ensuring that analytic models are properly architected, trained, and validated again and again, amid rapid advancements in neural network technology. They need clear statements of use case objectives and criteria, along continuous model evaluation after deployment. Data scientists are the wrong persons to craft those requirement statements, which require an intimate understanding of the business.
It is management, from top to middle, who are responsible for those statements. Finally, guess who should be bridge between all levels of management and the technical specialties especially data science , ensuring that the objectives and criteria for analytic use cases are accomplished properly. How are these people currently being used?
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What is the balance of biz-savvy with tech-savvy, relative to their organizational position? How should their role evolve to support future analytic systems? What new responsibilities should they have? And, what new skills and tools do they require for these new analytic-centric tasks? If successful, they should become best buddies with the data scientists, rather than trying to substitute for them as citizen data scientists.
Take-Away : Think strategically about the function of the data science team. Do you really want to hire an entire team as employees? Maybe a couple coordinating data scientists would be sufficient.
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Custom analytic work could be out-sourced to highly specialized development groups, while common analytic tasks could be web-serviced from cloud vendors. I immediately observed that attendees at both events were curious, energetic, motivated, and under It was a bit jarring for me to enter the room and dramatically increase the average age. There was revival energy in the attentiveness of the audience, like this technical stuff really mattered to them personally.
But, why? Over breakfast, I talked with a random Alteryx customer. I really like the artistic details in the icons.
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Makes them distinctive. Easy to learn.
My workday goes fast. The energy seemed to be coming from their gut, not their mind or wallet. Here is my explanation to the WHY behind this youthful energy….
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First, their motivation has shifted from tech-centric fondness to story-centric curiosity. They do not care about the tech, just about acquiring the tools and skills to tell stories based on the data. They do not care about analytic-generated insights by themselves, but about the stories that those insights foretell.
Second, analytics adds color more depth and drama to their stories. It is like a who-done-it mystery that investigates how Ms Helen was killed. Their role is becoming like that of an investigative reporter who has a 6pm news deadline. Third, any story does not end with who-done-it.
There must be resolution. This will require knowing the relevant casual relationships to be employed. Take-Away : Executives, support your next-gen data geeks by validating their contributions to the organization. Too often, their contributions get buried several levels down or never come to the attention of management. Take-Away : Next-gen data geeks are very aware of the social implications and ethics issues of their job responsibilities. It is now considered part of their story-telling. Executives should support this trend by promoting open and honest discussions about these issues and making these discussions a safe and normal part of the organization culture.
My opinion is that we are nowhere close to the mountain pass. For several more decades, the BI evolution will continue upward with its surprising shifts and twists. So, tighten your seat belt.
Enjoy the views! Here are a few final suggestions that summarize the previous…. Take-Away : Once you have a viable analytic use case, shift your thinking from analytic-centric applications to analytic-driven systems. Because of the value multiplier, analytic use cases that are properly implemented will start to change organization behavior externally. A conspicuous analytic application is nice, but several insignificant analytic modules embedded within the enterprise system can have huge business impacts.
Deal with the issues of deployment, operations, and governance. Take-Away : Forget what you think you know about analytics. Analytics is evolving daily, and the latency from applied research to practical applications is rapidly declining. For instance, research papers are posted on arXiv in the morning; successful github forks are reported in the afternoon, and web service available the following day. Potential use cases for off-the-github next-gen analytics now exceed the ability of industry to absorb these new BI innovations.
Take-Away : To expand the thinking of your team about next-gen analytics, try this exercise: Click here ;. Thank you for helping me untwist my thoughts! Will post a link here when published in Towards Data Science.