Blog post

We Are All Witnesses

By Carlie Idoine | July 03, 2018 | 3 Comments

It happened. The end of an era. When the Cavaliers lost the NBA championship as only a Cleveland team could 🙁 , I asked one of my sons to take one more picture. One more picture of the backdrop that has consistently encouraged us to pause – to take a moment to recognize the uniqueness of what we have been so fortunate to observe – over the last four years and the seven years prior to that, and even the years dating back to the early 2000s, when LeBron was a kid from Akron, Ohio, still working on his free throws in his high school gym.

We Are All Witnesses

We Are All Witnesses – And what a journey it has been!

But this sign has resonated with me beyond LBJ, beyond basketball and even beyond my beloved northeast Ohio roots. We were ALL IN – we followed the hype – all the way up the peak of inflated expectations, deep down into the depths of the trough of disillusionment and now – onto the plateau of productivity (at least for him – not us!)

Sound familiar? It does to me, too. And often when I would see this sign, I would reflect on my own work – my 30+ years in analytics. How fortunate I am to bear witness to the transformation of both the BI/analytics and data science/machine learning industries – yet again 🙂 – as the hype around these technologies not only sustains, but grows. Now, more than ever, the forces are aligning to add significance and relevance to the technologies and dynamics that comprise these markets. Both are pushing the boundaries. Analytics and BI vendors are adding more advanced predictive and prescriptive capability. Data science and machine learning vendors are addressing the end-to-end analytics pipeline. And all data and analytics are increasingly consumable by new and different types of users due, in part, to augmented analytics – a new wave of technology that incorporates machine learning to transform how analytic content is developed, consumed and shared (see “Augmented Analytics is the Future of Data and Analytics”). Take the Citizen Data Scientist, for example, and even application developers (see “Maximizing the Value of Your Data Science Efforts by Empowering Your Citizen Data Scientists”). Both now can independently and proactively work to weave analytics into their business processes and applications for both internal and external consumption. What a difference from when I started in this field when both of these activities were relegated the sole property of technical “experts” and “specialists”. Who would have thought that my worlds would recently collide in a machine learning model that predicted where LeBron would be most likely to go next!

With this increased power, however, comes increased responsibility. It’s up to all of us to continue to push the limits, identify the relevance, build and hone our skills, and apply them in new and productive ways that move us all ahead. My favorite example of this is an initiative that is gaining ground in the analytics circles and has been dubbed “Data for Good”. Data for good is a movement in which people and organizations transcend organizational boundaries to use data to improve society.  Check out this newly published research entitled “How to Use Data for Good to Impact Society”, led by Cindi Howson, to learn more about this important effort. We should all strive to include this in our playbooks. Variations on this theme include AI for Good and ML for Good. It’s about harnessing the power of the analytic resources at our disposal to leave this place a little better than we found it. It’s about the transformational power of applied resources, tools and skills.

Which brings me back to where I began – with where we are now.

LeBron ABJ 201806

Today, the sign came down. We are, admittedly, a bit sad, but grateful. We are definitely better for having the experience and looking forward to the opportunities we now have to build anew.

And all of you in the wonderful world of data and analytics, don’t miss YOUR own opportunity. What will be your next steps to not only witness, but actively participate in today’s analytics challenges in your own game-changing way?

Leave a Comment


  • Chris Lenzo says:

    So true. My career has been focused on building the enterprise systems that collect and store data for all sorts of purposes. With each and every implementation, the “reporting” part of the project was unique to the client. The questions posed and the answers provided helped clients surface areas for improvement and for concern. Modeling human processes with systems is tricky. The data was often limited, processing power constrained or the modeling incomplete. Now with AI and machine learning, new insights are being surfaced that change how we think about the problem. Predictive models are becoming the norm. Moving from simply stating the “what happened” to more “what will happen”. People need to understand how these models work, not just follow them blindly. With great power, comes great responsibility…

    • Carlie Idoine says:

      Hi, Chris – Yes! There’s a need for responsibility across many dimensions. Predictive modeling, too, will organically evolve to the next step with prescriptive models answering, “what’s the BEST that can happen?”

  • relax says:

    There is certainly a great deal to learn about this topic.
    I really like all the points you have made.