Long time, no news summaries…what happened? I’ll share the scoop on that soon. In the meantime, Cloudera and Hortonworks announced a merger that was really “big” news yesterday. That merger may impact many of your organizations big data environments. Other interesting analytics industry announcements do get continuously added to my Alerts page as they happen since I simply can’t write about all of them these days.
Upcoming Fall Events
Analytics industry peak event season is upon us once again. Here’s a list of upcoming events that I will be attending or watching this year. Interestingly, I got a complimentary invite to attend Ignite last month but I was unable to attend with my crazy schedule. I do plan to catch up on that segment at PASS Summit.
Alteryx Inspire London
I’ll be attending Alteryx Inspire on Wednesday and giving a session on the Art of AI Storytelling at 2:45-3:30PM that day. Effectively communicating throughout advanced analytics, machine learning and AI projects is one of my passion topics right now. Last month I wrote an article for InformationWeek called “Stop Talking Gobbledygook to the Business” about what I’m seeing and why this topic is so important. For all of the promise that AI and machine learning have for an enterprise, you won’t realize the technology’s potential if you blather in data science speak.
I think Dan Hare from Continuum is also speaking. I know he has been working on amazing machine learning automation and also robotic process automation projects these days. Thus, his session would be a must attend one.
AI Experience London
On Thursday, I’m honored to be giving the keynote at AI Experience. I plan to share how our industry is evolving and discuss key lessons learned from early adopters of machine learning automation. Other speakers from industry leading analytics organizations will be discussing their AI projects. AI Experience is a free half-day event for learning how AI is impacting every facet of business.
Qlik Data Revolution London
If I can, I’ll pop in on Qlik’s event in London. Not sure if that is physically possible of not given all that is happening at the same time. For Qlik fans, here’s the link to that event next week.
Tableau Conference 2018
Following the London events, I’ll be back in the USA and thrilled to be giving a free hands-on workshop at Tableau Conference. Tableau’s event that now has grown to over 17,000 attendees. It is my favorite one of the year. Yes, I do have favorites! Can’t wait to see what they will unveil in the keynotes. I always have super high expectations and sense we will see cool innovations in the area of “smart analytics” since that has been a recent theme for them.
In this workshop, Scot Barton, Head of Business Insurance R&D at Farmers Insurance and former Tableau Zen Master, Jen Underwood, will introduce automated machine learning and teach you how to get started using DataRobot with Tableau. They will cover when to use machine learning, how to build automated machine learning models, and how to best visualize and explain predictions with beautiful, brilliant Tableau dashboards. Workshop participants will: Learn common business use cases for “intelligent” dashboards Understand how to get started using DataRobot automated machine learning Explore the new DataRobot extension and other integration options Build an intelligent dashboard “hands-on” using Tableau Desktop
Then I’ll be heading to PASS Summit in Seattle for two days to see several wonderful peers out there. It has been three years since my last go at this show. I’ll be giving a session on Storytelling for Machine Learning and Advanced Analytics and spending time with DataRobot in the expo hall.
After PASS Summit, I’ll be giving another hands-on machine learning automation workshop at TDWI in Orlando. No machine learning or programming skills needed.
How To Get Started with Automated Machine Learning
Today there is way too much data to manually analyze and a data scientist shortage. Stop waiting for data scientists and learn how you can accelerate insight to action with no-code DataRobot automated machine learning with your existing analytics talent.
In this session, we will introduce automated machine learning and teach you how to get started using DataRobot. We will cover how to select appropriate business problems to solve, how to prepare data, analyze data, build a machine learning model and put it to work for the business in dashboards or applications. Lastly, we will provide tips for effectively translating quantitative insights and telling a compelling story throughout the entire project life-cycle.
Workshop participants will:
- Walk-through how to structure machine learning projects
- Get an introduction to DataRobot automated machine learning
- Learn how to effectively communicate results to the business
Artificial Intelligence Live
I think – hope – my only other in-person event event this year will be Artificial Intelligence Live. I’m scheduled to give three several sessions there.
- AIT06 – Fast Focus: Career Planning for the Next Era of Analytics
- AIH06 – How To Avoid Building Bad Predictive Models
- AIH08 – Innovations in Automating Analytics and Machine Learning
So if you are still with me…that’s not all. There are also a couple online events. (Are you seeing now why I haven’t been able to blog! Hint: Extreme sleep deprivation)
Successful analytical communicators don’t wait until the end of the analysis but rather use the entire process as a vehicle to communicate with the business. In today’s era of artificial intelligence and machine-assisted analytics, business analysts are crucial for bridging the growing data literacy gap. Accurately defining projects, understanding what to data to use, preventing bias, interpreting and effectively communicating findings are all important skills for helping stakeholders trust and make sense of results to get the most actionable value from analytics projects.
AI, machine learning and advanced analytics can be difficult to understand and explain. Describing the problem, the model, the relationships among variables and making sense of findings can be subtle, surprising and technically complex. Effectively translating quantitative insights and telling a compelling story requires planning, compelling design, and visualization choices. Please join me in this session to learn the essence of storytelling throughout the entire project life-cycle.
More to Come
I know this is not my ideal industry news wrap up. I’ll try to get a better one published soon now that most of the new content, hands-on training and demos have been created for the analytics industry event madness.
I will quickly share with you that I’m a little worried about our industry and what will happen to those of you diligently working in industries that adopt new technology slowly. I already see a massive AI divide happening between early adopters that are moving from BI to AI and the laggards. It is unlike anything that I have witnessed in my 20+ year career. I’m thrilled to be at the leading edge of innovation in our space. I love it so much and learn so much every single day.
I also see a lot of silliness and confusion in the market when it comes to augmented analytics and automated machine learning. It seems like everyone is saying that they have AI or automated machine learning when they really don’t have anything even close to it. Classic.
What is real versus wannabe automated machine learning? Stay tuned for an upcoming session on that hot topic.
I’m also seeing weak button click machine learning cooked into BI tools that will likely cause a plethora of bad models to plague our industry in the coming years. According to PwC’s 2017 Global CEO Survey, 67% of the businesses leaders taking part in the survey believe that AI and automation will impact negatively on stakeholder trust levels in their industry in the next five years.
67% of the businesses leaders believe that AI and automation will impact negatively stakeholder trust
We can’t let that happen. Last year I delivered a session on how to avoid building bad models. I’ll be updating that session soon to help you succeed in understanding what is real versus wannabe automated machine learning, how to responsibly deploy automated machine learning and how to build good models with it.