Last week, we hosted an ElevateData dinner, the last 2019 gathering in the series that unites Data Leaders to delve into hop topics in the industry. Just in time for driving New Year’s resolutions, we focused on a headline-generating buzzword and a top-of-mind topic for businesses: Machine Learning (ML). Given the continued commotion around this topic, we wanted to foster a reality check on the progress and potential of Machine Learning with a group of data leaders from different industries and companies. The result was a wonderfully insightful and lively conversation. Here’s what we’ve found:
Machine Learning is still at the beginning of its journey
When asking the data leaders how they define Machine Learning, it was clear that there is still variance in how data science vs. ML vs. AI are defined. Yet, it was widely agreed that we’ve just started to scratch the surface of the potential of ML. It’s interesting to note that the group overwhelmingly agreed that not every business problem requires ML and it is not perceived as a magic solution to throw at any problem. In fact, there are so many business problems across industries where the right solutions are data-related but not ML-driven, like reporting/dashboards, forecasting, inference, experimentation, etc.
Current impediments to Machine Learning
The discussion then focused on what’s standing in the way of Machine Learning delivering on its promise. Here are the four major obstacles that were identified:
- Lack of organizational readiness: It seems that many companies and industries haven’t yet reached the maturity level in data infrastructure and optimization/efficiency needs warranting investment in ML. Clear frameworks for measuring the impact need to be defined, tested and implemented. There is also the human factor: in many scenarios, there is friction around adopting ML, as intelligence and automation delivered by ML are perceived by many as a black box creating loss of control, as well as driving job loss. The group agreed that leadership is critical to ML success. We’ve observed that organizations where Data Science directly reports into the CEO (like Stitch Fix) often see their data endeavors propel.
- Technology deficiencies: Technology needed to make ML ubiquitous is still in the development phase. This is true for both development and deployment technologies and is leading to inefficiencies in ML solutions production cycle. Many companies are making progress in that direction but we are still far from having a full end-to-end ML solution.
- Continued ML expertise gap: Despite an abundance of training programs, we continue to have a shortage of true ML expertise which is a combination of multiple skill sets including but not limited to programming skills, full understanding of algorithms’ inner workings, expertise in statistical concepts, and business acumen.
- Next wave of innovation not enabled: Another factor inhibiting growth is skewed perception of where ML is often considered for optimization and increasing efficiency as opposed to something with a promise to deliver true innovation.
Machine Learning in 2020 and Beyond
After discussing the obstacles standing in the way of ML, the data leaders shared their perspective on driving forces which will help scale ML to next level in 2020 and beyond. Here are the five key takeaways:
- Moving beyond the buzzword: It seems that the ML overhype is stabilizing. With growing awareness around hype vs. reality, just adding “ML” to every pitch and story is no longer creating the perception of the proposed data product being extra valuable. We are getting better at asking questions to validate the how, what and when about ML. This will help build credibility for ML application.
- Education and messaging to build real belief in ML: To help ML realize its full potential, data leaders will continue to engage in demystifying machine learning so that it is not considered to be a black box. This will require using the communication tools and strategies tailored to specific situations and org structures. By reducing uncertainty associated with ML solutions, we can truly help unlock the full potential of machine learning. Data leaders will continue to work on positioning ML-driven solutions as enablers for people to do better at their jobs to mitigate the fear of job loss.
- Focusing on broader data skills in hiring: Industry players are starting to think more holistically about data skills, with a growing acceptance that all data skills are valuable. Data leaders will have proactive conversations on this topic, reenforcing the message that analytical and statistical skills combined with business domain knowledge are critical to building credible ML solutions.
- Technology advancement: Through continued investment in ML platform development, some players will emerge with affordable and comprehensive technologies. That would likely speed up the adoption of ML but as development takes off, we would need to remember that ML systems have deeper technical debt challenge compared to software engineering. Therefore, it will be critical to set up the right processes and shape perspectives to ensure sustainable development.
- Data ethics evolution: ML can lead to negative outcomes due to poor quality data, biases in the modeling, and more. A recent example is the Apple Credit Card controversy, with reports that the algorithm was discriminating against women. There are other scenarios where simple lack of judgment or not thinking through the broader impact of ML can lead to negative consequences. The group wondered if the role of a Chief Ethics Officer will emerge to focus on the ethical usage of data, and it expects an ethics discussion around data usage to become more prominent.
Our guests: Thank you to our guests from Unity, Snowflake, Stitch Fix, Uber, KeepTruckin, Shipbob, ZScaler, Streamlit, SAP Aruba, and Ethos for joining us for this dinner.
Thank you to all of the ElevateData members for joining us and passionately exchanging your ideas, ElevateData would not be what it is without all of you! We can’t wait for what 2020 has in store.
Let us know if you’d like to join this amazing group at the next ElevateData dinner!
Your ElevateData Founders,