Maven Wave sponsored the 2017 Chief Analytics Officer Forum in Boston on October 3-5, 2017, hosted by Corinium Global Intelligence. The conference is led by CAO’s and attended by 300+ top-level analytics executives. It was a well-executed event with insightful speakers, peer-to-peer networking, and valuable actions for analytics teams from all industries and sizes. Below are our top takeaways and insights from the conference.
1. Do not discard traditional techniques.
On the first day, Matthew Marolda, CAO of Legendary Entertainment discussed the impact data has on the movie industry. Today there are many advanced machine learning techniques to collect and analyze data. Marolda recommends leveraging several strategies, including traditional techniques like conducting customer surveys. You will get different information from surveys compared to facial recognition technologies, as an example. Because there is no silver bullet in data, you should use several techniques to triangulate the solution.
2. Customer data is still not reaching key parts of the organization.
In a session on “Trends and Developments in the World of Data and Analytics”, PwC presented findings from two recent studies (20th CEO Survey and PwC’s Global Data and Analytics Survey 2016). One of the interesting findings was how much opportunity there is for using customer data to make better decisions across the organization. Today, customer data is mainly being used by customer facing functions like customer services (85%), sales (84%), and marketing (81%). Meanwhile, only 39% of organizations are using it for finance, 36% for IT, and 29% for HR. Knowing how to apply customer data insights throughout the enterprise can unlock new possibilities to make smarter and faster decisions.
3. Promoting a data-driven analytics organization requires influencing the micro-level.
Eric Huls, Chief Data Officer and Chief Data Science Officer at Allstate, led a keynote on “Fueling an Analytics-Driven Culture Across the Enterprise.” He provided several levers to influence individuals and promote a data-driven culture:
- Position yourself as a partner: integrate the core analytics team with the business.
- Develop business and soft skills: help data scientists speak the language of the business, understand business needs, and the underlying hang ups.
- Offer holistic solutions: support holistic efforts to gain actionable insights, enable change management, and facilitate connection with technology.
- Create tangible business value: link value and insight to business goals and push beyond being just a research team.
- Educate leadership: leaders have desire to be on the cutting edge and understand this space, so provide thought leadership and guidance on trends like AI.
- Institutionalize analytics – go after operational processes. How you hire, make operational decisions, measure, and reward success all have the opportunity to be rooted in analytics.
4. What got you to where you are today, won’t take you into the future.
The world is different now, there’s no question about it. On day 2, Rick Davis VP, Global Data Governance at The Kellogg Company, presented a keynote on “Thriving in a Changing World.” Representing a perspective from a company that failed to keep up, he provided valuable lessons learned for more traditional companies. Today, the world’s largest taxi company owns no taxis (Uber) and the world’s largest accommodations provider has no real estate (Airbnb). Industry leaders today are all disruptors, doing business completely differently than the market leaders of the past. The most successful companies today are data-obsessed, leveraging data as the primary competitive advantage. Companies need to think differently to evolve and disrupt the processes that are inside out.
5. AI technology still has its limitations.
The last day of the conference was a dedicated to machine learning, artificial intelligence, and deep learning for strategic innovation. Dr. Jerry Smith, VP Data Science at Cognizant provided one of the most intriguing sessions of the conference on “Cognitive Computing, Machine Learning, and Applied AI for Improving Humanity, Innovating Products, and Creating Efficiencies.” While the advancements in AI technology are amazing, machines simply don’t have the cognitive ability that humans do… for now.
Contextualization is one of the shortcomings of machine learning. For example, in the recent State Farm commercial in which two people say “O.M.G. my car!”, for 2 very different reasons. Machines do not currently have the ability to differentiate the positive sentiment from the negative, as a result of context. In the future, AI will be much more sophisticated by combining neuroscience + data science + human science to give us unifying view of intelligence and a better read of human thought processes.
On the final day, Brad Foster from Maven Wave presented on “Generating New Revenue with Machine Learning”. Look for a recap coming soon! For more information on Maven Wave’s Data & Analytics capabilities, contact us.