Winning with AI: Industry POV on how to change the game, part 2
Leaders from a broad range of industries and expertise will gather at “Change the Game: Winning with AI,” a September, 13, 2018 event taking place live in New York City and broadcast live online. Rob Thomas, IBM vice president of analytics, will be joined by host ESPN anchor Hannah Storm to explore the transformative potential of AI and the importance of a multi-cloud.
Ahead of the event, we asked a virtual roundtable of analytics experts and analysts to wrestle with some of the core issues surrounding AI. Check the part 1 post here for bios and earlier responses.
How can companies use data, their most valuable asset, as a competitive advantage?
- Theodora Lau: The key to leveraging data as a competitive advantage comes down to the amount of good customer data companies have, and their ability to discover actionable or impactful insights from it. Having the right data strategy and tools provides the ability for organizations to mine the data, deliver exceptional customer experience and stay competitive.
- Bob Hayes: To leverage data as a competitive advantage, companies need to focus on the right outcomes. Research shows that analytical leaders focus their analytics on increasing customer understanding and improving customer loyalty. Analytical “laggards,” however, use analytics primarily to reduce enterprise costs and improve resource allocation.
- Kevin Jackson: To be leveraged properly, assets must be properly cataloged, tracked and managed by a responsible custodian. Data is no different. This is why executive leadership must invest in data loss prevention (DLP) and digital rights management (DRM) technologies today to properly catalog, classify, track and manage data resources.
- Tony Flath: With better sourcing of big data that uses machine learning and AI tools, organizations are uncovering predictive AI capabilities that customers will pay for.
How can enterprises be proactive about data privacy and regulation?
- Theodora Lau: Data protection and privacy best practices, along with compliance and data privacy programs, must be central to each organization’s operations, especially when new technologies are evaluated and deployed. With increased awareness of personal data collection and use in the new digital era, along with a rapidly expanding IoT ecosystem, privacy is no longer just an IT or policy topic. It’s one that matters greatly for your brand and the future of your business.
- Tony Flath: The best advice here is make compliance mandatory, being driven by things like GDPR. Look to have effective security controls and data integrity controls and reporting to support compliance requirements.
- Chris Penn: Honestly, no one has the foggiest clue what AI is going to look like in five years. Now you can say what’s been developed today and how will it be deployed over the next five years? Because that’s a valid question today. All these techniques in deep learning and reinforcement learning and things like Watson Studio, for example. These are technologies that are available in market now and that will take several years to deploy them in market.
- Carla Gentry: Of course, your staff has to be aware of all privacy laws and regulations. But things like GDPR were not meant to put companies out of business. They were meant to protect consumer rights. Companies that put it all on the table will fare better than those that use “dark data science.”
How do you think businesses can use data today to develop a winning strategy for AI five years from now?
- Chris Penn: Honestly, no one has the foggiest clue what AI is going to look like in five years. Now you can say what’s been developed today and how will it be deployed over the next five years? Because that’s a valid question today. All these techniques in deep learning and reinforcement learning and things like Watson Studio, for example. These are technologies that are available now and that will take several years to deploy them in market. (Chris shares more here.)
- Kevin Jackson: Plan for a future when real-time data is used to customize your products and services in real time. That requires identifying data use metrics today and directly linking them to business model-related goals.
- Tony Flath: Start now with better control of all data sources and define better controls for structuring that data. Find how to most effectively start with the building blocks that will enable AI deep learning and future AI capabilities.
- Carla Gentry: Hire someone who has an understanding of data engineering and architecture, build a team of experts, pay them well, give them a few freebies every once in a while and let them know they are doing a good job. The talent is out there, the question is, will you use this talent correctly and be able to retain that talent? AI isn’t just lines and lines of code. It impacts lives. If you respect this fact and use AI for good, your future will be bright.