Chad Meley, Vice President, Marketing, Artificial Intelligence, IoT and Customer Experience Solutions
Public clouds will move from competing on commodity compute and storage to competing on full stack data analytics offerings. AWS, Azure, GCP, Alibaba and other public clouds will aggressively partner with enterprise data analytic vendors to offer high value solutions optimized for their cloud and integrated with other components. In house data analytic offerings will become less relevant to large enterprises looking to avoid cloud lock-in (I’m looking at you Redshift and Big Query). These in-house data analytic offerings served their purpose when cloud providers needed to engineer these tools for the cloud but have since been out innovated. Enterprise players in the data analytics space have moved past the strategy of creating their own cloud and are now looking to partner with all of the big public cloud providers.
- After a few successful AI pilots over the last couple years, enterprises will put a renewed focus on enterprise data management and integration to serve as a foundation to scale up to hundreds and thousands of narrowly defined AI use cases. Every sort of machine intelligence that surrounds us today is narrow AI. Narrow AI operates within a pre-defined range. Narrow AI works within a very limited context and can’t generalize to take on tasks beyond its field without significant rewrites and retraining. So, you can’t expect the same AI algorithm that detects fraud to detect customers at risk of churn. That’s the task of a different narrow AI algorithm. A successful enterprise AI initiative will spawn hundreds, if not thousands, of use cases, each supported by a narrowly defined algorithm. Once that’s understood and anticipated, it’s evident that large enterprises need a common data foundation to scale their AI ambitions.
Customer Data Platforms
- The Customer Data Platform (CDP) space will be exciting to watch in 2020. CDPs are emerging to solve the challenge of fragmented customer data and disjointed customer experiences by enabling nontechnical business users to easily and quickly extend the customer profile, generate customer insights and deliver finely tailored instructions to the last-mile tools that execute personalized messages. While CDPs are spot on in defining the challenge, the current crop of CDPs fall well short of solving the challenge for large enterprises. All the technical ingredients are there for this category to mature fast: citizen data integrator tooling, no-code machine learning and autonomous real-time personalization. When it does, it will generate massive value and usher in a step change in the way enterprises sense and react to customer opportunities.
- There will be immense interest and adoption of “no-code analytics.” We’ve seen a steady democratization of advanced analytics by automating away certain laborious aspects such as feature engineering and model selection. But advanced analytics become truly pervasive when machine learning and other advanced procedural analytics becomes something that requires absolutely no coding or SQL skills. No-code analytics will become embedded in workflows or invoked through simple drop-down menus. They won’t make coding obsolete in the analytics world but will increase the number of use cases benefiting from analytics in large enterprises by a factor of one hundred.