“Facebook today cannot exist without AI. Every time you use Facebook or Instagram or Messenger, you may not realize it, but your experiences are being powered by AI.” — Joaquin Candela, Facebook’s Applied ML team leader
“Machine learning is a core transformative way by which we are rethinking everything we are doing. Google Brain is the way we are embedding this in everything we do.” Sundar Pichai (CEO Google)
Salesforce CEO Marc Benioff said at a recent conference: “This [AI, ML] is a huge shift going forward, which is that everybody wants systems that are smarter, everybody wants systems that are more predictive, everybody wants everything scored, everybody wants to understand what’s the next best offer, next best opportunity, how to make things a little bit more efficient.”
Machine Learning (ML), Deep Learning and AI powering “Systems that Learn at scale” are at the bleeding edge of data science, deep learning and predictive search today.
Every market leader is jumping on this AI enabled engagement trend in retail, banking and healthcare.
Machine learning is central to Facebook’s future. Creating the extreme personalized experience (“individual equation based on 1000s of attributes – your preferences, predilections, conversations and transactions “) is the killer app.
FaceBook – AI/ML Case Study
Facebook is a bleeding edge case study of where AI/ML are being used to augment user engagement and experiences. I am starting to see many leading firms investing in ML Accelerators and Platforms as part of their data science strategy.
Big Data has been replaced by Machine Learning and AI as the next “must have” trend. Machine learning has caught the attention of venture capitalists.
Chief Data Officers, Chief Analytics Officers, Chief Data Science Officers and Chief Digital Officers are everywhere. The job is to leverage the latest in predictive analytics, data science, machine learning, and multi-tenant cloud architecture to bring innovation to traditional processes.
This is a pivotal moment in data driven business models (“systems that learn”) but there is no getting around the inherent difficulties associated with either altering organizational behavior, data ownership politics or managing transformation of the data infrastructure. And while the challenges are real, many firms are getting closer to achieving a data science and data management environment.
What are data and analytics officers overseeing… A variety of foundational & plumbing strategies:
- Data-as-a-Service: Data Provisioning, Management, Lineage, Quality
- Reporting-as-a-Service: Dashboards, KPIs, Drilldowns/Aggregates…. Descriptive
- Analytics-as-a-Service: Predictive Modeling and BI… Prescriptive analytics
- Information-as-a-Service: Threshold based Alerts, Exceptions, Mobile Prompts
- Insights-as-a-Service: ML/AI based “Systems that Learn”…automated learning – augmented intelligence, Next best Offer/Action
At core of all these, Data Management and Data Science tools are core technical and business capabilities. Some firms are more mature and further along than others.
Why Mature Data Management as a Function
Organizations live or die by the quality of their data.
Data is an underlying factor of input into business operations and essential in order to facilitate process automation, digitize operations, support financial engineering and enhance customer facing analytical capabilities.
An effective data management program requires a planned strategic effort
- Integrate multi-discipline efforts
- Inculcate a shared vision and understanding
- Data is a ‘thing’ – vital infrastructure element foundation of the n-tier architecture
- Not a project, more than a program…it’s part of the core foundation
There is no question about it – the foundational levels of people, process, governance and technology required to establish data management on a sustainable basis are coming together under the CDO umbrella.