Conference Presentation, Panel, Fireside Chat, Interview
The Data Science Revolution
- Data is expected to become the core intelligence of next-generation applications, driving transformative changes across industries with a focus on connecting complex data components to reveal previously unknown correlations.
- The advertising sector, currently valued at $200 billion, will undergo deep disruption through programmatic advertising infrastructure capable of connecting buyers and sellers within 80 milliseconds to repair advertiser-consumer relationships.
- Storage and collection costs will continue to decline, shifting the primary industry challenge from data acquisition to managing data abundance and determining how to utilize existing information.
- New research models will reverse traditional approaches by testing hypotheses against existing data sets to identify correlations, rather than collecting data to test specific theories.
- Precision medicine will evolve to treat broad patient populations with specific compound sets, supported by computational tools that parse previously unmanageable data to identify effective genes and compounds.
- Enterprises will increasingly own their data processing capabilities, sharing only derived insights and conclusions rather than raw transactional or source data.
- Real-time processing infrastructure will enable proactive service by treating individual devices based on usage patterns, frequency, and break likelihood to facilitate predictive asset management.
- Cybersecurity defenses will shift from signature-based detection to real-time anomaly analysis to identify data leaks as they occur over time.
- A new phase of enterprise re-education will see data scientists assuming leadership roles to help technologists and analysts overcome preconceived analytical constraints.
- Markets will likely develop for both public datasets, such as those from city governments, and private datasets, with niche markets emerging where specific groups like clinical trial patients are paid directly for data participation.
- Consumer data sharing will generally rely on existing value-exchange models like loyalty programs rather than direct cash payments, though exceptions for specific high-value groups are anticipated.
- Computational availability will facilitate rapid, effective interpretation of complex algorithmic outputs, allowing the boundary of solvable problems to expand to include previously "unsolvable" challenges like earthquake prediction.
- The line between data sets suitable for big data analytics and those that are not will remain dynamic, fluctuating as analytical techniques improve.
- Business realization of disruption potential will increase as the capability to know previously unknown information leads to "slap-the-forehead" moments across various sectors.
- Privacy concerns are expected to be largely managed by consumer demand, with a growing emphasis on the social value of data for the collective good.
- The role of the data scientist will remain critical, requiring a blend of scientific, artistic, and business acumen to formulate the correct questions for data pools.
- The Internet of Things will generate sufficient sensor data to reveal previously unrecognized needs, leading to unexpected data discovery opportunities.
- Operationalizing predictive insights into real-time decisions will require establishing historical baselines and models before full deployment.