Historical data is dead. While global corporate enterprises are restructuring their balance sheets to accommodate for the value of data, organizations need real-time data which must be democratized throughout the enterprise. Data acquisition, governance, visualization, and virtualization along with advanced analytics and AI putting ‘math on top of data’ all make that goal possible.
AI & Data Democratization Live‘showcases lessons learned from those who are accomplishing the goal.
Data is more than just the currency of business, it’s the lifeblood of growth, innovation and profitability. The upcoming’AI & Data Democratization Live Virtual Event‘will dive into concrete strategies to grow true data-driven decision making at your enterprise through all employees.
On‘April 27 – 28, you’ll have the unique opportunity to learn from hundreds of Data and Analytics leaders on our custom event platform built for virtual sessions, Q&A, chats, booths and more.
Don’t miss key discussions on:
- Orchestrating True Enterprise Business Intelligence
- Data Insights from Predictive, Prescriptive Analytics
- Data Enrichment for the Soul of Your Enterprise
- Attaining Checks & Balances Data Governance
- Diving in on Data Mining
- Capturing Data Virtualization
- Optimizing Your Enterprise Data Platform
Presenters Include:
- Adri Purkayastha, Global Head of AI and Digital Risk Analytics,
BNP Paribas - Catherine Lopes, Head of Data Strategy & Analytics,
ME Bank - Kate Carruthers, Chief Data & Analytics Officer,
University of New South Wales - Timo Etzold, Senior Data Consultant,
Continental
Sessions Include:
Building An End-To-End Analytics Strategy
Data analytics strategy cannot bring value on their own. There must be integration between people and data. There must be integration between business and technology. The intersection of these four forces is the sweet spot where value resides.
Data Governance 2021
Gone are the days when only data people handled data. Data democratization has meant that new data governance tactics, strategies and methodologies need to be implemented to ensure that data remains pristine in the hands of the masses.
Utilizing ML To Detect Historical Patterns To Then Detect Real Time Patterns
What is one to do when historical data loses implicit value to the enterprise? One way to ensure that partial value is restored is to find the patterns in the historical data to inform the detection of real-time patterns.
