Why Data Virtualization is the most critical ingredient in realizing the full potential of Analytics, Machine Learning, and IoT
In 10 years, 4 out of 5 decisions will be machine assisted. Today, according to McKinsey Digital humans are making decisions on only 1% of the available data and 85% of AI and machine learning projects fail because of the complexity of retrieving and analyzing data from all of the distributed sources and types. With the data to decision cycles getting shorter and more fluid, with the increasing volumes and variety of data, and with the tightening data regulations needed to keep data secure and jurisdictionally compliant, the need for instantaneous retrievability of very large, fragmented, and geographically distributed datasets to power decisions is becoming critical. We must move away from traditional ways of accessing, aggregating and storing data if we want to advance into the intelligence era.
What data do Advanced Analytics, Machine Learning and IoT need to be successful? It’s often very different than the data that’s being accessed for human-scale use cases. Modern data techniques thrive when trained with instant, high-fidelity data, which is too often thrown out or summarized. Molecula’s Zero-Copy Data Virtualization platform allows all of your Enterprise data to flow at the speed of thought.
Assess your Data Virtualization readiness with these five questions:
- Is all your Data accessible for AI/ML algorithms?
- Is your Data Density optimal for fast Data to Decision Cycles?
- Is your data securely portable to make decisions where it needs to be made?
- Do you have a Data Integration strategy to power AI/ML workloads?
- Is your organization ready to break down the barriers between the data consumers (business units) and IT/Data Engineering?