Data Virtualization is the most critical ingredient in realizing the full potential of Analytics, Machine Learning, and IoT
Today we are making decisions on only 1% of our data
(Source: McKinsey Digital)
79% of Executives say their data is not ready for AI/Fast Analysis
(Source: MIT-TI Global Report)
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?
- Enterprise Data is fragmented across multiple systems, structures and locations. Is all of this data available for use with models and algorithms, for business analysts, data scientists and application developers?
- Do you have a strategy for data preparation and pre-processing for machine learning?
Is your Data Density optimal for fast Data to Decision Cycles?
Data density is the rate of collection vs. the rate of data decay. If the business need is to make decisions with real-time changing patterns in data, you need to have the right infrastructure in place to feed queries, analytics and ML models with this ever changing data.
a. Do you have a streaming data pipeline infrastructure in production to support real-time decisions?
b. Do you have capabilities where you merge real-time streams with batch processing infrastructure to pipeline data to ML models and applications?
Is your data securely portable to make decisions where it needs to be made?
- Do you have to move your data to where the application/ML model is, to do predictions?
- Do you have a model training pipeline to retrain models running in production without having to move your data back and forth?
Do you have a Data Integration strategy to power AI/ML workloads?
- Do you have an integration strategy across your batch, streaming, data lakes and Data Warehouses to power ML based decisions?
- Do you have a data model that allows efficient interconnection between various data sources to provide a comprehensive view of the data at an Enterprise level.
Is your organization ready to break down the barriers between the data consumers (business units) and IT/Data Engineering?
- Is your IT data access request cycle long for large, complex datasets?
- Do you have a secure self-service model for business users to gain access to datasets?