Data is the most critical ingredient in realizing the full potential of AI
84% of Executives believe AI will be a competitive advantage
(Source: MIT-BCG survey)
79% of Executives say their data is not ready for AI/Fast Analysis
(Source: MIT-TI Global Report)
Over the past two decades, data volume has grown exponentially, yet query times have remained relatively flat. Because of this, the analytics & BI movement have led to compromises like batch processing, pre-aggregation, sampling, deresolution & long information request cycles that will be unacceptable going forward. Analysts, scientists, engineers and most importantly the machines running our AI workloads should be able to ask questions of 100% of the data in real-time, no matter how large or complex the job.
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?