Timely and virtually frictionless access to data is a critical requirement for the expanding need for data science and AI/ML. The precious time of skilled practitioners is often spent managing data instead of building models. Ingenious technology that allows them to think about data, and get it without delays, requests, and errors is here now. Data in mind, data in hand is a concept that shrinks the effort and latency from conceiving of a model and having the data to run it.
For those who relied on a steady source of integrated, conformed data, this new reality posed a severe problem. With the number of source systems identified and captured, it was no longer possible for an analyst to identify, much less qualify, a data source for their investigations. The situation became so complicated that a new approach emerged: DataOps.
In this paper you will read about:
- Data in Mind to Data In Hand concepts
- Revealed Complexity vs. Complexity
- Customer Example: Complexity with Data Science and AI/ML
- Customer Example: Streaming analytics at the Edge
This report was authored by Neil Raden, an analyst, consultant, a widely published author and speaker, and the founder of Hired Brains Research. Hired Brains provides thought leadership, context, and advisory consulting and implementation services in Information Management, Analytics/ Data Science, AI, AI Ethics, and IoT Edge Analytics.