Despite investments, hopes and expectations, many businesses are struggling to see returns from machine learning and AI projects. What lies behind this discrepancy between the promise and the delivery of AI and machine learning?
The answer lies in the ability to deploy analytics at speed and scale. Machine learning and AI are first and-foremost a data problem. The importance of ‘Tidy Data’ and standardizing the structure and processing of analytic datasets has long been recognized but progress in this area has been hindered by a proliferation of tools, technologies, data silos and “one-pipeline-per-process” thinking.
Teradata’s Analytics 123 strategy establishes a straightforward roadmap for both business and analytics leaders that creates robust, efficient and easily deployed processes that ensure machine-learning and AI projects live up to their promise and deliver real business value.
Analytics 123 decouples the different elements of the analytics process and ensures appropriate weight is given to each:
- Stage one is feature engineering with reuse at its heart.
- Stage two gives data scientists the flexibility to use their preferred tools to create predictive models with value to the business.
- Stage three deploys those models to score live data.
Features are engineered to be reused, documented and catalogued in an Enterprise Feature Store reducing duplication, increasing efficiency and consistency. Data scientists are freed to use the tools and languages they feel are best for each specific task—knowing that trained models can be easily ingested back into the enterprise to score live data in the Enterprise Feature Store. And that scoring can leverage the massively parallel, high-performance, enterprise scale capabilities of Teradata to drive real-world, business-critical analytics that can transform organisations.