Infosys TechCompass #59: AI - Data engineering

AI - Data Engineering

Trend 1 – AI technologies enhance data scientists’ experience

Data scientists often manually analyze and cleanse data, lacking standardized tools for tasks like data wrangling, feature engineering, and model experimentation. However, privacy concerns, regulations, cost pressures, and data bias are driving a shift toward automated processes. One such approach is synthetic data generation, employed when real data is scarce or lacks outliers. This ensures safe, reliable, fair, and inclusive ML models.

A European telecommunications company aimed to leverage customer data for improved client retention. By constructing predictive data sets, they effectively anticipated customer churn, leading to a 10-15% reduction through tailored offers.

Responsible data crucial for safe and sound AI development

In the evolving landscape of Explainable AI and responsible data use, the potential bias in data can have far-reaching consequences, posing ethical and regulatory challenges for businesses and society. The implementation of responsible and ethical data policies in AI development is crucial. Additionally, as AI increasingly relies on data for algorithm development and training, the importance of secure and dependable systems cannot be overstated. This involves considerations such as identifying data origin, distinguishing between internal and public data usage, detecting data anomalies, safeguarding individual data privacy, fortifying against cyber threats, and ensuring compliance with legal and regulatory mandates.

Explainable AI through responsible data is still evolving. The bias on data can devastate business outcomes, causing serious ethical and regulatory issues. The application of responsible and ethical data policies in AI development benefits businesses and societies.

Trend 3 – AI tools enhance data quality

Whether for executives, frontline staff, or ML models, every intelligent enterprise relies on high-quality data. Yet, data quality issues are common. AI-driven data quality analysis is now vital in ML Ops. Enterprises now view data engineering as a core aspect of their data strategy. Tools like Lakehouse, metadata management, data lineage, data quality, and data discovery are pivotal in data engineering architecture. For large enterprise data sharing, cooperative computing is noteworthy. It is used to generate robust datasets quickly for innovative corporate ML models. Here, various users consolidate and encode datasets for efficient and effective use.

An investment firm wanted to build a data pipeline on AWS for corporate customers. It involved identifying, ingesting, cleansing, and loading the existing data in its legacy IT ecosystem built on mainframes. The company improved the marketing campaign effectiveness by 70%, with effort savings of around 45% for the commercial sales line.

Find more about these trends and their use cases:

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