Augmented Analytics: Extending AI Across the Telecom Enterprise

Companies that adopt augmented analytics report a 50 percent increase in analytics efficiency and decision-making confidence.

End-to-end automation

Everything from cleansing data to deploying the model is automated. This includes data preparation, tuning the hyperparameters, selecting the best-fit model, deploying it into production, and monitoring its performance. Augmented analytics provides a governing framework to execute these steps and to ensure transparency across the entire lifecycle. The efficiency gains here are significant.

Explainable and ethical AI

Augmented analytics helps users interpret black box models, so they can understand the logic behind predictions. It does this across three levels: global, regional, and local explainability. It also provides mechanisms for machine learning de-biasing, so that business users can trust the model’s outcomes. This is imperative when the model’s results govern actions or recommendations related to fraud, customer service, business assurance, revenue leakage, and so on. A robust augmented analytics platform also helps monitor key evaluation and performance metrics like precision, recall, feature drift, model drift, and more.

The business advantages

Intelligent automation of all data management tasks greatly improves efficiency and productivity of existing data scientists. But the greatest value driver is that augmented analytics platforms empower business users to become citizen data scientists. They can easily leverage AI to solve business problems without having to depend on exhaustive training and domain knowledge.

The benefits are clearly quantifiable.

Companies that adopt augmented analytics report a 50 percent increase in analytics efficiency and decision-making confidence. Automated feature synthesis helps data scientists roll out more accurate models, iteratively, quickly, and without user bias. Data processes run up to 100 times faster, accelerating time to insights. A bonus with augmented analytics is crisp visualization of insights and patterns. Conversational analytics makes it even easier for business users to consume these insights. 

Efficiency gains are great, but because ROI is one of the key drivers of adoption, how does augmented analytics help with revenue? For one, telecom operators can expect operational profitability to increase by 23 percent. Employees become more productive, and retention increases by 31 percent as does the newfound scope for value-adding tasks. This includes nurturing citizen data scientists who can then build AI models for other functions, amplifying value and ROI across the enterprise. Some companies report increasing their citizen data scientist pool by nearly five times thanks to augmented analytics platforms. On the front end, customers enjoying increased personalization, faster issue resolution, and network quality (among other benefits) report greater satisfaction. Some adopters report 35 percent year-on-year increase in customer acquisition.

Final thoughts

Telecom is evolving, reorienting business models to match the pace of change. Augmented analytics platforms can help players accelerate the data-to-decision lifecycle, giving them a sharper edge. They also minimize costs and maximize revenue by optimizing existing processes and unearthing new business opportunities.

Expectations from AI are soaring; augmented analytics is the crucial differentiator that will separate those who win big through AI investments and those who lag.


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