What is Augmented Analytics, and how can Telcos benefit from it?
Big Data is a vast industry that is only growing with each passing year. According to IDC, data created by connected Internet of Things (IoT) devices is expected to expand from 13.6 zettabytes (ZB) in 2019 to 79.4 ZB by 2025. Although Big Data is critical to an organization’s ability to make efficient business choices, most businesses fail to effectively understand the data’s insights. There are two reasons for this: the vast amount of data accessible and most firms still rely on human, bias-prone processes throughout the data value chain. Here’s where augmented analytics can come in handy. Augmented analytics is the future of data and analytics.
An Era of Disruption
Existing business models failed due to COVID-19’s disruption, and as a result, organizations are drowning in big data. There’s a demand for actionable information. Forrester Research estimates that just 0.5 percent of all data gets evaluated and used. Only 12 percent of enterprise data is considered when making business decisions, highlighting the limitations of unlocking the value from data. Even more unnerving, Forrester estimates that expanding data usage for decision-making by just 10% may result in an additional USD 65 million in net income for a typical Fortune 1000 organization. The answer to addressing the issues organizations experience in uncovering insights and realizing the benefits of Big Data could be an augmented analytics platform.
What is Augmented Analytics?
Augmented analytics is the application of enabling technologies like machine learning (ML) and artificial intelligence (AI) to data preparation, insight creation, and explanation in analytics and business intelligence (BI) platforms or AI in telecom. According to Gartner, augmented analytics aids both expert and citizen data scientists by automating various parts of data science, machine learning, and artificial intelligence model development, management, and deployment.
How can telcos benefit from augmented analytics?
While the obstacles that businesses confront are enormous, augmented analytics can help them overcome many of them. Automating data preparation, lowering time to insights, eliminating human analytical bias, and reducing the chance of missing critical insights are just a few of the advantages of augmented analytics at a high level. It also allows less business-savvy people, such as citizen data scientists, to democratize data analytics for those who lack specific training or expertise in data science or analysis for augmented analytics in telecom.
1. Understanding Customer’s Shifting needs
Using augmented analytics, telecom companies can instantly analyze tens of millions of CDRs, identify patterns that may indicate problems, create scalable data visualizations, and use predictive maintenance technologies to reduce dropped calls, poor sound quality, and various other issues that may cause customers to switch providers. It can also be used to create service plans that please clients while also being profitable.
2. Helps Prevent Fraud and Churn
The biggest challenge for telecoms is the high churn rate in the industry, which is estimated to be between 20% and 40% every year. Using churn analysis and churn prediction approaches, providers may better profile their customers and devise a strategy to keep their loyalty by determining who is likely to churn and who might still respond positively to marketing activities. Scams using automated calls or premium rate charges may not constitute a direct threat to telecom companies, but they do have the ability to diminish customer happiness over time. Using AI methods and techniques such as enhanced anomaly detection, fraud is easier to detect.
3. Improves Accuracy and Speed
Traditional BI tools necessitated a lot of IT assistance and required a lot of manual work. The human aspect raises the risk of a mistake in most BI software operational activities, such as cleaning and preparing a massive amount of data, analyzing and processing it, and presenting the results correctly. Robust IT systems with superior Augmented Analytics in telecom at their core can perform tasks with extreme precision and zero errors.
4. Prevents Roadblocks
For organizations to give ultra-fast results, there are various pre-built analytics use cases in the marketing, finance, and technology verticals. Customers can also create AI-powered analytics solutions that are tailored to their specific needs.
5. Predict Network Anomalies
Telecom can boost marketing performance through trend recognition, augmented analytics, and data science. Augmented analytics software can process vast amounts of data, particularly call detail records (CDR), and discover trends to detect and anticipate network anomalies. This function allows companies to notice emerging patterns affecting their operations, such as market shifts or rival activities. It allows marketers to respond more swiftly to rapidly changing market conditions by freeing up time that would otherwise be spent manually investigating trends.
The beauty of augmented analytics is that it can take data from various sources, such as Google Ads, Facebook, Shopify, or any other platform, and apply powerful machine learning algorithms to uncover critical insights that can save money and increase profits. For example, a budget allocation tool can determine which mix of expenditures across several marketing channels will yield the maximum revenue for a given budget. Data science in Telecom accomplishes this by first learning about previous outcomes and recent spending patterns, then creating a regression model for each channel to analyze how spending on each has affected sales over time.
Derive Maximum Value From Your AI Investments