Big Data Predictive Analytics – The Difference and The Value

Big Data and Predictive Analytics Technologies – The Differences 

There are fundamental differences between big data and predictive analytics. In terms of basic functions, big data is the collection of large volumes of raw data from various sources that are then systematically stored. Predictive analytics utilises big data to identify insights and patterns to predict future outcomes and events. 

In terms of technology use, the two are also quite different. Big data technologies include:

• Data storage – this refers to the use of computing and storage technology that collects and manages large data sets and allows for real-time data analytics. 
• Data mining – is the process of sorting through big data sets to identify patterns and relationships. 
• Big data analytics – is the process of examining big data to identify insights, hidden patterns, trends, and customer preferences that will ultimately improve decision making.
• Data visualisation – is the graphical representation of information and data through the use of graphs, imagery, maps, platforms, etc. that facilitate the efficient understanding of the data at hand.

On the other hand, predictive analytics uses several different predictive models and predictive analytics techniques that many businesses choose to combine to provide a full representation of the complex data available. These models utilise different data analytics and are set to provide accurate information on elements that affect the business. These include: 

• Regression models – this regression analysis is often used to predict demand during various months and how operations can meet this demand.
• Clustering – this model is often used for customer segmentation as it involves grouping data and variables, based on similarities, into clusters. This enables the identification of patterns concerning customer demographics, locations, etc.
• Classification – this model establishes the shared characteristics of large data sets and determines the category of a new piece of data based on its characteristics.

Predictive Analytics, Big Data, and How to Make Them Work for You as a Telco

From the above, there is no doubt that predictive analytics tools and big data add value to a business and offer the relevant data and insight needed to enhance operations, performance, services, decision making, and more. These elements impact business profitability and customer churn, which is necessary for business success. In the context of Telcos, this technology can be used to: 

• Detect fraudulent activity– in a digital age, fraudulent activity is a real threat. This technology allows data statisticians and data scientists to identify, detect and prevent cybersecurity issues and fraud. The algorithms are used to process data and find patterns in real-time to identify vulnerabilities and threats to protect your business and customers. 
• Optimise marketing campaigns – the insight gained from big data, and predictive analytics help businesses target the right customer base with the right product offerings, enhancing profitability. These analytics allows businesses to determine customer responses and therefore improve targeting. 
• Improve network operations monitoring and management – using predictive analytics, Telcos can identify traffic patterns, peak use times and forecast industry needs and demands. Using this insight, the business can put measures in place to ensure that the network runs smoothly and that the customers have access to what they need. This technology is also used to manage and optimise backend workflows, maintenance activities, alerts on equipment failure, and provide real-time updates on the network. 
• Enhance customer experience – predictive analytics provides historical data on customers who have already churned as well as insight into their behaviors and reasons for the churn. By addressing the reasons for customers leaving, you will also identify opportunities to enhance the customer experience.

Conclusion

Big data and predictive analytics are technologies that businesses can use to give themselves a competitive edge in the market. In terms of Telcos, this technology is used to provide valuable insight into the business processes, the performance of services and offerings as well as customer experience.

This insight is then used to enhance operations, offerings, and customer experience as well as make informed decisions which in turn impacts profitability. Going forward we expect to see big data and predictive analytics continuing to impact the industry as a whole, especially with the increase in the use of the Internet of Things (IoT). Telcos that effectively use big data and predictive analytics will have a competitive edge.