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The predictive analytics market is witnessing strong vendor activity, with an increasing number of sophisticated and vertical-specific solutions. In an effort to differentiate their look-alike offerings, many BI and data warehouse vendors are adding predictive analytics capabilities to their core platforms. Currently, vendors are focusing on overcoming obstacles standing in the way of broader adoption of predictive analytics. Traditionally these solutions have been complex, requiring costly additional infrastructure, time, and programming to build predictive models based on high-quality, well-integrated data.

A new Ovum report, Telcos Unlocking the Power of Prediction, looks at the current drivers and challenges of predictive analytics for IT vendors in the telecom markets. The report provides insight into emerging opportunities on the supply side, outlines key IT vendors’ strategies, and recommends ways for them to them to exploit the rapid changes under way in the telecoms market.

Harnessing data is more important than ever

IT vendors in the telco space are well aware that their customers are rapidly turning toward improved quality of customer experience as a primary means to reduce churn. Vendors are explicit in their marketing messages to telcos, hammering home the point that telcos’ opportunity lies in harnessing the value of their most important asset: the customer.

Whether the goal is to reduce network operating costs, enable service personalization, provide richer customer insights that can drive more relevant advertising, or simplify customers’ interactions with various service departments, telcos must rely on fact-based insights rather than intuition. Vendors are promoting the idea that forward-looking telcos must treat subscriber data as a strategic asset – the oil that will fuel their future growth – and utilize predictive analytics to transform this data into revenue.

The siloed approach hinders analytical platforms from achieving their full potential

The validity of any predictive model depends on the quality and quantity of data available to it, so a major challenge that telcos (and all other industries) face with predictive analytics is deploying the right data mining tools. Most telcos today have vast amounts of data on which to base predictive models, but the data may be stored in legacy systems that are isolated from each other and incompatible with the systems running predictive analytics software.

Converting data in legacy systems to a usable format can be time-consuming and costly. A typical telco may have as many as 200 different major systems, 15 to 20 data warehouses, and hundreds of data marts. In addition, when provisioning a new subscriber, an operator may store data in 70 to 80 different databases and create a separate profile for each service to which the customer has subscribed. User profiles, identities, and preferences become splintered in the different databases.

Collecting, integrating, and cleaning up all this data is a huge challenge, but unless it is done properly, predictive analytical tools can never deliver their full potential, and telcos cannot optimize service delivery across access technologies.

Vendor strategies show clear commitment to address the challenges

Cost and complexity have traditionally restricted the use of predictive analytics to relatively small numbers of people within the company. Vendors are actively addressing this situation with a wave of new automated discovery and self-service data-mashup technologies that enable business users to easily connect to and access BI data sources and generate their own analysis, reports, and dashboards.

To promote the use of predictive analytics among nontechnical users, vendors have introduced web-based interactive querying and reporting tools along with interactive visualization tools that have improved both implementation speed and user acceptance of predictive analytics tools. Vendors need to take this process further so that end users can easily customize reports for their daily jobs.

In addition, as vendors seek to break down the cost, complexity, and interoperability barriers typically associated with predictive analytics, they are integrating predictive analytics capabilities into their core BI platforms, so users don’t have to juggle multiple web applications or export data from the collaboration environment. This will help improve the overall performance of predictive analytics while substantially reducing the storage costs.