Communications service providers (CSPs) are now well aware of the power of the data they collect regarding their customers and see it as a key means to run their operations more efficiently, achieve compliance, improve customer experiences and ultimately enhance their profitability. The telecoms industry, however, is composed of a complex web of organisations created through mergers and acquisitions (M&A) and infrastructure that involves multiple generations of technology. This makes the process of enabling data-driven decision making multi-layered and encompasses cultural as well astechnological challenges.
Mel Prescott is a principal consultant in the telecommunications practice at FICO, the data analytics specialist. He helps companies to use the most advanced analytics, decision rules, orchestration, mathematical optimisation and other systems and technologies so they can achieve the potential to optimise all aspects of their operations that lies in their data. He has more than 15 years of experience working in analytical telecoms roles to provide subject matter expertise and has held credit risk management positions at EE, Orange and Bank of America. He holds an MSc in Analytical Credit Risk Management from Sheffield Hallam University in the UK.
Here he tells George Malim how FICO is helping its CSP customers through the use of big data and mathematical algorithms to predict consumer behaviour. The company focuses on enabling CSPs with tools and software that allow them to optimise operations through fighting fraud and managing risk more effectively, complying with government regulations and creating more profitable customer relationships.
George Malim: What are the challenges communications service providers (CSPs) face in making effective use of data to drive their decision making?
Mel Prescott: We’ve been talking about use of data and ability to create new solutions by exploiting data for a long time but the reality on the ground is that a lot of CSPs are hamstrung by operational silos. They are still having to process data in large batches rather than in near real-time.
The data is sometimes inconsistent and different departments have different data warehouses. They are sometimes protective of these and suspicious of other business functions using that data. This means there isn’t the holistic view of the customer that is needed to make effective data-driven decisions.
For example, CSPs often take only isolated approaches to different systems’ data, keeping network data seperate from customer data. There’s no real two-way flow of data taking place and they are not really stepping in any customer-side data which means they can’t prioritise access to the consistently most valuable customers or those who are most active on social media.
I don’t think there’s any real use of holistic data to the greater good within many CSPs and they are further hampered by their heritage of growth by M&A, which means there hasn’t been the ability to bring the different departments together.
For example, when I worked at EE in the UK, we started as Orange, then merged with TMobile. We had no way to link a broadband customer with a mobile customer, or an Orange mobile customer that also had a TMobile mobile subscription, or to provide seamless experiences.
There’s an old Indian parable about blind people in a village being asked to identify an animal by touch. The animal’s an elephant but because the people all operate in isolation they can’t provide a coherent description of the animal. CSPs’ fragmented structures and the barriers between departments result in the same incapacity… Continue reading in Tech Trends magazine »
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