Opinion

Data privacy rules.

Nov 25, 2019

Digital intelligence and data privacy must become inseparable forces.

Communications Service Providers (CSPs) have explored sophisticated analytical treatments of their customers’ data. They do so for many reasons: to better serve their clients, to open up new possibilities of revenue streams in both consumer and enterprise applications, and to create new business models in general. While pursuing those opportunities, CSPs have also sought to employ cutting-edge technologies to ensure that their data explorations do not undermine fundamental data privacy principles. Not doing that would breach both trust and privacy protection laws.

Within the entire wealth of data processed by CSPs, information gleaned by network analytics is perhaps the most powerful and deserving of protection.”

Network analytics builds upon existing network traffic and subscriber data, providing telcos with a platform that performs broad traffic inspection, data aggregation, and data analysis. It enables comprehensive knowledge management, operational optimisation, and investigative discovery by leveraging granular network usage detection.

Against those capabilities, rigorous data management and oversight regimes are non-negotiable. Four key protection measures must be tirelessly reinforced:

  • Precision data control and auditability: network analytics platforms must provide an access control tool for permissions management so that data access, use, and sharing can be regulated with extreme granularity. Data access must be logged and auditable, deterring employees from performing any activity that could violate data privacy.
  • Encryption of personal identifiers at source: personal identifiers (IMSI, MSISDN, IMEI) must be encrypted at the tapping point. Afterwards, data can be transformed, integrated and used for business and operational purposes without compromising the privacy of the telcos’ individual customers.
  • Purpose-driven data analytics: network analytics platforms should only capture data that is required for identified use cases. It should also make sure that data is not stored for longer than necessary for the purpose it was originally meant to serve.
  • Intelligence augmentation: a powerful and privacy-preserving network analytics platform should put the analyst and decision-maker at the top of the analytical chain. While software can handle the more tedious workflows required to surface the essential data and insights from the network traffic, end users must focus on the data problems and on the privacy implications that they bring about.

The challenges to building privacy-sensitive network analytics platforms are, at their core, mental and economical. Embedding protections at different stages of a data handling pipeline consumes development time – it requires more, and more complex, coding. It also makes software hungrier for hardware resources. Unsurprisingly, then, it gets often overlooked.

That shall change. The proactive offering of privacy-protective services is quickly becoming a central requisite for any business. An attribute that no client will, soon, be willing to compromise on. And, consequently, a major force in reshaping network analytics platforms for a safer reality.