From CSP to DSP – why digital integration is the difference between success and failure

Claus Nielsen, Global VP of Marketing - Neural Technologies will be at Digital Transformation World next week examining the challenges involved in digitally integrating event data. Join him and 3000+ of your senior telecoms industry peers from across the world for three immersive days of inspiration, innovation and networking.

When it comes to embarking on the journey to becoming a digital service provider (DSP), it can be difficult to know where to start. A question we often get asked is: “What is the quickest and maximum improved business value achieved from minimum effort?”

The problem is that turning into a fully-fledged DSP is not straightforward. Communications service providers (CSPs) face increasing complexity in the integration, retention, analysis and delivery of event data – data generated by logging uses, events, or transactions which have been generated or triggered by people, machines and services.

This is without even considering the complexity of high volume and velocity, which has resulted in the explosion of big data that we see today. Examples are call detail records (CDRs), the Internet of Things (IoT), data points, and customer related activities data such as orders, invoices, payments and complaints.

Done wrong, a CSP’s journey to digital transformation could be cut short. So what’s the answer to ensuring these digital transformation projects don’t fail? A practical approach to digital integration must be developed, and it must be centered around the event data and its associated business processes.

Successful digital integration can be divided into four stages:

1. Event data classification and standardisation


2. Event data collection and distribution


3. Event data life cycle management


4. Event data driven decision making

Event data classification and standardization

Event data classification and standardization is the preparation step for all instances of digital integration. This involves putting in place programs for defining the format, loss prevention, protection, age-based retention of event data, as well as the cleaning, enrichment and transformation as per business lexicon of event data.

Event data collection and distribution

Nowadays, managing customer data or contact centre data is rarely a challenge. The difficulties start with network related events and usage data on both sides of collection and distribution. This is due to the volume, velocity and variety of the event data and their input and output format requirements. Then there are the various service level agreements (SLAs) around processing speed and expected information and format, source/target application and users plus security and privacy. What’s more, there are limited resources, meaning the event data must be used efficiently (avoiding the duplication and multi-processing of the same event).

Consequently, most CSPs are required to replace their legacy mediation platform to deal with event data collection across numerous sources.

Storage cost-effectiveness and SLAs play a big role here, which is why big data technologies and open source tools have become a recurring requirement. However, these bring their own difficulties. CSPs will need to consider the multiple tools required – and how they will integrate, stabilize and maintain them – as well as what skills are needed to maximise big data and high-volume computing.

Additionally, rolling out a digital integration solution for standardizing event data collection and management leads to other challenges. What about event data enrichment and stitching for example? What happens when volume and velocity increase further – will streaming and processing meet SLAs? Are complex event data retention policies, especially in light of the General Data Protection Regulation (GDPR), being considered too? Some of these challenges are often overlooked, contributing to the 70% failure rate of digital transformation projects.

Event data lifecycle management

Crucially, CSPs also need to plan how they will manage the entire event data lifecycle – from collection and storage right through to consumption and retention. Such plan needs to take into account the creation of event data values that do not yet exist in the enterprise, as well as the pre-processing of event data to points at which data synthesis and data usage can occur. Then there is the distribution of event data, plus the archival and removal of every copy of a data item from the enterprise.

Event data driven decision making

Finally, for event data to drive decision making, CSPs need to standardize the enterprise reporting framework, and report of uniform business KPIs across all stakeholders. To achieve this goal, there are several actions to take. This includes defining an event data quality strategy and a common lexicon across all business functions, plus leveraging TM Forum’s Information Framework (SID) model, giving business users ad-hoc reporting ad self-service capabilities.

This calls for CSPs to adapt a smart information delivery model. Specifically, a telecommunications-driven data warehouse (DWH) model which is aligned to SID, and establishes the different levels of analysis capability – i.e. descriptive, predictive, forecasting and prescriptive analysis – to deliver business values.

The bottom line? Taking this four-staged approach to digital integration is the most efficient and effective approach to becoming a DSP and overcoming the challenges that come with this hard-to-reach but necessary goal.

Done right, the failure rate of digital transformation projects will decrease, and the telecoms industry’s collective goal of an open digital world will become a reality.