The new digital age we are living in is dominated by companies whose data is central to their business: The Data-Driven Company.
For these companies, in a VUCA (volatile, uncertain, complex and ambiguous) world that is rapidly evolving, decision-making can no longer be based solely on past experience or flair. Data mining helps in making strategic decisions at the senior management and business management levels.
Several ingredients are needed to build a data-driven company: not only a real change in culture and new skills, but also the right organisational structure and technological environment. As you can see, being “data-driven” is not something you can just do by improvising.
What data do the operators have? Where do they stand currently when it comes to using data for decision-making?
Operators’ data and their main issues
After the initial buzz, big data is now a reality in the telecommunications sector.
Depending on their size and service offering (fixed, mobile, internet, TV, IoT, etc.), operators collect large volumes of customer, vendor or employee data.
Whether it is customer identification data, point of sale data, customer inquiry/complaint histories, consumption data, billing and payment data, call data (CDR), network and traffic equipment data, website data, TV usage data, connected object data (IoT) or ERP data, data is everywhere and is just waiting to be exploited.
After phases of data mining, the launch of data projects is now based on more precise objectives. Overall, operators are generally looking to improve customer knowledge and the customer experience. They are also exploiting data to improve their operational efficiency and networks.
Being “data-driven” is not just a matter of implementing use cases, so let’s take a quick look at the various applications.
A wide range of use cases to support business decision-making
The TM Forum, an association of companies in the telecommunications and digital sector, lists more than 70 Big Data & Analytics use cases. Most of them are already operational and others are in the planning stages.
I have gathered these use cases for you in the form of a word cloud. For the technophiles amongst you, this dataviz was coded in Python on Spark (data-processing technology) with a Jupiter notebook. I will come back to the benefits of Spark in a future article on big data technologies.
Customer! Network! Real Time! Proactive! Personalised!
Real-time data at the service of the customer…
As the word cloud shows, the customer is central to data enhancement. Data analysis allows companies to be proactive and to make personalised decisions in “virtually real time” to respond to the client’s needs.
In the case of marketing, it involves offering customers or prospects real-time offers tailored according to usage, location, type of terminal or cookies collected during a web browsing session.
For customer relations, rapid detection of customer sore points enables solutions to be put in place to improve the customer experience. Whether it be in the proactive reduction of billing errors or in the early provision of support to customers impacted by a network incident, data operations are already delivering convincing results.
… and for the proactive management of telecom network infrastructures.
Beyond marketing and customer relations, the data collected on the network via probes allows proactive maintenance of the infrastructure. Analysis of this data helps to automatically identify incidents and correct them with or without human intervention. Using machine learning algorithms and historical traffic, as well as failure and performance data, some operators can predict failures and thus plan for equipment maintenance.
Still some way to go before the goal is reached, not to mention the priorities for 2018.
Telecommunications companies, like other sectors, have made progress in the area of data over the past few years. Real-world applications are delivering outstanding results in the areas of marketing, customer relationship management and network management. However, there is still a long way to go for operators (and companies in general) before they reach the target of being data-driven companies. The organisational, cultural and technological dimensions (predictive analytics, AI, etc.) are all areas where further work needs to be done.
However, before then some urgent steps need to be taken in 2018, such as compliance with the GDPR (General Data Protection Regulation), which will come into force in May, as well as data quality and security.
