KNect365's Telco Data Analytics Europe 2017 show took place in Madrid in late October. Having attended the show last year, I noticed that this year's show was bigger and more popular with operators and vendors.
I listened to the keynotes from Deutsche Telekom and Orange, but the real gems were in the more hands-on discussions with operators tasked with understanding customer segments, buying journeys, improving customer experiences and optimizing networks by simulating customer demand.
Overall there is a healthy interest in effectively using data and analytics to run telecom businesses better -- so how well are operators doing and what are the key challenges?
Data strategy and governance does what it says on the tin
These roles are responsible for deciding where data is stored (e.g., Hadoop data lakes) and who can access it, organizing a group data strategy and giving guidance to local regions. Data governance is a hot topic, with the new General Data Protection Regulation (GDPR) coming into effect in May 2018. Ensuring the data privacy and security of European residents will be important, while ensuring that customers have clear explanations of the data they are agreeing to share, as well as the ability to request and/or transfer their personal data. Orange suggested that operators should conduct privacy marketing and make trust the center of customer experience, using pitches such as, "Telcos take care of your data privacy unlike other digital companies." However, data strategy and governance roles tend not to lead the innovation or funding of analytics projects. Although there was some discussion around replicating use cases, this is driven at a line-of-business level: chief commercial officers, chief operating officers and chief marketing officers are the key players in moving operators to be data-driven businesses.
Data scientists want to work on real business problems
The challenge of attracting data scientists comes up frequently, as does the issue of retaining talent. After a while, they become disillusioned because they feel they are not having an impact on the business. Part of the issue is that data scientists don't want to work for operators in the old-school way (e.g., being sent impersonal requests to pull data or build a model), and instead want to be seen as key partners in solving real business problems. The recurring organizational silo issue is a factor here for telcos, as is a disconnect between IT and network operations teams and the commercial teams. The most successful data-driven operators will be those who set up cross-functional teams with common business KPIs to redesign dysfunctional processes and come up with new ways of driving the business forward.
Data is the new oil, but the refinery process is hard work
This is one of the top challenges for operators -- getting the right data in a usable format, ensuring that it's quality data. Again, organizational constraints are cited as a roadblock in terms of smoothing out this process and making it more efficient. Business users are not familiar with networks and IT systems, and there is a general lack of unified collaboration and governance. Complex integration of data in some cases is sucking up 80% of total project time, along with data validation, errors and quality problems. Huawei discussed the need to de-duplicate, cluster and consolidate data by automating data sources and dependencies and using human-guided machine learning algorithms to do automatic data mapping. Another key point is that subject matter experts/domain experts need to work very closely with data scientists on models, tracking the health of algorithms and confidence level in data quality and insights. In a panel on network analytics with Dell/EMC, Netscout and Nokia, the issue of laying the foundational tools and systems to ensure data and intelligence flows from the network efficiently came up as a key issue. Going forward, it will be critical to be able to do this on a real-time basis for on-demand NFV/SDN networks and services, where monitoring actual service quality will be a key requirement. A critical competitive differentiator for a data-driven operator will be figuring out how to get the right data from your network, as fast and efficiently as possible.
Integrating big and small data will help telcos understand customers
Telefónica discussed what's ahead in terms of big data analytics and talked about big data and small data integration (e.g., social media, CRM, behavioral data) to better understand what customers would like to do, as well as how competitive intelligence teams can use big data analytics platforms as a tactical and strategic tool. This was shown by Turkcell, which has developed a crowdsourcing analytics tool that keeps management updated on the mobile network quality of Turkcell relative to its competitors in different regions. A visualization tool and comparable KPIs have captured leadership's attention. In addition, Telefónica touched on the issue of (disruptive) innovation relative to business cases for analytics projects, with the advice, "Don't start with innovation unless it has a business case." Also, Telefónica's view is that the business owner should take over the analytics use case once data feed, integration and algorithms have been vetted and it's working well.
AI will revolutionize the customer experience
IntentHQ CEO Jonathan Lakin believes artificial intelligence (AI) can help operators build human-like customer profiles that will anticipate what customers want to do, interpret their intent and automate actions to deliver a personalized experience -- a customer data hub/cloud platform that takes all of customer interactions, interests, habits and behaviors, based on visits to websites, etc., ingesting at event level. The giant data set can then be tuned with AI algorithms to micro-segment customers, reduce the cost to serve certain segments, run data-driven marketing campaigns, etc. Although Telefónica cautioned that AI is not the solution for everything, Lakin believes that most operators are still figuring out how to move to being data-driven, and that the current ways of working in silos have to dramatically change. One of the first things his company does with any operator is spend time with each department to determine maturity in terms of using data intelligently to improve the business processes, interactions and data sharing between groups to drive common business goals.
Operators are making progress using analytics effectively in key areas that are linked to revenue growth, such as reducing customer churn and digital marketing. However, in order to have the dramatic operational improvement that's needed, hard work must be done to address organizational silos, redesign broken business processes and develop a team spirit and common business goals/KPIs. This is firmly a culture and management issue that's not going away -- and one that the best machine learning, AI, data scientists and consultants alone cannot solve.
— Sandra O'Boyle, Senior Analyst, Heavy Reading