Escaping silos to pursue scale: The top three big data challenges for telcos
When looking at common problems organisations face around building strategic big data analytics programmes, it’s interesting to look at the challenges and how they differ from industry to industry. While banks and insurance companies may struggle with legacy technology, and retailers fight against unmanageable volume, there is one thing that is certain: when it comes to big data, telcos are all starting from different places and perspectives, and there is no commonality in the use cases they are pursuing as they aspire to maximise monetisation.
However, unlike the use cases, the challenges that telcos face when it comes to capitalising on big data are usually similar. Here are the top three challenges telcos are facing today, as well as some background on what some telcos are doing to overcome them.
The silo stumbling block
Few telcos have managed to break out of the traditional silo approach in order to adopt an integrated big data strategy that will be along with them, across an entire company, to better identify what would be the best recommended action for a client. It is only when telcos escape these silos that they will be able to work at scale with big data, sharing it effectively across the business to enable more accurate predictions about what to do next with customers - whether that is from a customer service perspective, including customer experience management or churn management, or from the network perspective, to focus on predictive maintenance of the network.
Lost optimisation opportunities
It’s unfortunate that nearly everyone can recount a scenario that goes something like this: As a new customer, you set up a package for unlimited broadband yet soon found yourself with a package of an upper limit 40 GBs of broadband rather than the unlimited at home option. It takes you three months to get in touch with your provider to actually upgrade to unlimited broadband. Once you’ve been assured you’d been upgraded, you arrive home to find that your fibre optic service has been cancelled in favour of an ADSL package which is significantly slower than what you’re paying for. A series of calls and engineer visits later and there’s still no clarity on what happened. Several months into service as a new customer, it’s safe to say things are not going well.
The point here is not limited to complaining about poor customer service. It’s about highlighting gaps and missed opportunities. Why were two different orders placed in the first place? Who was looking at the data and making decisions based on it? In a situation like this, the provider has not even had a chance to capitalise on the opportunities to optimise service for the customer as they are still preoccupied with providing the service originally requested.
This is where the silo approach does not work. Because it cannot consider at all times multiple threads from customer contact centres to engineers in the field, to order and device delivery and management, it cannot work with continuity or cohesion between different departments.
Moving past silos and overcoming the challenges described above takes a good big data analytics strategy, as well as the ability to execute against this strategy in a strategic way. Many companies choose one or two new big data technologies and make a concerted effort to move from a silo to an integrated approach, but for mammoth telcos, this approach is like using a teaspoon versus an industrial sized excavation.
Telcos are effectively made of four core and rather independent businesses, including mobile, fixed, broadband and data. In each business, there are further independent silos such as operations, which is further divided into departments for field service management, device management, fault management and so on.
So scale is a challenge, but in more ways than one: telcos are big, and they want to be using big data on a much bigger scale than they are currently managing. Partnerships with social media giants are inevitable, at which point the amount of data coming into the networks will reach record levels. The opportunity to monetise on this level is huge, but only if telcos can manage smarter frameworks to capitalise on today’s big data technologies.
One company, one big data vision
Whether they are looking to move past silos, to evolve past re-active customer service based fixes or to make big data work at scale, telcos will have to move beyond thinking of big data as a simple technology fix. Simply opting for one new big data technology over another will not work as the basis of an effective big data programme.
Telcos, like all other large organisations, are going to have to move to consider big data from a joint architectural, engineering and data science perspective. Unless this happens, companies usually end up with a lack of infrastructure, approach, strategy and process that may ultimately cause them to lose both competitive advantage and market share.
The challenge for telcos is going to be looking at the use cases that have been successful within silos, and then removing those silos between company data stores to try and replicate the success across the business – and to share data easily and efficiently across the entire organisation.
In achieving this, telcos may find themselves able to treat customer interaction as optimisation opportunities, instead of constant issue fixing and complaint resolution.
In getting the foundation right, telcos can begin to develop a future-proofed, well-defined architectural vision to shape the kinds of big data capabilities they are looking to develop, and to test these amongst departments before rolling out across the enterprise. Once gaps are identified and addressed, telcos can begin to implement some of the newest cutting edge big data tools and technologies to in order to serve customers and the network better than ever before.
- » Google stops waiting on lazy carriers, starts RCS rollout on Android
- » Virgin Media blocks popular image-sharing website Imgur
- » Nokia, Ericsson, and SK Telecom collaborate on 6G research
- » Opinion: How machine learning can help operators detect small cell anomalies
- » Nokia CEO: Huawei crackdown would harm the whole industry