Why telcos need to make data visualisation a priority now
Big Data holds the key to optimising telecoms business processes and driving efficiencies across them. But the ever-increasing volume of data means that it's becoming more difficult to harness insights from this data. So how can telcos tackle this? Data visualisation holds the key, argues Alistair Carwardine.
You must have heard the saying “A picture is worth a thousand words!” The need to 'see' information clearly has never been greater than in the connected economy where the vast clutter of data presents a complex challenge for businesses. It is common to see armies of data scientists trying to glean actionable insights for businesses from this deluge of data. After all, correctly correlated data across multiple sources presented in a meaningful fashion is worth its weight in gold!
So, what's the big fixation with data visualisation?
No business likes to wade through endless spreadsheets or pages of CSV data. Least of all telecoms businesses, which are essentially in the business of data. Some estimates have suggested that global mobile data traffic will generate 30.6 exabytes of data by 2020! This phenomenal amount of data will require some serious storage, processing and computing power for telcos to leverage it for business gains.
Traditional business analytics tools simply lack the fire power required to make sense of this data. Data visualisation gives you the modern range of powerful tools capable of managing and extracting meaningful insights from this data. Telcos which leverage the power of visualisation can take predictive action and maintain their competitive edge in an increasingly crowded market.
Is there any such thing as too much data?
Before we look at the merits of data visualisation for telcos, let's look at a critical Big Data dilemma – is too much data bad?
Just sitting on tons of inaccurate data or collecting data for the sake of it simply doesn't make sense for businesses. For instance, continually accumulating charging data records (CDRs) can lead to a massive data build-up over time. Is all of it useful? Likely not! Quality is always more important than quantity. Don’t be misguided into thinking that more data will lead to more accurate results and higher business efficiency. Often, reality is quite the opposite.
Large data sets can often be less accessible, fraught with errors and costlier to store and manage. Even if storage is way cheaper nowadays, why pay for unnecessary storage space? In addition, this data is vulnerable to security breaches. We have already seen the massive impact of the WannaCry Ransomware attack on telcos.
Even worse, data might not be correlated correctly
The real value of Big Data can be unearthed only from data correlation. This is the ability to understand how one set of data applies to another set in the right context. Essentially, data correlation helps you understand the relationship between two data sets and derive benefits from this data.
For instance, motor insurance companies are correlating usage data to offer discounts to drivers. For these businesses, it is important to determine the risk each driver represents so that they can set their premiums accordingly. Using data from the usage patterns, average speeds and distance covered, along with other metrics, they can easily assign a risk level, and offer the users a competitive premium. This is the kind of value that can be derived from correctly correlated data.
Of course, this data needs to be treated carefully to extract the correct meaning, and should only be used for the purposes specified while obtaining it. The results can also change drastically depending on the questions you ask of your data. Here are some fun examples of spurious correlations. If you compare apples with oranges, then your business data is as good as no data!
Another common Big Data fallacy is confusing correlation with causation. Without getting into too much detail, it is important to understand that if two data sets seem related to each other, it doesn't mean that one causes the other. Simply put, causation implies that X causes Y, whereas correlation means X and Y happen at the same time. The best-selling book Freakonomics is full of examples of this kind.
Data visualisation for telcos
One of the most important benefits of visualisation is that it allows interpretation of huge amounts of data in easily digestible chunks. Complex data formed into diagrams and pictures that reveal truths which aren’t immediately obvious. Merging huge amounts of real-time data in a single picture allows CSPs to tap into critical information which can be used to improve business functions and processes.
This is significant since the telecommunications industry has spent billions of dollars to establish their network infrastructure. Through visualisation, they can find out their best performing products or services across geographies, price or any other important metrics, to better forecast sales and marketing activities. Visualisation can also help organisations to reduce data packet loss and delays in networks, leading to a better customer experience.
Data visualisation is a wonderful enabler of understanding that information to reveal what your customer might be thinking; where there might be provisioning or operational challenges; to identify any carrier reconciliation issues or revenue assurance shortfalls; and, of course, to be better equipped to handle customer care demands.
The value of an integrated BSS/OSS solution
To aid this data visualisation process, effective information management is extremely critical. Having a complete end-to-end picture of network data with performance data and BSS customer data, vastly helps telcos to tie their loose ends together, and an integrated BSS/OSS solution will provide that crucial 360 degree view of your business processes and customer behaviour.
At Cerillion, our Enterprise BSS/OSS suite uses a common workflow engine to enable business rules and processes to be mapped across the whole telco business. This ensures that the data is aligned according to your business needs, and as a result, yields more accurate insights through data visualisation. This intelligence can be used to make business processes more efficient and improve the overall customer experience.
