We talk about big data a lot because of the countless possibilities – but even though there are many possibilities there are also potential pitfalls and challenges.
According to the 3Vs model, the challenges of big data management result from the expansion of ‘volume’, ‘variety’ and ‘velocity’ – the three defining properties or dimensions of big data. Gartner analyst Doug Laney first introduced the 3Vs concept in a 2001 MetaGroup research publication, 3D data management: Controlling data volume, variety and velocity.
James Taylor CEO of Decision Management Solutions says that “The solution to these three main big data challenges can be found in a six letter acronym – BWTDIM: Begin with the Decision in Mind.”
He goes on to explain that BWTDIM means identifying the decisions that matter to your organisation; i.e. the decisions that make the difference between hitting your targets and missing them. These decisions can be strategic, tactical and also often operational, day-to-day decisions that need to be made at the front line. For example, a network operator would often find most benefit from making them at the call centre.
Most information systems are developed around business functions, data or processes. However, they all share a common challenge; they either assume that people will make all the decisions involved in the business processes being automated or that how these decisions are made can be changed.
Focusing on the decision first provides the analytics required. It can then be determined what might help make the decision in a more profitable and effective way, i.e. if it’s known how likely it is for a geographical location to lose network connectivity then extra resource can be provided in that part of the network. With this in mind we would always add the other two Vs to the main three, the hardest ones – validity and value. How do we validate data that’s useful, reliable and accurate and once we’ve done that, how do we extract value from it?
Once it’s known which analytics to use, it’s easier to see which data can help build those analytics. A decision first approach helps step away from the data challenges and enables focus on the possibilities.
For example, Swisscom, the main Telco in Switzerland, needed to identify customers impacted by resource faults as quickly as possible and to relay the information to the call centre. We provided a data application product that provides the required interface functionality to forward network alerts in real-time.
And at Turkcell SuperOnline, we provided use cases for customer impact analysis, where resource faults are related in real-time to VPN services, and customers. Incidents are created automatically for the impacted customers, providing Turkcell with invaluable knowledge prior to the customer calls reporting the issues.
So instead of gathering all available data and using it to make decisions – identify the business case and then gather the data that is needed to support that decision.