Data Analytics

Whether they realise it or not most large organisations are awash with valuable data. More often than not, there is a mountain of information tucked away in a number of departments, in varying states of accessibility, accuracy and formats.

Additionally there are numerous sources of external data readily available from reputable sources. In our own industry the likes of the Bank of England (BoE), the BBA and the CML have data available that can hugely enrich an already growing pile and there are all the recent opportunities afforded by ‘big data’.

Many companies are now realising the value and insight that data can bring them. However, it is worth remembering that only when the information is held in an ordered and structured format can it be properly mined and drive usable insight. Without access to the data in an organised and structured manner, decision making is often based on gut feel and intuition rather than being fact based and driven by robust data analysis.

Historically data has often been loosely aggregated through operational and other systems, leveraging individual organisational knowledge to ad-hoc mine it and drive assumptions out. Whilst this may have worked in the past when there was less data available, a modern structured approach is required to integrate and manage data in a way that is standardised for careful and controlled interrogation.

Once a company has properly structured and base-lined its data, they are then in a position to quickly and effectively draw out insight and actionable conclusions. This is where value can really be added – firms are able to tweak their pricing models, adapt servicing strategies, implement new initiatives and ultimately make improvements to their processes, all based on data rather than guess work. This gives them an analytical edge and if utilised a real competitive advantage.

Properly implemented data analytics really comes into its own when a new system, price or process has been implemented. Structured data regularly analysed can provide early indicators on the success or otherwise of the stratagem. This allows a quick, iterative response to tweak any changes and ensure driving the anticipated improvement, rather than waiting for later downstream indicators that run the risk of costly mistakes.

In essence, properly aggregated and analysed data allows you to move through the test and learn cycle quickly and ensure changes are driving the desired outcomes. Properly implemented this also allows champion challenger initiatives to be run to test new initiatives against existing models in real time.

At a time when firms in many industries offer similar products and use comparable technologies, business processes are among the last remaining points of differentiation. Whilst the steps may sound logical and straightforward, appropriate resource and commitment needs to be applied to ensure that robust systems are in place that allow data to be properly aggregated and presented in an appropriate and actionable format for all layers of your management. All these steps have to be co-ordinated as part of an overarching strategy championed by top leadership and pushed down to decision makers at every level.

Data Analytics

For more information on how we can support your business requirements with our data analytics services, download our fact sheet or call us on 0845 650 6200.

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About Target

Target’s experience with business process engineering, including data positioning to facilitate management insight into core performance drivers, is augmented with experience in re-engineering our own internal MI platforms to facilitate state of the art data mining techniques for the benefit of our clients and their customers.