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Big vs Small Data

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Concept definition

In a literal sense, Big Data is just that; large volumes of numbers and/or information taken from numerous sources/channels from which conclusions and direction may be extracted or inferred. That is, however, not really what Big Data is about, it’s a mindset, an ethos, a perspective.

Big Data is a data-centric approach, supported through the application of specialised software, tools and knowledge. With a firmly held view that key nuggets of insight and illumination will flow forth from the Big Data pipeline, once the data platforms/processes have been constructed and integrated.

Similarly, Small Data is not simply ‘small’ (although it almost always is) – this is again too limited a definition. It is more of a customer-centric mindset, which requires the purposive identification of groups of individuals – gleaning from them information that can be extrapolated to become indicative of market trends and direction.

Commonalities & differences

There are core similarities (Big vs. Small Data) in terms of analysis approach, interpretation and implementation – i.e. looking for consumer trends (attitudinally and/or behaviourally) within the data, then ascertaining the opportunities these represent (or not) – along with the optimal routes to activation.

In essence, the analysis and interpretative foundations are the same – analysis application, visualisation and commercial implementation – all of which predate Big Data.

Where the approaches differ is in the volume, variety, velocity (and veracity) of Big Data – which provides its sobriquet. It is the volume of data that delivers the complexity needed for the emergence of correlations that were unpredictable and undetectable at the small scale. We now have the data volume and the tools to analyse these emergent or fledgling correlations in real time.

Some pitfalls

Doing Big Data ‘well’ is hard. The sheer scale and volume of the information can be, at best, daunting and, at worst, impossible to navigate. Working with Big Data is also extremely time consuming and can prohibit effective time management for both the insight professional, as well as the stakeholder team. For some brands, Big Data is simply TOO Big.

Furthermore, with Big Data solutions, it can take time for trickle-down benefits to materialise, quite a considerable pressure when the investment is often rich (in terms of finance, resource and headspace).

Across both Big & Small Data disciplines – as with all insight deliverables – some basic fundamentals apply though. Ultimately, analysis, distillation and dissemination are everything. If the data is not actionable it has little value – swamping the senior leadership team with data achieves nothing.

Simply collecting data does not unleash its business effectiveness. It is not the amount of data that is important. It is what organisations do with the data that matters.

Ultimately, it's not a binary decision

So, in our view, this is not some David vs. Goliath contest. There is ample room in the Insight professional’s toolkit for both Big & Small Data approaches.

The key to the successful implementation of either route is knowing when best to leverage the respective strengths.

Furthermore, recent technology-based developments (machine learning/AI, DMPs, data visualisation, etc.) have evolved to the point where both Big & Small Data solutions can be deployed in tandem – to elevate a brands’ knowledge and insight capability exponentially.

Illustrative of this are the new customer relationship management (CRM) solutions, incorporating social media. Social CRM – with Small Data at its heart – can be employed to create a complete picture of customers, their segments, influencers and even competitors. When these social insights (Small Data) are fused with web analytics and transactional (Big) data, you have a very powerful customer engagement and planning tool.

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