#three v

[ladynoirisback]

Vox and Valentino relationship so far

[chughp]

Are we ignoring the value from the third V of Big Data

Amongst the three Vs of Big Data, Volume, Velocity and Variety, many companies focus on deriving value by addressing the first two, and relatively less from the third. However one could argue that the most transformative solutions could come from tapping into the third V – the Variety of data.

  Often companies solve Big Data problems within the confines of a specific organization function, with the variety of data available with that function. For example, the online sales functions tend to address how they can give real-time and granular recommendations to the customers by improving the characterization of the products and the customers through online browsing and purchase patterns. This taps the volume and velocity of data well, and leverages the few varieties of data that are accessible to the function. Solving such problem is certainly critical, but it is also worth noting that most competitors are likely working on the same very problem, and the customers have already started to expect personalized recommendations i.e. this is already becoming table stakes!

  Today, not only has there been significant proliferation in the variety, but also the technological ability to use this variety is unprecedented. There are machine logs that come from manufacturing shop floors, data coming in variety of formats from suppliers, social data from customers, demand forecasts from resellers, weather projections, competitive information…. The list goes on. 

  Effectively tapping this variety is where the big opportunities could be. Could for example, the online sales team improve the recommendations further by looking at the data coming from shop floors that predicts a likely shortfall due to supplier quality issues. The company could steer the customer towards a different product or variant by giving a price discount.  Similarly, could the manufacturing operations function improve the yield on the shop floor by more closely monitoring the data coming from the field service engineers and customer complaints showing up on social media?

  These problems are certainly more complex to solve, mainly because they need for companies not to frame the right answers of the traditional questions, but to frame the questions themselves – They need to look at all data sources they could potentially tap into, and imagine how they could use them, and what value would that generate. Furthermore it requires them to resolve the very tricky issues that can hinder the access to data variety, especially data ownership rights, cross-department data access, and the technology infrastructure to allow the data access. Certainly more complex, but it is worthwhile thinking about all this before competitors start thinking about it!