For many senior managers, right now the fear of failing to maximise the corporate value of big data is leading to massive inertia. Where to start? How can the business deliver that essential Single Customer View? What's needed to achieve real-time customer personalisation? Where can a company access the essential data scientist resources required to make timely sense of this data. This article is copyright 2015 The Best Customer Guide.

Fear of failure and lack of resources is preventing numerous organisations from taking any steps at all, so it is time to take a step back. Organisations still need the deep insights enabled by big data - but it is essential to look beyond purely the strategic and instead work out how to exploit new insights quickly and effectively at different levels across the business.

Once a company has real-time granular data and some analytical tools, it should use them! Moreover, there is no need to rely on data scientists for everything - as Katharine Hulls, VP, Marketing, Celebrus Technologies, insists, the latest generation of BI tools is democratising data and enabling right-time decision making across the business.

Strategic Challenge
Big data continues to dominate the senior management agenda with organisations across every market intrigued by the role big data can play in customer analytics (48%), operational analytics (21%) and fraud & compliance (21%), according to Datameer. However, the sheer breadth of opportunities offered by this data - especially real-time data - is proving to be problematic.

One of the most compelling aspects of the new data model is the potential power of real-time streaming data to transform operational performance. A handful of organisations are exploring real-time data to drive personalisation in a bid to improve cross-selling - such as ancillaries like extra luggage or lounge passes for airlines. There are exciting opportunities in areas such as second screening, using an individual's real-time response to TV advertising or other events to deliver highly relevant content that actively reinforces brand value.

Such activities, however, remain far from mainstream: the evolution from the big data strategic objective to the big data deliverable value is slower than many would have hoped. How many organisations are still struggling to transform this mass of data into tangible additional results with proven return on investment (ROI)? Whether it is the cultural gulf between IT and marketing or the siloed mindsets that exist between digital marketers responsible for web analytics and those running more traditional marketing activity, there is clear evidence that a lack of collaborative approach is undermining the value of big data.

Right Time Decisions
Companies face a myriad of challenges associated with such projects - not least attaining adequate resources. Data scientists are in short supply and while training programmes are in place, there is a built in delay and inertia to big data projects as a result. Organisations also fear the risk of mis-using data - especially real-time data - and the potential negative business implications.

There are additional issues associated with data usage - to make the most of streaming analytics a company needs more people to be able to use the data to make decisions otherwise analytics becomes a bottleneck. But are these big strategic, real-time objectives missing the point? Big data offers organisations a huge raft of opportunities to gain incremental value - it is not all about personalisation or strategic marketing insight. In the right hands, real-time and right-time big data insights can drive effective decision making across the organisation.

With a new generation of powerful data visualisation tools designed to present simple data to a wide range of users across the business, via mobile and tablet, to support rapid decision making, there are now significant opportunities to quickly and effectively explore big data. And it is this democratisation of data that can quickly deliver ROI - with minimal risk.

Democratised Data Value
The key to this model is the delivery of data in a simple, usable format at the right time to support, not overwhelm, those in operational roles. A retail store manager, for example, can be presented with twice daily feeds of products to be shifted to the sales rail and those that should be moved to more prominent in-store positions. The store manager can drill down into the data if they want to see that this insight is driven by up to date information about website and app browsing, combined with local sales data and weather forecasts - or they can simply chose to act upon the suggested changes.

Similar support to existing activity can be delivered to a Telco's call centre agents tasked with encouraging existing customers to re-sign contracts in order to reduce churn. Timely data about an individual customer checking current contract terms and conditions, combined with that customer's lifetime value, network usage and existing contract data, enables the Telco to both determine the right incentive to offer each customer and prioritise outbound customer calls. The call centre team are empowered by big data - without any need for complex data skills.

The great thing about using data in this way is that it is all about right-time, not just real-time. It enables organisations to explore less sophisticated techniques to deliver immediate value from the raft of big data now collected and it can be done in a way that supports existing day to day activity. Staff can be empowered with timely information - not deluged with reams of real-time data that they may find difficult to decipher. And importantly the organisation will start getting value from their data quickly…the omnichannel predictive modelling can come in the future.

By broadening and democratising the usage base of big data across the business, a company can both achieve and prove ROI quickly. It can also build broader expertise and experience to drive more innovative thinking and ideas, creating further value and supporting an extension of the big data project. Once a company is confident and has the commitment to big data, using real-time streaming analytics to flag up fraud risks or improve online personalisation is just the start - with machine data, sensors and everything offered by the Internet of Thinks real-time streaming is clearly the future.

"But organisations cannot jump straight into this vision," warned Hulls. "Incremental steps that build up both expertise and confidence are key. It is the use of new and improved BI and data visualisation tools that enable many more people to access and make decisions based on this data that are set to pave the way for organisations to realise their big data expectations."