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Incorporating playtime into your big data initiatives

Read Accenture’s report on integrating playtime into big data initiatives and how companies should incorporate creativity into their analysis of big data.


It’s hard to comprehend the sheer volume of data created on a daily basis. But more and more companies understand that mining this ‘big data’ can uncover potentially lucrative opportunities.

According to IDC, the big data market is expected to grow from US$3.2 billion in 2010 to US$16.9 billion in 2015 – a compound annual growth rate of 40 per cent or seven times that of the overall information and communications technology market. In Australia, 84 per cent of mid-market businesses have either deployed big data solutions or plan to explore them in the next year.

Typically, the possibilities of big data are seen through the prism of specific and pressing challenges. However, this approach shrinks the scope of analysis and potentially the benefits that big data and analytics can deliver. Instead, companies should incorporate a greater element of ‘play’ and experimentation in their analysis of big data.


In some businesses, big data is mainly deployed to resolve immediate business challenges, such as increasing sales, cutting costs and accelerating expansion.

As the big data market matures, we recommend companies dedicate some of their analytics activity to initiatives that look beyond current issues and focus on harvesting data sets in a more unstructured fashion.

One way to think about this is to think of locking a creative and an analytics expert in a room and getting them to brainstorm – in an unconstrained manner – ways to combine and analyse data. By synthesising management, marketing, consumer and social media data, what possible analysis could be performed and what new insights could this lead to?

It’s an exploration process without a particular end goal. The emphasis should be on the unconventional and unexpected; on keeping the scope as wide and open as possible, rather than settling for the most obvious set of questions and concerns.

For example, in a recent client engagement, Accenture’s Management Consulting team identified multimillion dollar savings for a client within four hours of analysing combined data sets. The team identified the potential savings through its initial sweep of the data and largely before it had begun tackling the key business challenges at play.

In addition, by using big data, companies can analyse social media activity to predict loyalty rates and customer churn. This analysis could include:

  • The relationship between customers’ negative experiences and churn levels

  • Whether positive experiences could lead to up-selling opportunities and increase in revenue

  • How quickly social media activity could decrease churn or generate additional revenue.

To demonstrate the potential of open-ended approaches, consider the example of telecommunications companies.

Telcos currently use big data in a number of ways to address core business challenges, including reducing customer turnover, promoting loyalty and personalising services. Some are also looking at approaches that move beyond analysis and focus on monetising customer data.

By implementing an open-ended approach to big data and analytics, telcos and other companies could discover a range of potential new revenue sources.

For instance, telcos could track consumer behaviour by analysing the types of content accessed by mobile users at different times and locations (known as geotagging). Companies could then use this information to send targeted advertisements to customers. By sending promotions at the right place and the right time, telcos may be able to increase sales and boost engagement.


We recommend companies begin by allocating a portion of their big data budgets to conducting more wide-ranging analysis. This could be around 5 per cent, and would give analytics professionals the time and resources to ‘play’ in companywide data sets.

As part of this process, companies could also encourage greater interactions between analytics staff and creatives – two groups that have traditionally been quite separate.

We appreciate that not all companies are in a position to do this. There are resource-based challenges, as companies need to find the staff and budgets to drive additional analytics activities.

On a return on investment basis, there are no guarantees of what may emerge from an open-ended approach. The results may be lucrative or they may be more mundane. However, in a highly competitive marketplace, this extra analysis may be the key to gaining additional (and sustainable) competitive advantage.

Overall, big data could help companies make better decisions more rapidly. Through an open-ended approach to big data analytics, companies could go one step further and find answers to critical questions they didn’t even know existed.