By: Matt Roberts, Marketing Director for Big Data and Strategic Innovations, Amdocs

With all the hype around big data, has it really reached the stage it is truly prevalent across all aspects of our lives?

I think not. While I believe it is safe to say that big data has been accepted into the world of reality, we need to start talking less and doing more to make it a more integral part of our daily lives. To explain this better, let me break down big data into three layers that I call “the hard work”, “the toys” and “the money”.

The “money” layer of my model refers to the analytics – or the “sexy” area of big data. This includes the previously unthought-of insights that can be derived by combing mass data sets to come up with algorithms that drive business value. These insights are what enables service providers to know (before the subscriber does) when their subscribers are about to contact the call center, and proactively take action to prevent the call and hence enhance the customer experience.

The “toys” layer refers to the new technologies, many of which are open source, such as Hadoop and Spark. IT departments have rushed out to buy these latest toys, which has allowed the companies providing these technologies, such as Hortonworks and Cloudera, to thrive.

And until now, all the talk that propelled big data to #1 in the hype hit parade has really focused on these two areas. But the third layer – the “hard work” – is the area that has really been overlooked, and which needs to be understood before big data can truly shake of its hyped past and become prevalent in our lives.

To take full advantage of all the value that can be derived from big data, service providers need to transform themselves into data-driven businesses. But with many organizations only beginning the journey, they are discovering that the path towards reaching this goal is beset on all sides by challenges.

To overcome these, they need to evolve their data management environments from traditional, appliance-based data warehouses into “data lakes”, which are hybrid landscapes of mass-volume, low-cost storage that can house structured, non-structured, third-party and in house data. This enables them to create rich and huge data sets from which valuable insights can be uncovered. But the next step is where the real hard work lies: extracting and cleansing relevant data from the source systems, such as billing, networks and CRM, and hydrating the newfound data lakes – something that takes both time and money – and the data needs to be fresh, relevant and analytics-ready.

To understand this better, let’s use oil as an analogy. In 2012, on CNBC Squawk Box, the host asked legendary investor Ann Winbald what she thought would be the next “big thing”. Her response was, “data is the new oil”.

And I think this is a perfect way to understand big data. If we apply it to my model, the analytics and the technologies (the toys and the money) are the combustion engine and the motor car, while the hard work is the extraction and refining of oil into petroleum. Only when these challenging and expensive activities are performed, does oil become valuable. Similarly, only once data is found, extracted and cleansed, can the previously hyped benefits of the toys and money be realized.

This idea was validated by Gartner when they predicted that firms will spend a total of $44 billion on big data projects this year, with much of it going to the services required to transform organizations into true data-driven businesses.

So while it may seem a little early to completely remove big data from the hype cycle, many in the data management business would probably agree that one thing is abundantly clear: the battle of the hype is over and the battle of business value is about to begin.