Big data in the real world: how to move from buzzword to strategy?


Big data has become one of the most widely debated topics particularly in the past year. The market for big data will reach $16.9 billion in 2015, growing six times faster than the overall IT market, according to IDC.

With some years of experimentation and getting to grips with big data, lessons have been learned. How can enterprises now exploit the power of big data best in 2015 to support their overall business strategies?

One of the fundamental elements of data is how we use it to make decisions. We have to handle critical information all the time to help us to make better assessments. This data can be used over and over again to test these assessments, enabling us to change our decisions if necessary. It’s the ability to modify these conclusions that gives organisations more agility and power. Thanks to big data analytics organisations have been able to get ahead of competitors in many ways; improving supply chains, refining products and services, adjusting pricing, cultivating more effective product promotions and improving customer retention strategies.

Where big data really comes to the fore is when it gives businesses the power to solve problems that were previously hard to tackle. This is when industry experts see where the real value of data lies.


Data is crucial to almost every industry, with the data landscape becoming more complex than ever before. For those organisations operating in more data-complex scenarios, such as ecommerce providers, reducing complication is important. For instance, on travel sites there are a number of steps that customers have to go through in order to get the pricing for their holiday. There can be up to 5 million different combinations across hotels, flights, car hire, insurance, upgrade options, transfers, and so on, for just one booking.

Snapping at the heels of these travel companies are competitors, direct and indirect, ready to serve price-savvy customers. To speed the process up and help customers make decisions, some travel providers are beginning to use big data technologies to search non-complex relationships and create dynamic pricing and recommendations with a sub-second response. This helps them take advantage of taxes, fuel and exchange rates to improve their operating margin.

Consumer behaviour

How can brands use big data to better understand their customers? There is a huge amount of data on individual consumers, from behaviour to purchasing history, across a multitude of touch points. Retailers need to dissect this information to get a better insight into their customers. The problem is that as our ability to gather more information increases, so too does the complexity in gathering and analysing this information. Furthermore, there is increased consumer expectation of excellent and tailored products and services. The pressure is certainly on.

Big data analytics

Big data analytics is at the heart of future success. In September 2011, Philip Carter at IDC wrote a paper ‘Big Data Analytics: Future Architectures, Skills and Roadmaps for the CIO’. Here he observed that one of the key differences between analytics in the traditional mode and analytics in the big data era is that the variables and modes are likely to be entirely new, requiring a different infrastructure strategy as well as new skill sets.

Over three years later the biggest challenge facing organisations is still this need to plan appropriately to get the most out of big data. Bringing together the business and IT functions to jointly develop architecture-led planning or a capability-led roadmap will help today and in the future.

A capability-led roadmap puts business capabilities at the heart of the IT strategy and delivers a business aligned IT framework. An IDC survey in 2011 posed a question to CIOs asking what technologies would be most useful to harness competitive advantage. The leading answer was better “business intelligence and analytics”.

The challenge comes because using data effectively to define business strategy is difficult. Traditional business intelligence solutions fail here as frequently you will not know the questions you want to ask. Data warehouses have to be created to store data without a clear idea about how that data will be interrogated. Call it a data lake, refinery or reservoir, we’re not precious, but we do think that CIOs need to understand that all data has potential value (if not to you, then to a possible partner), and placing that data in a container then allows it to be explored by the increasingly ubiquitous data scientist.

Data scientists can work with the business to really understand what their data is telling them. This will help reveal trends and patterns that will shape the business strategy. This then drives the capability aligned IT framework, and so the process repeats. With every cycle, more data is harnessed, thus improving the business’ ability to make strategy decisions, so long as the data interrogation techniques can cope with the ever-increasing volumes and complexity of the organisation’s data. Herein lies the biggest challenge facing big data at the present time.

Businesses should create a structure to measure success and a target path that allows for inevitable change. This will equip organisations to make much smarter, data-led decisions on a longer-term basis, delivering the power to improve the way organisations understand and respond to their customers.

We urge organisations to do two things. The first is to take the time to understand the data that they have access to and how this could benefit all parts of the business. The second is to develop a roadmap that sets the foundations to support the business in exploiting that data over the next five years, and beyond. This will ensure that big data delivers back to the heart of the business.

John Sidhu is a partner at Glue Reply, the specialist consultancy within Reply Group focusing on business-led IT planning and execution


Copyright © 2015 IDG Communications, Inc.

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