Big data could be simply seen as a natural progression of how organisations access, analyse and use information for the running of a business. Therefore, big data could be viewed more as an evolution (rather than a revolution) that, above all, compels organisations to re-examine what they traditionally consider as business information.
Big data is characterised by volume, velocity, variety and veracity. If there is one key take-away from the issues of big data, it is that organisations now have a much broader spectrum of data available to them. This can be both from within and outside of the organisation - and can be utilised and transformed into valuable insights to improve decision making.
Whilst big data is clearly a key business priority that is expected to add significant value to organisations, it has also become difficult to get beyond the hype.
Today businesses want relevant information at their fingertips, in real-time. They want the ability to analyse both structured and unstructured data inside and outside the organisation to better understand and predict customer preferences and behaviours, improve operational insight and reduce risk at a level and speed never before possible.
The challenge is that data has become so vast and varied that the traditional approaches to managing and analysing data can no longer meet the increasing demand. This may be due to numerous factors such as speed or quality, which could provide essential insights for organisations that want to be able to make fast, informed business decisions.
The good news is that the technology is available to tackle these challenges, and big data tools can deliver new levels of insight fast. However knowing where to start can be overwhelming. The real key to success lies in how you go about identifying the data that will be useful and relevant to your organisation, how you examine this data, and then understanding how to store, categorise, organise and use it for competitive advantage.
There are plenty of real-world applications of big data today, with many organisations actively using them to better target customer-centric outcomes and improve business performance; tapping into internal and external data to build vastly improved information systems. For example in the insurance world, catastrophe and loss modelling are the two biggest data analysis challenges.
Big data is helping insurance companies better understand how events are developing and the effect this might have so that they can better manage risk. Retailers are using big data so that they can provide dynamic pricing and predictive analytics. They are creating up-to-the-minute customer profiles that allow them to better predict buying patterns.
Big brands are also using big data to provide better customer service. By harnessing unstructured information that sits outside the organisation, they can find out what customers are actually saying about them at any given moment and respond accordingly in real-time.
However, whilst the benefits of big data are potentially immense, a good dose of common sense and pragmatism needs to be applied when approaching a big data project. Regardless of whether you are looking to analyse corporate data or external data, it’s important to take on board that the value of big data doesn’t always lie in the data itself; it’s what an organisation does with the data that really matters.
Successful big data projects first clearly identify the business requirements before applying the analytics to support the business objectives. In this way, new insights can be gleaned from existing sources of information at a speed and level that was previously impossible to achieve through traditional methods.
Key questions that should be considered are largely based around a best-practice approach that any organisation should adopt when embarking on an IT project and include:
- What are the key big data requirements that will provide the most value to the business?
- Has the organisation established a strong business case based on measurable outcomes?
- Does the organisation have business leader sponsorship for the project and can they establish a pilot project that will deliver a quick win?
Organisations who are seriously considering a big data initiative should first focus on the tangible business outcomes, and then think small to think big. It might be counter-intuitive given all the hype around big data, but it could make all the difference in achieving successful outcomes. Here are some practical recommendations on how organisations should approach a big data project to ensure success:
Big data is a business-driven solution
Success will be dependent on meeting the needs of lines of business - IT is the enabler. First identify the business requirements, and then look to the infrastructure, data sets and analytics to support the business opportunity.
Establish a clear business case
For many organisations, the traditional approach to data analytics has limitations. Put a cost on it - it’s the difference between having information at your fingertips in minutes as opposed to days, weeks or even months.
Take a staged approach and focus on quick wins
Don’t try to analyse everything at once or you will struggle. Focus on a specific area that will deliver a return to demonstrate the capability of the technology. Then look to broaden your wish list.
Start a pilot programme by selecting a business unit or function where you think the big data opportunities and benefits will be. Develop proof of concepts or prototypes before you make huge technology investments. A gap analysis between your current state and desired outcome will be helpful! Where possible, benchmark yourself with industry best practice.
In today’s increasingly competitive business environment, the advent of big data is driving greater demand for faster and more advanced analytical insight and better management of today’s increasing volumes of data. If approached without achievable and measurable goals however, big data can be somewhat of a minefield.
