Big Data: Seeing Is Believing

Big Data Visualisation

For all the hype about Big Data, it offers no value to businesses unless users can actually find what they’re looking for. As the dust settles and advanced analytics with intuitive data visualisation become the new priorities, it seems Big Data’s role might finally come into its own – as the driver of intelligible, personalised and – most importantly – actionable insights.

It’s easy to be dazzled by technology hype, and in the world of Big Data there are probably a lot of would-be emperors strutting around wearing no clothes. That’s because they have bought into the promise of Big Data without making sure the substance is there.

Data volume isn’t the same thing as data value; yet companies seem preoccupied with the en masse nature of Big Data, as though by starting with everything they will automatically end up with something of value. But all too often that ‘something’ results in a large cost and confused, overwhelmed users; and the point of Big Data – Big Insights – has been missed.

Of course this isn’t the first time something like this has happened. As with any big business trend, there is a huge wave of initial interest, followed by a period of hype, which sometimes leads to a crash. Big Data is currently teetering around this critical point – at which the hype is obscuring the reality, and a lot of companies are on the verge of being bitterly disappointed. Respected market analyst firm Gartner described Big Data technologies as nudging the ‘peak of inflated expectations’ in a recent report and it is hard to argue with its conclusions.

But as the first Big Wave of Big Data subsides against those unrealistic (and sometimes uniformed) expectations, could it be that something calmer and more harness-able will wash up behind it? Quite possibly.

What is more certain is that, if CIOs become determinedly pragmatic and focus on easy and effective analysis of the most telling data, Big Data may avoid a scenario where it is left to languish for years on the CIO’s desk. If the industry was able to filter out 90% of the noise and concentrate on just one thing – namely making Big Data the powerful driver of useful data insights via advanced analytics and effective visualisation – companies might yet take away something useful from the phenomenon.

Defining Data Visualisation For Insights

Data visualisation is the presentation of statistical data from myriad sources in an appealing visual format that distils trends and causes alerts to pop on the screen. Optimised tools are the best way to discover the nuggets of suggestive/rewarding insights which are always – and sometimes deeply – buried within a mass of collected Big Data.

These tools can be particularly impactful in helping users identify patterns, outliers, anomalies and cause-and-effect relationships, as well as other relationships within the data – which is, after all, why the business agreed to write the cheque for the Big Data project in the first place.

At the same time, don’t forget the need for advanced analytics, including data mining and predictive analytics, to support these worthwhile aims and objectives. Such tools bring understanding and meaning to your Big Data: technologies like predictive analytics, for instance, can analyse and model Big Data to help make predictions about future events, while text mining and natural language processing (NLP) solutions can be fantastic at understanding sentiment and extracting underlying meaning from textual data. Successful data visualisation efforts, in fact, depend on a solid bedrock of proper Business Analytics.

Enabling Exploration

What does this mean in practice? Advanced analytics and visualisation tools are fantastic at presenting the kind of very complex relationships found within unstructured, structured and multi-structured Big Data. These tools do that by querying and modelling the underlying data sources (in many cases, via the power of an in-memory computing engine) before presenting a visual analysis of the data to users.

Such systems are particularly suitable for the more exploratory style of analysis demanded by Big Data, by virtue of their ability to recognise patterns and communicate data in a way that business users find much more immediate and meaningful – and much easier than picking through reams and reams of tabular data.

At the same time, successful data analysis requires a fresh approach if it is to deliver the personalised insights now demanded by various internal user groups. That is to say, each visualisation needs to truly ‘tell a story’ about the data and provide an environment for users to interactively explore and probe that story as far as they can, letting emerging insights lead them to their next logical question, and so on through the Big Data journey.

The message is clear. Users need to stand back from the Big Data hype (and its accompanying emotion: fear) and look at how best to achieve optimal payback for their Big Data initiatives. If configured and designed correctly, advanced analytics coupled with intuitive and engaging data visualisation tools provide the logical way forward, producing game-changing insights to drive brand loyalty and profitability.

It is only through such a push that Big Data can be used to create new business opportunities for an organisation. In the meantime, the (somewhat self-congratulatory) hope can be that competitors become mired in their own unwieldy and ill-defined Big Data projects, creating space for those who master the Big Data journey to move to the head of the pack.

Nobby Akiha

Nobby Akiha brings over 25 years of experience in high technology and consumer packaged goods marketing to Actuate. He joined Actuate in 2000 as vice president of Marketing. Prior to joining Actuate, he was vice president, Marketing and Business Development at Inference Corporation. Nobby also served as senior consultant at Regis McKenna, Director of Marketing Communications for CASE vendor Interactive Development Environments (IDE) and Group Product manager at Oracle Corporation. He started his marketing career consulting to consumer packaged goods companies at Management Decision Systems and Burke Marketing Research. He holds a Bachelor of Science in Commerce from the University of British Columbia and an Masters Science in Management from the Sloan School of Management at M.I.T.