Forget Data Science, Data Art is next!

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I’ve news for all the budding data scientists out there; you have missed the boat! All is not lost however, as producing a ‘painting’ covering the story of its journey is likely to reap more rewards to your business in the near future than the current machinations of your advanced algorithm-crunching statisticians. Acquisition and marshaling of data […]

I’ve news for all the budding data scientists out there; you have missed the boat! All is not lost however, as producing a ‘painting’ covering the story of its journey is likely to reap more rewards to your business in the near future than the current machinations of your advanced algorithm-crunching statisticians.

Acquisition and marshaling of data is rapidly becoming a ‘commodity’ as the underlying technologies of database appliances, in-memory, ETL, Hadoop and unstructured engines have reached a mainstream consumer ‘tipping point’ and, the rapid adoption of cloud applications is further accelerating this trend.

So, on which development areas do we focus over the next few years to ensure we are ahead of the next data exploitation wave?

Move over Einstein, Van Gogh is calling!

Whilst data science will remain an integral foundation for our organizational ‘data health’ promoting insight and consistency of the corporate asset (namely data), data art is rapidly emerging as the ‘next best course of action’ in the field of big data.

If we think of data science as our regular banking transactions (in short capturing the business event data across our points of party interaction), data art is the VISA™ card (as it enables us to make sense of our spend in a format that has personalized context); this acronym is important as is represents, the four quadrants of data art necessary to deliver meaningful intelligence into the heart of your organization’s processes with exploitable business outcomes at the forefront of your design approach.


Figure 1:
 Successful next generation big data will mean hitting the information sweetspot across all four quadrants of data art. 


More Art than science?

The main issue with data science in its current guise is that it is firmly wedded to our data assets and therefore, is extremely difficult to both understand and exploit without a team of statisticians at hand. Its outputs force each of us to consume information through a ‘fixed’ cognitive lense on a daily basis; we must first interpret and then understand what the information means to our situation and finally, respond accordingly which wastes time, duplicates effort and ultimately detracts from servicing our customers efficiently.

Data Art however will be embedded into our operational fabric and as such, will be more accessible to knowledge workers who ‘live the story daily through a process’. The resulting intelligence will be ‘to the point’, insulated from the underlying data complexities, instantly consumable in the context of the interaction and therefore, instinctive in its application.

Our ability to balance between these two supporting disciplines will be critical to the quality of intelligence that we can provide and, the bottom-line impacts that a big data startup can yield within our organizations.

A picture paints a thousand words; it could also ensure your customers ‘remain in touch’

There are a lot of ‘tell-tale’ signs in the technology landscape which point to the rise of the data art discipline.

1) Infographics

From infogr.am to easel.ly, through piktochart to visual.ly, we are increasingly gravitating towards infographics as a presentation vehicle to ‘simplify the complex’, to ‘cut to the chase’ in our corporate data exploitation, but also to make it more accessible to a wider variety of consumers in a variety of different formats.

2) Accessible Visualisation

From Many Eyes to Wordle and, GeoCommons to Vizualize.me, our visualisations are being rationalised and simplified en masse from ‘traditional complex charts’ (more representative of a school mathematics lesson), to ‘visual metaphors’ representing everything from a personal online presence at a glance to the opinions and sentiment of a generation.

3) Intelligence Presentation

The rapid emergence of flexible presentation technologies such as Prezi, Sliderocket and Slideshare continues to grow as ‘cloud presentation technology’ moves mainstream and, design approaches to delivering ‘stories’ change for the better. These global collaboration frameworks are increasingly making ‘good design’ a given, are ensuring we can not only ‘tell our story’ effectively but also make our presentations widely accessible and, finally and perhaps most crucially, come complete with an embedded social media feedback link plus in-built analytics so we can continuely refine our content to meet the needs of our target audience. 

4) Actionable Metrics

This framework as described by Eric Ries, although originally aimed at entrepreneurs, is gaining significant traction in tier-1 corporate institutions. The ability to focus on a small but perfectly balanced set of metrics through which, the development of your business can grow and prosper is not new; Managing the business through a trusted dashboard as a window to your consumers changing needs and adjusting operations accordingly is more radical and, has traditionally been a ‘hit-or-miss’ experience for many organisations. In order to make our data intelligence ‘meaningful’ we will increasingly need to embrace this type of methodology as a means for delivering context-appropriate in-process analytics.

Science delivers ‘BIG’ but Art delivers ‘RIGHT’

As previously discussed, producing iterations of data specific to a process touch-point or outcome at the speed of business is incredibly difficult.

Data science provides the ‘pump’ (acquisition and marshaling big data components) and ‘filter’ (the algorithmic analysis of big data) to ensure a constant supply of clean data is readily available.

Data Art needs to provide the ‘smart-meter‘ to ensure the resulting information is presented visually with context as an actionable story where the ‘ending’ is firmly in the hands of the knowledge worker to influence.

This sounds straight-forward but it is NOT!

In the future, big data ecosystems will undoubtedly be able to consolidate, intelligently analyze and filter information to the point of interaction in fractions of a second.

The question is, when will we have a repeatable mechanism that can dynamically and visually present information at each ‘decision-point’ without significant human intervention in a format that ensures that each course of action our knowledge workers take, is the best one for the customer and the right one for our business?

That is where the emergence of Data Art will reap huge dividends in the future and, it’s coming to a boardroom near you soon!

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