Big data and visual analytics

There is a whole new array of software packages and analysis engines which are meeting the challenge of tackling big data.

Big data and visual analytics

There is a whole new array of software packages and analysis engines which are meeting the challenge of tackling big data. They achieve this in one of two ways: Either by creating logical samples of the data and using them as the solution engine which can be applied uniformly. Or, by creation of template charts and visuals which would help accepting certain types of information and thus leading to trial and error based solutions.

First major problem with such approaches is that it skips the innate pattern recognition ability of the human brain. Instead, if we combine big data with visual analytics where by the data is paired with the option of “how best to present it”, then the data researchers are free to explore the useful insights such approach provides.

Visual analytics is like a prolific cinematographer and editor knowing precisely when and where to edit your big data movie. It does this to achieve tangible coherence and easier to recognise patterns which are easily understood.

The second challenge faced by organisations today is the need to develop hyper-competitive speeds which are required to crunch the vast amounts of data. And the challenge doesn’t simply end there: there is an innate need to develop details and insights from this sheer amount of data being crunched and churned.

The key solutions which have been offered in combination with visual analytics in order to solve these problems are as follows: A)Bigger and faster hardware support. This method uses parallel computing and several arrays of data crunching hardware put together to achieve the data efficiency which the modern day big data tasks demand.

Or, B) Using in-memory grid computing approaches where the data crunching engines are harness their combined power to solve one big problem and then this solution is being applied to other smaller yet complicated data sets.

Both of these approaches allow the organisations to solve big data problems in real life situations with ease and required trust.

Data visualisation has been a keystone technology which has been used effectively by large organisations to tackle big data. However, with the increase in data granularity, ambiguous multiple sourcing of the data and a vast amount of data providing channels; there is an increased need to strengthen the very basis of data visualisation on which the concept rests.

For a company which deals with data from social media sources for example, it is necessary to understand the demographics and their product usage understanding of what your organisation has to offer. Here even the visual analytics is dependent on the keen insight and deep understanding of the data researchers employed to handle the task. Only they can identify the clues and patterns which would then be used by the visual analytics engine to present a tangible picture of complicated big data sets.

Another page from within the same book is of assessing the data quality before it can be crunched and understood. The need to separate ambiguous and bad data from the useful and purposeful data sets is as necessary as having a visual analytics support structure in modern day data computing environment.

In order to achieve higher standards of data quality and uniform and intelligent data governance policy should be utilised. The uniformity would ensure that redundancies are weeded out of the processes. While the intelligence component would suggest that the right questions are being asked to address the big data questions at hand.

Data binning and identifying the ‘outliers’ in your vast sets of data is also a crucial component. This means that, say you have a 10 billion lines of retail data at hand and there are almost a similar number of graphs being generated. Instead of individually sorting this data, clustering the data together to have a higher level and lower levels of data resolution or simply data binning is necessary.

Outliers are the ghosts which can cloud any data scientists judgement and efficacy while dealing with big data. Visual analytics provides you with tools which can simply allow you to understand these ghosts better by putting them on separate visual graphs which can then be studied more closely to understand the trends amongst these outliers.

Thus, we can clearly understand the need of having better visual analytics engines in today’s big data driven business environment.

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