The evolution of data visualisation tools and the demand for faster data processing

Human response to visual representations of data is far more engaged and responsive than any other form of data representation. Hence, the advancement of data visualisation tools over the last decade and forward into the 2020s has been, and will continue to be, seismic across businesses looking to ingest and act on ever growing data points and volumes. 

There is no doubt that the data dashboard and the tools used to represent data have advanced massively over the last decade or so. However, as Gartnerrecent report on the top 10 data and analytical trends of 2020 notes, we are moving beyond the era of the data dashboard and new innovations in data representation and their ability to tell automated, interactive stories for all types of data are moving far beyond that of many established techniques. 

According to Gartner, the rise of visualisation innovations that embrace advanced AR/VR, augmented analytics, AI/ML, structured and unstructured data sources, NLP and so on, will cause a “shift to in-context data stories [meaning] that the most relevant insights will stream to each user based on their context, role or use. 

The Data Bottleneck 

Therefore, as we look to these increasingly powerful and enriched tools to represent and overlay our ever-growing sources and volumes of data, such as with the wide-scale adoption of IoT or the onward march of social media on top of more traditional data sources held within businesses, we need access to data like never before – enabling us to stay ahead of, or at worst keep up with, the competition in an ever more data driven world.  

The traditional solution for this has been to have large datasets pre-defined, pre-aggregated and preloaded for the given data we are looking to visually represent and queryThis is by nature restrictive as in itself, only specific questions can be posed and only performed on the pre-loaded data available.  

Furthermore, this is inherently a slow process, requiring specialist resource dedicated to the data set builds, the flexibility of which will be limited by the quality and availability of this resource. Finally, a consequence of the previous issues is the cost to the business – firstly in terms of the utilisation of specialist resource and secondly in terms of the lack of agility that a business will have relative to its competition.  

The solution 

Deploying BrytlytDB enables speed of thought data processing with Brytlyt’s advanced GPU accelerated database for data performance at scaleCrucially deploying BrytlytDB is complementary to existing database infrastructure, thus businesses do not have to rip and replace existing data infrastructure, but instead can have access to next generation data processing within hours of deployment. 

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