Digital Transformation
Big data optimisation that turns analysis into insight, and insight into the best action.
From analysis to action
Big Analysis
Big data analytics refers to the strategy of analysing large volumes of data, gathered from social networks, videos, digital images, sensors and sales transaction records. The aim is to uncover patterns and connections that might otherwise be invisible.
Big Answer
The insights gained through analytics are incredibly powerful, and can be used to grow your business while identifying areas of opportunity — allowing brands to intimately understand their customers and create meaningful engagement.
Best Action
With best analysis and insight, it is just as important to follow with appropriate action. This action will have a measurable effect and, in turn, lead to new insights — an increasingly significant cycle for any competitive organisation.
Working with big volumes of data collected through many applications in multiple storage locations is both challenging and rewarding. Extracting valuable information from data means combining qualitative and quantitative analysis techniques. One of the main promises of analytics is data reduction with the primary function of supporting decision-making.
Many big data optimisations have critical performance requirements (e.g., real-time big data analytics), as indicated by volume, variety, velocity and veracity. To accelerate big data optimisation, users typically rely on detailed performance analysis to identify potential bottlenecks. Due to the large scale and high abstraction of existing frameworks (e.g., Apache Hadoop MapReduce), tuning massively distributed systems at fine granularity remains a major challenge. BA3 is one of the performance tools that can enable this.
The big data problem can be seen as a massive number of data islands — ranging from personal, shared and social to business data. The data in these islands is getting large in scale, never ending, and ever changing, arriving in batches at irregular intervals. Linking and analysing this potentially connected data is of high and valuable interest. In this context, the Linked Data approach can enable Big Data optimisation by facilitating accessibility, sharing and enrichment of databases.
Applying data optimisation provides information to decision makers. As available measurement data grows, so does the need for reliable interpretation. Big Data optimisation techniques enable designers and engineers to realise large-scale monitoring systems in real life, by allowing these systems to comply with real-world constraints in performance and reliability.