The Challenge of Relational Analytics and IoT Data
Relational analytics, including analyzing data from the Internet of Things (IoT), presents myriad challenges when it comes to litigation. The diversity and number of mobile devices, wearable devices, vehicles, buildings and other items (like light bulbs, picture frames and tea kettles) embedded with connective technology, and thereby chock full of valuable IoT data, are growing at an alarming rate. As the 3V’s (volume, variety and velocity) impact the ever-expanding dimensions of big data, structured data systems are becoming more disparate and increasingly complex. And these systems can be highly distributed throughout an organization and third party vendors, especially when considering Cloud-based and IoT datasets.
When legal issues arise, there’s often a need to leverage relational analytics surrounding this data and its related systems. Identifying, gathering, analyzing and understanding IoT data – what it is, where it’s stored, who touched it and why it’s relevant – can be critical to resolving a case. It’s also vitally important to bring distinct structured data systems together so data can be examined collectively, rather than in isolation, in order to uncover patterns or aberrations and support legal arguments with proven facts. But how can such an enormous chore be tackled quickly, accurately and defensibly?
Relational analytics encompasses not only the analysis of IoT (relational) data, but the building of relationships between countless structured data sources. At iDS, our relational analytics and IoT data solution is a unique, customized methodology, process and suite of tools that, when used in concert, powerfully leverages technology to help connect the structured data dots, see the big data picture and tell the story through facts.
It’s not all about technology. At iDS, our group of experts have the knowledge, experience and technical expertise to understand complex legal issues – partnering with legal teams to help resolve the case. Our solution maximizes the value of information, decreases risk, increases efficiency and delivers defensible processes for every relational analytics and IoT data challenge.