As highlighted by analytics professor Jonathan Choi, just because datasets may be small doesn’t mean that they are not valuable. In the age of big data influenced eDiscovery, we often neglect the power of small data. However, if considered and used effectively, even the smallest of datasets may provide great value.
As highlighted by Jennifer Zaino in BizTech, a data lake is an architecture for storing high-volume, high-velocity, high-variety, as-is data in a centralized repository for Big Data and real-time analytics. And the technology is an attention-getter: The global data lakes market is expected to grow at a rate of 28 percent between 2017 and 2023.
Though it is challenging, having visibility into all this legal data is paramount not only for efficiency and cost-saving purposes, but more importantly, to meet regulatory demands.
There are at least two schools of thought that are very different about what constitutes the meaning of what is and what is not structured data. One school of thought, as stated previously, is that everything not in a standard DBMS is unstructured. Another definition is that something is unstructured only if there is not a rational way to explain the structure.
Algorithms can be as flawed as the humans they replace — and the more data they use, the more opportunities arise for those flaws to emerge.
Putting aside the dystopian views that sensationalize AI, bright prospects are ahead for corporations that embrace this transition to new ways of thinking. However, to make the leap, some radical adjustments in the ways of working are necessary.
Don’t think that data is only a value driver for stratospheric M&A valuations. It can also form a significant portion of the remaining value of a company during the bankruptcy process.
Rarely does Big Data present itself in a way that is ready for analysis. Companies must first deal with three important considerations of today’s data: format, sources and grain.
Big Data velocity refers to the speed at which data is created, made available, ingested by those who want it, and processed to gain valuable insights.
Data has become so large that the words we use to describe its size are not part of our everyday vocabulary. This leads to confusion.