Size Does Matter: Considering the Great Value of Small Data

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.


Data Lakes: An Important Technological Approach for Data and Legal Discovery

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.

Untangling the Definition of Unstructured Data

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.

Bigger Data Isn’t Always Better Data

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.

A Critical Shift in Thinking About AI and Big Data

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.

3 Guidelines to Maximize Value of Data

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.

Big Data: Finding Value in Diverse Types of Data

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.

How ‘Big’ is Today’s Data?

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.