Big Data is one of the most potentially dangerous and destructive new technologies to come about in the last century. While a new fighter jet or a new type of bomb can certainly wreck havoc, big data has the potential to insidiously undermine and subtly (and not-so-subtly) change almost every aspect of modern life.
Automation of data management can be transformational in the enterprise. Eliminating low-level manual processes frees up people resources, amplifying human potential to deliver more value and creativity further up the value chain.
“Big data is data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or doesn’t fit the strictures of your database architecture.”
The growing usage of Software-as-a-Service, or SaaS-based, applications in the enterprise means that software will displace traditional services to account for almost half of all Big Data revenues by the middle of next decade.
The EU Data Protection Directive 95/46/EC (the “Directive”) creates the legal framework for national data-protection laws in each EU member state.
Digitization and Big Data can undoubtedly bring big benefits to the legal profession. Some are obvious, such as freeing up space formerly needed to keep mountains of case files in filing cabinets, to the less obvious such as finding unexpected judgments or decisions which could swing a case in favor of a data- savvy lawyer.
Big data is no longer the hot buzzword it was a few years ago that people strained their brains to understand. It’s now entered the mainstream and can be viewed as an extension of traditional data crunching.
“Once you understand the data and you understand the problem you’re trying to solve, that’s when you can match the algorithm and get a meaningful solution.”
With data being the obsession of business executives, entrepreneurs and IT technology investors, there’s a justification to want and store increasing amounts of data.
Data integration involves a lot of details (systems, transmission, structure, security, etc.) to address, but when it comes to pleasing the consumer the biggest and the most important task is normalizing the data.