The session explored the following questions:
- How do you implement Big Data?
- Does Big Data mean All Data?
- How is big data being used? (case studies/use cases)
- Will in-stream analytics lead to knee-jerk management?
- A major barrier to implementing a big data plan is in understanding what big data really means.For non-technical professionals, big data should be thought of less as a tangible thing and more as a condition. Organizations are far less likely to invest in “big data technologies” when they aren’t presented with the real business problem that the technology is needed to solve.
- The Cloud used to be viewed as a silver bullet to Big Data because is provided organizations an opportunity to store all their data and not worry about it. This sort of ‘out of sight, out of mind’ attitude has changed somewhat. Many organizations are coming to recognize the hidden costs and hidden risks of the Cloud and aren’t simply relying on cloud storage as a way to deal with the explosive growth of data.
- The future of big data technologies lies in analytics. More specifically, analytics across data repositories and data types that connect the dots and give people real, actionable information about their data as a whole. Social media sites, like Facebook, are great examples of the power of analytics. Facebook takes endless amounts of data from hundreds of millions of users and presents it in a meaningful and cohesive way. Organizations are trying to move in the same direction.
- Privacy is a major issue with Big Data. Information governance professionals have to strike a balance between getting the most business value out of data and information while also minimizing risk and damaging information exposure. Privacy legislation simply cannot keep pace with the information boom. It needs to be addressed at an organizational level. It’s also important to note that privacy issues evolve. Generally we have sacrificed some degree of personal privacy in our pursuit of information access.
E-Discovery Beat’s Key Takeaway
The concept of Big Data conjures a lot of fear for organizations. The reality is that Big Data should make more informed business decisions possible. The first step in implementing a Big Data plan and strategy is to determine what data is actually valuable and needs to be managed. From there, classification of the data is critical. In the e-discovery world, the holy grail is analysis of data in-place, prior to it being collected and moved to a centralized repository. Automated document classification is a significant first step to achieving that goal.