Big data is the leading buzzword among businesses and government agencies nowadays. No matter the industry in that a company operates, data is a vital resource that is changing the economic landscape. In government agencies, big data has helped avail information that can be used to in governance issues, inclusivity and to build sustainability. For a comprehensive agenda to be established, the economic and environmental data needs to be collected and analyzed to show patterns.

Data loss protection, anti-viruses, and endpoint security are just some of the technology available to defend against a cybersecurity attack. Organizations use these platforms to keep their existing data secure from potential threats. Often, this protection does not extend to information stored in data lakes. Because of this – information located in data lakes are vulnerable to attacks – a problem that hackers exploit when infiltrating data lakes.  

Big data is now a topic of discussion in many boardrooms where most organizations believe that if they collect all data from different streams into their companies, they can apply analytics and they will, without doubt, gain significant value from it. However, although big data can have a massive impact on your business if data is gathered and used appropriately, it can also present a challenge if too much data is collected without regard to verifying accuracy. This article highlights reasons why you need to scale back your big data in favor of accuracy to realize better results.

In 2018 there were numerous Big Data predictions from industry leaders. For instance, there will be a high demand for data scientists. Big Data will become more accessible. Information retrieval and NLP will see a surge in use. Now that we are in Q4 of 2019, did those and other predictions live up to the hype or go beyond industry expectations?