Strategies for limiting the negative effects of big data

 A well-executed big data strategy can streamline operational costs, reduce time to market and enable new products.

Below are listed some strategies that some organisations use.

1. Managing large volumes of data: Big data by itself definition typically involves large volumes of data. Once you have a sense of the data that's been collected it is easier to narrow in on insights by making small adjustments.

2. Finding and fixing quality issues:  The analytics algorithms and artificial intelligence applications built on big data can generate bad results when data quality issues creep into big data systems. These problems can become more significant and harder to audit as data management and analytics teams attempt to pull in more and different types of data

3. Dealing with data integration and preparation complexities: Some enterprises use a data lake as a catch-all repository for sets of big data collected from diverse sources, without thinking through how the disparate data will be integrated. Various business domains, for example, produce data that is important for joint analysis, but this data often comes with different underlying semantics that must be disambiguated