Creating value from big data is top of mind for many executives across a wide variety of industries. These executives perceive the potential to substantially improve operations by creating insights into the terabytes of existing data stored in their organizations’ databases, with more data flooding in every second. Ironically, the best way to approach this problem can often be to start with little data problems and insights before progressing to big data solutions.
With big data opportunities come challenges, and perhaps the greatest is the sheer volume of data. There is often a temptation to tackle the issue all at once, with mega-scale projects ambitiously gathering all the data from various sources into a data lake, either on premise, in the cloud, or a hybrid of the two. Once stored, this data must be cleansed to remove anomalies, and this is an ongoing process as more data floods. Another issue arises because big data often comes with significant dilution of insights as data naturally starts to normalize and hide intricacies of events.
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