Data discovery refers to collecting and analyzing data from several sources with a view to glean meaningful insights and drive profitable business outcomes. The data discovery definition pertains to making sense of raw data and structuring it with a view to make it palatable for the end user.
What is data discovery
Data discovery helps in driving key insights within large sets of unstructured data. On their own, data sets are meaningless unless business owners are able to identify patterns and trends hidden within them. The use of such data sets can improve KPIs and unlock specific distributional patterns. Executives can then use this to rejig processes or switch core focus areas.
Data discovery analytics tools aggregate all data sources with a view to facilitate meaningful analysis. That’s because only the right data in the right format can be analyzed properly; unstructured and messy data has negligible impact on business outcomes.
The proper use of data discovery also enables different business units to look at the same data but leverage it in different ways. This means it’s possible to scale it across teams and create insights unique to each department.
Efficient data discovery also necessitates that you clean existing data and store it properly with a view to using it in the future. That’s because data scraping is never a static process, there will always be new data sets emerging as your company increases its scope of operations.
Valuable data discovery uses both past data as well as new data in order to come up with more efficient and reliable insights.