How metadata prevents inconsistencies when working with Data Lakes

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Good metadata management can prevent inconsistencies that alter the results of big data analysis when working with data lakes.
The exponential growth of data and the increasingly pressing need for businesses to base their decisions on valuable information means that we pay special attention to the entire data strategy of the organization.



Within effective data management , there is metadata that, when properly managed, can prevent inconsistencies in data lakes that alter the results of big data analysis applications .


A data lake is a place to store a lot of information from a wide range of data sources and put it into a single repository. However, that massive amount of information can create problems when it comes to engineer database and preparing the data in a consistent manner. Big data analytics applications may have trouble extracting results, but proper metadata management can help prevent this.







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A data lake architecture gives analysts the ability to impose their own structures and transformations on datasets as needed. This approach offers greater flexibility in data usage, but poses a risk to data consistency and analysis. It is quite possible for different users to deduce completely different meanings from the same dataset. The risk grows even more when analytical applications include external datasets whose provenance may not be fully known.

Data preparation tools provide some relief by standardizing the approaches used for profiling , evaluating, and transforming raw data. But such tools are often used on an individual basis. This leads, at best, to duplicated efforts, and at worst, to inconsistent analytical results.



Associating metadata tools with collaboration
The goal is to reduce confusion, simplify data interpretation, and reduce the level of effort required to integrate and prepare data. And that can be achieved by combining collaborative processes with the use of metadata management tools. When done correctly, maintaining a shared set of metadata definitions can help foster consistent treatment of data by analysts, thereby reducing the risk of conflicting interpretations.

Modern metadata management tools are equipped with sophisticated methods to facilitate collaboration. For example, many tools now support discussion threads that are used to share current and historical context information about how data is integrated, prepared, and used.