What is an Enterprise Data Lake?

March 1, 2022  |  Mark Hillary

Big Data, and data analytics in general, is becoming more important across a number of industries. Many companies are now tracking their customer purchases, browsing behavior, and blending this with other external data – such as weather records – to create insight into customer preferences. In some cases, companies can even predict what customers will do before they do it.

But underneath all this analysis is the data itself. Accessing large amounts of constantly-updating data from different sources is more feasible than ever before, but it requires some planning.

A ‘data lake’ is a large storage area containing a huge amount of raw data in native form – it’s not cleaned or filtered when stored in the lake. Therefore an ‘enterprise data lake’ (EDL) is just an enterprise-wide data lake – it’s raw information from across all of your organization.

But what can I do with all the raw data inside an EDL?

The main benefit is the ability to centralize all your various sources of data into a single location. Once the data is grouped together into the EDL then you can apply techniques to process it in a way that would usually not be possible if you had to create a database that accessed all the various sources in real-time.

Because the data is sourced from different locations you can create security rules for users of the data lake. There may be some information that is proprietary or sensitive and this can be shielded from a set of users. Some users will be granted rights to access all data and therefore have the ability to create the most insight.

Some users may also not require access to raw data, but will only need to access areas of the EDL after processing has taken place – this is also simple to organize and adds further to the security that is possible.

Data normalization and enrichment can be applied to the raw data so it makes more sense to human readers and also allows processes such as indexing or aggregation to take place. Enrichment processes might be the extraction of metadata from images. In the raw form, an image file just looks like an image, but inside the image, there will be additional information on where the picture was captured, when, and with which equipment.

An EDL often differs from a traditional data warehouse by avoiding a lot of up-front processing and normalization of data. These processes are generally applied when required, so data that is not being searched or checked will just reside in the raw format without additional processing.

Your enterprise users from all departments in a business can access the EDL and apply searches and analytic processing. It should reflect the entire universe of your company activity, but with additional data feeds that can then be correlated to help create more insight. Ultimately this should drive business decision-making activity by allowing more insight into the way that your employees or customers are using your systems.

A well-designed and managed EDL is extremely powerful. It allows information from across the entire enterprise to be in the hands of any authorized employee so your business can be smarter, can create more insight, and can test new ideas more easily – leading to a more innovative environment.

The COVID-19 coronavirus pandemic has offered data analysts some great opportunities to study Big Data trends across the world. Read more about Big Data In A Time Of Pandemic

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