Big data offers businesses huge opportunities to innovate and optimize their processes and product offerings. But it also requires significant resources to implement and maintain. Even as cheap storage becomes increasingly available, many companies are spending more on data storage. Over the last few years, 60% of companies have set aside additional funds for more storage space.

In response to the need for agile, scalable data storage solutions, some companies have turned to custom data analytics, known as data lakes. However, the waters of data lakes are rife with sharks. Here’s why most data storage experts recommend against data lakes.

What are Data Lakes?

A data security lake is a type of data storage repository that hosts a large amount of data in its native format. Unlike traditional data warehouses, which are structured hierarchically, data lakes do not store data in files or folders. Data lakes utilize a flat architecture.

Every piece of data within a data lake is assigned a unique identifier and tagged with metadata tags. Users can access specific data by imputing queries. Until a query is made, the data’s schema is undefined.

The appeal of data lakes lies in their ability to store vast amounts of data efficiently, without organizational silos that prevent departments from accessing relevant data. Proponents of data lakes argue that they offer both cost savings and the ability to adapt to future systems easily.

Top 3 Reasons to Avoid Data Lakes

#1: Dirt

Data stored in data lakes can easily become dirty, thereby becoming unusable. Although the major selling point of data lakes is the ability to pull data from anywhere without extensive governance, this also means that data can easily become dirty and therefore unusable. Business users then cannot depend on the quality of any data in the lake of dirty data.

#2: Dams

Data lakes pose major barriers that prevent business users from accessing and analyzing data effectively. The great promise of self-service business intelligence (BI) tools is that business users will be able to conduct their own data analyses even if they don’t have a degree in data science. Data lakes undermine this promise because they lack semantic consistency and governance of metadata.

As a result, non-expert users tend to have difficulties finding the data they want and using it. In many cases, data lake users find that they can conduct basic keyword searches but not advanced analytics.

#3: Leaks

Using data lakes poses major security risks. Company-wide data governance does not exist in data lakes. As a result, companies can’t implement strong controls over data access—meaning that people who don’t need to access sensitive data can do so. Compliance regulations can be difficult if not impossible to implement. Many vendors that provide data lake products cannot provide adequate protections for the data.

In short, any data stored in a data lake is at risk and is likely to become unusable.

It is possible to implement a data storage solution that will allow your company to reap the benefits of data analytics without putting sensitive data at risk. Contact Ntirety to learn more about how to implement a security solution that meets your needs, without falling into the dangers of data lakes.