Data lakes are the key to streamlining data collection and analysis. However, there is no denying the obvious benefits of these lakes but, like most technologies, there are some disadvantages to using a data lake. It’s important for organisations to be aware of its shortcomings before investing in it. This blog post attempts to address some of the problems that come with data lakes. If not implemented properly, the lake could end up hurting the organisation more than benefiting it.
There are several technical and business challenges of using data lakes.
Data lakes are an open-source of knowledge designed to streamline the analytics pipelines. However, the open nature of the lake makes it difficult to implement security standards. The open nature of the lake and the rate data is inputted, makes it difficult to regulate the data coming in. To eliminate this problem, data lake designers should work with data security teams to set access control measures and secure data without compromising loading processes or governance efforts.
However, it’s not just security that’s causing problems with data lakes. It’s also an issue of quality. Data lakes collect data from different sources and pool it in a single location, but the process makes it difficult to check data quality. It is problematic because it leads to inaccurate results when the data is used for business operations. When the data is inaccurate, the findings will be inaccurate, causing a loss of confidence in the data lake and even in the organisation. To resolve this problem, there needs to be more collaboration between data governance teams and data stewards so that data can be profiled, quality policies implemented and have action taken to improve quality.
Metadata management is one of the most important parts of data management. Without metadata, data stewards (those who are responsible for working with the data) would have little choice but to use non-automated tools like Word and Excel. Moreover, data stewards spend most of the time working with metadata, as opposed to actual data. However, metadata is not implemented on data lakes, which is a problem, in terms of data management. The absence of metadata makes it difficult to perform vital big data management functions like validating it or implementing organisational standards. Since there is no metadata management, it becomes less reliable, hurting its value to the organisation.
Data lakes are incredibly useful, but they are not immune to clashes within the organisation. If the organisation’s structure is plagued with red tape and internal politics, then little value can be derived from the lake. For example, if data analysts cannot access the data without obtaining permission, then it holds up the process and hurts productivity. Different departments might also have rules for the same data set, leading to differences in rules, policies and standards. This situation can be somewhat mitigated by having a robust data governance policy in place to ensure consistent data standards across the whole organisation. While there is no denying the value of data lakes, there need to be better governance standards to improve management and transparency.
Identifying data sources in a data lake is not often done, which is a problem in big data management. Categorising and labelling data sources is crucial because it prevents several problems like duplication of data. Yet, this is not done regularly, which is problematic. At the very least, the source of metadata should be recorded and available to users.
Big data management is made much easier with the use of data lakes. However, there are some challenges when it comes to using the centralised repository. These challenges can hinder the use of the data lake because it becomes harder to discover actionable insights when the data is flawed. If there is a problem with the data, then insights are useless. The main challenge of fixing these problems is implementing multi-disciplinary solutions. Fixing problems with data lakes requires comprehensive technical solutions, adjusting business regulations and transforming work culture. However, organisations need to address these problems. Otherwise, they will fail to draw maximum value from their data lakes.