Many modern enterprises are finding it difficult to gather, store, and process in data lakes and get valuable business insights.
Business leaders need to have real-time information on their business performance and ROI. With the advent of industry 4.0, businesses are generating a large amount of data through various channels, and to process this vast amount of data, many CDOs have embraced data lakes in their tech stack. Integrating data lakes into the enterprise tech stack has benefited the DataOps team in multiple ways, but these data lakes usually lack to deliver the theoretical promises because of the following challenges:
Designing, developing, and deploying a data lake can be an expensive process with high maintenance costs. There are a few cloud data platforms that offer enterprises with open source platforms to develop a tool and manage by themselves. However, it can be a tedious task for organizations to manage and maintain these assets and will require expertise. Enterprises can also leverage managed cloud data platforms which are easier to deploy. However, this usually will have higher costs and will be difficult to manage.
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It is a daunting task for even skilled DevOps teams to design and manage data lakes. Even if organizations leverage an open-source cloud data platform or a managed service
CDOs find it challenging to develop a data lake that is scalable and deals with redundant data without compromising on security.
Gathering data from all the available channels and making sense of it can be a difficult task for many enterprises. Bygone are the days when organizations had copies of crucial data from their transaction systems consolidated into a data warehouse. It is a time-consuming task to structure data and cleans it with a lack of resources which creates a lag in the information flows, and users will not have real-time updated information to make strategic decisions.
One of the most significant challenges with managing data lake houses is to manage through the large amount of data generated. Moreover, data gathered through third-party channels might not be structured, which makes it even more difficult to interpret.
Enterprises to generate a return on investment on their data lake expenditure will take longer than expected. Because once the organizations implement a data lake, it might take a while for the entire time to get acquainted with the systems and deliver value through it.
The traditional cloud data system will not be highly efficient while securing data and enforcing stringent data governance policies. Enterprises, while embracing data lakes, need to consider data security and governance as a concern, which will reflect in higher operational and management tasks.
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Another significant challenge while developing and implementing a data lake house which an enterprise has to be prepared for, is the lack of skills and resources.
There is a tremendous scarcity of data scientists and engineers that makes it challenging for enterprises to make the most of the data gathered and stored.
CDOs need to resolve the data inconsistencies while merging various data sets. The challenges of a data lake resemble a lot of the pitfalls of data warehouses. Even though the underlying data storage layer might have evolved, but the concerns like data governance, security, metadata, data quality, and consistency are inherent challenges of the data lake that it exposes the business too.
These are a few challenges that CDOs need to consider before implementing data lakes into their IT infrastructure.
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Nikhil S is a Tech Journalist with OnDot Media. He is a media professional with eclectic experience in communications for various technology media brands. He brings his eye for editorial detail and keen sense of language skills to every article he writes.
