Data lakes: 5 key challenges for enterprises and how to overcome them


The move towards an entirely data-driven business ecosystem has been underway for a while, but it hasn’t always gone smoothly. Although 96% of companies said that they saw success from data and AI initiatives in 2020, only 24% reported that they’d achieved authentic data-driven cultures.

Estimated reading time: 4 minutes

One of the most critical steps in driving a data-led culture is moving data storage and processing to the cloud, but that’s easier said than done for large corporations with legacy infrastructure.

As with so many things, the trend received a boost from COVID-19. When enterprises shifted to remote work, they also had to switch their data centers to the cloud to ensure that all stakeholders could access the data they need, wherever they may be located. Cloud-based data lakes adoption grew immensely in the last year and a half.

But enterprises have also had to overcome several obstacles to access the benefits of data lakes. Here are the five main challenges to using data lakes successfully for your business and what you can do to resolve them.

1. The challenge of data ingestion

Loading data into a cloud environment can be time-consuming and tedious. Most data storage in the cloud is immutable, meaning that it’s resistant to incremental changes in a database. The trouble is, if you can’t load data incrementally, you have to reload the entire massive data table, which is slow, laborious, and challenging in the cloud.

For that reason, many enterprise users prefer a combination of data lakes and data warehouses. The data lake serves as a batch loading source for the cloud data warehouse, which holds the refined data. Use a SQL-based ELT process to refresh databases quickly, without transferring huge and slow databases.

2. The challenge of data portability

The data storage and processing landscapes are changing fast. Many enterprises are nervous about becoming locked into either a specific cloud vendor or an on-prem data storage environment, narrowing their options for data processing tools.

To avoid this, it’s crucial to choose applications that have data portability baked in as a default. You want to shift your data tools and storage providers according to your changing organizational needs, data formats, and applications and respond to fluctuations in data quantity, use, and storage location.

3. The challenge of data speed vs. data quality

The move to cloud data lakes has only sharpened the existing conflict between the need for real-time data and high-quality data. Enterprise users can struggle to collect data and bring it to their analytics solutions without delay while also assuring that data isn’t corrupted or weakened along the way.

Dealing with this issue generally requires creating a process that preserves data context, rather than any “black box” solution that doesn’t let you see how data is crunched and calculated. Organizations may need to upgrade legacy ETL systems to support real-time data from IoT or streaming devices and connect data transformation tools that automate data preparation to reduce the risk of manual errors and speed up preprocessing.

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4. The challenge of democratizing access to data insights

You want all departments to draw on data insights for their decision-making, but it doesn’t always work out that way. Confusing interfaces and hi-tech processes often defeat Non-tech employees, plus IT departments can get possessive about data control. As a result, it’s easy to find that data queries are channeled through your IT or data science teams, leaving them without much time for their fundamental tasks and creating a bottleneck in business workflows.

Prevent these unhealthy data access dynamics by choosing low-code data analytics solutions with intuitive, easy-to-use interfaces that even non-techies can handle. At the same time, set up clear data ownership rules to prevent any department from being shut out of your data insights.

5. The challenge of standardizing data governance

Increasingly hybrid data storage and analytics environments open up new challenges regarding the standardization of data governance. Enterprises must maintain security, access settings, and metadata privileges when data workflows flow across public cloud, private cloud, and on-prem environments, but that’s a challenge when vendors apply varying business logic and inconsistent governance regulations.

Choose data transformation and processing solutions that integrate powerfully with your preferred cloud data warehouses to address this issue. Look for specific governance capabilities like data lineage, audit logs, auto-documentation, data preparation, and ETL/ELT tools so that you can set up consistent governance practices throughout your workflows.

Advance planning can help you overcome data lake challenges

Although there are many obstacles in your path, it is possible to create streamlined, user-friendly, cloud-based data analytics workflows with data lakes. By using incremental data loading, choosing tools with data portability and an intuitive interface baked in, establishing transparent governance and data ownership policies, automating data preprocessing, and upgrading legacy tools, your organization too can achieve data lake nirvana.

What do you think of data lakes? Please share your thoughts on any of the social media pages listed below. You can also comment on our MeWe page by joining the MeWe social network.

Last Updated on October 4, 2021.

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