Has your company implemented a data lake? It wouldn’t be a surprise. With the rise in big data demands, a data lake implementation has the potential to improve analytics, create wider data availability and produce more efficient data throughput.
Unfortunately, many companies fail to realize the true benefits of data lakes and are left disappointed. Organizations that lack the infrastructure, segmentation or even a clear use case for their data lake could be missing out. Luckily, new strategies can help solve these shortcomings.
Here are the changes your company can make to overcome the biggest barriers to data lakes and get the most out of your implementation:
Lacking A Clear Data Lake Use Case
Since data lakes don’t have the same restrictions as database management systems, there doesn’t need to be any defined storage structure. However, this can promote poor data governance that leads to no organization and makes security and access more difficult.
A big reason for this is most organizations don’t have clear use cases for their data lake. This results in it not being used to its full potential. Set some governance structure and schemas related to your initial use cases that still allow you to store unstructured data and remain flexible for yet-to-be-determined future scenarios.
Setting a more loose, flexible schema — while still providing the ability to store unstructured data — makes it easier for your existing analytics to draw on a much richer data set. Processing previously unavailable and unstructured data that isn’t restricted by a set schema offers endless ways to query data for any use case. In fact, an Aberdeen report found that companies with a data lake in place were more likely to report more sophisticated, powerful analytics.
Building An Unsegmented Data Lake
A problem many organizations run into is offering too diverse of a data pool for departments to draw from. When you have access to every piece of data generated by the entire organization and don’t have the right schema in place to organize it, your data lake can quickly turn into a data swamp that no one takes advantage of.
However, eliminating data silos allows you to share data across departments to offer richer data access to everyone. Creating a single, unified data storage solution across the entire organization is crucial for a data-driven organization.
The solution is to add segmentation into your data lake for each department or business function. This allows you to increase operability while still maintaining a democratized data lake. These mini data lakes, or data pools, are all connected together to make sure data sharing across the organization is maintained.
Building mini data lakes gives each department the ability to subscribe to data, access insights from other data pools and create schema specific to their needs. The most successful data lake users are more than twice as likely to have a process to support data sharing across business functions.
With the ability to store a wide variety of raw data into these smaller data pools, your organization gains much more diversity and availability in your data. Data lake users from each department are able to leverage diverse sets of complex data, such as the internet of things, geospatial data, rich media and more. Most organizations ignore this type of data because they don’t have systems in place to store and process it or because their data lakes are too unstructured to be of any use. This leaves out major insights that could give a clearer view of your organization and market for a competitive advantage.
Using A Rigid Data Lake Structure
Once your organization begins collecting all available unstructured data, you may quickly struggle to adapt to your growing data storage needs. As new use cases are discovered, you must also be able to quickly create new schema to allow departments to leverage any data they may need. In order to fully benefit from a data lake, it must be agile and flexible enough to scale with your organization and adapt to its needs.
If your organization isn’t seeing the agility benefits of data lakes, a SaaS solution is another option to consider. Data lakes as a service increase the scalability and flexibility of your data lake as your big data storage needs increase.
Since data lakes receive and store information in its native form, the amount of processing required to adapt data to the data lake structure is very low. This makes for a more efficient stream of data. Data lakes also provide a multi-terabit throughput architecture that allows data to reach applications faster. Not only do data lakes offer more data diversity, but they can also increase efficiency by offloading capacity from legacy systems.
Above all, the biggest reason your data lake may not be living up to its expectations is it wasn’t designed with these benefits in mind. Despite the unstructured nature of data lakes, implementing and managing them with clear intentions about how they will help your organization is key to seeing any positive outcome.