As data analysis has become more and more essential for companies to move forward and thrive, the process has also become increasingly overwhelming to maintain and interpret. Permanent solutions such as data warehouses are no longer cost-effective and don’t allow the customization necessary to explore the data thoroughly. Many eCommerce companies have begun incorporating data lakes, which bring together data in raw, diverse forms to maximize how it can be utilized.
Why Data Lakes?
A data lake will not only save significant time upfront because of its schema-on-read nature, but will also increase options in coordinating and interpreting raw data. While a data warehouse allows for efficient engagement, it is also by design limited in accessing and connecting data within deliberately structured categories. This can make it challenging for businesses to attempt new methods of analyzing data. With an Aberdeen Group study indicating that the average company sees its volume of data increasing at a rate of 50% annually, limiting the scope of how to analyze that data can prevent businesses from discovering strategies that could increase efficiency and profits.
As is often the case, eCommerce companies that pursue new and emerging technologies find themselves with a competitive advantage. That same study reports that organizations that are incorporating data lakes effectively communicate 32% higher satisfaction with their data quality, and 9% higher gross revenue than their competitors. It’s important to note, though, that successfully incorporating data lakes in place of or in addition to preexisting structures requires careful planning and strategic thinking. Otherwise, companies run the risk of creating a “data swamp” that results in inconsistent, unorganized, and unusable data.
How to Effectively Implement a Data Lake
If a company decides to implement a data lake solution, it needs to make sure that the technology is serving the business rather than the other way around. Ask yourself, “What problem are you trying to solve?” Just because data can be stored does not mean that all of it will ultimately prove fruitful. The first step is to identify the content that is already or may one day be valuable to your organization. This will help to establish the amount of necessary cloud storage and avoid overly complicated retrieval. Of course, this can all be modified as needs evolve.
It is also wise to test multiple options and apply new techniques incrementally yet rapidly, to quickly determine the best fit. These experiments will soon reveal the methods that work most effectively and which ones are unnecessary for your organization. There is no “one size fits all” solution when it comes to storing and analyzing data, but the most significant benefits companies will find in using data lakes is that they can evolve concurrently with the needs of the business. Because the data is stored in its raw form, any time a company wants to analyze a new variation on customer habits, everything they need is already in place.
It is crucial that any organization establish its own individualized approach to data maintenance and analysis in order to work intelligently and remain competitive. By scheduling a consultation with Katalyst Software Services Limited, you can collaborate with our experts to ensure that you will discover the ideal strategies to make the most of your data.
