From data lake to a more agile model: the data market

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Harnessing the potential of data is key for any company. To do this, moving from a data lake to a data marketplace is a great solution.
Organizations rely on data lakes to store the new information they collect. However, it is all too common for these repositories to become passive storage areas, missing out on valuable opportunities to gain new business insights and generate business value from intelligent data.

Today, best practices encourage transforming data lakes into active engines to respond quickly to business needs. Let's see why and how.


First of all, it is worth remembering that a data lake is a shared data environment in its original form (i.e. raw data), comprising multiple repositories and leveraging big data technologies. This solution can provide data for a wide variety of analytical processes. By centralizing disparate content sources, a data lake makes it possible to combine and process them using big data and perform searches and analyses that would otherwise have been impossible.

In short, it allows you to manage multiple types of data in ig database native formats with a high degree of flexibility, scalability and security.

This growth will be driven by the trend towards extracting comprehensive information from consumer data to carry out marketing and sales strategies. This makes it easier to achieve a competitive advantage, or more accurately, an analytical advantage . This will also simplify access to organizational data from departmental silos, mainframes and legacy systems. In addition, the shift towards cloud-based data warehouse or data lake platforms (i.e. within the framework of cloud computing schemes) is expected to offer opportunities for greater adoption of data lakes.





The data lakes market is estimated to grow from $7.9 billion in 2019 to $20.1 billion in 2024, growing at a compound annual rate of 20.6%.

Source: Data Lake Market by component


Advantage to grow
One study found that companies with data lakes saw 9% higher organic revenue growth than their competitors. Why? Basically, because this model allowed them to perform new types of analytics, employing machine learning on new sources like log files, clickstream data, social media, and internet-connected devices stored in the data lake.

As the same report indicates, companies implement a data lake to take advantage of more advanced and sophisticated analytical techniques, applied to a more complex and diverse information base. And, in addition, to make traditional activities, such as data access and retrieval speed, more efficient and easier to perform.