How Data Lakes Drive Business Value

There is now more data coming from more channels than ever before. Businesses are faced with the tasks of storing information from social platforms, IoT sensors, and transactional systems all while needing to stay competitive. Organizations have no choice but to store this information in some way even if they don’t know how they’ll use it down the road. Traditionally this information would be modeled in accordance with pre-defined data structures before being stored.

However, as the influx of information continues to expand, it becomes harder and harder to scale this strategy. So with the continued adoption of a more digital, agile architecture capturing data in raw format has become a technical requirement. Raw storage means information is captured at its most detailed level. You can then use it for several types of analysis later. Knowing exactly how a data lake will benefit your organization is crucial to expanding your strategy past simple reporting and into predictive analysis. If you build it, you can destroy silos and create a single foundation for all of your data needs.

In this blog, I will discuss what is a data lake and then we will move on to talking about its benefits in depth.

Data Lakes

They allow enterprises to collect data "as is", i.e. without first having to understand what they want to do with it. Schema and definition of the data take place at the time of consumption. This is referred to as schema on read. Therefore, the raw information can be repurposed for different uses. This is where they differ from traditional databases. For example, one set of raw data can be used for Big Data processing or machine learning. Data lakes consume information in their native format. Since they are typically housed on cheap scalable storage, data lakes allow enterprises to store petabytes of data that wouldn't fit in with a traditional structured database because of the high cost.

Top Benefits of Data Lakes for Businesses

Data lakes empower organizations to unlock value from vast, diverse datasets by enabling faster analytics, scalable storage, and unified access. They break data silos, support advanced analytics and machine learning, and help teams collaborate effectively while making data-driven decisions with greater speed and confidence overall.

Let’s talk about core benefits;

● Accelerated analytics: A data lake speeds up the time one needs for analytics. Data scientists and analysts gain immediate access to raw data in the lake. With no need to wait for data to be modeled or organized into a predefined schema before it is collected, data scientists spend less time waiting for data to be available. They have more time to experiment, plus more opportunities to run advanced analytics such as machine learning and predictive modeling against unfiltered datasets.

● Cost-effective storage: Data lakes store data on low-cost hardware or cloud-based object storage. A data warehouse requires expensive high-performance disk storage for all data stored. By comparison, storing petabytes of data in a data lake costs a tiny fraction of the price of a data warehouse. Organizations can afford to take the "store everything" mentality and hang on to historical data for possible future use.

● Centralized data: Essentially, this data solution consolidates all information that belongs to your organization into one place. It eliminates silos between departments by capturing data from logs, clickstreams, social media, transactional databases, and more. With a data lake keeping everything together, your teams have one primary source that they can both rely on. Not only does everyone have access to the same information, but a data lake gives you a more holistic view of your business than you can get when your data is spread out over several disjointed systems.

● Better quality of collaboration: Having everything stored centrally in raw form also means that different departments don't need to replicate datasets to work on them. Data engineers, analysts, and business stakeholders can all work off the same raw files for their own purposes. There's less friction introduced when data is moving between departments and validation can occur across teams when they're working with the same information.

Final Words

By combining the scalability of data lakes with AWS’s robust cloud ecosystem, organizations can accelerate analytics, reduce costs, and innovate faster. Partnering with an experienced AWS development services provider team ensures secure, well architected data lakes that deliver long term business value.