Data and analytics have become key components of how businesses operate. More than ever before, businesses rely on analytics to understand customer preferences, cut down on costs, and identify growth opportunities.
With an increased focus on big data and its unique insights, data lakes have become popular assets for companies of all sizes.
Understanding Data Lakes
What is a data lake? Think of it as a storage repository that can hold large amounts of data in its raw or native format. Currently, most businesses use data warehouses for storing and analyzing data. These warehouses tend to store data in a more structured format (such as files and folders) that has been cleaned, modified, and arranged for a specific use value.
On the other hand, data lakes contain information in a “flat architecture,” where files can be stored in a structured or unstructured manner. Data can also arise from multiple sources and be stored in multiple formats. To identify a specific file, metadata tags are used. This makes it easier for businesses to query results according to many different search parameters.
The idea behind having a large storage depository of raw data is that you can use different data processing tools to analyze, categorize, and even extract information according to your specific needs. In other words, you have more freedom to query subsets of data and obtain unique insights about your business.
The Benefits of Having a Data Lake
You may wonder how a data lake can give you a competitive edge. Here are some benefits your business can enjoy by using a data lake.
1. Have more control over data analysis
Data analysis is now slowly shifting towards intelligence analytics. Intelligent analytics refers to using a more comprehensive and effective approach to data analysis- with the ultimate goal of gaining relevant insights and improving business performance.
Data lakes are an important step toward intelligent analytics. Having your business data in a common pool allows you to use complex algorithms and analytical tools to sift through large amounts of data (and unlock valuable insights).
The data contained in a data lake can be derived from multiple sources- including mobile apps, social media, IoT devices, supplier systems, and much more. Therefore, you have more control over analyzing this data according to your established objectives. You’re also less limited by statistical biases, human error, and data timelines (such as historical vs. current data).
2. Reduce costs
Data lakes also make managing large amounts of data easier and more efficient. As opposed to setting parameters for incoming data (as you would have to do with a data warehouse), a data lake allows you to converge multiple sources and file formats at a lower cost.
From XML files to sensory data collected by IoT devices, you can conveniently store this information in a common pool. The process is more cost-efficient and can easily retrieve individual pieces of data by using tags.
3. Make the data security and auditing process easier
You will also have an easier time securing data that is stored in data lakes. This is because you can efficiently structure how personnel accesses various subsets of information when needed. You can also conduct regular audits using queries and data mining tools, thus ensuring data integrity.
4. Make scalability easier
Data lakes also enable you to scale up your data analysis processes. You can now analyze large amounts of data to facilitate machine learning, which drives automation and growth. Generating valuable insights from data is also key to better performance and new business opportunities.