Data Lake: Improve Business Operational Efficiency

Technological solutions in this modern era are very profitable for businesses, one of which is a data lake solution. On this occasion we will provide a little brief information about data lakes such as definitions, definitions, benefits, and also advantages and disadvantages. 

What are the advantages if a business uses a data lake platform? And whether it can help more effectively in the development of a business. We will try to discuss it further below. 

What Are Data Lakes?

Data lake is a centralized storage that allows to store all types of data both structured and unstructured on a large and small scale. Data lakes can store data as is without having to do data preparation first. Usually, data lake platforms provide visualization of the results of big data processing with certain algorithms in real time. With the results of this data processing, businesses can make decisions for business strategies quickly, effectively, and efficiently. 

What Are the Benefits of a Data Lake for Business?

Businesses that succeed in getting development strategies from data will be ahead of their competitors. According to several survey sources from Aberdeen, companies and businesses that use data lake solutions are more successful in growing in terms of organic revenue by 9% compared to competitors. These businesses are able to obtain new types of analysis from various data sources including log files, social media, and internet devices connected to data lake platform solutions. 

From data stored using a data lake solution, businesses will identify the collected data faster and more efficiently generate business decisions based on the analysis of the data that has been collected. This will provide opportunities for faster business growth because it can retain customers, attract customers, increase productivity. 

Data Lake vs. Data Warehouse

Every business has a different characteristic so that the decision to use a data lake or data warehouse is sometimes quite confusing. 

A data warehouse is a database that is optimized for analyzing data relations from transactional systems in business applications. The data structure, and data schema are defined before the data storage process so that they can be optimized when using data such as using SQL queries. 

Meanwhile, the data lake is slightly different. A data lake is a storage of all types of data whether relational or not from various applications, devices, and even social media. The data structure as well as the schema are not defined during data retrieval. 

The following are brief differences between data lake and data warehouse: 

1. Characteristics of Data A

data warehouse has the relational characteristics of data from transaction systems, operations, from a business application. As for the Data Lake, all data is relational and does not come from IoT devices, websites, mobile applications, social media, to other business applications. 

2. Data Schema

Data Warehouse schema has a predetermined data schema, while the data lake is generated directly from the analysis process in real time. 

3. Performance

Data warehouse has a query speed that consumes more resources from storage compared to data lakes. 

4. Data Quality

In terms of data quality, the data warehouse is slightly more accurate because it has been filtered beforehand, while the data lake is raw data that is not filtered first. 

5. Users and Users The

data warehouse is widely used by businesses, especially for business analysts, while data lakes are usually used by data scientists, data developers, and business analysts to be more accurate in results. 

6. Analytics 

data warehouse has batch reporting capabilities, as well as Business Intelligence and visualization, while the data lake uses several advantages such as machine learning, predictive analytics, data discovery and profiling. 

Architecture and Elements In Data Lake Solutions

are several elements that must be considered before implementing data lake solutions in a business. The following are some of the architectures and elements to watch out for. 

1. Data Movement

The data lake allows data movement such as importing large amounts of data in real time. This collected data coming from various sources is transferred into the data lake platform in its native format. This process allows us to provide scalability of any size and save time for generating analyzes by cutting time from data structures, schemas, and transformations. 

2. Secure Storage System With Data Catalog Data

lake solutions allow for secure data storage such as databases from various business applications and devices. The data lake has an algorithm that is able to understand what data is in the data lake using a data scraping, with data cataloging and indexing. All these processes are protected with a security architecture to ensure the stored data remains safe. 

3. Analytics

Facilitate roles such as data scientists, business analysts to access data with analysis tools to produce accurate information. Several choices of framework tools that can be used, such as Apache Hadoop, Presto, and Apache Spark, are usually the main data choices. Data lakes make it easy for us to run analysis without having to move data to a specific analysis platform. 

4. Machine Learning

The last element of the data lake is machine learning that makes it easy for companies and businesses to generate various types of insights including reporting from historical data and processing it with machine learning models to predict the desired output. 

What Are the Benefits of Data Lakes?

The ability to process more data, from more sources, in less time makes data lakes very useful for businesses. The following are some of the benefits of data lake solutions for business people. 

1. Increasing Customer Interaction

On various occasions the data lake can be combined with various other applications and frameworks including CRM. If you add data from social media, as well as other business applications such as purchase history, then we can find out what customer interactions are like with our business. From this data we can make strategies to increase customer interaction and loyalty to our business. 

2. Improving Product Innovation

The research and development team will be greatly helped by data lake solutions because of course access to product testing will be more complete. The team can look at various data to produce the right product performance for the target audience. 

3. Improve Operational Efficiency

Data lakes make it easier to store data, run analyzes quickly and efficiently, shortening time without sacrificing quality. By using a data lake solution we can also reduce operational costs significantly but still get optimal results.