“Data-driven companies leveraging the insights of their entire organization and implementing them to create competitive advantage are growing an average of more than 30% of annual growth rate and expected to achieve $ 1.8 trillion revenue by 2021.” Quoted from Forrester.
Today, many companies are struggling to become data driven. We all know the importance of using data to help us make better business decisions.
Although many organizations have adopted business intelligence and hired data scientists to achieve data-driven, they still face a lot of problems.
How to Become A Data-Driven Company
The company commonly faced:
- “I have many type and volume of data in my company”
- “I can’t take the real-time data of other departments”
- “I don’t have enough resource, time and talent”
How to fixe those problems?
A data lake can provide a big competitive advantage for an organization. We believe that the end goal of every organization is becoming a data-driven organization. Today, some companies have announced their intentions of doing data analytics.
and AI/ML. Some organizations simply don’t have any idea to start this work. If the organization wants to do data analytics or AI/ML, the first thing is they must break down the data silo, shown in Figure 1. The data lake can help to solve the problem of data silo.
Figure 1: Traditional Data Warehouse Approaches Don’t Scale
The Power of Data Lake
Companies like the data lake. It is because data is loaded in “raw” format, rather than pre-configured when entering the company’s system, their use is not limited to basic capture. The data lake is characterized by three key attributes:
- The data lake can collect everything: Data Lake contains all data, both raw sources over extended periods of time as well as any processed data.
- The data lake can dive in anywhere: Data Lake enables users across multiple business units to refine, explore and enrich data on their terms.
- The data lake is flexible access: Data Lake enables multiple data to access patterns across a shared infrastructure: batch, interactive, online, search, in-memory, and other processing engines.
The benefits as below:
- Single source of truth
All the dependents can store their raw data in the data lake. The data lake does not need to define data through the architecture without a difficult process. As a result, everyone can get the most real data using a data lake.
- Real-time decision analysis
With the huge processing power of the data lake, the user can use tools to ensure the high quality of the data to arrive at real-time decision analytics.
- Data democratization
Data democratization means that everybody has access to data. The data lake makes data available to the entire organization. Every user is empowered to access any and all organization data if they have the proper privileges.
Next Generation End-to-End Data Analytics Platform
After deploying 30+ data projects for our customers, eCloudvalley offers an end-to-end modular eCloudvalley Competency: approach to help companies to build their data analytical platform on AWS, shown in Figure 2.
Figure 2: build their data analytical platform on AWS
Successful Case Study: Aviation Analysis
Including domestic and international destinations, the customer offered 60+ destination with 3,000+ employees of the company.
- Multiple data systems from 15+ business units caused the issue of data silos.
- The great amount of data causes the difficulty when the customer tried to analyze the data using BI tool.
Solutions from eCloudvalley
- eCloudvalley used DLMS (Data Lake Management System) to help the customers to build a data platform with a single source of truth and remove the barrier of data silos.
- Through eCloudvalley DLMS, the customer built up data lake and enterprise data warehouse in only days.
- it helps customer to save up 80% of data processing time.
- With a unified platform, 15+ business units can access data from different departments and have real-time and complex analysis anytime.