Alteryx Success Stories
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SUBMISSION INSTRUCTIONSKB Securities Merges Several Databases to Analyze Customer Data
Overview of Use Case
KB Securities is one of the biggest investment bank in South Korea. Their departments of Data Analytics and Data Platform needed to integrate multiple databases and analyze data in various formats, including Json. Different people in the teams were using different coding language which slowed the process down considerably. They used Alteryx Designer and Server to integrate more than eight databases (Teradata, Oracle, AWD, Hadoop, etc.) and automate many processes like ETL, Prep & Blend (Json Parsing) and scheduling which saved hundreds of hours.
KB Securities is one of the subsidiaries of KB Financial Group, Inc. In 2016, through the merger between Hyundai Securities with core capabilities in WM and S&T businesses and KB Investment & Securities with excellent capabilities in IB and Wholesale businesses, KB Securities has secured a well-balanced business portfolio in all business units. Designated as one of the five largest IBs (aka the Big 5 securities firms) including Mirae Asset Daewoo Securities, NH Investment & Securities, Korea Investment & Securities, and Samsung Securities, KB Securities is a supersized securities firm with more than $4 trillion won in capital equity.
- We carried out a “Big Data Platform Implementation Project” to evolve into a data-driven organization. Before building the big data platform, we built a different database and used it for each project, and this caused us to operate with a wide variety of data sources. It was difficult to efficiently utilize data structurally in the organization.
- In order to solve this problem, we carried out the “Big Data Platform Implementation Project” that migrates independently implemented Databases (Teradata, Oracle, AWS, SQL Server, Splunk) to the new Database (Hadoop, Hive, Impala) to integrate and systematically manage data.
- The “Big Data Platform Implementation Project” is not just an ETL process but contains all processes for transformation to a data-based organization.
- Among them, the key challenges included (1) analyzing customers’ behaviors in mobile media, (2) analyzing the use of screens by customer/in-house media, and (3) analyzing customers’ search intentions after pre-processing log data occurring from KB Securities’ mobile app. During implementation, the biggest problems were ETL, Prep & Blend (Json Parsing), and scheduling, all which were effectively solved through Alteryx.
- Department of Data Analytics – In the past, they analyzed data from several databases using Python, R, and Teradata. Each member uses a different coding language, and the recently increasing data volumes made the processing speed very slow; the complicated coding also required much more time. Alteryx has enabled us to build logics uniformly regardless of programing languages and has allowed for easy maintenance. Besides, it has improved work productivity at a rapid speed without limiting the amount of data. Above all, Alteryx is helpful for those who do not know much about coding in approaching and handling big data and improving productivity. Ultimately, the mid- and long-term objective is to enable statistical analysis as well as data analysis using AI and machine learning.
- Department of Data Platform – They spent too much time to manage a variety of databases, since they each use a different query syntax and encounter different errors. The code-free and code-friendly functionality of Alteryx has made database management easier. We intend to improve productivity through data management automation by using server scheduling in the future.
At present, we are improving the UI/UX of user media and streamlining marketing and internal tasks and affairs. First, KB Securities uses it mainly for pre-processing and ETL. In particular, it was effective in processing Nested JSON Data. It has many advantages including ease of use and high productivity compared to other solutions.
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|
Required time in the past |
Required time after adoption |
|
ETL |
NA (No ETL job before) |
Significant Productivity Enhancements |
|
Prep & Blend |
8 hours |
3 hours |
|
Scheduling |
NA (No Scheduling job before) |
Significant Productivity Enhancements |
In the future, our goal is to automate analytics using AI and machine learning.
