Data Solutions

We specialize in building powerful and scalable data analytics solutions that empower businesses to extract actionable insights from their data. Our expertise covers everything from data management, integration, and cleaning to building advanced analytics platforms for informed decision-making. Whether it’s creating Data Management Systems (DMS), Customer Data Platforms (CDP), or leveraging AI and machine learning for data prediction, we provide end-to-end solutions that help organizations maximize the value of their data.

We focus on integrating big data technologies, cloud services, and advanced analytics tools to design systems that handle large volumes of data while ensuring scalability, security, and real-time performance.

ㅤ

【 Data Collection 】

As a first step, it is necessary to consolidate all company data in one place.
While some companies may already have centralized data, in many cases, systems are developed separately for each business function, resulting in data being stored in different locations.
An infrastructure (container) must be prepared to store the data in one place, and a mechanism must be built to integrate the data.

Some data may be linked in real time, while others may be updated every hour, daily, or even monthly.
Such data must be organized and gathered into a central repository.

ㅤ

【 Data Cleansing 】

The process of organizing the collected data is called data cleansing.
Data collected from various systems is often inconsistent. Below are two examples of data cleansing:

1. Time Format:
   In one system, the time might be stored as "2025-06-26 12:43:20", while in another, it might be stored as "June 26, 2025 at 12:43:20 PM".
   These inconsistencies prevent accurate analysis, so the data must be standardized—e.g., to the format "2025-06-26 12:43:20".
2. Age:
   Even if someone was registered as "20 years old", they will become "21 years old" the following year.
   Using age as-is in data analysis would result in outdated insights.
   In such cases, it's necessary to calculate the current age from the date of birth dynamically.

   ㅤ

【 Data Integration 】

Once the data has been cleansed, the next step is integration, also known as record matching (名寄せ).

For example, if user A has ID "001" in one system and "abcd" in another, they are actually the same person, but when analyzing based on ID, they may be treated as two different individuals.
To avoid this, a common identifier needs to be found.
If both systems contain the user's email address, it can be used as a common key to link the data.
This allows for a more accurate understanding of user A’s behavior.

ㅤ

【 Data Utilization 】

After cleansing and integration, the data can then be put to practical use.
This includes data aggregation, analysis, condition-based extraction (target segmentation), and visualization.

• Data aggregation involves summarizing data based on certain conditions.
  For example, aggregating the number of purchases of product A by location over the past 3 months.
• Analysis may involve applying machine learning (ML) or generative AI to predict purchases, forecast inventory, or perform data mining.
  This can lead to insights such as discovering correlations between product A and a specific location.
• Condition-based extraction (target segmentation) is used to isolate users based on specific conditions—for example, for sending targeted email campaigns.
• Visualization uses BI tools to represent data clearly through tables, charts, and graphs.

ㅤ

【 Conclusion 】

By carrying out these initiatives, companies can make effective use of internal data (information assets) to support and enhance their business activities.

ㅤ

ㅤ

‏‎