Why Morph?

Morph is a data workspace focused on utilizing data rather than just storing it.

While the importance of collecting and storing data is well understood and many companies operate data warehouses and databases, effectively utilizing the collected data is not an easy task.

In an era where important business data is collected not quarterly but daily or even hourly, where decisions are required on a daily or weekly basis, it is essential to quickly build a flexible data utilization foundation and understand the true meaning of the data.

This section explains why Morph is the best tool for agile data analysis and insight extraction, and deepening understanding of data using AI.

Challenges of Traditional Data Tools

Traditional data analysis tools and BI tools have the following issues:

1. High learning costs and long construction times: Many data tools require high learning costs as they use their own extended programming languages or have very complex settings. Also, building these tools requires engineers, and time for construction must be considered.
2. Sluggish response: Suppose you finally complete construction of your data pipeline and gain insights from the data. However, if new metrics need to be monitored from those learnings, you will have to rebuild the analysis flow and dashboard from scratch. You will need to get the engineers back to reflect the business department's requirements into the dashboard.
3. Complex workarounds: When trying to do more than the tool's capabilities to meet real business requirements, the problems become complex. In many cases, you will need to ask engineers to build workarounds.

In other words, it is like a waterfall model. If you can spend weeks or months on the sequence of requirements definition, design, construction, and operational testing, and then use what you have built for years, this might be fine.

However, in an era of rapidly changing business environments, don't you feel that a faster data utilization cycle is needed for data-driven decision-making?

Agile and Flexible Data Analysis and BI

While the agile approach is widely adopted in software development, it can also be applied to data analysis and business intelligence (BI).

Benefits of the Agile Approach

1. Quick feedback loop: The agile methodology allows for quick feedback by repeating short iterations (sprints). This enables you to verify analysis results immediately and make adjustments as needed.
2. User-centered development: Agile progresses based on user needs, allowing data analysis tailored to specific business requirements. This ensures that users quickly obtain the necessary information, facilitating smooth decision-making.
3. Flexibility: The agile approach can flexibly respond to changes in the business environment and requirements. Adding new data sources or changing analysis methods can be done quickly, enabling decisions based on the latest information.

Specific Methods of Agile Data Analysis

1. Incremental data collection and integration: Initially focus on the most important data sets and gradually add and integrate data. This provides early value while gradually expanding data coverage.
2. Continuous communication and collaboration: Frequently communicate within and outside the team and actively incorporate stakeholder feedback. Share progress and make necessary adjustments through regular meetings and review sessions.
3. Automation of data pipelines: Automate data collection, integration, cleaning, and analysis to reduce manual workload. This improves analysis efficiency and effectively implements the agile process.
4. Deployment to the operational environment: Smoothly deploy what has been tested in the sandbox environment to the operational environment. This allows you to quickly leverage the results of data analyses in the next sprint.

By adopting agile data analysis and BI, companies can quickly and flexibly derive value from data and enhance their business competitiveness. Morph providing an environment where users can obtain useable information right away.

Features of Morph

Complete Cloud Infrastructure for Data Utilization

Morph includes all the necessary cloud infrastructure for data analysis. This includes a cloud-based Postgres with advanced computing power, scalable storage solutions, and the management of directed graph models for building data pipelines. Users can easily utilize this infrastructure and quickly start data analysis.

SQL and Python Support

Morph supports both SQL and Python, the main languages for data analysis. This allows users to choose the optimal language according to their skill set and efficiently perform data queries and analysis. Simple queries can be executed with SQL, and advanced data science and machine learning models can be built with Python. Moreover, Morph seamlessly integrates these languages, enabling easy data exchange between them.

Supports Various File Formats

Morph supports various file formats, including CSV, JSON, and Excel. This makes it easy to import data from different sources and manage it centrally. Users can integrate data of different formats and provide a unified view, greatly improving data analysis efficiency. Data export is also flexible, facilitating integration with other systems and tools.

Understanding Data with Morph AI

Morph features AI capabilities that deeply understand user data and assist in the analysis process. Morph AI automatically detects patterns and trends in the data and provides important insights. Additionally, the AI assistant supports users in creating queries and visualizing data, making complex analysis tasks easy to perform. This enables users to conduct advanced analysis and quickly obtain information useful for decision-making, even without specialized data knowledge.