Why Morph?
Morph is a data workspace that focuses on ‘using’ data rather than ‘storing’ it.
Today, the importance of collecting and storing data is well understood and many companies operate data warehouses and databases. However, making good use of the data collected is not an easy task.
In an age where critical business data is collected on a daily basis and new actions are required on a daily or weekly basis, it is essential to build a quick, flexible, data utilization infrastructure to understand the true meaning of data.
This section confirms the importance of incorporating data analysis and insight extraction in an agile manner to your workflow, and using the power of AI to better understand data… and why Morph is the best tool for such workflows.
Challenges of traditional data tools
Traditional data analysis and BI tools have:
- High learning costs and long build times. Many data tools require high learning costs, with proprietary extended programming languages and very complex configurations. They also require engineering to build and that must also be taken into account.
- Long lead times to make changes. Suppose you have finally completed the build and have gained insights from your data. But if new indicators emerge from the learnings that you want to monitor, you have to build the analysis flow and dashboards from scratch. You need to convene the engineers again and get the dashboard to reflect the requirements from the business unit.
- Complex workarounds. The problem becomes instantly more complex when you try to do more than what the tool has been designed specifically to do. In many cases, you will be forced to ask an engineer to build a workaround for you.
In other words, it is like a waterfall model. This may not be a problem if the sequence of requirements definition, design, construction, and operational testing can take weeks or months, and furthermore, if the system is built once and then used on an annual basis.
However, in an era of rapidly changing business environments, you may feel the need for faster data utilization cycles to make data-driven decisions.
Agile and flexible data analysis and BI
Agile approaches have been widely adopted in software development, but the mindset can also be applied to data analysis and business intelligence (BI).
Benefits of the agile approach
- Rapid feedback loop: agile methodologies allow for rapid feedback through a series of short iterations (sprints). This allows immediate validation of the results of the analysis and, if necessary, a change of direction.
- User-centred development: agility allows development to be based on the needs of the user, so that data analysis can be tailored to the specific requirements of the business scene. This enables users to quickly obtain the information they need and make decisions more smoothly.
- Flexibility: an agile approach can be flexible as the business environment and requirements change. New data sources can be added and analysis methods can be changed quickly, so that decisions are always based on the latest information.
Specific methods of agile data analysis
- Incremental data collection and integration: focus on the most important data sets in the early stages and adopt a phased approach to adding and integrating data. This allows for gradual expansion of data coverage while providing value early on.
- Continuous communication and collaboration: communicate frequently within and outside the team and actively incorporate stakeholder feedback. Share progress and make necessary adjustments through regular meetings and review sessions.
- Data pipeline automation: automates data collection, integration, cleaning and analysis, reducing the burden of manual work. This improves the efficiency of analytical work and makes agile processes more effective.
- Deploy to the production environment: deploy what you have tried in the sandbox environment smoothly to the production environment. This allows the results of data analysis to be quickly utilised in the next sprint.
Agile data analytics and BI enable companies to extract value from their data more quickly and flexibly, giving their business a competitive edge, and Morph supports this approach, providing an environment where users can get the information they need in a timely manner.
Morph Features:
Complete cloud infrastructure for data utilization
Morph has all the cloud infrastructure needed for data analysis. This includes cloud-based Postgres providing advanced computing power, scalable storage solutions and management of directed graph models to build data pipelines. Users can easily utilise these infrastructures and start analysing data quickly.
SQL・Python
Morph supports both SQL and Python, the main languages of data analysis. This allows users to choose the language best suited to their own skill set and efficiently query and analyse data: they can perform simple queries using SQL or build advanced data science and machine learning models using Python. Furthermore, Morph seamlessly integrates these languages, allowing users to easily exchange data between different languages.
Support for all file formats
Morph supports a variety of file formats, including CSV, JSON and Excel. This allows data from different sources to be easily imported and centrally managed. Users can integrate data from different formats and provide a unified view, significantly increasing the efficiency of data analysis. Data can also be exported flexibly and easily integrated with other systems and tools.
Data Applications
Build custom data apps for flaexible, performant useage of your data.
Morph AI
Morph includes AI capabilities that help users gain a deeper understanding of their data and assist in the analysis process; Morph AI automatically detects patterns and trends in the data and provides key insights. In addition, an AI assistant helps users in creating queries and visualizing data, making it easier to perform complex analysis tasks. This allows users to perform sophisticated analyses and quickly obtain decision-useful information, even if they do not have specialist data knowledge.