Data Collection
An early step of each cloud transformation is to first get a full overview of your in-scope applications and their dependencies. Based on a proper data foundation of your applications, they can be assessed for cloud readiness with confidence and actually migrated. There are different ways in Txture to support collecting this data efficiently, hence speeding up the transformation process and enabling well-informed transformation decisions.
So far you have already considered the potential data sources and the stakeholders that can provide data on your applications and IT landscape. In this step, you will make use of these data sources to populate Txture with the available data. Always be aware the quality of the data you collect forms the basis for the quality of all subsequent activities, like cloud readiness assessments or the generation of target architectures.
1. What data is required?
For different phases of the transformation different data is required. The Assessment requires a different set of data than the Target Architecture phase.
The visualization below gives a glimpse into the data requirements per transformation phase. Not all data is always mandatorily required but the more data you have, the more accurate the results will be. Please note that the list below is not complete! You will get the full version of the Collected Data Report from your Txture customer/partner service.
2. The three steps of data collection
In Txture, you can use three main sources for data collection:
- Existing data sources, such as CMDBs, EAM tools or virtualization environments, etc.
- Crowdsourcing data from stakeholders via Txture's Assessment Surveys.
- Manual (graphical) modeling together with stakeholders, like application owners.
The recommended process for populating Txture with the necessary data is the following:
- Use existing data sources to initially populate the Txture Repository. New data sources and data collection methods can be added later as well.
- Crowdsourcing of missing data from application owners and other related stakeholders via the Surveys functionality. Either use Txture's default surveys or create your own to fill in custom data pieces.
- For each application assess the data completeness and your confidence. Set up Txture modeling workshops with the corresponding stakeholders to fill the gaps and to enhance the existing data.
For each application that has a sufficient data quality you may complete its data collection stage in the data collection phase of the Transformation Cockpit and continue with assessing its cloud readiness.
Step 1: Initial data ingestion by integrating existing data sources
In most IT organizations, some data is readily available. Txture can connect and synchronize various data sources and performs automatic mapping to the data model (structure). The imports can be can be scheduled to keep Txture up-to-date continuously. By matching the assets from different sources and combining them, Txture creates a central and consolidated report of your applications and IT landscape. This information forms the basis for the assessments and target architecture generation.
The following screenshot shows an excerpt of the data sources that Txture can handle. For more details about how to import data, please read our documentation about Importers.
Step 2: Crowdsourcing data with Assessment Surveys
If the imported data is insufficient or you lack data sources with good data quality, you can fill remaining gaps by collecting data from the relevant stakeholders.
To speed up gathering data from countless stakeholders, Txture offers the Assessment Surveys functionality.
Surveys are also a great way to easily evaluate what data has already been collected, as they are pre-populated with all the information that has already been discovered.
An in-depth description of the survey functionality can be found in the Survey section of the documentation. The screenshot below shows a survey in action.
Txture CT comes with pre-defined surveys already, but you can adapt them or add new, tailored ones as you need. Our standard surveys ask for the following details of an application:
- Business processes it supports and their criticality
- Types of data it processes (criticality, privacy-relevance, etc.)
- Interfaces to other applications
- Technical deployment stack
- Regulatory requirements
Step 3: Data fine tuning with manual modeling
Sometimes it is necessary to manually add missing information in collaboration with the application owners and hence enrich the data foundation about the application. As this is the slowest and most time-consuming way to gather information, it is usually only used to refine application data that is known to a large extend or if no data source for automation can be used.
Modeling workshops are typically done by using the Dependency Report. Depedency reports provide the necessary overview to manually work through the application dependencies. Also, it has similar usage patterns to other modeling or graph-drawing tools. The difference to classic modeling tools is that the modeled data is also stored in the central repository. This ensures that modeling takes place on up-to-date information. Additionally, the resulting visualizations can be shared as reports or for quality assurance.
The following screenshot shows the dependency report with which you can model the application architecture collaboratively.
Additionally, you can always search for specific assets in the repository and edit its information in the sidebar.
Want to learn more about Data Collection?
This page is intended to give you an idea of the data collection functionalities in Txture from an introductory point of view.
Find more details about how to use the Assessment feature in our dedicated documentation about Data Collection.
Also, have a look at our technical brief about Txture's Data Collection on our Resource page.
Once you have collected enough high-quality data on application and infrastructure landscape, you can proceed to the next step of calculating the assessment of the application.