Combining All Data To See Opportunities
In every organization, both managed and unmanaged data exist. Most of them are disconnected. Think of the business processes and the software applications. They are closely connected, but their relationships and interdependencies are often unclear. Therefore, the impact of changing either one is not transparent.
Dragon1 enterprise transformation platform helps to combine all of these datasets, so you see opportunities that you as a data miner did not or even knew of before.
Common categories of sets of disconnected data are:
- Strategic Data
- Operational Enterprise Data (i.e., Process Logs)
- Concepts and Principles (Literature, References)
- Norms, Values, Legislation, and Standards (Benchmarks)
- Solutions by Vendors
- Solution Requirements
- Transformation Data
- Reference
- Current Situation (CMDB)
- Audits and Lessons Learned
Imagine all the improvement and innovation you would see if you had all of this data managed and connected in your organization.
What is Data Mining?
Data mining is the computing process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. It is an interdisciplinary subfield of computer science. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.
Read more about the Data Mining definition here.
Techniques for Data Mining
Data Mining: Follow these four steps.
1. Data Sources
First, you define and set up your data sources. Ensure you are familiar with the data source holders and the data quality. Make sure you agree on a format in which the data is extracted or delivered from the data source.
In the Import module of Dragon1, you can define a list of data sources from which you get your data. And you define how the data is imported: manually, scheduled, or digitally (via SOAP/WSDL interfaces or REST APIS)
2. Data Collections
After defining your data sources, it is time to collect and gather data from these sources so you have something to work with. Here, you use the Import module to import the data manually or by schedule.
You collect data using the Import module, and you enrich the data using the Architecture Repository web application.
3. Data Modeling
I. Models
Ok. You have now imported both managed and unmanaged datasets. Perhaps because of your work, data sets have become higher quality. This is what attention does.
Modeling means relating data: what is related to what. Suppose you know which servers support an application, which applications support a process, which process produces a product, and which product is bought by clients. In that case, you know what products can be produced and sold if a certain server functions correctly. And one million other things, of course.
Screenshot of the Import module. You can import any data from any data source.
Modeling is done using the Visual Designer. You can create, design, or draw meta models and next-user models. A metamodel is like the language rules, and a user model is like a story written in that language. With your meta models, you can test the quality of your user models.
An example is: If you draw in your product metamodel that every product in your company may only use fair-trade materials, you can test every product module to see if non-fair-trade materials are used. Alternatively, you may only work with certified suppliers for specific materials. Also, this can be checked.
How well you do this job depends on your knowledge of meta models. However, Dragon1 also has some reference models and templates to help you get started.
II. Views
A unique feature is that you can filter models. This is called creating views. Suppose a financial person is only interested in the financial data of a product model but not in the process data of the product model; you then create a financial view of the product model. This makes the financial person more likely to use the model to support his decision-making.
Creating views is done in the Visual Designer.
III. Visualizations
But there is more. After creating views, you can link any number of views to a visualization (a canvas or a template) and decide which symbols are used to show the data in the view or the model. You may want to visualize financial data using a relevant financial icon to make the information more effective and engaging.
Creating visualizations is done in the Visual Designer.
IV. Business Rules and Visual Indicators
Suppose there are patterns and rules you want to discover. Why not have Dragon1 help you see them? You can define patterns, rules, and visual indicators to indicate whether patterns are present in a visualization and whether business rules are followed or breached.
Working with visual indicators is done in the Visual Designer.
Deploying Data Models
After creating wonderful, good-looking visualizations, views, and models, people need to take action with them.
By publishing the data models in the Dragon1 Viewer, you enable people to access them and make decisions with them.
Screenshot of an example Enterprise Architecture Visualization published in the Viewer. This is where users can slice and dice the visualizations and see patterns.
Start using Dragon1 for Data Mining
If you want to use the Data Engine in your company, do not hesitate to contact our sales via phone or email.