The process of Data Modeling is playing an increasingly important role when creating or improving a Data Governance program. Data Governance has become extremely complex, and the use of Data Modeling promotes understanding. One basic reason for the increasing complexity is the expanding use of data analytics for purposes of research. Another reason is complying with the laws and regulations that have been developed for internet businesses.
A data model displays a simplified, symbolic representation of how data flows through a data system, and how the organization classifies and uses the data.
Data Governance has become the heart of the organization’s data flow. It is used to set internal standards – data policies – for determining how the organization’s data is collected, saved, processed, and eliminated. It limits who has access to certain kinds of data and can enforce compliance with the standards and regulations set by government agencies. Data Governance ensures the data is usable, available, and secure. It can also be used to:
- Gather high-quality data: A good data model should promote the collection of high-quality data from a variety of sources.
- Make better decisions, faster: Identifying problems and trends becomes simpler, promoting less confusion and faster decision-making.
- Enhance regulatory compliance: Respecting people’s privacy and avoiding legal fines has become quite important. Good Data Governance helps avoid the risks of non-compliance with regulations.
- Reduce costs: Data Governance helps to manage resources more efficiently by eliminating data duplication and reducing errors and mistakes.
Using a data model in developing or improving a Data Governance program helps with defining and analyzing a business’s data needs.
The visualization offered by a data flow model simplifies the complexity of an organization’s data flow. Because Data Governance includes changing the workplace culture, a data model showing the flow of data throughout the organization is, in fact, representing the Data Governance program in its entirety. (While Database Management is a separate system, the two should be supporting one another. If there is master data management software, it is normally a part of the Data Governance program.) A good data model will display the types of data that are used and stored, the relationships shared by the data, and ways the data may be organized.
Automation plays an important role in the Data Governance process and should be included in the data model.
The Benefits of Data Modeling
A data model is often a visual representation of the organization’s entire data system (or perhaps a smaller part of the system) and is used to communicate improvements that will be made (or, initially, to determine problem areas that need improvement). Data models should be designed with the business’s needs in mind. Rules and requirements can be integrated into the model’s design for a new system or alter an existing one.
Data models can also promote collaboration between departments and research teams, because other people are made aware of any problems a department has with data flow issues. (The data model initiates conversations.)
While data models are often based on standardized schemas, the model’s designers must be flexible enough to adapt the model. It should present an accurate model of the business, rather than a frozen, nonevolving version. The model can be used to support a consistent way of managing data throughout the organization.
Data Modeling supports effective Data Governance, as well as other positive outcomes, including:
- Improves database and software performance
- Simplifies data mapping
- Improves communication between departments
- Reduces errors during software development
Making data understandable increases the value of the data. Profits may increase after developing a data model as more and more opportunities for savings and sales are realized. Data Modeling supports both the infrastructure needed for metadata management and the Data Governance program.
Metadata and the Data Governance Program
The integration of metadata into the modeling process helps to streamline developing Data Governance programs and business intelligence initiatives.
Metadata is an important aspect of Data Governance and should be included in the Data Governance model. The data model can be used in visualizing the most effective use of metadata and harnessing its strengths. Governing data efficiently and developing business intelligence depends on efficient metadata management.
Data Governance defines the rules that must be followed as the data moves through the organization. Metadata, a labeling system that helps in finding the data, is used in this process and is technically necessary to locate the data. Data Governance can use metadata to enforce the rules used to collect and manage the data.
Metadata supports Data Governance policies and access to the data. It is critical to an efficiently operated Data Governance program.
The term “metadata management” describes the use of metadata within an organization to promote the efficient handling of data. It supports the collection of high-quality data through the use of automation. The use of automated metadata management allows data inconsistencies to be captured in real time, assisting in improving the overall quality of the data.
Automated Data Processes
By automating its data processes, an organization can improve its accuracy levels significantly. For example, automated metadata management will gather metadata from a variety of data sources, and will also map all the data sources. These automated processes should, of course, be displayed on the data model.
The use of automated and repeatable Data Governance processes can promote more efficient productivity and reduce costs.
Automation can be used to comply with privacy laws and data regulations. The GDPR (General Data Protection Regulation), the HIPAA (Health Insurance and Portability Accountability Act), and the CCPA (California Consumer Privacy Act) must be complied with when doing business with citizens or organizations residing in the state or country that implemented them. The use of automation can ensure sensitive data will be flagged and tagged automatically.
Modern Data Modeling
When creating the data model, or diagram, there are essentially two techniques: Data Modeling software and whiteboards. (A combination of both can be ideal.) The advantage of a whiteboard is it’s big, generally publicly available to staff, and easy to work with. (For technology enthusiasts, a very large “smart” TV could serve the same purpose.)
If software is used in creating the data model, there are primarily two diagramming tools: Unified Modeling Language (UML) and Entity Relationship Diagram (ERD). ERDs are a model used for databases. (That’s not the one you want.) You want the UML, which includes a broad range of model types. If the goal is to develop a data model that shows the flow of data throughout the organization, avoid ERDs.
Examples of data models that can be applied to the whiteboard, and then tweaked, filled out, and detailed, are offered by Visual Paradigm, as well as free software. Some other popular software for developing data models include:
- Open ModelSphere, which is open source. This is a UML modeling tool with a great deal of flexibility.
- Enterprise Architect, a software tool that supports “enterprise” Data Modeling. It is based on object-oriented languages and standards.
- Lucidchart, which allows flowcharts and diagrams to be created online, can be quite useful. (No download required.)
Data Modeling often moves through three phases. The process typically starts with the conceptual model, progresses to the logical model, and finishes with the physical model. (This process has traditionally been applied to database models but can be applied to other models for learning purposes.)
The Future of Data Modeling
Over the last few years, Data Governance and metadata management have grown significantly in importance. As their importance has grown, the value of Data Modeling has also grown, but, unfortunately, not its use. We can anticipate data models becoming a standard feature in organizations that work with data.
The process of Data Modeling, with all data flowing through the Data Governance program, will promote the use of automation. Management will see where the problems lie, and install the appropriate automated services, in turn minimizing human error and accomplishing the tasks much more quickly. Without the use of a realistic data model, an organization risks making poor decisions in how it handles its data.
Machine learning and artificial intelligence can also be expected to take a greater role in automation, metadata management, and Data Modeling. At some point in the next decade or two, artificial intelligence will be used to create an organization’s data models, which will then be approved by humans.