Data architecture is the system behind how an organization collects, stores, and integrates data.
All organizations should have a structure for their datasets to ensure efficiency in handling and manipulating them. Some of the advantages of having structured datasets include:
- Availability: Datasets are available to use and are in the format required by those who need them within the organization.
- Order: The datasets are in the proper order for manipulation and execution in the data repository.
- Data integrity: Datasets are accurate, complete, and consistent with required standards or guidelines. Compliance teams within the organization or regulatory bodies, such as GDPR, provide these guidelines.
But to benefit from these advantages, you need the right resources to support your organization's data architecture.
In this post, you'll learn about the people, the processes, and the technology behind data architecture, along with the best practices to implement.
Who are the people involved in data architecture?
The people involved in data architecture create, use, and manipulate data entities. Some of the roles that support data architecture include:
- Data architects: Data architects are responsible for creating the structure for the datasets. They guide data collection, storage, and integration and take charge of how data flows within the data/information cycle of an organization. Data architects also need to understand the technology and data needs within their organization because their job is to translate these needs into a structure that fits the data/information flow within the organization.
- Data engineers: Data engineers carry out tasks such as extracting, transforming, and loading (ETL) datasets. They conduct ETL on the datasets from the different data silos to a single source of data. They also manipulate datasets for purposes such as updating or deleting unwanted data.
- Data analysts: Data analysts are responsible for cleaning or wrangling datasets. They use tools like Python to clean these datasets. They also gather insights from data using analytics and visualization tools to create reports from these datasets. Essentially, a data analyst is a user of data architecture.
- Solutions architects: They build products or services based on the organization's business requirements. They work with both the business and technology teams to understand the needs of both parties and then build products or services based on those needs.
Other people who might be involved with data architecture
Organizations with more complex data architecture needs may require more people to create their structures.
These people include:
- Cloud architects: Cloud architects ensure that the cloud technology deployed to house datasets provides optimal performance.
- Information security analysts: They ensure there is no breach in stored data.
- Network engineers: Network engineers ensure that data is available and accessible to relevant stakeholders.
- Project managers: Project managers ensure that the data architecture planning and building processes work according to plan.
How these people build an efficient data architecture system
Now that you know who is involved with data architecture: let's look at how they work together to build an efficient data architecture system.
I'll use a financial institution as an example system.
To begin with, the data architects oversee how the organization stores "customer transaction data."
In addition, they work with data engineers to ensure the storage and availability of all necessary transaction data in a data warehouse.
The solutions architect ensures that the products meet the customer's needs.
They also work with both the data architect and the data engineer to ensure the database used to store the information about this product is available on the central repository.
Additionally, when management needs to make strategic decisions, data analysts will work with the data engineer to ensure that datasets for reporting and insights get stored in the required format.
The processes behind data architecture standardization
We've covered how people play different roles to create efficient data architecture, but there are also processes to define how these people interact with the data architecture.
These processes ensure that the ways datasets are collected, organized, integrated, and maintained are standardized:
- Manipulation and handling of data entities: This process defines how and where you create, store, transport, and report data entities. Data should only get manipulated when necessary and by personnel with an appropriate access level. Some examples of data entities are tables, procedures, and models.
- Data governance policy: When implemented, a policy document on data architecture should ensure a standardized process for data collection, storage, transformation, distribution, and consumption. This policy document should also include a policy on information access and control. The information access and control policy ensures that data is only accessible to individuals who should have access to datasets. Other policies that this document should include are data quality management and data standards and processes policies.
- Procedure for data infrastructure acquisition: This is the process an organization uses to get data infrastructure. All infrastructures should be within the budget and meet the data needs of the organization. Additionally, they should ensure efficiency in the organization's data architecture. Some examples of these infrastructures are database servers and network systems.
- Data integration and support: Lastly, you should adequately train staff handling datasets on the technology used for the data architecture.
What technology gets used to implement data architecture?
The final component of an efficient data architecture is technology. The specifications of the data infrastructure or technology vary according to the requirements of the organization.
These infrastructures consist of the following:
- Data warehouse: This is the central repository of all the databases, business intelligence, analytics, and reporting tools. Some examples of data warehouses are Panoply, Microsoft Azure, and Teradata. Panoply offers a cloud-based infrastructure for organizations to sync, store, and access data.
- Databases: A database, in the simplest terms, is a collection of data. Databases can be either relational or non-relational (SQL vs. NoSQL).
- Relational (SQL) databases: They store structured data in tables organized in rows and columns. You use SQL to manipulate these datasets. Examples of relational databases are Oracle DB, Microsoft SQL Server, and MySQL Server.
- Non-relational (NoSQL) databases: They store semi-structured or unstructured data. Additionally, you use programming languages to manipulate these datasets. Examples are MongoDB and Cassandra DB.
- ETL tools: They collect and refine data from different sources and deliver them to the data warehouse in three stages. ETL stands for "Extract, Transform, Load."
- First, you extract data from various sources.
- The transform stage involves sub-processes that include data cleaning, standardization, verification, and quality management. This stage ensures that the datasets meet requirements for data quality and reporting.
- Last, you load the transformed datasets to the repository. Microsoft SQL Server Integration Services (SSIS) and Panoply are examples of some of the best ETL tools.
- Data modeling tools: They define data flow and relationships. An example is Oracle SQL Developer Data Modeler.
- Data analytics, visualization, and reporting tools: These tools are suitable for gaining insights from datasets using visuals such as charts, maps, and tables. Data analysts use these tools to create dashboards and reports to help management make informed decisions. Microsoft Power BI, Tableau, and Qlikview are examples of good data visualization tools.
How technology contributes to an efficient data architecture
The people we discussed earlier use the technologies and processes above to build an efficient data architecture in an organization.
Let's return to our previous example to see how people, processes, and technology work together in a financial institution.
- First, the data engineer conducts ETL on historical data to assemble it in a single repository.
- Next, the data analyst uses a suitable data reporting tool to provide insights to managers on how customers have previously interacted with an existing product.
- Then, the solutions architect improves existing products by collecting more customer data, which they can use to provide better services.
However, to ensure that the people, processes, and technology I discussed above perform efficiently in an organization, you need to put some best practices in place.
Data architecture best practices
When creating an efficient data architecture, you should follow these best practices:
- Data should not exist in silos: The goal of creating a data architecture is to ensure that datasets are in a central repository. There should also be a flow of information between these datasets.
- Standardize data entity creation: Follow the highest standards when creating data entities. For instance, apply constraints like primary keys and null allowances for relational datasets.
- Use entity-relationship diagrams (ERDs): They help you create and understand the relationships between data entities. Thus, they should be a part of the standard procedure for datasets in relational databases.
- Update data architecture and ERDs: As you create data entities, update the existing data architecture and ERDs.
- Create a data architecture document: As stated earlier, create a data architecture document. Furthermore, a compliance team should review this document regularly to keep it up-to-date.
- Make data structures consistent: The data structure in the repository should be consistent with data visualization and reporting requirements. Also, the data architect needs to ensure that there is consistency in the different data flows and architecture for the various business products and services used within the organization.
- Automate the ETL process: Data is automatically loaded to your data warehouse when you automate the ETL process.
Summing up data architecture
Your organization's people, processes, and technology need to work together to ensure an efficient data architecture. Furthermore, there needs to be strict adherence to the best practices listed above.
One thing I would suggest is to implement an appropriate data governance policy. If implemented and adhered to, it would benefit the entire data architecture process.
If you need a platform that can help you implement these practices within your organization, you should check out Panoply.
Panoply is a great platform that can store your data in a central cloud repository to meet the needs of the data engineers, data architects, and cloud architects in your organization.
It also allows data analysts to access datasets that are readily available for analysis.