Data architecture is a logically organized structure focusing on how data is collected, stored, distributed, secured, and used in an organization. It eliminates data silos in your tech stack, and empowers people to securely find and use relevant data.
In the past, data access required a tedious process—one in which a data engineer would manually write a custom script to deliver the request. This gap in time led to slower access to data, more pressure on personnel (data engineers and data architects) to deliver frequent internal requests, and an overall reduced velocity in business processes.
Thanks to automation, things have changed.
An example of data architecture in use today is in sales. Let’s say a sales representative wants access to customer billing information. Having the right data architecture in place allows them direct access to all data types instead of having to request it from the accounting team. This streamlined data flow means no wait time. No custom coding.
Data architecture accesses and organizes information from data pipelines, data processing, etc. to meet business and stakeholder demands. It solves problems in real time and provides necessary insights into business needs and ongoing strategies.
Data architecture should:
- Have scalability and high performance
- Support open data formats
- Allow seamless data movements
- Support diverse platforms and consumption needs
- Be secure and governed
Data architecture provides a framework for a data strategy to be implemented within an organization. This is done according to a consistent, logical set of underlying principles.
Data architecture principles
View data as a shared asset
It’s common for teams to focus on specific domains and have their own data warehouses. However, cross-team collaboration and data integration are also often necessary. This requires access to derived insights gleaned from other teams.
Data architecture shares real-time data from one data warehouse to another without duplication. Maintaining a single source ensures data integrity and provides easy access to data sets, showing their context and relevance to business functions.
Establish new norms using your existing tools
Teams use various tools to collect and share information, which leads to data fragmentation and data silos. Well-designed data architecture incorporates interfaces that your team already uses in their day-to-day operations. This reduces search time and removes the need for training and familiarization with dozens of tools—which becomes increasingly important as teams scale.
Use cases include SQL interfaces for data analysts, OLAP interfaces for business intelligence, and real-time APIs for targeting systems. The goal is to build around the asynchronous tools your team already uses rather than reflexively adopting new ones.
Ensure security and access controls
Privilege abuse, ignorance of policy, configuration mistakes, and poor auditing processes are all common IT security challenges—and all of them are mitigated through a robust data architecture that puts security first.
With the increasing adoption of unified data platforms such as Snowflake, Google BigQuery, and Amazon Redshift, more enterprises are baking security into their raw data from the ground up. This means using data governance tools and implementing user access control policies on the raw data, instead of on individual applications and data warehouses.
By automatically flagging suspicious activity and requiring additional user authorizations, enterprises can protect their data without having to allocate significant IT resources. For instance, sending push notifications to users’ registered mobile devices can easily verify their identity, whether they are trying to sign into email or other company platforms.
Simplify data hubs with a common vocabulary
As companies become more data-driven, data hubs are called on to help simplify data integration. To begin, you need to establish a common vocabulary to speed up data accessibility. Ensure all users are speaking the same language by centralizing all your requirements, definitions, policies, etc. in one format instead of incurring the overhead of separate documents.
For example, you need to establish common definitions for all your KPIs, product catalogs, feature descriptions, and modules. This way, instead of debating what each data source means, your users will all be on the same page.
Avoid data duplication and movement
Enterprises often move data from temporary data storage to siloed data warehouses and databases for analytical decision-making. Part of this process is to extract, transform, and load (or “ETL”) this data to put it into a more usable format.
The challenge is that, as data volume grows, these movements increase the risk of data loss and security threats. Data can also be inadvertently duplicated (or partially duplicated), which causes delays, inaccuracies, and increased costs.
For example, an individual from a marketing team could create a new record for a lead that is already present in the sales team’s database. This leads to confusion among the users who will access that data and wastes a lot of time.
The simplest solution is to minimize or eliminate the need to move data. By doing so, modern data architecture strengthens security, improves accuracy, and optimizes data agility in a cost-effective manner.
Tools for quick wins with data architecture
Segment is one of the best data layer and data management tools there is. It allows you to collect, unify, and route customer data to any system (~200 destinations) with little to no manual intervention. Teams can send coherent and consistent data to relational databases to track all the actions of any user. This helps them to better understand customer experiences.
Fivetran helps teams reliably replicate data from SaaS tools into their preferred destinations or cloud data warehouses. It does so using connectors that work as independent processes persisting for the duration of one update.
Fivetran’s connections install within minutes, need no maintenance, and automatically respond to changes in the source. When vendors modify schemas by adding or deleting columns, changing the type of data elements, or adding new tables, Fivetran’s connectors automatically adapt. An excellent mapping system means you can analyze maps both in real time and at scheduled intervals (i.e. when your complete data sets have been updated).
Fivetran also offers intuitive information accessibility permissions. This means it keeps the information secure by only authorizing access to appropriate users based on predetermined access controls.
Snowflake is a cloud data warehouse that acts as a single integrated system. It has the ability to automatically scale storage, analytics, or digital resources for any business function. On-demand cloud data warehousing makes it a competitive choice—you only pay for the time Snowflake is running.
Snowflake is fully automated—no need to worry about software updates, configuration, failures, or scaling your infrastructure as your data grows.
It supports modern features such as auto-scaling warehouse size, big data workloads, auto-suspend, and data sharing. This means your team can focus on making the most out of useful data instead of worrying about the underlying complex of data architecture development.
Snowflake makes aggregating, processing, managing, and sharing data easier across your business. When combined with a data lake, it can make your data readily available on Azure or Amazon S3, so your information is redundantly stored across multiple structured data lakes, facilities, and devices.
dbt simplifies the transformation part of your ETL or ELT workflow by working on the data stored in your data warehouse. Powered by SQL and YAML configuration files, dbt gives you data documentation, lineage, and a version-controlled way of writing transformations. It’s an extremely easy tool to understand, especially for a less technical data team members, allowing for increased shared data knowledge between engineering and non-engineering teams.
With its flexible data model, teams have full control over data refreshes if any changes are made to the underlying logic. This makes it easy to recreate, trace, update, and fix data in the pipeline instantly.
Tableau is a business intelligence software application for centralized data analysis and management. You can create visualizations of complex data, find highly specific information using relevant filters, and even specify a time interval to take snapshots of the dashboard.
With its granular access permissions, Tableau helps teams manage user roles. Authorized individuals can easily create, edit, view, publish, and share information (data sources, reports, and resources) across your organization without sacrificing security.
Tableau is also the ideal data visualization tool. With it you can create dashboards and generate easy-to-understand reports from trusted data sources. This allows anyone from your organization to make high-impact business decisions quickly.
Augmentation (Reverse ETL)
Hightouch is a reverse ETL tool. It helps companies synchronize their customer data from data warehouses, data lakes, or databases with 80+ SaaS destinations (like CRMs, Google Sheets, Slack, ad tools, etc.). By doing so, it improves data transparency and visibility across teams in an enterprise.
For example, Hightouch can push meaningful, relevant sales metrics and product data into your CRM, which your product and sales teams can use to improve product design, increase customer retention, and boost sales.
Shipyard is a data orchestration platform that leverages automation to help teams launch, monitor, and share data workflows with ease. We bring together the entirety of your data architecture without you needing to familiarize yourself with the backend of these individual platforms or tools. The platform services as the single source for automating and managing your data from beginning to end.
If you select any of the vendors listed above to build out your data architecture, Shipyard offers pre-built low-code templates designed to help you perform key actions on these platforms and connect each of these tools together. For example, with Shipyard you could kick off a Fivetran sync, run your dbt models once they complete, refresh data extracts in Tableau, then send updated Tableau dashboard PDFs to your clients.
Shipyard lets you visually construct workflows in the UI and make changes seamlessly. With 50+ integrations you can connect all tools that construct your data architecture—from major cloud storage platforms to databases, marketing tools, and messaging services.
When you launch custom workflows, Shipyard lets you monitor performance in real time, automatically retries if issues occur, and sends notifications to ensure you can fix errors before they negatively impact your business.
How to get started with data architecture
Collecting, managing, and analyzing data sets might seem intimidating. But a simple data architecture can change your customer experience, product design, or the way you look at your business. Tracking individual data points from multiple sources helps you determine tangible, actionable steps to move your business forward.
Building data architecture is about how efficiently you connect your existing tools to automate and accelerate your data analytics journey.
If you want to learn more about how you can build your data architecture effectively, schedule some time with our data experts. If you already have a strong data architecture in place but want to drive value with the data you have on hand, sign up for our free Developer plan. You can start building powerful workflows in a matter of minutes!