You’ve identified your data sources. Finalized the data sets. Built robust data pipelines to feed into your AI or analytics systems. Yet, you find yourself submerged in data quality issues and ownership confusion that slow insights, increase risk, and derail your analysis process.
Today’s AI models are fed millions of terabytes of diverse, high-velocity data to ensure favorable, unbiased outcomes. While organizations use modern data engineering techniques to implement flexible data pipelines within these models, AI training data faces quality, consistency, and reliability issues, which dilute model performance and results.
With AI applications demanding real-time insights and continuous learning, data contracts are now central to scalable data engineering. Read further to uncover how data contracts eliminate data quality issues and enable organizations to maintain governance standards.
The Scalability Challenges of Traditional Data Pipelines
Data pipelines have long been delivering data to end users for analysis. However, traditional pipelines have largely monolithic architectures, with a central data team owning all the data. As the world moves towards distributed data ownership, these pipelines cannot handle today’s data demands. With different teams being held accountable for different data products, several challenges arise due to their rigid nature:
- Traditional data pipelines are built on rigid architecture, making them ill-suited for rapidly growing data volumes and evolving data needs.
- As AI initiatives surge, these pipelines cannot handle increasing data loads.
- Archaic data pipelines do not integrate well with different data sources and fail to maintain data quality as they scale.
- They do not adapt to new data requirements and lack the automation and flexibility for efficient scaling.
- Current data pipelines cannot maintain consistent, high-quality data, impacting business decision-making.
- With data residing in many databases and various formats, these pipelines fail to offer a single source of truth.
- As data volumes soar, these pipelines need technical experts to optimize workflows to handle large, diverse datasets efficiently.
- Data arriving into data pipelines also needs extensive cleaning, enriching, and appropriate structuring, as most lack basic levels of encryption, access controls, and compliance.
Why Data Contracts are Growing in Popularity
Let’s face it. Today’s data sets are more complex than ever. They are generated from several diverse sources and come in different types and formats, each with varying quality and compliance. Maintaining proper governance, security, and privacy standards becomes increasingly challenging as data grows in complexity.
Data contracts are official contracts that ensure the data structure, quality, and availability. They are turning out to be a central tool for achieving changing data management objectives, serving as a long-needed mechanism to ensure data quality, consistency, and dependability, particularly in data-centric ventures.
They formalize agreements between data producers and consumers using various data governance rules and service level agreements. This helps avoid quality problems early on, enabling good cooperation and easy scaling of data pipelines.
With data products evolving, there are several reasons why data contracts are becoming so important:
- They simplify data ownership: Data contracts clearly classify and establish agreements between data producers and consumers. This includes data structure, format, and usage, which helps clarify responsibilities for ensuring data quality and maintainability.
- They clarify how data will be shared: Data is shared across different teams and tools, and data contracts bring much-needed clarity to the exchange process. By defining the structure, format, and exchange rules, these agreements ensure no uncertainties or undocumented assumptions about data.
- They address the limitations of traditional data pipelines: Data contracts provide a standardized way to define data quality expectations and ownership. This enables more robust and scalable data workflows, which traditional data pipelines struggled with.
- They enhance data quality and reliability: Data contracts specify certain expectations regarding data structure, format, and quality. This removes data inconsistency and errors and results in more reliable data.
- They enable enterprise-wide collaboration: Because data contracts create a standard expectation for data, they allow more cooperation and communication among business stakeholders.
- They allow automated data quality testing: Data contracts allow organizations to automatically perform data quality testing and monitoring, allowing data engineers to focus on more strategic work.
Leading the Data Revolution with Data Contracts
Data pipeline failures are rampant, from unplanned schema changes to data quality and performance issues. To ensure robustness and scalability, enterprises must embrace data contracts. These contracts can set standards for monitoring and optimizing data quality., thus preventing errors in data pipelines. Data contracts enhance compliance and enable effortless teamwork by establishing clear data storage, analysis, and usage rules.
Successful adoption of data contracts requires organizations to strengthen their change management policies. They must ensure a culture of data quality to drive adoption among engineering, data, and product teams. They must also clearly define data quality requirements and terms of use for all data users – both internal and external. This is essential to ensure that data is handled consistently and reliably and following the defined specifications.
Remember, data contracts aren’t just a technical imperative but a mindset shift toward treating data as a product with high quality and governance standards. Begin your journey today with Wissen! Contact us to learn more.
