By Andy Longworth, Data Practice Lead, Hybrid Cloud practice, HPE, and Lena Weiring, DualStudy, Hybrid Cloud practice, HPE
However, AI systems require vast amounts of high-quality data for both training and inference. The effectiveness and accuracy of AI models are directly tied to the quality of the data they are trained on. A recent study showed that poor data quality can lead to an average loss of up to 6% of annual revenue, or about $406 million.
Why is this a problem? While data is abundant, organizations often struggle to access it effectively. Data frequently exists in silos and may contain errors, even if it appears clean and high-quality at first glance. Additionally, the deployment of tools, platforms, and pipelines is often manual, time-consuming, and error-prone. A survey conducted by HPE and TechTarget’s Enterprise Strategy Group revealed that 76% of respondents felt their current data management capabilities could not keep up with their business demands.
Any successful AI implementation hinges not only on code but also on high-quality data and the expertise of those executing it. However, many organizations lack access to high-quality data, even if it exists within their systems.
What is DataOps?
DataOps is a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and data consumers across an organization. Unlike DevOps and data analytics, DataOps is a process-oriented approach to working with data. It combines processes, culture, and technology to enhance the business value of data. DataOps spans from the origin of ideas to the final chart that creates value, aiming to merge multiple sources and pipelines to provide high-quality data quickly.
Why is DataOps important?
Data is often described as the new oil of today’s world. However, like crude oil, data has relatively low value in its unrefined form. Organizations strive to maximize the value of their data, refining it into high-value insights and information. Despite widespread data collection, few organizations fully capitalize on its value.
DataOps follows principles of automation, collaboration, integration, monitoring, and security that are crucial for most businesses. One primary benefit is enhanced data quality and reliability. Continuous monitoring and data cleansing provide consistent data quality, leading to more reliable and accurate data for informed decision-making and AI implementation.
DataOps frameworks also offer robust data governance, maintaining data integrity, security, and compliance with regulations. Scalability and flexibility are inherent advantages, supporting scalable data architectures that grow with organizational needs, handling increasing data volumes and complexity without compromising performance. This adaptability allows for continuous improvement and the integration of new tools and technologies.
What makes DataOps different?
Unlike big data analytics, DataOps applies to any size of data. It adopts regular innovation intervals from agile methodology and includes the collaboration methods of DevOps. While DevOps focuses on software development, DataOps manages how data evolves. Additionally, DataOps emphasizes the people interacting with data, simplifying data collection and cleaning processes to facilitate ease of use.
Overall, DataOps implements the best features of other methods and tailors them to the context of data analytics.
How does DataOps play into an AI journey?
AI is only as good as the data that trains it. This principle underscores the importance of quality data in AI implementation. Poor quality data leads to erroneous or wrong results from AI models, resulting in bad decisions and potential financial losses. Moreover, it erodes trust in data-driven initiatives within the organization, hindering cultural change.
DataOps addresses these issues by cleaning and monitoring data as a preparation and ongoing process for AI implementation. By streamlining data acquisition and integration from different sources, DataOps combines data for more insightful results. Automation within DataOps ensures consistent pipeline deployment and data flow, enhancing reliability.
The DataOps approach facilitates collaboration between data scientists, engineers, and domain experts, enabling better results and fostering a high-performance data culture.
Where does HPE play?
DataOps thrives on core principles such as automation, collaboration, integration, monitoring, and security. HPE Services can guide you through the DataOps journey, transforming nascent ideas into automated, pipeline-driven data teams. Here’s how:
- Automation: Provides consistent and replicable changes to data and pipelines
- Collaboration: Promotes teamwork towards common goals and improves data usage and quality
- Integration: Incorporates new data into decision-making processes
- Monitoring: Quantifies changes to make sure they are beneficial, with automation for rollbacks if necessary
- Security: Protects data as a high-value asset with security by design
By embracing DataOps, businesses can significantly improve data quality, reduce the time and effort required to process raw data, and enhance data-driven decision-making. HPE Services offers a range of services to support your DataOps journey, from initial conversations and workshops to strategy engagements, proof of value, design, planning, and implementation.
Read our whitepaper about DataOps and AI
Learn more about HPE Data Services
Lena Weiring is a DualStudy employee who works within the HPE’s worldwide Hybrid Cloud practice.
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