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Maximize Upstream DataOps Efficiency Through AI and Machine Learning to Accelerate Analytics  Maximize Upstream DataOps Efficiency Through AI and Machine Learning to Accelerate Analytics 
What is DataOps? At Zaloni, we’re talking about  all things Dataops. Though it is a recent buzzword in the data management... Maximize Upstream DataOps Efficiency Through AI and Machine Learning to Accelerate Analytics 

What is DataOps?

At Zaloni, we’re talking about  all things Dataops. Though it is a recent buzzword in the data management industry, the concept of DataOps is not new. DataOps is a data management methodology that considers the technology, process, and people involved in improving data supply chain efficiency. DataOps encompasses an agile and extensible approach to end-to-end data management, allowing for the convergence of data management capabilities to produce a unified view of an entire data cycle.

Presented in the graphic below, Zaloni sees DataOps as a continuous optimization process. At the center lies a collaborative data catalog that bridges the gap between data producers like data engineers and data stewards and consumers like data scientists and analysts. Once data is ingested, it can be:

  • Classified and profiled to ensure data integrity
  • Evaluated for data quality standards 
  • Mastered for golden records 
  • Tracked for data lineage purposes to help users understand what happens to data overtime
  • Application of technical, operational, business and, in some cases, custom metadata may be applied

Once data meets quality and security requirements, it’s published in the collaborative catalog and made accessible to data consumers. Data consumers can enrich, transform, annotate, and share data before provisioning the selected data to a downstream environment, application, or analytics tool. Every action performed to a data set is logged, tracked, and fed back into the collaborative data catalog. Data is governed with security, quality, and other workflows applied at each step of the DataOps process.

What is DataOpsThe DataOps cycle continues to improve over time through automation, AI, and machine learning, to reduce costs and increase efficiency. An integrated supply chain allows you to standardize governance and ensure security throughout the process. DataOps drives down data down costs, accelerates time to analytics, and maximizes user collaboration and productivity.

Improve Upstream Efficiency with AI & machine learning

Companies are laser-focused on the downstream activities, wanting to understand how they can utilize data to enable data-driven decision-making and harness potential business opportunities. What many companies overlook is the potential that lies within upstream systems. Optimizing data supply chain steps with ML and AI can create stronger, more efficient downstream opportunities that companies feverishly seek. Below are a couple of examples of areas Zaloni customers are leveraging AI & ML to improve their DataOps process:

Data Mastering

One area where we see ML being used to improve efficiency and data quality is data mastering for master record creation. Companies today struggle with unifying data silos and integrating 3rd party data sources. Machine-learning can help match and merge disparate data sources into master records that serve as a single source of truth.

This machine learning approach is commonly used for creating customer golden records.  For example, using machine learning techniques, customer data from multiple sources can be integrated even without unique identifiers. These approaches include:

  • Probabilistic matching for record linkage
  • Data clustering and classification techniques 
  • Reinforced learning techniques to train matching models based on live sample data that improves and can be adjusted over time

What is DataOpsData mastering adds significant value by accelerating and improving analytics and data science projects. End-users have the peace-of-mind knowing they are pulling reliable and accurate data.

Data Classification 

Another area for leveraging machine learningto improve data quality and ensure data security is through data classification. machine learning classification algorithms can automatically identify data categories and associate them with data quality rules or flag sensitive data such as personally identifiable information (PII) and obfuscate the data based on governance policies.

ML-powered data classification reduces the time it takes to deliver quality, secure data to data consumers resulting in faster, higher quality analytics outcomes. Additionally, this approach automatically enforces governance policies to reduce risk and ensure compliance.

What is DataOpsImproving Analytics Outcomes with Arena DataOps   

Zaloni’s end-to-end DataOps platform, Arena, streamlines data pipelines for better analytics, AI, and ML through an augmented catalog, automated governance, and self-service consumption to reduce IT costs, accelerate analytics, and standardize security. 

Arena provides visibility and control over the entire data supply chain, allowing companies to quickly identify areas for upstream process improvements that result in better downstream outcomes.  With better data for the end-user, one can achieve actionable insights that allow businesses to maximize everyday business practices and opportunities.

What is DataOpsIf you are interested in learning more about what is DataOps, join Zaloni’s very own Solutions Engineer, Cody Rich, on October 28 at 3:30 PM PDT at the ODSC West Virtual Conference. Cody will give a live Arena demo, walking viewers through our unified DataOps platform that bridges the gap between data engineers, stewards, analysts, and data scientists while optimizing the end-to-end data supply chain to process and deliver secure, trusted data rapidly. We will also be in the exhibit hall. Stop by to learn more about Arena can help you achieve your analytics goals. We look forward to seeing you there!

ODSC Community

ODSC Community

The Open Data Science community is passionate and diverse, and we always welcome contributions from data science professionals! All of the articles under this profile are from our community, with individual authors mentioned in the text itself.

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