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Data Operations

Improving Data Efficacy and Data Reliability

Data Analytics Services

What is Data Operations Management?

Managing data is not your core business but it is critical for innovation and making your business grow. Any company that strives to be on the cutting-edge needs datasets to be readily available. We realize that for most of our customer’s data is critical for the analysis needed to make accurate predictions to ensure business growth. DataOps (data operations) refers to a practice that brings speed and agility to an end-to-end data pipeline process, from collection to delivery. Improved data management leads to a better quality of available data, which results in better analysis. That single source of truth translates into better insights, business strategies and higher profitability.

Challenges Data Management Platform

While working on data operations for our customers here are some of the common data challenges we encountered:

        Data Science company

        Multiple disparate data sources with no single source of truth

        • Large amount of incorrect or missing data
        • Hidden costs of data growth – also the data is not easily scalable

        Inaccessibility of data – the data is on multiple systems that are not accessible

        • Buried and messy data that’s not easily available for analysis
        • Duplication of effort in data creation and extraction

        Tibil’s Data Operations Solutions

        Managing data is at the core of everything that we do at Tibil. Tibil’s DataOps Solution strives to foster collaboration between data scientists, engineers and technologists so that every team is working in sync to leverage data more appropriately and in less time. Our DataOps strategy enables our customers to leverage data for superior customer outcomes. Machine learning and deep learning applications require constant new data in order to learn and improve. From our experience, any company that strives to be on the cutting-edge needs datasets to be readily available. Self-service data access and the infrastructure to support it are also essential.

        Our Some of the real-life value adds from or customer engagements in Datops are:

          Feature engineering services

          Rapidly building ML models and analytical applications

          Data driven decision

          Creation of standard interfaces and reusable building blocks

          Feature engineering services

          Allowing Data scientists to focus their efforts on high end work rather than waste time on DataOps

          Feature engineering services

          Improved ETL for faster and better data quality

          Data driven decision

          Working with Artificial Intelligence and Machine Learning techniques

          Data Analytics companies

          Deploying text analytics and data preparation

          Benefits of Data Operations Management

          In Tibil’s view, DataOps is a critical data related activity that is much ignored in many organizations that are generating large amounts of data. We believe that by focusing on improving DataOps, our customers can benefit with:

          • Rapid Time-to-Value: Application framework to cut down development speed for each model
          • Robustness: Audit framework to increase trust for accelerated & simplified compliance
          • Scale & Performance: Scalable models with new use cases and new data
          • Managed Evolution: Rapid iterations on the features and model development
          • Dynamic Data Platform: Eliminate data friction, accelerate innovation and secure data
          • Data Privacy and Security: Continuously deliver data without the risk
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