DATAOPS

Data Challenges

• Is your data management not able to deliver high levels of productivity and                   quality?
• Is there a big Time lapse between the new idea of a proposal and the deployment      of finished analytics “cycle time?” This happens due to, the following

• -> Lack of teamwork
• -> Process bottlenecks
• -> Sluggish process to avoid poor data quality
• -> Inflexible Data Architectures
• -> Poor data quality
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Data scientists spend 75% of their time in exploration, massaging data and executing manual steps increasing the analytical cycle time.

DATAOPS:

• -> An automated, process-oriented methodology, to improve the quality and reduce        the cycle time of data analytics.
• -> Covers entire data lifecycle, from preparation to reporting, and recognizes the            interconnected nature technology with business.
• -> Incorporates the Agile methodology to shorten the cycle time of development in        alignment with business goals.
• -> Utilizes Statistical Process Control to monitor and control the data analytics                 pipeline.
• -> Not tied to a particular technology, architecture, tool, language or framework.
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