- 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
Data scientists spend 75% of their time in exploration, massaging data and executing manual steps increasing the analytical cycle time.
- -> 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.