However, analytics are only as good as the underlying data data preparation and management. Access to the right data at the right time is critical for getting timely insights.
Three preconditions for successful
data management for analytics
24 / 06 / 2016
Three preconditions for successful data management for analytics
Data and analytics form a foundation for better communication and relations. Whether it’s online shoppers, citizens applying for passports, or an internal client or colleague, data-driven analytics drive insights towards personalized information and services. This enables a better customer experience.
Analytics sink or swim with data. And that data is spread out over various systems, operational applications, data warehouses, or even streaming from sensors. This can create real data quality challenges and requires robust data management strategies.
Three requirements for data management
In order to ensure that data is suitable for analysis, it is important to treat data management as a business-critical process. A robust and flexible data management platform is key to improving, integrating and governing data. It needs to these three requirements:
- Ease of use and flexibility
Choose a platform that allows users, business analysts, and data scientists to prepare the data themselves.
- Integration possibilities
An integrated platform for all data management and analytics tasks not only enables faster development and production of new insights, it also reduces time spent on maintenance.
Data management must be just as innovative as new technologies and come with functionality such as the simple and easy input of large amounts of real-time sensor data and unstructured data.
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