As the volume of data is increases rapidly, it becomes harder and harder to turn it into information to analyze. You can be data rich, but information poor – overwhelmed by data complexity and volume. As a result, you’re basing business decisions on incomplete, inconsistent or inaccurate data. In addition, data relationships may be unrecognized or be misinterpreted if not properly visualized.
Simply accessing information in the traditional ways can fall short, such as when IT queues are long. Or the methods for getting data can be complex – geared to IT people, not the business people who need to consume the data.
Data discovery combines analytics and data visualization to better equip business people to build data models and make predictions. Aimed at business people, it is visually-oriented, interactive and iterative, and allows data to be combined and shared.
How we've helped others with their data discovery needs:
- A provider of online marketing solutions had a legacy BI system but needed better access to data. We helped identify and implement data discovery tools to support self-service analysis by business analysts and data scientists.
- A data services provider needed to identify and implement data discovery tools to replace their cloud application-specific reporting and spreadsheets, which contained conflicting versions of their data.
Our insights on data discovery:
- Understanding BI analytics tools and their benefits
- Analytics Best Practices: The Analytical Sandbox
- Designing the analytical sandbox: data across the enterprise needs to be accessible and timely
- Suggestion #2 for the analytical sandbox: time-to-solution must be fast and disposable
- Suggestion #3 for the analytical sandbox: the business analyst needs to be “in control”
- Suggestion #4 for the analytical sandbox: sufficient infrastructure must be available for conducting business analytics
- Suggestion #5 for the analytical sandbox: solutions must be cost- and resource-effective