Big Data Analytics, Smarter
Datameer pioneered point and click big data integration, analytics and visualization and now with Smart Analytics, we make data mining as easy as drag-and-drop.
With clustering (a K-means algorithm) Datameer automatically finds non-obvious but related groups within your data by automating the process of identifying and measuring common attributes within the dataset. The obvious benefit is that if you can segment your data into groups, you can treat the groups differently.
For example, drag-and-drop to identify groups in:
Customer databases Health records Text documents Social media Product libraries Online gaming logs POS data Clickpaths Weblogs
Drag-and-drop to find groups and relationships hidden in your data
Datameer’s decision trees help you understand the different combinations of data attributes that result in a desired outcome. Decision trees are often used when enriching a dataset with additional data sources to optimize a process for a better outcome. The structure of the decision tree reflects the structure that is possibly hidden in your data.
For example, find out what common attributes influence:
Disease risk Online signups Fraud risk Root-cause Customer churn Product conversions Purchases
Want to know how strongly a single data attribute like age, location, or gender, relates to other data attributes like income, college degree, or credit score? The column dependency algorithm automatically compares every possible data attribute combination and visually ranks the strengths of those relationships so you can instantly see where to focus further. Those relationships are important themselves and is often used to help target further analysis.
For example, see the relationship between:
Title and purchase amount Age and disease type Transaction type and frequency Account age and product type Location and product selection Age and number of SMS messages Average session length and virtual goods purchase
Datameer’s recommendation engine automatically predicts interests of a person based on historical observations of similar people’s interests so you can increase engagement, recommend more relevant choices, increase customer satisfaction, and more.
For example, predict interest in:
Music Products Movies Documents Content Applications Services
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