Anna's best insights come from time series data. This article will help you understand the features of time series data, and identify if your dataset is good for Anna. If you're looking to quickly validate if your dataset is Anna ready, please refer to our checklist here.
Date: Time series data typically has one or more date fields. Anna needs at least one date field, to assist in the identification of patterns, trends and seasonality over time. Anna’s insights depend on the period your data spans, for example:
Anomaly detection: 7 months of data or more
Seasonality analysis: 24 months of data or more
The preferred date format is YYYY-MM-DD.
Measure: Your data should have at least one measure. A measure is a number or value that can be summed or averaged. Examples of measures include total value of sales, quantity ordered, spend and more.
If your measures have any special characters ($, %, #, &, etc.), thousands separators or currency indicators (e.g. AUD, USD, NZD etc.), these should be removed from your data before uploading. If you would still like to see the currency indicator in Anna, create a new column for these values.
Segment: In order to upload your data to Anna, the data should have at least one segment, but the more the better! Segments are the qualitative fields of your data, for example, office locations, departments, product types, job ranks and more.
Make sure your segments use user friendly language and natural language terms.
For each segment identified, Anna will also perform a distinct count. For example, with 'Department' set as a segment, she will calculate the number of departments and automatically provide additional calculation on Average sales by Department.
When you upload data, Anna will automatically detect the format of each of your columns. You have the option to change this selection during the upload process. See the Related articles for more information on uploading your data.
If you have any questions, please contact us at email@example.com.