Search driven analytics is a new way how you can make ad-hoc queries and get answers simply by entering a search query. This works like Google search but for your own dataset: instead of list of links you get a report that is most relevant to keywords from the query. Search driven analytics is especially good when you need to enable data-driven decisions in your company by non-IT staff - they don't need to know anything about pivot tables and visualization, as reports are configured automatically by search.
SeekTable Ask Data function allows you to perform data exploration with simple keyword-based search queries. It is enough to enter a free-form natural language query and get an answer in the form of pivot table or chart. SeekTable helps to form the question with an auto-completion: suggestions come from the dataset itself (depending on the data source) and the cube configuration.
Everyone can use search-driven analytics and get the results for data-driven decision making. Try these online demos:
average television by age range and gender
show sales sum in 2011 vs 2012 by region and month
Sometimes you can get something different from what you expect; in this case you may back to your search query by returning to the cube view ("back" in a web browser or click on the cube name in the breadcrumbs) and add more keywords. Keep in mind that SeekTable does not really understand your query (like a human), it just matches keywords you entered and tries to propose most suitable report.
Search-driven reporting is enabled by default for CSV cubes; for databases you may enable search interface with a checkbox in the cube configuration form.
total sum or
sum of total.
To use first "Sum" measure it is enough to specify just
city Berlin (or
Hint is required if you want to filter by a high-cardinality column (when SeekTable cannot recognize a value).
closed (may refer to 'state' or 'status' column).
Entities recognition works differently for CSV and databases. In case of CSV data SeekTable performs quick scan of CSV file and suggestions/by-value-recognition works for all dimensions. However, this approach is not possible for DB cubes, and suggestions/by-value-recognition works only for dimensions explicitly specified in "Match Dimension" list on the cube configuration form. It is recomended to specify here only low-cardinality dimensions that may be quickly loaded.
May 1 or
2019 Mar. Note that these 'partial' dates are applied correctly only when your cube has separate dimensions
for date parts (year, month, day).