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Search driven analytics with natural language queries

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:

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.

Keywords recognized in search queries

Dimension Names
Word or phrase that matches dimension Label (or Name if label is not defined). Words order is not important.
Measure Names
Word or phrase that matches measure Label (or aggregate column name + measure Type if label is not defined). Word order is not important; for example, if you have "Sum" measure for column "Total" you can reference it either with total sum or sum of total. To use first "Sum" measure it is enough to specify just sum.
Hinted Comparison
To filter by some dimension's value you may specify 'hint' keyword that refers to the dimension (or measure) before an entity name to clarify which exactly dimension (or measure) you want to match. For example: city Berlin (or city:Berlin), name John. Hint is required if you want to filter by a high-cardinality column (when SeekTable cannot recognize a value).
Entity Name
You can use specify full or partial name of some entity (company, person name, country, city etc) without a hint and SeekTable will try to match most appropriate dimension that corresponds to this name. For example: Canada, New York, John Smith, 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.

=, equal, equals, not equal, not equals
Less Than
<, before, below, less, less than, fewer, under, ending with
Greater Than
>, after, after, above, greater, greater than, more, more than, larger, over, starting with
SeekTable recognizes dates written in many different formats; this can be even incomplete date like 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).
Relative Dates
yesterday, today, tomorrow, this month, current month, prev month, previous month, last month, next month, this year, current year, prev year, previous year, last year, next year
Are you interested in the search-driven analytics functionality? Feel free to contact us and ask for more details.