Transform SQL/Excel/CSV data to Self-Service BI tools

Kyubit Analytic Model is a Self-Service BI multidimensional analytic data source that is quickly set up using your data from SQL query results or Excel/CSV files. With 'Analytic Models', any business user can create analytics, reports, visualizations, and dashboards, using measures, dimensions, slicers, and many features related to self-service analytics.

Self-Service BI Tools Overview

Unlock the power of your data with Kyubit's Self-Service BI tools. As a business professional, you're often faced with a wealth of data that holds the potential for valuable insights. However, the process of creating data warehouse/OLAP cubes can be time-consuming and requires specialized knowledge and tools. That's where we come in. With Kyubit, you can swiftly import data from SQL query results or Excel/CSV files and establish Analytic Models. These models offer multidimensional analytic capabilities akin to OLAP cube analysis. Your data is transformed into analytic models, making our self-service BI tools ready for all Kyubit users to utilize in their analysis, reports, and dashboards. What's more, you can schedule regular updates and reprocessing of values from your data sources based on your preferences. This ensures your insights stay fresh and relevant. Discover the ease of data analysis with Kyubit.

Prepare Data for Self-Service Analytics

If you can write a simple SQL query, it will return the results that you can use to create an analytic model for self-service analytics and business intelligence dashboard visualizations and management. Also, if your data is stored in Excel/CSV files, it could be quickly utilized and transformed into analytics models.

Transform SQL query to a beautiful Dashboard analytics

Kyubit's business intelligence platform enables its users to smoothly transform relational databases into business intelligence dashboards and self-service analytics for data exploration and quickly isolate business important data and insights. The created analytical dashboards could be shared by URL to all authorized users. The dashboards are not one-time created static objects but could be scheduled for reprocessing with the new data from SQL databases data which would automatically refresh all dashboards and other objects based on the same analytic model.

Excel to Dashboard and self-service analytics

Self-Service BI Tools for Data Analytics, examples...

Once the self-service data model (Analytic Model) is created with Kyubit BI tools, the same user, as well as other authorized users, can quickly create self-service analytics, charts, and reports based on the same Analytic Model with the simple and comprehensive approach (drag-and-drop) that does not require special skills or training. Furthermore, created analysis/report could be used while creating dashboard charts, tables, and KPIs, to visualize prepared data insights by the regular end-user. Kyubit BI includes features to quickly design dashboard layout by drag-and-drop various charts, connecting with previously prepared queries or analyses, positioning and resizing dashboard elements, setting display options for individual dashboard elements, and setting overall dashboard style.

Self-Service BI Tools Tutorials

After Kyubit Self-Service BI 'Analytic Model' is processed, authorized end-users can start the self-service analytics, which will look almost the same as if they are analyzing OLAP cube structures (very similar). End-users can create analyses, reports, and dashboards based on created analytics the same way they are doing with OLAP-based analyses. Most features, like drill-down, drill-through, expanding, slicing, ordering, and data isolating are included in the Analytic model analysis.

Read Step-by-Step tutorials on creating and using Kyubit Self-Service BI 'Analytic Models'...

Self-Service BI Tutorial

How it Works

After you import your data from SQL query results or Excel/CSV files and process 'Analytic Model', Kyubit creates special data structures in Kyubit's internal "KyubitAnalyticModels" database, that is suitable for quick analytic SQL queries. While analyzing data Kyubit is creating SQL queries to bring analytic results from the Kyubit Analytic Models database. In other words, Kyubit is using SQL technology, combined with 'ColumnStore indexes' and some smart caching to deliver data analytics. The only technology prerequisite is the MS SQL Server, which is a prerequisite for the whole Kyubit BI platform product anyway.

Pros

The main reason to use the 'Analytic Model' is for a regular user to quickly add a set of data for analysis, dashboard usage, scheduled subscriptions, and sharing with other users.
Excel/CSV data format should be friendly to all users while preparing data to be used. Any set of data could be exported from Excel to a CSV file (semicolon (;) delimited).
Great usage of Date filters (if data contains date values) that are much friendlier to be used than OLAP ‘date’ structures. Quickly select absolute or relative date filter values in the analysis, report, or dashboard filters.

Limitations

There are some limitations to Kyubit's self-service BI 'Analytic Model' usage, that should be known before using this Kyubit's technology. Kyubit 'Analytic Model' data sources are not created in mind to replace more robust analytic engines, like OLAP or Tabular technology, but to bring a simple solution for smaller data sets that should be analyzed quickly with very little knowledge of data analysis and structures.

'Analytic Model' will perform great with hundreds of thousands of rows of data, even a couple of millions, while we would not recommend being used with tens of millions of rows of data. This question also greatly depends on the hardware on which the SQL server is running.
There are no limitations to the number of category members (rows) in grid analysis and reports, while analytic grid and report can contain a maximum of 128 series (columns) of values in the analysis for each measure in the analysis.
On the category axis, there could be multiple category levels expanding (drill-down) to explore data in more detail, while series members cannot be expanded.