Self-service BI is a term for business intelligence (BI) tools aimed at business users, instead of the IT department. These tools help analysts explore databases through visual interfaces, instead of SQL queries and custom scripts.
We'll take a look at the types of self-service BI tools on the market and explain how they differ from traditional platforms. Here's what we'll cover:
To answer this question, you have to understand traditional BI.
Back in the 80s and 90s, BI platforms were so complex that IT staff had to help business analysts create custom reports by writing SQL queries and custom scripts. Moreover, organization-wide reports were typically produced by the IT department.
IT was traditionally in charge of BI because traditional BI platforms store data in a data warehouse, which is a dedicated database system for historical business data (e.g., many years' worth of sales data, accounting data, customer information etc.).
Essentially, data warehousing involves pulling data from business applications (CRM systems, accounting systems etc.) and storing it so that analysts can spot and diagnose issues with the business's operational and financial performance:
Before data could be loaded into the warehouse, however, IT departments had to prepare it for analysis by normalizing dimensions (e.g., ensuring that “customer ID" means the same thing in all the tables in the database), aggregating certain metrics, cleaning dirty data etc. This is known as the “extract, transform, load" or ETL process, because data is extracted from applications, transformed into standard formats for analysis, and loaded into the warehouse.
When an analyst had to combine a new source of information, such as a web analytics system, with data in the data warehouse, the IT department would often have to reinvent the whole ETL process.
This was definitely not a user-friendly situation for analysts, and the IT department didn't really like being reduced to the work of data waitressing either.
Self-service BI tools emerged in the last decade as a response to this situation. They differ from traditional systems in the following ways:
Self-service data modeling in Looker BI
Visual data exploration in Board
Analyst workgroup. Analyst workgroups typically need solutions that are robust when it comes to data analysis features—for instance, the statistical algorithms found in IBM SPSS and SAS STAT. They don't typically need strong dashboard capabilities or governance functionality.
IT department. Even though self-service tools are designed for business users, IT departments are still tasked with purchasing and configuring these platforms in many organizations. IT departments will need to find solutions with strong data modeling capabilities, since they'll need to invest in tools that can support data modeling for a whole organization rather than just a workgroup concerned with specific analytical tasks. Moreover, they'll need to invest in solutions that offer strong data governance to protect sensitive data from business users who don't need to access it as part of their roles.
Self-service BI tools offer the same benefits as traditional BI platforms, including:
|Data governance||Restrict access to sensitive data sources and visualizations.|
|Role-based dashboards||Customize end-user dashboards for different roles throughout the organization.|
|Data mashups||Blend data from many different sources in visualizations.|
|Metadata management||Manage metadata (time stamps, classification tags etc.) across the organization.|
Self-service tools also offer the benefit of user-friendly, visual data exploration, which isn't a strength of either traditional BI platforms or of tools for statistical analysis and data mining.
The need that self-service tools don't cover is data storage for analysis, since these tools typically lack support for data warehousing.
Additionally, since self-service tools lack support for analytical data warehousing, they are not as robust when it comes to data governance as traditional platforms. Organizations in which data quality is of utmost importance (e.g. financial institutions) will still need to use traditional data warehousing alongside self-service tools.
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