Subscribe to DSC Newsletter

9 Questions to Determine If a BI Solution Is Truly Self-Service

A Tool That Grants Independence to Business Users

We all know the constant struggle between IT and business users when it comes to BI software: Business users want to access data in order to make fast decisions independently, without having to use IT as a middle man every time a new requirement arises, query is run, or data is added. Yet, IT is overwhelmed by constantly changing requests and requirements, and struggle to deliver data in an actionable time-frame. The question is: how do more people independently explore business questions? A popular solution to this challenge is self-service BI.

Self-Service BI Friction Points & Solutions

Data, skills, and infrastructure can measure the degree of self-service supported by a Business Intelligence tool using these 9 questions. Conditions for self-service can be balanced against the friction points that need to be addressed. This results in a matrix of nine types of questions to test the degree of self-service supported by a BI tool:

Self-Service BI Table

9 Questions for Self-Service

  1. Acquire - Data: Access recent data from all sources in the rawest formatData access requires you to connect, join and synchronize diverse data sources. This gives rise to questions like: Is it possible to access all the required data sources? What needs to be done to join different data sources together? Can a connection be made directly to the raw data?
  2. Acquire - Skills: Skills to format data for analysisLet’s assume you work with many data sources that have data quality issues. You need to check the level of skill to make corrections to the data before analysis. Relevant questions are: Does the tool require a professional DBA? Do users need to learn different skills to prepare different data source?
  3. Acquire - Infrastructure: Infrastructure to process the data for analysisWorking with large varied data sets is a common requirement. This needs to be supported by the appropriate technology infrastructure. You should ask: What hardware is required to process data sets in a reasonable time-frame?
  4. Analyze - Data: Ability to connect to data and run any type of analysisYou may need formulas to perform multiple aggregations on the data. For example, calculating ‘Average Total Sales per Month’ requires both an average and sum. You may ask: Can formulas be created with multiple aggregations without the need to first summarize data?
  5. Analyze - Skills: Perform desired analysis and easily explore resultsAnalysis requires both the ability to generate the formula and explore result sets. The leads to questions such as: Can the query be generated through the UI without the use of complex language like SQL? Is it easy to drill between the summary and detail levels of results?
  6. Analyze - Infrastructure: Hardware required to quickly run dashboards with many queriesFor efficiency, you need to be able to display many queries in a single dashboard. A relevant question is: Does my laptop support dashboards that return results in a reasonable time-frame with many formulas, visualizations and filters?
  7. Sustain - Data: Hardware infrastructure to scale to large and varied data setsBoth the volume and variety of data grows over time. An assessment should identify if the solution supports large data sets, and what hardware changes are required as data grows.
  8. Sustain - Skills: Skills required for users to manage a single version of the truthAs the team grows, you need to ensure they get answers quickly and can make the necessary changes to the data and analysis. A critical question: is it easy for users to make changes, but still work from a shared data source?
  9. Sustain - Infrastructure: Hardware infrastructure to support many user needsAs teams grow, more users must interact, create and change dashboards and data. What is the appropriate hardware to maintain performance for users? Is the processing done on a central machine or the user’s own machine?

Read full article here.

Views: 935

Comment

You need to be a member of AnalyticBridge to add comments!

Join AnalyticBridge

On Data Science Central

© 2019   AnalyticBridge.com is a subsidiary and dedicated channel of Data Science Central LLC   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service