Why do you need BI tool selection criteria?
Selecting the right Business Intelligence tool for your company isn’t easy. People generally have their own favorite BI tool and they often don’t tell you the complete picture. Neither do the BI-vendors. That’s why we have created a list of 197 BI tool selection criteria, a list of business intelligence requirements. With this list you can measure how a BI tool really scores in comparison with other Business Intelligence tools. Based on facts.
What are the most important BI requirements?
This page contains a small sample of the Business Intelligence requirements we have used to compare all the major Business Intelligence tools that are available in the market. In our Business Intelligence Tools Survey you will find all the questions and data reflecting the key selection criteria. Do you also want the answers to the questions of all the major Business Intelligence tools right now? Click here.
1. Infrastructure & architecture
This category, which contains 24 criteria, measures how well a Business Intelligence tool supports your IT infrastructure. Which operating systems and which computers and server platforms are supported, and how the Microsoft Office Integration is implemented. It also covers important aspects like re-usability, caching, zero-footprint, load balancing, fail-over and In-Memory techniques.
2. Security & connectivity
This category, which contains 14 criteria, covers all aspects regarding security: single sign-on, authentication, content authorization, etc. In addition we look at the connectivity. Does the BI tool have native connectors to a wide range of data sources like SQL Server, Oracle, DB/2, MS Analysis Services (OLAP), etc.
3. Cloud architecture
This category, which contains 7 criteria, covers all the aspects of running your Business Intelligence platform in the cloud. The type of service offering is examined and which cloud models are supported (SaaS, PaaS, etc.). Which third-party Cloud providers offer your Business Intelligence solution?
4. Mobile Business Intelligence
In this category, which contains 24 criteria, the support for Mobile Business Intelligence is examined. Does the Business Intelligence Tool support (native) mobile access? Several criteria were taken into account, for example which platforms are supported (iPad, Android, BlackBerry), whether they have native support, and if the Business Intelligence solution supports the principle of ‘create a report once; run on every device’ (desktop, laptop, browser, Smartphone, Tablet).
5. Core functionality
This category, which contains 29 criteria, examines the Business Intelligence tool’s core functionality (out-of-the-box). For example is there support for slowly changing dimensions and role-based dashboarding and reporting? And if so, to what degree? Is basket analysis supported? Are there facilities to export to PDF and Excel?
6. Performance management & planning
This category covers 13 criteria about performance management & planning facilities. Is there support for management models like the Balanced Scorecard and strategy maps out-of-the-box? About fifty percent of the Business Intelligence & reporting tools don’t have any functionality for performance management and planning.
7. Predictive Analysis & data mining
This category, which contains 7 criteria, examines if and how data and text mining is supported and to what degree. Data mining is widely used to be able to predict behavior of customers, vendors, web visitors, employees, etc. Text mining is generally used to classify Word documents, Twitter messages, webpages, unstructured text, competitor information, and other social media profiles.
8. Usability & visualization
This category, which contains 15 significant criteria, measures the usability of the Business Intelligence tool. Is the product easy to use and easy to learn? Is Mobile Business Intelligence (smartphones, tablets) supported? What type of graphs and visualizations can be used?
9. Performance issues & Big Data
In this category 13 criteria are used to measure how well the Business Intelligence tool supports techniques to optimize report and dashboard performance and is able to process Big Data. A good response time is really important for the User eXperience (UX) because business users and especially higher management are not patient. Some example criteria: aggregate awareness, OLAP, MOLAP, ROLAP, caching, and Big Data connectors.
Criteria are suitable for
10. Search & alerting
This category covers 4 criteria about search on data and meta data and it also includes criteria to evaluate the alerting & notification functionality.
11. Self service Business Intelligence
This new category contains 9 criteria. It measures the degree to which an end-user is able to do self service Business Intelligence & reporting, for example, is it possible that the end-user accesses data from self-defined internal and external sources? Is data blending and data wrangling supported?
12. Collaborative & Social BI
This new important category covers story telling, commenting, liking, guided analysis, and much more. It contains 11 criteria.
Business Intelligence requirements & our verdict
In our Business Intelligence Tools Survey 2019 we have examined all the major Business Intelligence tools against the list of 197 business intelligence requirements. Our survey provides you with true insight into both the strengths and weaknesses of the different Business Intelligence tools. It’s 100% vendor-independent: vendors can’t pay to get included our receive a better review. Download our verdict.
Vendor and product information
This category, which contains 13 criteria, provides general information about the Business Intelligence vendors and the Business Intelligence tool like the product name(s) and the version number(s) that are examined.
Minimal coverage of selection criteria
The BI tool selection criteria should at least cover:
- the desired functionality
- ease of use for both developers and end users (with a specific focus on managers)
- maintainability, scalability and expandability
- tool compatibility with existing environment
- rough price estimate (per CPU or per source system type)
- compatibility with (support of) standards such as ANSI SQL, MDX and XML
- support for the existing infrastructure, such as operating systems, networks, platforms and databases
Start a proof of concept
We can start a so-called ‘proof of concept’ (POC) – a pilot project – and ask two or three suppliers, who meet the essential criteria, to show their solutions. During such a POC, it is wise to test various scenarios and to assess the tool on aspects such as:
- the processing of large volumes of data
- whether large numbers of users can perform actions simultaneously
- large number of ‘members’ in dimensions
- its connections to the source system and the exchange of meta data
- the degree of openness of the meta data repository
- the handling of historical data
- whether slowly changing dimensions are supported
- the possibility to use predefined, complex indicators
- the financial stability of the supplier
- the exact price and possible room for negotiation
- how the tool copes with status information such as inventories, subscriptions, prices and other data that are not additive over time (so-called semi-additive indicators)
Tools shouldn’t effect the architecture
It is highly desirable that the selected tools do not affect the architecture; however this is not always possible. Sometimes adjusting the architecture is unavoidable. This may be the case if the organization (internationally) opted for a standard solution in the area of Business Intelligence and this tool is not completely compatible with the designed data warehouse architecture.
Speed of the discs is also important
During this phase, we also select the hardware on which the tools will run. Therefore, we will need to determine what capacity we require. An important aspect is the processing speed of the discs on which the data will be physically stored. This is often a major bottleneck when it comes to loading the data warehouse, reporting, and analysis. These processes often involve large amounts of data.