Enterprises rely on business intelligence applications to identify and predict the potential outcomes of their business strategies. Whether or not the business intelligence application is effective at delivering measurable business value depends on the accuracy of the application's predictions, and how those predictions affect the organization.
SoftwareReview's 2021 Business Intelligence Data Quadrant Report asked 1,234 IT decision makers to weigh in on the factors that separated the most and least admired BI vendors. Of the 16 vendors evaluated for the report, respondents identified Zoho Analytics, Tableau, Dundas BI, TIBCO Spotfire, and Qlik Sense as delivering the greatest business value to their users. Zoho Tableau, Qlik Sense, and Yellowfin received the highest scores for reusability and intuitiveness, and Board, Qlik Sense, and Looker were rated as being the most customizable, according to the survey.
Emotional response ratings across 25 questions were aggregated to create an indicator of overall user feeling toward the vendor and product. Two of the metrics in the survey indicated how favorably the respondents viewed the BI applications: Value Index, or user satisfaction given the costs paid; and Net Emotional Footprint, or high-level user sentiment about the application. Dundas BI, Tableau, Board, Looker, and Zoho Analytics had the highest combined Value Index and Net Emotional Footprint scores across 16 BI vendors included in the study.
Users were vocal about their satisfaction levels regarding features in BI apps, such as advanced analytics and data science. Tableau received 158 survey responses and a satisfaction score of 79%, the second-highest in the survey. Microsoft Power BI received the most survey responses with 207 and had a satisfaction score of 75% on this attribute. The chart below ranks how respondents ranked vendor support for advanced analytics and data science in BI applications.
Another use case is enterprise-wide BI deployment. In that scenario, key criteria to consider include support for governance, centralized manageability, and scale to deliver access and content to a broad community of analytics users.
A third use case is augmented BI, defined as automating manual processes involved in data analysis, integration, and visualization using machine learning. The most important criterion for evaluating augmented BI applications is the automated insights module, which includes machine learning algorithms trainable by an organization. Additional key criteria include natural language query, natural language generation, and support for data storytelling.
Depending on which use case is most important, different BI applications might work better for an organization. Regardless, such applications need to progress beyond data visualization and dashboards by adopting machine learning techniques to generate insights that deliver more business value.