Nucleus Research Identifies Birst by Infor, IBM, Microsoft, Qlik, Sisense, and Tableau as Analytics Leaders
Nucleus Research has released the 2019 Analytics Technology Value Matrix, an assessment of the analytics market based on the value customers realize from the product usability and functionality that analytics vendors are currently delivering with their solutions.
The Nucleus Analytics Technology Value Matrix measures the ability of analytics vendors to deliver value in usability and functionality and is placed into four categories: Leaders, Experts, Facilitators, and Core Providers. Customers can use the Matrices to evaluate vendor short lists as well as to make the case for maintaining existing applications. Analytics Technology Value Matrix Leaders include: Birst by Infor, IBM, Microsoft, Qlik, Sisense, and Tableau.
Analyzing data to make informed business decisions is a mission-critical function for any organization. Previously an arcane practice conducted by people outside of the data science wing, analytics are now accessible thanks to self-service tools that use conversational, natural language interfaces. With the current shortage of data scientists, vendors are rolling out self-learning and automated functionality in business intelligence (BI) applications to maximize users’ time and skills. Vendors are also leveraging artificial intelligence (AI) — particularly automated chat bots and classifiers for pattern recognition — to enhance data collection and analysis.
“Analytics are no longer optional for modern organizations they are a requirement,” said Ian Campbell, CEO of Nucleus Research. “The ability to collect and analyze data lets companies differentiate themselves from competitors and shift from a reactive to proactive operational strategy.”
“Vendors are continually developing new ways to make collecting, storing and analyzing data easier and more productive for the user,” said Daniel Elman, analyst at Nucleus Research. “As analytics become more effective due to an increase in connectivity and computing power, it is critical that vendors develop tools that are performant, but also easy-to-use for those who are not data scientists.”