TrustRadius has just published the 2016 Buyer’s Guide to Business Intelligence Software, based on 2,250+ end-user ratings and reviews across 22 BI software products. This is an update to the original guide published in 2014, and is a significant revision containing brand new TrustMapsTM that compare products in the category based on end-user satisfaction ratings and research frequency by prospective buyers on TrustRadius.
The guide also contains individual profiles for each of the main vendors and products in the category, advice on how to buy a BI solution, and a discussion of recent trends in the BI market, identifying several trends that have either occurred, or gained in strength, since the first version was published. The grand theme is a change in focus to the needs of business users, and away from IT . Major trends identified are:
The shift away from centrally governed, IT-led tools to agile, self-service BI.
Most of the full-stack tools like SAP, IBM, SAS and Microsoft have now released data discovery and visualization tools designed for business users/analysts that are not dependent on IT.
Data discovery and Visualization vendors building more enterprise-level features.
The data discovery and visualization vendors are being pressed to provide more enterprise features as they become more widely deployed across the enterprise. Most of them have responded to this pressure by providing some measure of security, data governance, data preparation and even report generation to satisfy these broader deployments.
Big Data has greatly increased in importance and Hadoop is now much more widely adopted.
Business data is no longer just operational and transactional data, but now includes unstructured data from sensors and machines along with social media data and images. Hadoop is now being widely deployed to analyze this kind of data.
Emergence of self-service data preparation.
Preparing data for analysis (removing redundancies, organizing into tables, etc.) used to be performed by the IT department as part of the Extract, Transform, Load (ETL) process. But vendors are now making data preparation tools much easier to use and designing them to meet the needs business users or analysts instead of IT professionals. Data discovery and visualization vendors like Tableau are gradually building these capabilities into their products. Data preparation is also important for big data and Hadoop, and a new generation of data preparation tools like Trifacta and Paxata has emerged to meet this need.
Co-existence of multiple data storage systems.
Hadoop will not replace the traditional relational data warehouse. Both Hadoop data lakes and traditional data warehouses are likely to co-exist for the foreseeable future. The value of data warehouses for structured data is still clear, particularly when running against very fast massively parallel processing (MPP) relational data warehouses.
Was this helpful?