Gartner Magic Quadrant for Insight Engines 2019
Attivio was recognized for our completeness in vision and ability to execute.
Chief Data Officers and the Challenges of Big Data
Once Chief Data Officers have identified, confronted, and hopefully overcome the challenges at the bottom and in the middle of the Big Data stack, what’s next? As Andrew Brust of Datameer notes, “At the top of the stack, there are seemingly endless choices. Whether Enterprise BI stalwarts, BI 2.0 challengers, or big data analytics players, the number of vendors and their similar positioning makes it really hard for customers.”
Plus, although some analytics tools are designed for a broad range of users, many require significant training and knowledge of data modeling and architecture to be used effectively. These workers are in short supply or absent entirely in many organizations. They’re also expensive to hire and getting harder to find. And the proliferation of tools mentioned above makes it difficult for even seasoned data analysts to keep pace.
In addition, the focus on embeddable analytics is another trend in Big Data that makes the top of the stack more complex. Not all tools embed easily. Some of the most popular desktop BI tools are based on old technology that was never intended for embedding. Others cannot connect natively to structured and unstructured data sources. Workarounds that address this can degrade performance. And developers often want embedded analytics to incorporate real or near-realtime data. Newer tools with as yet unproven track records are typically the ones that offer this kind of capability.
For a deeper look at the top of the Big Data stack and its challenges, including the emergence of self-service data discovery and provisioning tools, download our 5-minute guide.