Which data warehouse does your organization use?

CloudData & Analytics

Snowflake12%

Google21%

Amazon24%

Microsoft24%

Cloudera3%

Oracle8%

IBM2%

SAP1%

Other2%

680 PARTICIPANTS
3.3k viewscircle icon2 Comments
Sort by:
Senior Enterprise Architect, Application Consulting in Healthcare and Biotech4 years ago

We use multiple OLAP repositories and analytics tools.

Lightbulb on1 circle icon1 Reply
no title4 years ago

To clarify my previous remark, we used "multiple OLAP repositories and analytics tools" when I was at IBM.  At Oracle we employ an extensive portfolio of homegrown and open source-based data repositories, analytics and data science tools.

Lightbulb on1

Content you might like

We use Microsoft PowerBI extensively at the company. Over 15,000 reports.
For our "corporate" reports, like financials or HR, we centralize those requests and attend them with my team.
We currently "extract" information from multiple systems (ie. SAP, SuccessFactors, CRM, etc...) and put in into a SQL Analysis Services. For interpreting the data we have a Semantics model in PBI.
The problem I see is we can't scale enough to comply with the reporting demands as the semantic model is "unique" and we end up having only one or two developers that can work at a time. 
Anyone has an idea how to scale their PBI teams without having to redevelop the semantic model per report?

AI and Research in Marketing and Communications: How Are You Ensuring Quality and Truth Over Speed? As AI becomes embedded in our day-to-day marketing operations, from customer insight to content strategy, it’s clear we’re gaining speed and scale. But are we sacrificing veracity and quality of output in the process? One of the biggest challenges I'm seeing is the rise of hallucinated content in AI-generated research.   - How do we ensure AI systems are surfacing trustworthy, contextually accurate, and well-sourced information? - Are we building enough verification layers into our AI-driven content and research workflows? - And how are you educating teams (and execs) to challenge AI outputs, rather than accept them at face value? - What frameworks, tools, or governance models have you adopted to maintain integrity and depth in AI-led research and insight creation? - Have you had success (or struggles) balancing speed vs. substance in AI-powered strategies? Your thoughts, examples, and recommendations would be incredibly valuable in helping others try to navigate this shift responsibly.

What are the biggest technical challenges preventing organizations from being able to use their existing data to enable digital transformation?

Finding data and putting it to good use13%

Controlling the security and privacy of data45%

Understanding how data is currently being used20%

All of the above19%

None of the above1%

View Results

How do you think AI will disrupt business across industries? Add to my list: 1. Content creation 2. Photos and video production 3. Basic coding and debugging 4. Strategic analysis to be highly complimented 

Read More Comments

What's your most indispensable cloud infrastructure tools to use in addition to what you get from GCP, AWS, Azure?

HashiCorp (Terraform, Vault, Packer, etc.)22%

Cloud infra automation (Ansible, Puppet, Chef, etc.)56%

APM (Datadog, AppD, SignalFX, NewRelic, etc.)10%

Others?10%

View Results
Which data warehouse does your organization use? | Gartner Peer Community