Data connectivity, data transformation, and web editing, because being able to connect to a variety of data sources and bring them together, and store that dataset in an environment where others can also access it, consistently, drives a lot of self-service within the organization without a lot of fear of dispersion of truth. The Web Editing capabilities allow us to grant end users enough capabilities for them to do self-serve discovery without the added cost of needing to get everyone desktop licenses.
Improvements to My Organization:
Still working to get properly implemented at my current organization. However, it has helped my previous locations be able to get a pre-built visualization that anchors them on the truth, with access to datasets for them to explore and continuously push the envelope on new inquiries. The more questions we ask, and get answers to, the more informed we are to make better decisions, which leads to revenue growth and cost reductions.
Room for Improvement:
At the organizational level, increasing the servers' capabilities to support us as an enterprise tool: data management, semantic layer, and integration with other collaboration tools. At the analyst level: Better connection with Python data frames.
Use of Solution:
I have used it since 2011.
Yes. In earlier versions, it was tough to scale without a lot of expense. With the addition of Web Editing, you can control your costs better and keep your usage to the things they do the most frequently. Still experiencing challenges in the VM world, but sounds like they are exploring options to address this.
If you are fortunate enough to have a dedicated technical guy, it is great. If you have to work through the common help desk, it is tough. In most cases, analysts have problems they need to solve right away; having them spend hours pouring through KB files to only find something similar, but not exact, and then waiting 5-10 days to get resolution from a help desk agent generates a lot of frustration on the team.
Several companies ago, we were using Cognos and switched to Tableau due to ease of deployment, total cost of ownership. At my last three places, Tableau was already in-house.
Pricing, Setup Cost and Licensing:
Really work across the organization to understand the user personas of your audience. Who is a builder, who is an audience member? Being able to set up the server licensing right (core vs user seats) is the fastest way to manage your costs. Paying for users you never setup or buying expensive desktop licenses for users who can solve their users with web editing on the server are the two biggest expenses.
Other Solutions Considered:
First and foremost, outline the value proposition of BI. Getting a plan on whether you are going to go with a centralized, decentralized, or blended approach will help aid you in how you can maximize the tool. On its own merits, it is a great tool. Most failures come from a mismatch in the organizational needs and the implementation approach.
Disclosure: I am a real user, and this review is based on my own experience and opinions.