In our ongoing effort to understand the BI and analytics marketplace, we here at Blue Hill just finished up our latest survey of 160 data analysts and BI professionals.
We asked a lot of questions around what BI tools people use, and why they chose them. An interesting side product of asking such questions is that we got an idea of how many tools people are actually encountering in their organization. As it turns out, people are encountering a lot of tools over the course of just doing their job. In fact, the average number of tools was 2.8. In other words, the average data analyst/BI professional deals with about three unique tools in some form or fashion throughout the course of their job.
It’s worth noting that this number doesn’t include the ever-present Microsoft Excel. So depending on whether or not you want to classify everyone’s favorite spreadsheet software as a data analysis tool, this number could in fact be closer to four unique solutions.
We were able to split our survey respondents into two distinct buckets based on their job roles: IT professionals and Non-IT professionals. When we cut the data this way, we found some interesting results. On average, IT professionals interact with about ~3.5 BI/Analytics tools, while those in a different line-of-business function encounter ~2.6 tools.
In a marketplace where each vendor wants you to believe that their solution can do everything under the sun, these findings certainly seem a bit high. Based on our analysis of the data, as well as a number of interviews and conversations with individual users, it’s clear that businesses are supplementing their existing larger deployments (think enterprise-wide legacy deployments, or homegrown solutions) with smaller, more agile and tactical solutions for specific pain points. For instance, the majority of users also interact with dedicated data visualization tools, or solutions designed specifically for certain function (such as reporting on Salesforce.com data).
This is a reminder, of course, that there are always unique scenarios where nuances between products are significant enough to bring more analytics solutions into the fold. This also speaks to a state of general fragmentation in the data value-chain. Having a number of solutions for individual use cases isn’t bad in and of itself, but it does create pitfalls if organizations are trying to scale a deployment or foster collaboration across disparate sources and teams.
Are these findings consistent with your experiences? Do you find yourself working with a number of BI/analytics tools over the course of your job? We will be publishing our full results with additional analysis and context in the coming month and would love to incorporate your insights.