For the uninitiated, Gartner’s Magic Quadrants are market research reports published by IT consulting firm Gartner. They use qualitative research to rank and rate technology vendors and help buyers understand the competitive landscape.
First off, a short disclaimer. We’re not in the Gartner Magic Quadrant. One day, we’ll be in there, and look forward to making our way from the niche players' corner to ‘leaders’. In the meantime, though, we love these reports as they’re a great benchmark for the analytics and business intelligence industry.
As we read this year’s magic quadrant report, we thought we’d share our thoughts and offer four insights…
1) Why do you want a BI platform?
You can barely turn on a podcast or read a tweet without hearing the phrase ‘data-driven decision making’. While COVID has undeniably accelerated companies' digital transformation efforts (well, digitization and digital workplace…), they’re now wanting to put the power of data in everyone’s hands. We love, love, love this. Ease-of-use across the enterprise driven by embedded, augmented analytics is absolutely the goal. It’s a world where people will use BI without knowing they’re using it.
But (it’s a big BUT) this is definitely a future vision. We’re doing some work in this space now, but the nirvana of voice-activated complex BI queries is a long way off. Though we can’t wait to say “Hey Siri! What was the percent change in market share for a grouping of my top 30% of products for the current six-month period versus the same period a year ago for clients that grew by more than 15 percent in revenue?”, we’re not holding our breath.
The reality is, we’ll still need people making dashboards and reports. And we’ll definitely need SQL expertise, augmented by data modeling languages, to help make the magic happen behind the scenes.
2) Head in the Clouds?
We learned that almost all of the seven largest cloud infrastructure and platform service vendors have an analytics/business intelligence offering. This is no surprise as the inevitable cloudification of data and software marches on. Lock-in-averse tech buyers are still looking for alternatives, preferring hybrid or multi-cloud providers. And research shows that two-thirds of CIOs would prefer to use cloud services from several vendors.
While COVID has accelerated the shift to the cloud, there will also be on-premise requirements for some time to come. Most companies maintain a lot of on-prem data, especially in the healthcare and financial services sectors.
From bipp’s perspective, there’s a need in the market for cloud-agnostic business intelligence platforms. Clients should never need to move data in order to gain insight from it. Ideally, a BI platform should support on-prem, cloud, and migration from one to the other.
3) With Great Power…
But, putting our cap aside for a moment, it’s worth noting that Power BI can only be deployed in Azure. This limits your ability to use services from other cloud providers, and also potentially creates high switching costs.
There’s also a challenge if Power BI users need more than basic dashboards then analysts are required to learn a complex language called DAX that requires considerable training. Alternatively, a SQL-based data modeling language like bipp’s bippLang can define a single source of truth - a universal data dictionary of sorts. Using this ‘dictionary’, users can easily explore and create ad-hoc reports on their own. These reports can then be grouped together as tiles on an interactive, connected dashboard.
4) The Future will be augmented
Gartner talks about a “…collision course…” where the ABI, data science, and ML platform markets are tumbling into each other like asteroids between Mars and Jupiter. This collision course was the genesis of bipp (see our stories here) but that’s a tale for another time.
Given increasingly pervasive BI needs, the sheer volume of data, and the complexity of connecting a myriad of data sources, augmenting analytics using AI is inevitable. The opportunity to remove pre-existing data biases and also help non-technical users obtain real insights is very compelling.
As the asteroids accelerate, though, It will be critical to consider how to build trust in the output. Ensuring ‘augmented’ isn’t replaced by ‘artificial’ will be important as companies begin to rely on machine-generated insights. It will also never be 100 percent automated. There will continue to be hard-working BI and data analysts writing SQL queries in the engine room.