Hollywood has the Golden Globes and Oscars. The music industry has the Grammys. And BI and analytics…we have the Gartner Magic Quadrant. In the most anticipated report of the year for our industry, there are no LaLa Land snafus or other major suprises…but there are shortcomings. While the Gartner report elicited from us a hearty Amen! in some areas, it fell short in others. As always, we find the MQ to be an interesting take on the industry. But ultimately, we see it as a helpful aid in regards to tool evaluation, not the Gospel, and certainly not a substitute for taking a top-down approach to technology that starts with understanding your organization’s real business needs and then matching the right tools to get that job done. Now for the envelope, please… let’s get into our take on the 2017 Gartner Magic Quadrant for BI and analytics platforms.
This year’s Gartner MQ for BI and analytics isn’t the earth-shattering bit of news it was last year, when they fundamentally re-categorized BI vendors into two variants: “modern” and “legacy.” There was considerable uproar over this re-categorization, owing to the potential ramifications to the vendors, both positive and negative, as well as the interpretation of “legacy” as pejorative out of the gates. Gartner has since revised “legacy” to “traditional,” but you could still argue it sounds a bit like Thanksgiving at Grandmas – substantial but not very exciting. Couple that with Gartner’s discontinuation of a formal MQ for the traditional set (relegating them to a less exhaustive “Market Guide”), and the old 800-pound gorillas have found themselves kicked out of the proverbial jungle, exiled from their long held spots in the high branches of the leaders quadrant trees.
Most of the surprise in this year’s MQ is that the trend seems to have continued with only three vendors inhabiting the Leaders quadrant. Two of those are clearly dominant – Tableau and Microsoft – with lots of white space below and to their left. Qlik manages to barely remain as a leader, but has lost ground since last year, and privatization doesn’t appear to be doing them any favors. The remaining vendors appear to sink ever further from nirvana.
“Modern agile business-led” analytics is now considered mainstream, owing to decreasing feature differentiation, desire for large enterprise deployment functionality and price pressure. Additionally, buyers want to cover an ever-broadening array of use cases, featuring increased complexity and more and varied data sources – all without requiring lengthy, or even any – ETL tools.
Gartner does, however, throw the venerable big boys a bone in saying that the likes of Oracle, SAP, IBM and MSTR have improved their offerings sufficiently to appeal to their install bases and make them the standard, citing the specific benefit of leveraging “years of investment in data models and analytic content.” To that we say, “Amen!” as we point folks to our Senturus Analytics Connector, which was designed to address the specific benefit of leveraging these rich metadata models with Tableau as the front end.
Gartner sees the market moving heavily towards Artificial Intelligence (AI), Natural Language Processing (NLP), Natural Language Query (NLQ), and/or Natural Language Generation (NLG). Many of these figure prominently in Tableau’s and Microsoft’s roadmaps and are already central to vendors like IBM with Watson Analytics. Everyone’s claiming something in this area given they are the new hot buzz words – the reality of their implementations and practical realities have yet to be seen.
There is a significant jump in planned cloud deployments – from 46% to 51% – as organizations realize cost savings and such deployments are no longer regarded as “bleeding-edge.” Likewise, buying has shifted back from the Line of Business back to IT as these implementations become larger and more centralized.
Some accept Gartner’s word as Gospel, while some flat out mock it, such as the sage, but opinionated Stephen Few. And there are plenty of shortcomings to point out. For example, nowhere does this MQ discuss important business needs like financial reporting, pixel perfect reporting (invoices, statements), security or deployment and distribution. These are critical elements, many of which the good old enterprise BI tools do an amazing job at, and oh-by-the-way are non-trivial to productize. This means the modern vendors have a lot of work to do to implement these, and organizations will find themselves sorely lacking in critical functional areas, perhaps even resulting in the need to implement the dreaded multiple tool solution, with its attendant higher costs and complexity, should they make the wrong choice.
We are also left in the dark regarding how exactly Gartner calculates the position of the dots. We see the features they value, but nowhere do we see the weightings, which would almost by definition vary – possibly by a lot – based on the business requirements. Again, use cases should drive the functionality requirements and the relative weights, not the other way around.
Senturus cautions against a bottoms up or feature function approach as laid out in the MQ (granted they do frame up some typical use cases, but it’s way too high level to be useful in our opinion). Analyst reports such as these are useful tools and serve as a good starting point for vendor selection, especially in crowded spaces such as this. However, we continue to espouse using the “right tool for the job,” driven by real business requirements and value, and the underlying technology flows naturally from that point.
If you still have questions or concerns about the Gartner findings or would like to speak about your existing platform and where your organization is headed with business analytics, contact us at email@example.com.
You may also be interested in our webinar: Enterprise BI Platforms vs Self-Service Analytics Tools: The Right Tool for the Job in which experts from both the enterprise BI and self-service analytics practice areas explore the benefits and drawbacks of the various options. Also, you might be interested in reading our review of last’s years MQ.