After a momentous year of innovation, industry consolidation, and unstoppable big tech market domination, controversial placements, and omissions linger on in Gartner’s Magic Quadrant for Analytics and Business Intelligence Platforms 2020. I’d share the latest BI MQ image but Gartner asked me to remove all their images several years ago after a vendor complained. They probably didn’t like my annual evaluation of the results. Thanks to Sean Miller, you can explore a decade of dot placements. You can also search for the latest BI MQ image and read the full report from BI vendor websites. In this article, we’ll briefly discuss the findings and explore what is NOT covered.
With all due respect, I truly value the incredible work Gartner does each year along with the rigorous effort vendors put forth to be included in this report. Working through the entire process takes several months of resource and time investment. The following tidbits merely reflect my initial take-aways from reading the report details, speaking with peers, and talking to several vendors.
The Elephants in the Room
If cloud is a top-level influencing purchase factor as Gartner’s opening highlights, then where is Amazon QuickSight? Cloud copycat Alibaba Quick BI, a vendor that primarily services China, got added. Amazon’s QuickSight did not. Amazon’s solution should be mature enough by now to be included. Amazon has enough customers. All of my Amazon QuickSight classes sold out in 2017. The Amazon Web Services cloud analytics ecosystem surrounding it is market-leading.
Amazon QuickSight’s glaring omission is bizarre in 2020.
If I had to take a guess on why no Amazon QuickSight, I’d bet that Amazon chose not to participate.
Google’s Data Studio also got omitted. That one would likely make the customer survey cut but it would not get past a revenue bar as a free offering. On the other hand, Google’s little Looker secured an excellent Challengers quadrant placement.
The Future Outlook
Gartner shared ongoing cloud migration and augmented analytics trends again. They also touted old-school reporting as a new differentiator. Yes, you read that correctly. SAP (BusinessObjects), Oracle (Business Intelligence Suite Enterprise Edition) and IBM (Cognos, pre-version 11) were mentioned.
What is old is new again?
Gartner included vertical strategy in the completeness of vision. Across the BI industry, I’m seeing a noticeable shift towards vertical app strategy. YellowFin’s CEO recently wrote an excellent article on that trend. I’m giving an upcoming webinar with a healthcare vendor next month. The vertical movement makes sense since the horizontal BI market is mostly commoditized.
Shift to vertical market strategies
Interestingly, Google Cloud’s CEO just publicly shared his vertical focused go-to-market strategy. Don’t forget – Google has over 5 million G Suite customers. They are a formidable force to reckon with. Google’s vertical strategy could explain why Looker, popular with app devs, got acquired when Google already had Google Data Studio in its analytics portfolio.
Expect increased analytics automation
Analytics automation continues to improve. Basic exploratory analysis, trends, changes, forecasts, and outlier detection reporting is already automated by numerous BI vendors – often with lovely natural language descriptions of the findings. Gartner claims “By 2025, data stories will be the most widespread way of consuming analytics, and 75% of stories will be automatically generated using augmented analytics techniques.” Data storytelling courses will need to add critical thinking modules to validate machine created stories.
Machine Learning a Must Have
According to Gartner, “By 2022, 40% of machine learning model development and scoring will be done in products that do not have machine learning as their primary goal.” That stat is not surprising since I’ve been stitching machine learning into BI for over a decade.
So many buzzwords, so much confusion. Last year I shared important tips with you on how to demystify automated analytics and machine learning hype from reality. Although these technologies may sound or look alike in popular BI solutions, how they work behind the surface is significantly different.
- TIBCO Spotfire had wizard-based machine algorithms in 2014 – K-Means Clustering, Hierarchical, Regression Modeling, Classification Modeling, and others. They’ve come a long, long way since then with Enterprise Runtime for R (TERR), R, Spark MLlib, AutoML and more.
- Microsoft’s Power BI, Google’s Looker, Oracle’s Analytics Cloud, Amazon’s QuickSight and others include automated insights, AutoML, and other machine learning capabilities natively.
- Salesforce Einstein includes proprietary, limited embedded machine learning model building capabilities that are simple for anyone to use.
- Tableau includes R, DataRobot ML integration and more. The latest update to TabPy 1.0 better connects pickled machine learning model scoring capabilities into Tableau’s world. I’ll be playing with that one soon.
- Qlik’s cognitive engine is powered by machine learning. They also offer exceptionally good APIs and partner extensions to integrate machine learning functionality.
- Sisense, YellowFin, Pyramid, SAP, SAS, Log Analytics, ThoughtSpot, and the list goes on and on all include analytics automation and integrated machine learning capabilities.
I haven’t seen any BI tools replacing data scientist tools. I usually see a better together story of embedding high-quality machine learning models built by data scientists into BI tools for the business to use. There aren’t any BI tools out there today seriously deploying load-balanced, machine learning model workloads with model ops, drift, bias, A/B testing, canary updates, traffic routing, and other robust machine learning management capabilities. If you see them, I’d like to know.
2020 Movers and Shakers
Same, same, same – at a glance. If you skim past the Leaders quadrant, read the report details, and then talk to the vendors directly, this report gets far more interesting. Although a few BI vendors are growing and all vendors are quickly innovating now, most struggle in light of Microsoft’s Power BI being bundled into Office 365 with claims of $3/per user per month pricing. Predatory pricing? The last time I checked Google Data Studio was totally free and Amazon QuickSight changed to per-session pricing with a maximum charge of $5/per reader per month for unlimited use.
Big tech is more powerful now than any government in the world
Even as the big tech vendors entertain more US Federal Trade Commission inquiries and global anti-competitive behavior scrutiny, I expect nothing to change. Big tech will continue to drive world policy and shape our business landscape. Their unprecedented access to data influences individual behaviors and public opinion. Their ability to control markets, define and/or ignore laws, decide what we see and what we don’t, and scale through network effects remain unrestrained. Here’s a good read to understand those market dynamics.
Thus, BI and analytics vendors today need to deliver truly exceptional customer experiences, tangible business value and get creative to retain customers. There should be a large enough worldwide market for multiple BI vendors to serve different customers’ needs. Smaller vendors can become your strategic partners.
Congrats to TIBCO Spotfire, Looker, Oracle, and YellowFin
Vendors that made noteworthy strides in the BI MQ 2020 include:
- TIBCO Spotfire finally got better placement this year in Challengers. Their depth and breadth of capabilities across the entire analytics portfolio were key. Gartner dinged TIBCO on marketing and market awareness. Gee, that should be the easiest thing to fix!
- Looker also moved into the Challengers quadrant. I expect them to advance further next year as Google gets serious about business apps. A former Power BI GM leads that team.
- Oracle moved into Visionaries quadrant. That team is making significant progress to improve customer experience and cloud offerings. A former Microsoft BI peer of mine is leading the charge over there under Larry Ellison. Oracle also announced an AutoML offering that I’ll be checking out soon. As a strong business apps vendor, they are positioned nicely for the future.
- Yellowfin got a much better and well-deserved Visionaries placement. Having personally worked with this group in the past, I know that they are innovative and provide a strong offering. Much like TIBCO and other players, they don’t get much attention in the market.
ThoughtSpot Hot or Not?
This year I’m hearing ongoing grumbles about ThoughtSpot’s optimistic placement. The highly funded newcomer is a Silicon Valley darling with high profile investors. Love ‘em or hate ‘em, ThoughtSpot’s search and SpotIQ are nice. They do provide an on-prem offering that I suspect fills a void Microsoft consciously opts not to pursue. ThoughtSpot also hired a former Gartner analyst that is widely respected. Thus, market awareness improved last year.
Questions remain over ThoughtSpot’s Leader placement
Will that be enough to sustain ThoughtSpot in a cloudy, big tech-dominated market long-term? They don’t have a large customer footprint or the breadth of needed capabilities other vendors bring to the table. I feel the rumblings I’m hearing might be valid.
To get an unbiased perspective of the current BI market pulse, I did a quick Indeed.com job search and Google Trends Compare by vendors. I know these are not ideal indicators of market conditions but they do illuminate market demand and interest. Here are the results.
If you guestimate vendor demand from tools mentioned in public job openings, Amazon QuickSight has 3x more demand than ThoughtSpot.
If you guestimate vendor interest from tools mentioned in Google searches, here is the Google Trends report from 2004-2020 for the BI MQ Leaders quadrant vendors. Tableau and Power BI clearly get the most search interest.
Looking at that same report for BI MQ Visionaries quadrant vendors and ThoughtSpot, Looker stands out. Looker searches peaked during news of Google’s acquisition of them. You can start to see ThoughtSpot searches rise around 2015-2016.
That’s All for Now
While I could go on and on about this report, I’ll leave it at that for now. I’m much more interested in the imminent Gartner Machine Learning Magic Quadrant this year. It published late…likely due to vendors challenging the results. More to come on that one soon.