Market Analysis From Gartner’s 2017 BI Magic Quadrant

A review of the market analysis within Gartner's 2017 BI Magic Quadrant and recommendations for analytic investments based on the report. 

Gartner’s 2017 BI and Analytics Magic Quadrant is out. As per usual, it’s what everyone in the industry is talking about right now. While is it fun to look at the vendors and see who made it to the leader’s quadrant and how the vendors change position over the years, the market analysis is even more interesting and relevant.

Gartner is now describing three “waves” of business intelligence:

1. IT-Centric Semantic-Layer-based Approach (eg. classic Business Objects, MicroStrategy)
2. Visual-Based Data Discovery (e.g. Qlik, Tableau)
3. Smart Data Discovery

They describe the third wave as being an evolution of data discovery that automates the analytics workflow through machine learning and interaction with the user through natural language. They suggest that within three years, this approach will be mainstream.

We’ve seen interest in Natural Language Generation (NLG) in our client base, and have recommended and implemented solutions from Narrative Science (one of the vendors mentioned in the Quadrant’s appendix) to satisfy that need. We’ve not seen a lot of requirements around Natural Language Processing (NLP) or Natural Language Query (NLQ).

The trend towards “Smart Data Discovery” is one we’re watching closely, and so should anyone interested in data and analytics.

Here are four recommendations to those trying to figure out where to invest in their analytics capabilities based on this latest Gartner Magic Quadrant.

1. Let Your Requirements Guide You

These waves are not mutually exclusive. Most companies need both a structured semantic-layer approach for some of their needs and visual data discovery for others. Rather than a march away from more structured centralized reporting to more decentralized, we see the market more as a pendulum. Right now, most of our clients have invested in the visual data discovery approach, but increasingly, we see the pendulum move back to the middle where companies want to reign in the wild-wild West through centralized administration and data preparation.

Let your requirements guide your technology decisions. If you are having trouble in business meetings with inconsistent data definitions, you might need to centralize part of your data and analytics infrastructure. If you have new challenges you are trying to solve (e.g. figuring out a discounting strategy that maximizes profit), you may want to give more of your smart employees access to the data and tools to do complex analysis.

2. Apply The Right Tool For The Right Job

It is early for “Smart Data Discovery,” but technology moves very fast and companies should consider what business initiatives could benefit from smart data discovery today with an eye to the future, knowing that this is where the innovation is going to be. But don’t just invest in technology without a real need for it. Just because a vendor has something they call “smart data discovery” doesn’t mean it is, or that you might need it. No tool or feature (machine learning included) is going to figure out your requirements for you nor are they going to do the hard work of understanding and modeling your business data.

Being successful in your analytic endeavors doesn’t mean you blindly adopt the newest wave or technology trend. Companies succeed by applying the right technique and right tool for the right job based on your overall data strategy and business objectives.

3. Be Aware Of Your Business Users’ Needs

What is considered “simple” for business users will get simpler and simpler at time goes on. The mainstream tool for interacting with business data in the 1960s and 1970s was COBOL (it stands for Common Business-Oriented Language). In the 1980s, SQL (and the relational database) was hailed as a way for a non-programmer to access databases. In the 1990s, tools like Oracle Forms and Business Objects used to be considered easy. Now the mainstream tools like Tableau and Qlik are considered relatively easy, but business users still want easier tools. Tools like Thoughtspot are squarely focused on easier access to business information for the masses, but all the vendors constantly work on bringing analysis capabilities to a broader audience.

Organizations should pay attention to the large numbers of employees and stakeholders who don’t have the time, ability, or interest to create content, but still need analysis capabilities that go beyond reading a report.

4. Don’t Forget The Importance Of ETL + Data Prep

None of this technology exists in a vacuum, and no technology is going to eliminate the need for hard work in understanding what data is important to a business, and how it should be curated.  The sometimes unsexy behind-the-scenes effort in ETL and data preparation will be the key to success with data and analytics for the foreseeable future.


It is a fun time to be in the middle of such a fast-paced industry and have the privilege to work with lots of interesting organizations and help them achieve their missions through data and analytics, and this latest Gartner Magic Quadrant is an indication that there is a very bright future for those companies that invest in analytics!

Access A Complimentary Download Of The Gartner Report 

In 2005, David founded Analytics8 in the United States. His focus is on leading and building Analytics8’s business in the Americas as well as our worldwide products business. His favorite way to spend a weekend is building something or doing an activity with his family. “Relaxing” on a beach sounds like torture to him.


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