Magic Quadrant for Analytic

Magic Quadrant for Analytics and Business Intelligence
Published 15 February 2021 – ID G00467317 – 72 min read
By Analysts
James Richardson, Kurt Schlegel, Rita Sallam, Austin Kronz, Julian Sun
The definition of self-service is shifting in this field as augmented capabilities pervade platforms.
At the same time, cloud ecosystems and alignment with productivity tools have become key
selection factors. This Magic Quadrant will help data and analytics leaders plan an analytics
andBI roadmap.
Market Definition/Description
Analytics and business intelligence (ABI) platforms are characterized by easy-to-use functionalitythat
supports a full analytic workflow — from data preparation to visual exploration and insight
generation — with an emphasis on self-service usage and augmented user assistance.
Vendors in the ABI market range from startups backed by venture capital funds to large technology
firms. The vast majority of new spending in this market is on cloud deployments, and major cloud
platform players are present in the market. In many cases, ABI platforms are entry points for wider
sets of cloud data management capabilities offered by these cloud vendors, examples being
Microsoft Azure Synapse Analytics and IBM Cloud Pak for Data.
ABI platforms are no longer differentiated by their data visualization capabilities, which are now
commoditized. All vendors can build interactive key performance indicator (KPI) dashboards using
common chart forms (bar/column, line/area, scatter, pie and geographic maps) and drawing on a
wide range of data sources. Differentiation has shifted to how well platforms support augmented
analytics. Augmentation utilizes machine learning (ML) and artificial intelligence (AI)-assisted data
preparation, insight generation and insight explanation to help business people and data analysts
explore and analyze data more effectively than they could manually. Rather than being a discrete
capability, augmentation is now threaded through platforms as ML is applied across the data-todecision workflow.
The scope of augmentation is extending. Originally intended to assist analyst personas using selfservice, augmentation and, increasingly, automation are now being applied to help end users directly,
giving rise to a new user category: augmented consumers. These are nontechnical people who
expect insights to find them, often in the form of machine-generated data stories driven by

automated insights based on ongoing monitoring of data relevant to their role, persona or job
function. To ensure relevance, this functionality includes usage behavior, especially natural language
query (NLQ) history and user feedback and ratings on automatically served content. This change has
the potential to push ABI beyond the approximately 30% adoption ceiling that has been in placndee
for many years. (For more information, see
Augmented Analytics: Teaching Machines to Tell Data
Stories to Humans
ABI platform functionality includes the following 12 critical capability areas, which have been
updated to reflect areas of change and differentiation, particularly in capabilities more closely
associated with augmented analytics:
Security: Capabilities that enable platform security, administering of users, auditing of platform
access and authentication.
Manageability: Capabilities that track usage of the ABI platform and manage how information is
shared (and by whom).
Cloud analytics: The ability to support building, deployment and management of analytics in the
cloud, based on data stored both in the cloud and on-premises.
Data source connectivity: Capabilities that enable users to connect to, query and ingest data, while
optimizing for performance.
Data preparation: Support for drag-and-drop, user-driven combination of data from different
sources, and the creation of analytic models (such as user-defined measures, sets, groups and
Catalog: The ability to automatically generate and curate a searchable catalog of analytic content,
thus making it easier for analytic consumers to know what content is available.
Automated insights: A core attribute of augmented analytics, this is the application of ML
techniques to automatically generate findings for end users (for example, by identifying the most
important attributes in a dataset).
Data visualization: Support for highly interactive dashboards and exploration of data through
manipulation of chart images.
Data storytelling: The ability to combine interactive data visualization with narrative techniques in
order to package and deliver analytic content in a compelling, easily understood form for
presentation to decision makers.
Natural language query (NLQ): This enables users to ask questions and query data and analytic
content using terms that are either typed into a search box or spoken.

Natural language generation (NLG): The automatic creation of linguistically rich descriptions of
answers, data and analytic content. Within the analytics context, as the user interacts with data,
the narrative changes dynamically to explain key findings or the meaning of charts or dashboards.
Reporting: The ability to create and distribute (or “burst”) pixel-perfect, grid-layout, multipage
reports to users on a scheduled basis.
Magic Quadrant
Figure 1: Magic Quadrant for Analytics and Business Intelligence
Source: Gartner (February 2021)
Vendor Strengths and Cautions
Alibaba Cloud
Alibaba Cloud is a Niche Player in this Magic Quadrant. As yet, it competes only in Asia/Pacific, but it
has global potential.
Alibaba Cloud is the largest public cloud platform provider in Asia/Pacific. It offers data preparation,
visual-based data discovery, interactive dashboards and augmented analytics through its Quick BI
platform. This platform is available as a SaaS option running on Alibaba Cloud’s infrastructure, an onpremises option on Apsara Stack Enterprise and an embedded analytics option with Alibaba
Business Advisor.
With release 3.9, Quick BI has improved augmented analytics capability with DingTalk, Alibaba
Cloud’s digital workplace collaboration tool, and thus addresses a broader range of data and
analytics consumers.
Vision for augmented analytics: Quick BI offers good support for data visualization and
dashboards. It also offers some capabilities enabled by AI, beyond reporting and self-service
analytics, namely augmented analytics features such as automated insights and NLQ to improve
user adoption. Another capability, currently on Alibaba Cloud’s roadmap, is integration with its data
science platform, PAI Studio, to provide more in-depth insights aided by its internal ecosystem as a
cloud service provider.
Modular architecture supporting composable analytics: Quick BI is utilized in Alibaba Cloud’s
“Data Middle Office” strategy, which delivers a modular and reusable data and analytics capability.
As such, Quick BI can be used to help compose analytic applications and provide businessoriented data products such as Quick Audience (for customer insights and marketing automation).
Expertise in e-commerce: Quick BI can integrate with Business Advisor, Alibaba’s market
intelligence platform, to utilize and blend industry benchmark data. It gives organizations a
stronger domain analytics capability, thanks to Alibaba Cloud’s expertise in e-commerce.
Capability gaps due to single-market focus: Cloud computing in China is growing rapidly, and
most Quick BI customers and prospects are in China. There is little incentive for Alibaba Cloud to
test Quick BI’s maturity by expanding into the more mature and demanding U.S. or EMEA cloud
markets. Currently, organizations in China have lower expectations of cloud products, which tend
to be of lower quality in this country. The market that Quick BI serves does not usually expect it to
be comparable to non-Chinese products. Organizations looking for leading-edge functionality
should consider competing platforms.
Geographical presence and market momentum: Alibaba Cloud is a China-focused vendor, with a
minimal installed base elsewhere. The newly released NLQ capability is available only in Chinese,
which limits its international appeal. As a SaaS offering, Quick BI is often packaged into the

vendor’s integrated Data Middle Office solution. Judging from the number of client inquiries that
Gartner analysts have received and from job postings, Quick BI’s market momentum as an ABI tool
is not as strong as that of products from local competitors such as FanRuan.
Product capabilities: Alibaba Cloud has improved Quick BI’s overall product capabilities
significantly, but these are still relatively weak, compared with those of some vendors in this Magic
Quadrant. All 12 functional capabilities evaluated are below average.
Amazon Web Services
Amazon Web Services (AWS) is a Niche Player in this Magic Quadrant. Despite AWS’s strong
adoption in other areas of the data and analytics stack, Amazon QuickSight is relatively new, and
AWS is not as well-known in the ABI platform market. Nevertheless, Amazon QuickSight has huge
potential to sell to the AWS installed base.
Amazon QuickSight is a fully managed, cloud-based ABI service for performing ad hoc analysis and
publishing interactive dashboards. The platform ingests data from a variety of on-premises and
cloud-based data sources into its parallel, in-memory calculation engine, SPICE, and AWS claims it
can scale to hundreds of thousands of users without any server setup or management.
In late 2020, AWS added embedded authoring capabilities with support for multitenant deployments
and dashboards with autorefreshing data. Supported sources include real-time data in Elasticsearch
and Amazon Timestream, as well as databases (such as Amazon Aurora, MySQL and
PostgreSQL),data warehouses (Amazon Redshift, Snowflake and Teradata) and serverless options
(such as Amazon Athena). Additionally, AWS has announced Amazon QuickSight Q, an MLpowered NLQ capability.
Potential price disruptor: AWS prices its QuickSight service at $216 per user per year for content
authors. For content consumers, QuickSight has a pay-per-session model, charging $0.30 per 30-
minute session, with a maximum charge of $5 per user per month. So the most a consumer would
pay is $60 per user per year. This is at least half the list price of other vendors’ per-user pricing
Cross-selling opportunity: AWS is the largest cloud service provider in the world by revenue, and
has an international presence and a global client base. Many organizations are investing heavily in
AWS for the backbone of their data and analytics stack. AWS has already made significant
progress with Amazon Redshift, Amazon Athena and Amazon EMR. Amazon QuickSight can draw
on that momentum, with many data and analytics and application developers eager to build on the
AWS stack.
Frequent updates: Although Amazon QuickSight is a relatively new product and therefore missing
some key features, the frequent refreshes of AWS’s cloud architecture, coupled with its extensive

development resources, indicate that QuickSight could close the functionality gap quickly.
Moreover, AWS has wisely resisted any temptation to make acquisitions in the ABI platform space,
which might enable quick gains in market share but would sacrifice tight integration with the AWS
Emergent functionality: Amazon QuickSight provides core data connectivity and data visualization
functionality. However, overall, QuickSight is less well-developed than competing platforms in a
number of areas, and particularly in its data preparation, manageability, Mode 1 reporting, NLQ and
catalog capabilities.
Lack of business applications: Beyond its contact center application, AWS lacks a broad business
application ecosystem to drive demand for its ABI offering. Similarly, its nascent personal
productivity and collaboration offerings (Amazon WorkDocs) lack significant adoption, unlike
those of Microsoft and Google. This may limit end-user demand for, and the comparative appeal
of, AWS QuickSight.
AWS centricity: QuickSight runs only on AWS. The lack of capability to embrace a multicloud world
clashes with the fact that most organizations will have data on multiple clouds. Although AWS can
make QuickSight work in a hybrid environment — by leveraging SPICE or direct query — hybrid
cloud is not a strong part of the QuickSight vision. As a result, AWS’s growth in the ABI platform
market will mostly come from its own installed base.
Board is a Niche Player in this Magic Quadrant. It mainly serves a submarket for financially oriented
Board differentiates itself by providing a decision-making platform that supports business processes
more fully than vendors of competing ABI products aim to. The company originated in Switzerland
and most of its customers are still in Europe, but it also has an impressive roster of customers in the
U.S. Board offers a subscription pricing model for on-premises and hosted cloud deployments.
In 2020, Board 11 was further developed by adding a set of REST APIs for front-end and back-end
integration, new data visualization types and improved centralized user access management for
distributed applications.
Low-code, closed-loop application creation: Board’s platform capabilities enable users to extend
beyond typical BI use cases. They can use self-service to build and publish process-oriented
analytic applications that include functions such as data entry and business rules using a dragand-drop interface.

Unified analytics, BI, and financial planning and analysis (FP&A): Board is one of only two
vendors in this Magic Quadrant to offer a modern ABI platform with integrated FP&A functionality.
As such, Board is highly differentiated for buyers looking to close the gap between BI and
processes such as those involved in planning, budgeting and financial consolidation.
Extensive system integrator (SI) partners: Board has a well-established network of SI partners.
These help to drive its growth and give it presence, by proxy, outside the nine markets where it has
significant direct operations, namely the U.S., Switzerland, the U.K., Italy, Germany, Australia,
France, Benelux and Spain.
Lack of market momentum: Board appears infrequently on vendor evaluation shortlists seen by
Gartner, and its new customer growth is limited. Furthermore, Board’s user community is one of
the smallest of the vendors in this Magic Quadrant, with very little user-created content available
on public video-sharing websites. Given the near functional parity between most ABI platforms for
core use cases, these ecosystem factors are increasingly heavily weighted in product selection
Minimal recognition outside finance departments: In most cases, Board enters a company via the
finance department, its brand being well-known there. Persuading end users in other functions to
use its platform as an alternative to better-known BI platforms can prove difficult. Board is rarely
named as the sole or main BI standard by users of Gartner’s client inquiry service.
Slow product release tempo: Board is not innovating as quickly as most ABI vendors. Compared
with the monthly frequency of product releases issued by competitors, Board’s approach is slower,
which means that the gap between the capabilities it delivers and those of other platforms is
growing. This is evident in areas like automated insight generation and NLQ.
Domo is a Challenger in this Magic Quadrant, thanks to significant improvements to its product and
its consumer-led vision for ABI.
This vendor’s focus on business-user-deployed dashboards and ease of use characterizes its appeal.
Domo’s cloud-based ABI platform offers over 1,000 data connectors, consumer-friendly data
visualizations and dashboards, and a low/no-code environment for BI application development.
Domo typically sells directly to business departments, such as marketing and sales, that are
attracted to its platform’s ease of use and fast time to deployment.
In 2020, Domo made significant improvements to the product, particularly in the areas of data
preparation and manageability. These changes are significant, given Domo’s reputation for delivering
attractive front ends that appeal to senior executives but are less appealing to power users and

business analysts. The data preparation improvements enable Domo to offer deeper analysis and
more end-to-end capability.
Business momentum: Despite tough competition, Domo’s subscription revenue increased by 25%
between the first nine months of 2019 and the first nine months of 2020. Domo is winning new
customers and increasing its relevance to enterprise buyers.
Speed of deployment: Domo’s ability to connect quickly to enterprise applications enables rapid
deployment. Domo’s connectivity is differentiated in that it maintains API-like connectors that can
respond dynamically to changes in source-side schemas.
Consumer design focus: Since 2010, Domo has been competing with a consumer-centric
approach in a market almost exclusively focused on “power users,” but new market dynamics
emphasizing the “analytic consumer” and the “empowered analyst” should work in Domo’s favor.
Lack of adoption drivers: Domo faces a competitive disadvantage against ABI platform vendors
that have their own application ecosystems and cloud platforms. In particular, some buyers will
prioritize ABI platforms that are embedded as integrated components in their cloud incumbent
(such as AWS, Microsoft Azure or Google) or application incumbent (such as Salesforce, Oracle or
Limited geographic presence: Although Domo’s platform supports multiple languages (English,
Japanese, French, German, Spanish and simplified Chinese), the company has a direct presence
inonly four countries: the U.S., Japan, the U.K. and Australia. Three-quarters of its revenue derives
from the U.S. This narrowness may impair its suitability for enterprises based in other countries.
Premium pricing model: Domo’s prices have decreased substantially in the face of stiff
competition, but it still prices at a premium compared with the low-cost cloud providers such as
Microsoft (with Power BI) and AWS (with QuickSight). Domo has been forced to readjust its pricing
model to keep pace, but evaluators will still need to consider its pricing.
Google (Looker)
Google (Looker) is a Challenger in this Magic Quadrant. Looker’s acquisition by Google in 2020
increased its market recognition and consideration by buyers, especially as Google made progress
integrating Looker into Google Cloud Platform’s (GCP’s) portfolio and go-to-market efforts.
Looker offers modern ABI reporting and dashboard capabilities using an agile, centralized data
model and an in-database architecture optimized for various cloud databases.

In 2020, Looker introduced enhancements to its user experience, including a mobile app (on both iOS
and Android) and an NLQ interface (Looker Q&A) that uses the LookML semantic layer. For
developers, Looker introduced the Looker extension framework, a hosted development environment
on which it built and released the Looker Data Dictionary, its first Looker-authored extension. Looker
also extended its integrations with Google Cloud applications, such as Google Marketing Platform
and the Google Contact Center AI solution. Additionally, it added optimizations with Google Sheets
and Google BigQuery.
In-database architecture and governed data model: The Google (Looker) offering does not require
in-memory storage optimizations. Rather, it leaves data in the underlying database and uses the
LookML data modeling layer to apply business rules. This enables power users and data engineers
to model data and then reuse data and calculations in other applications in a trusted and
consistent way. Looker is opening up LookML-governed data to other analytics and BI platforms,
having added a Tableau-specific connector in 2020. This approach exploits the performance and
scalability of the underlying database and supports data source flexibility.
Customer-facing application development: The developer is a key persona for Looker. Google
(Looker) offers extensive APIs, SDKs, developer tools and workflow integration support for enduser organizations and OEMs that want to create and embed analytics in application workflows,
portals and customer-facing applications.
Leverage within GCP ecosystem: Following its acquisition of Looker, Google has made progress
integrating Looker into GCP go-to-market activities, including the introduction of new Google
BigQuery product bundles. This, coupled with an acceleration in cloud data management and ABI
adoption, has contributed to an increase in Looker’s market momentum.
Power user skills requirement for data modeling: In contrast to the point-and-click and augmented
approach taken by competing vendors’ platforms, which are targeted at enabling less technically
skilled users, Looker’s data modeling requires coding. The product lacks data preparation
capabilities for visually manipulating data. Additionally, automated model generation from Google
BigQuery is a roadmap item.
Narrowness of product offering: Looker has added NLQ and offers access to Google BigQuery
ML-based functions and optimizations from within Looker. However, its current product is missing
important capabilities that are likely to define the future of ABI platforms, such as AI-automated,
augmented analytics and natural-language-driven consumerlike experiences.
Limited global presence: Although Google has expanded Looker’s global presence following the
acquisition, adoption of Looker outside the U.S., Western Europe and Japan remains limited,

compared with Leaders in this Magic Quadrant. Evaluators in those regions should take this into
IBM is a Niche Player in this Magic Quadrant. IBM Cognos Analytics is primarily of interest to existing
IBM Cognos customers who are looking to modernize their ABI use.
IBM Cognos Analytics has multiple deployment options — public, private and on-premises — and,
through IBM Cloud Pak for Data, offers multicloud support for AWS, Microsoft Azure, GCP and IBM
Cloud. IBM Cloud Pak for Data also gives customers the choice to use IBM Cognos in conjunction
with IBM’s containerized stack of data and analytics services, including storage, data virtualization,
data refinery, data catalog, and data science and ML services.
In 2020, IBM Cognos Analytics gained AI-assisted functionality to explore key driver relationships,
time series analysis for univariate and multivariate forecasting, and “what if?” analysis in a single UI.
In addition, its augmented data preparation capability was extended, to offer AI recommendations
that help with data modeling.
Comprehensive functionality for Mode 1 and Mode 2 use cases: IBM Cognos Analytics is one of
the few offerings that include enterprise reporting, governed and self-service visual exploration,
and augmented analytics in a single platform. In addition, as existing IBM Cognos Framework
Manager models and reports from earlier versions can be used in the single environment, there is
a migration path and the ability to use existing content.
Roadmap for applying analytics everywhere: Visionary elements of IBM’s roadmap include a
social insights add-on, AI-driven data preparation and analytic quality scores for data sources. A
big part of IBM’s vision is to unify planning, reporting and analysis in a common portal that offers
“what if?” scenario planning, Mode 1 reporting, and predictive models and forecasts.
Flexible deployment options: IBM offers a variety of deployment options to meet all customer
requirements. These include on-premises, cloud (IBM-hosted cloud and IBM OnDemand Cloud
Service) and “bring your own license” for any of the major infrastructure as a service (IaaS)
platforms (Microsoft Azure, Google, AWS), and IBM Cloud Pak for Data.
Fading brand value: IBM has put significant effort into its broad data and analytics marketing
efforts and vision centered on IBM Cloud Pak for Data, but this messaging is not resonating with
evaluators in the ABI market. The Cognos brand is no longer commonly bracketed with the leading
vendors in the minds of evaluators or potential users. IBM Cognos Analytics rarely appears on
evaluation shortlists seen by Gartner, unless IBM is already an incumbent vendor. Interest in IBM

Cognos Analytics from Gartner clients failed to rebound in 2020, judging from their inquiries and
Lack of sales adoption drivers: Despite being a large vendor with a wide data and analytics
offering, IBM benefits from neither personal productivity suite uplift (as Microsoft and Google do)or
an enterprise application “tailwind” (as Oracle and SAP do). This limits IBM Cognos Analytics’
touchpoints with organizations that might invest in the platform.
Price versus cloud vendor alternatives: Prices for IBM Cognos Analytics Standard, Plus and
Premium — at $15, $35 and $70 per user per month, respectively — are in line with those of
independent ABI specialists but significantly higher than those of some other large cloud
providers. Consequently, IBM struggles to be price-competitive in new deals.
Infor is a Niche Player in this Magic Quadrant. Its strategy aims primarily to meet the analytics needs
of the Infor ERP installed base, as well as OEM/embedded analytics use cases.
Infor Birst is an end-to-end data warehouse, reporting and visualization platform built for the cloud. Italso
runs as an on-premises appliance on commodity hardware. Judging by inquiries from Gartner clients,
most organizations that consider using Birst are Infor customers.
In 2020, Infor added new capabilities to further integrate Birst with Infor ERP applications with
context-aware filtering and workflows. It also added the capability to deliver real-time AI/ML while
users are performing interactive analysis, and unified Mode 1 pixel-perfect reporting and Mode 2
visualization in the same design canvas. With version 7.6 of Birst, Infor completed its redesign of the
administration experience, separating enterprise security from data engineering, and removed all
final pieces of Adobe Flash. In addition, in 2020, pricing and packaging were greatly simplified to a
single, all-inclusive user approach.
Range of prebuilt vertical applications: Infor Birst for CloudSuite gives Infor ERP customers
prebuilt extraction, transformation and loading (ETL), data models and dashboards that are fully
integrated into Infor business applications. It includes industry-specific analytics for
manufacturing, distribution, healthcare, asset management and human capital management. For
non-Infor data sources, Birst provides solution accelerators for specific domains, such as wealth
management, insurance, sales and marketing.
Flexibility and governance for self-service data models: Infor Birst’s networked semantic
metadata layer enables business units to create models that can be promoted to the wider
enterprise. Birst has patented capabilities that combine centralized and decentralized operating
modes of BI, supporting a process to enable agile end-user self-service while preventing analyticalsilos
and the overhead often associated with centralized BI.

Hybrid cloud capability: Infor Birst provides data preparation, dashboards, visual exploration and
formatted, scheduled reports on a single cloud-native platform. It supports live connectivity with
on-premises data sources and rapid creation of a data model and an all-in-one data warehouse ona
range of storage options. Infor Birst supports six data center options in the U.S. (including on
AWS GovCloud), Europe and Asia/Pacific. Customers can choose a single region or deploy across
multiple regions. Birst also offers a cross-site migration tool to enable customers to easily migrate
configurations across regions and hybrid cloud environments.
Strategic focus on Infor installed base: 2020 saw Infor shift strategy to focus Birst primarily on
meeting the analytic needs of Infor ERP customers. In some ways this is unfortunate, as Birst
remains a strong product for stand-alone use but is now rarely considered by those outside the
Infor installed base. As Birst’s development direction becomes less about the open market’s needs
and more about tighter integration with Infor ERP, it will become less relevant to those who may
otherwise have considered it an option.
Reporting centricity: Although Infor Birst now has a single interface for reporting and data
visualization, its interactive, visually driven charting functionality still lags behind that of other
products on the market. Birst is mainly used for Mode 1 static and parameter-driven reporting, for
which its functionality is well-developed, rather than for Mode 2 agile, visually driven requirements,
for which it is weaker.
Lack of consumerized, augmented vision: Although it has offered augmented data preparation
from the outset, Infor lacks a strong vision when it comes to augmenting the user experience.
Improved catalog and search capabilities are on its roadmap, however.
Information Builders
Information Builders is a Niche Player in this Magic Quadrant. Its WebFOCUS Designer is of most
interest to its installed base and not often evaluated in competitive sales cycles of which Gartner is
Information Builders sells the integrated WebFOCUS ABI platform, as well as individual components
thereof. WebFOCUS Designer includes components from the WebFOCUS stack that are intended to
satisfy modern self-service ABI needs.
Compared with previous versions, WebFOCUS 8207 has improved usability and performance,
amodern self-service analytics experience and key content authoring workflows.
In 2020, Information Builders agreed to be acquired by TIBCO Software.

Note: During our research for this Magic Quadrant, TIBCO Software announced that it had entered into
an agreement to acquire Information Builders. The acquisition was due to complete in the first quarter of
2021. As a result, product and company integration plans were not developed and available to share with
Gartner in time for consideration in this Magic Quadrant. Consequently, representing the two as
one entity is not warranted, nor would it be useful to readers at this point. TIBCO Software and
Information Builders are therefore represented separately in this Magic Quadrant.
External and large-scale deployments: Information Builders is well-known for deploying externally
facing analytic applications at scale, sometimes for thousands of users per deployment.
WebFOCUS offers flexible deployment options: Information Builders-managed cloud, private cloud
and on-premises.
Prepackaged analytic playbooks: Information Builders offers industry-specific solutions for
healthcare, credit union, insurance, law enforcement and public-sector customers needing
prepackaged data and analytics solutions without the need to invest in extensive upfront
configuration and development. In this way, Information Builders enables faster time to value.
Support for complex data: A core strength of Information Builders is data connectivity and
integration of a variety of data sources, including real-time data streams.
Lack of differentiated vision: Although augmented functionality is being delivered and appears on
its roadmap for 2021, Information Builders’ overall vision and product strategy are not clearly
differentiated from those of its competitors. Information Builders is more of a fast follower than a
market disruptor that others need to copy.
Lack of momentum: Although Information Builders’ product roadmap shows drastic
improvements to the existing platform, Gartner’s search and inquiry data, along with external
metrics such as social media following, indicate that its traction in the market remains low, relativeto
competitors. Consequently, skills in Information Builders’ ABI platform are less readily available
than for its competitors’ platforms.
Acquisition- uncertainty: Information Builders has agreed to be acquired by TIBCO
Software. Both organizations have offerings across the data and analytics stack, and they will
have to work out where each of their tools belongs. WebFOCUS overlaps with both TIBCO
Jaspersoft and TIBCO Spotfire. For potential buyers, this creates uncertainty. Information Builders
and TIBCO are currently unfolding integration and synergy plans with a focus on creating multiple
options for current customers and potential buyers.
Microsoft is a Leader in this Magic Quadrant. It has massive market reach through Microsoft Office
and a comprehensive and visionary product roadmap.
Microsoft offers data preparation, visual-based data discovery, interactive dashboards and
augmented analytics in Power BI. This is available as a SaaS option running in the Azure cloud or as
an on-premises option in Power BI Report Server. Power BI Desktop can be used as a stand-alone,
free personal analysis tool. Installation of Power BI Desktop is required when power users are
authoring complex data mashups involving on-premises data sources.
Microsoft releases a weekly update to its cloud-based Power BI service, which gained hundreds of
features in 2020. Notable additions include more augmented analytics in the form of AI-infused
experiences, including smart narratives (NLG) and anomaly detection capabilities for out-of-the-box
Alignment with Office 365 and Teams: The inclusion of Power BI in the Office 365 E5 SKU
hasprovided an enormous channel for the platform’s spread, making it “self-seeding” in many
organizations. The increasing integration of Power BI into Microsoft Teams, with its tens of
millions of daily active users, will further increase Power BI’s reach in the world of remote working.
Power BI is now often the option that organizations have in mind when using Gartner’s client
inquiry service to ask about ABI platform selection — “why not Power BI?” is effectively the
question most are asking.
Price/power combination: The influence of Power BI has drastically reduced the price of tools in
the ABI platform market since its launch. In this case, though, low price does not equate to limited
functionality. The Power BI cloud service is extremely rich in its capabilities, which include an
enlarged set of augmented analytics and automated ML capabilities. AI-powered services, such as
text, sentiment and image analytics, are available within Power BI Premium and draw on Azure
Scope of product ambition: Microsoft continues to invest in a broad set of visionary capabilities
and to integrate them with Power BI. It now claims to have 80,000 customers using AI services in
Power BI deployments. It continues to encourage usage at scale by, for example, applying MLdriven automatic optimization of materialized views on Azure Synapse (and soon other data
sources, including Snowflake and Redshift) to autotune query performance.
Functional gaps in on-premises version: Compared with the Power BI cloud service, Microsoft’s
on-premises offering has significant functional gaps, including in relation to dashboards,
streaming analytics, prebuilt content, natural language question and answer, augmentation (what
Microsoft calls Quick Insights) and alerting. None of these functions are supported in Power BI
Report Server, its on-premises offering.

Azure only: Microsoft does not give customers the flexibility to choose a cloud IaaS offering. Its
Power BI service runs only in Azure. However, customers that utilize Azure can take advantage of
the global reach offered by Microsoft’s cloud platform. Power BI Premium enables customers to
enable multigeography capabilities in their Power BI tenant, and they can deploy their capacity to
one of 42 globally available data centers.
Content promotion and publication process: The way in which Power BI handles the promotion
and publication of content can lead to a significant administrative overhead for customers. The
fact that there is a one-to-one relationship between published Power BI apps and Workspaces
(Power BI’s collaborative “development” environment) means that organizations may face a
situation in which they are manually managing many hundreds of Workspaces. Retroactively fixing
this issue is a complex task. How to govern self-service usage is perhaps the most common
question asked about Power BI by users of Gartner’s inquiry service. The Power BI team is,
however, investing in governance capabilities to help customers manage their Power BI
environments better.
MicroStrategy is a Challenger in this Magic Quadrant. It is functionally strong across a wide range of
use cases, and its direct query capabilities make it well-suited for use on cloud data warehouses.
However, its vision is narrow and fails to reflect other key selection drivers, particularly augmented
The MicroStrategy platform, which comprises an analytic product family for data connectivity, data
visualization, reporting and advanced analytics, is supplemented by complementary mobile, cloud,
embedded and identity analytics products. Its intuitive HyperIntelligence application uses a semantic
graph to overlay and dynamically identify predefined insights within existing applications. Unusually, the
MicroStrategy semantic graph is open to competing ABI platforms.
In 2020, MicroStrategy extended its HyperIntelligence capability to meet new asset management,
retail stocking and other use cases, by enabling images and thresholds (HyperVision), thereby
potentially putting ABI into the hands of decision makers on shop floors, for example.
HyperIntelligence was also made available as a SaaS offering (Hyper.Now), enabling business users
to author and share HyperIntelligence cards with little effort. MicroStrategy also maintained its
commitment to openness by adding support for Jupyter Notebook and RStudio to its semantic
graph, and further developed the enterprise deployment capabilities of its hosted service,
MicroStrategy Cloud Environment, available on Microsoft Azure and AWS.
Direct query: In the era of visual data discovery, BI architectures catered to business analysts by
ingesting data into BI platforms to bolster performance. As companies make significant
investments in cloud databases, they will be disinclined to take the data out of those databases.

MicroStrategy’s native data integration will help facilitate the architectural shift to a direct query
Mode 1 and Mode 2 reporting: MicroStrategy is one of the key providers for customers who want
all the security, manageability and scale of complex Mode 1 reporting and a modern agile Mode 2
analytical environment.
Stability of integrated product: MicroStrategy does not acquire codebases. All new developments
are built organically. This leads to more stable, less buggy code, especially compared with
competitors that fill product gaps with acquisitions.
Lack of advantage of stack ABI solutions: Much of the momentum in the ABI platform market
comes from the shift to deployment on cloud ecosystems, as well as to cloud-based business
applications. Although MicroStrategy’s platform is offered as a service on AWS and Microsoft
Azure, and interacts well with other cloud technologies, ABI solutions owned by cloud and
business application megavendors have a go-to-market advantage. This may impact how users
view MicroStrategy’s platform and their likelihood of evaluating it.
Lack of product differentiation: Launched in 2019, MicroStrategy’s HyperIntelligence capability,
which embeds insights, suggestions and actions directly into enterprise applications, is now its
key differentiator. It forms a central aspect of MicroStrategy’s growth strategy (along with an
increased focus on SaaS offerings) and has attracted new customers. However, in Gartner’s view,
it will face increased competition from other ABI platforms as they also begin to surface analytic
findings directly in the context of users’ workflows and collaboration. This may make it harder for
MicroStrategy to differentiate its platform.
Augmented analytics capabilities: Despite having had one of the most comprehensive ABI
platforms for years, MicroStrategy now has two big feature gaps: automated insights and NLG. For
organizations looking to help self-service users get the most from data and analytics adoption,
these gaps may prove deal breakers.
Oracle is a Visionary in this Magic Quadrant. Oracle Analytics Cloud (OAC) is an end-to-end cloud-first
platform that provides data ingestion, preparation, visualization, dashboards, reporting and mobility.
It offers pervasive augmented analytics, multilanguage consumer experiences, and Oracle cloud,
data management and application optimizations.
Oracle’s ABI capabilities can be deployed on the Oracle Cloud Infrastructure (OCI), on-premises in
hybrid mode, in Oracle cloud application environments with Oracle Fusion Analytics Warehouse, or in
third-party clouds.

During 2020, Oracle focused on enhancing its augmented capabilities and opening its platform to
third-party components. This focus included integrations with third-party ML platforms and opening
Oracle Analytics Cloud semantics to other ABI platforms. Cloud deployment options, once Oracleonly, have been expanded to third-party clouds and customer data centers using Oracle
[email protected] At the same time, enhanced optimizations within the Oracle stack include the
ability to use OAC with Oracle applications, and support for Oracle Machine Learning and Oracle
Autonomous Data Warehouse.
Cohesiveness of augmented analytics capabilities: Oracle implemented augmented analytics
capabilities across its platform earlier than most vendors. Users can leverage NLQ through the
Oracle Analytics interface, through Oracle Analytics Day by Day for mobile devices, as well as
through integration with a variety of chatbots and collaboration interfaces powered by Oracle
Digital Assistant. OAC also features NLG with adjustable tone and verbosity in English and French.
It is the only platform on the market to support NLQ in 28 languages.
Product vision: Oracle invests aggressively in augmented analytics capabilities and consumerlike,
conversational user experiences, including chatbot integration coupled with autogenerated
insights and integrated podcast generation, to boost adoption through multiexperiences.
Full-stack enterprise cloud: Oracle offers an end-to-end cloud solution, including infrastructure,
data management, analytics and analytic applications, with cloud data centers in almost all
regions of the world. In addition, Oracle Fusion Analytics Warehouse (FAW) offers native
integration and closed-loop actions for Oracle’s ERP, human capital management, supply chain,
customer experience and NetSuite products.
Oracle application-centric: Although OAC can access any data source, at the time of writing its
packaged analytic applications (FAW) work only with Oracle enterprise applications. To gain
similar capabilities, non-Oracle application customers would have to build applications for
themselves using OAC.
Mind share: Oracle has a strongly competitive product, but its brand is not associated with modern
ABI outside the Oracle customer base. As such, Oracle is not considered as frequently as the
Leaders in competitive evaluations known to Gartner. In Gartner’s opinion, organizations
considering OAC will find that business user preference is unlikely to be an advantage they can use
to drive adoption.
Customer perception: Selling OAC to Oracle’s wider installed base is a key part of Oracle’s sales
strategy. However, during 2020 it was evident from Gartner client inquiries that the views of Oracle
BI Enterprise Edition (OBIEE) and Oracle Applications customers regarding OAC, although

improving, were still often skeptical. Oracle is, however, making significant investments to
reestablish the perception that it is a trusted enterprise ABI partner to its existing customers.
Pyramid Analytics
Pyramid Analytics is a Niche Player in this Magic Quadrant. It is growing by expanding primarily
within its existing installed base, but also by adding more users and increasing utilization of its
platform across more of the analytics pipeline.
Pyramid offers an integrated suite for modern ABI requirements. This has a broad range of analytical
capabilities powered by a flexible cloud-based infrastructure deployable on any existing
infrastructure — on-premises, in a cloud or in a hybrid environment — in order to scale up and out
easily. Pyramid data connectors push compute down to the data source, rather than ingesting data in
memory like many ABI tools.
Pyramid has built on its cloud strategy by introducing deeper support for Kubernetes, with improved
elastic scaling to facilitate scaled processing of Python, R and SAS — and new options for multicloud
deployments. To complement the containerized approach, Pyramid is introducing a specialized AWS
Lambda version to support large-scale concurrent user deployments; additionally, a Microsoft Azure
version is planned. Pyramid’s adaptive augmented analytics platform now covers the whole data life
cycle out of the box, from ML-based data preparation to automated insights and automated ML
model building.
Broad range of use cases: Pyramid supports agile workflows, governed, visual data discovery,
report-centric content creation and data science functionality within a single platform and
Augmented capabilities: Augmented features such as Smart Discover, Smart Reporting, Ask
Pyramid (NLQ), AI-driven modeling, automatic visualizations and dynamic content offer powerful
insights to all users, regardless of skill level.
Comprehensive deployment, administration and workflow capabilities: Pyramid supports the full
data pipeline, from data wrangling, data discovery and sharing through to dashboards and report
publishing with enterprise-grade security and governance tools. The architecture is built around
cloud deployment, with a cluster-based design that incorporates a zero-footprint client and hybrid
data connectivity capabilities.
Execution of cloud vision: Although the latest version of Pyramid’s platform delivers strong core
product functionality and reflects a cloud-native vision, Gartner Peer Insights reviewers score
Pyramid’s cloud BI capabilities below average. Feedback from customers using Pyramid on

premises and trying to move into a hybrid environment is that the transition can be difficult. In
addition, Pyramid has no SaaS offering.
Limited availability of training resources: According to Gartner Peer Insights data, Pyramid scores
below average for the quality and availability of its end-user training. A lack of publicly available
training, help forums and video content, relative to competitors in the sector, may hinder end-user
Lack of ecosystem to sell to: The ABI sector is increasingly dominated by cloud data and analytics
ecosystems and business application vendors. Being an independent player without wider
application or collaboration offerings makes it difficult to gain traction in this crowded market. Like
many other vendors in the sector, Pyramid finds it difficult to differentiate itself on product
functionality alone, and, despite new partnerships and public cloud ecosystem channels, its
momentum remains low, relative to that of competitors.
Qlik is a Leader in this Magic Quadrant. It has a strong product vision for ML- and AI-driven
augmentation, but lower market momentum than the other two Leaders.
Qlik’s lead ABI solution, Qlik Sense, runs on the unique Qlik Associative Engine, which has
poweredQlik products for the past 25 years. Qlik’s Cognitive Engine adds AI/ML-driven functionality
to the
product and works with the Qlik Associative Engine to offer context-aware insight suggestions and
augmentation of analysis. Qlik offers deployment flexibility, with enterprise SaaS and customerhosted options including multicloud and on-premises installation, without limiting customers to any
particular cloud.
Qlik continues to enhance its platform’s open architecture and multicloud capabilities. It has built on its
augmented analytics vision, with key elements based on its Cognitive Engine. Insight Advisor now
enhances a full range of user experiences in Qlik Sense with search-based visual analysis,
conversational analytics, associative insights, accelerated creation and data preparation. Qlik
recently acquired RoxAI, Knarr Analytics and to enhance capabilities for alerting, continuous
intelligence and SaaS platform integration, respectively.
Flexibility of deployment: Qlik offers the flexibility to deploy on-premises or with any major cloud
provider, with multiclouds, or to use a combination of these approaches. Customers can also
utilize Qlik’s full SaaS offering.
Comprehensive portfolio of capabilities: Qlik’s purchase of companies will expand the breadth of
its capabilities across the data and analytics pipeline. Qlik Sense delivers self-service visual data
discovery capabilities for analysts or business users, while also supporting developer-embedded
analytics. Qlik Catalog is used for cataloging and governance. Also, although Qlik Data Integration

Platform (formerly Attunity) is a stand-alone offering, it adds powerful integration and data
movement capabilities under the Qlik umbrella.
Data literacy and customer focus: Qlik’s Data Literacy Program helps users of all levels, whether
Qlik customers or not, to understand and utilize data. Qlik’s Analytics Modernization Program
encourages and helps existing QlikView customers to migrate to Qlik Sense for new use cases.
Qlik’s Executive Insights Center is an executive portal focused on helping customers link analytics
to business outcomes; it is driven by Qlik executives and closely tied to existing marketing and
customer success programs.
Product pricing complexity: Qlik Sense offers core analytic and BI platform capabilities in a single
license, but also offers a number of add-on capabilities, such as Qlik Catalog, Qlik Insight Advisor
Chat for chatbot experiences and Qlik NPrinting for Mode 1 reporting. These entail additional
licensing and cost, if deployed on-premises. Qlik’s SaaS platform includes all capabilities as part of
the standard subscription, with the exception of reporting, which is not yet supported in the cloud.
Low market momentum: Relative to other Leaders, Qlik’s momentum remains the lowest, judging
by Gartner’s search and client inquiry data and a range of other indicators. Although Qlik’s
Analytics Modernization Program is meant to help existing Qlik customers move to Qlik Sense,
many customers looking to modernize are using the opportunity to reevaluate the market entirely
and assess other vendors.
Lack of product cohesiveness: Qlik made acquisitions in 2020, thus adding to an already complex
portfolio of previous acquisitions that are still on their own integration paths across the broad Qlik
portfolio. Although Qlik is experienced at integrating acquired technologies, evaluators should
consider how they would orchestrate wider use of Qlik’s toolset, if looking beyond using Qlik Sense
SAP is a Visionary in this Magic Quadrant. It offers augmented ABI functionality fully integrated
within the SAP enterprise application ecosystem.
SAP Analytics Cloud is a cloud-native multitenant platform with a broad set of analytic capabilities.
Most companies that choose SAP Analytics Cloud already use SAP business applications. SAP
Analytics Cloud offers an add-in for Microsoft Office 365 on-premises or in the cloud.
In 2020, SAP enhanced its automated insights capabilities by adding new “How has it changed?” and
“How is it calculated?” explanation functionality. Also, it rearchitected the self-service user
experience workflow to apply augmentation across the data-to-visualization process. Finally,

enterprise reporting was updated to add scheduled publication of data stories or PDFs, although it
has not achieved parity with SAP BusinessObjects’ capabilities in this area.
Unmatched SAP connectivity: SAP Analytics Cloud is primarily of interest to organizations that
use SAP enterprise applications. Seamless connectivity to those solutions is therefore of critical
importance. SAP Analytics Cloud has native connectivity to SAP S/4HANA and is embedded in
SAP cloud applications, including SuccessFactors and Ariba. Further, despite being cloud-only,
SAP Analytics Cloud connects directly to on-premises SAP resources (SAP BusinessObjects
Universes, SAP Business Warehouse and SAP HANA) for live data, with no data replication
required. Direct data connectivity to non-SAP sources still lags behind that of competitors,
Differentiated augmented, closed-loop capability: SAP Analytics Cloud’s integrated functionality
for planning, analysis and prediction differentiates it from almost all competing platforms. Its
ability to conduct “what if?” analysis is combined in SAP Analytics Cloud with a strong, multiyear
focus on augmented analytics as a core design tenet. SAP Analytics Cloud offers strong
functionality for NLG, NLP and automated insights.
Breadth of capability and content: SAP Analytics Cloud is part of a wider data portfolio that
includes SAP Data Warehouse Cloud. SAP Analytics Cloud offers a library of prebuilt content that
is available online. This content covers a range of industries and line-of-business functions. It
includes data models, data stories and visualizations, templates for SAP Digital Boardroom
agendas, and guidance on using SAP data sources.
Lack of large community: SAP’s platform has less market momentum than the ABI platforms of
some similarly sized vendors. Judging from public job postings, few organizations are looking to
hire staff with skills in, or familiarity with, SAP Analytics Cloud, which is surprising, given the size
of the BI installed base that SAP could cross-sell to. This means there is a relatively small user
community for SAP Analytics Cloud, at a time when community size is a key driver for selection
and adoption because technologies are only marginally differentiated.
Perception by potential users: Given its BusinessObjects heritage, SAP has been associated with
report-centric BI, and the legacy of this is a perception among potential users that does not reflect
SAP Analytics Cloud’s modern, self-service capabilities. The need to convince potential users that
SAP Analytics Cloud is worth considering puts SAP at a disadvantage to the competition in
selection processes.
Cloud-only offering: SAP Analytics Cloud is cloud-native and not available on-premises (although it
can query on-premises data). It runs in SAP data centers or public clouds (on AWS and Alibaba,
with support for Microsoft Azure planned). It is currently available on data centers in China, Japan,

Saudi Arabia, Singapore, United Arab Emirates, Europe, the U.S., Canada, Australia and Brazil.
For organizations that want to deploy an ABI platform on-premises, SAP’s answer is to offer the
SAPBusinessObjects BI platform.
SAS is a Visionary in this Magic Quadrant. This position reflects its robust and innovative product and
its global presence, as well as its challenges in terms of marketing and price perception.
SAS offers SAS Visual Analytics on its cloud-ready and microservices-based platform, SAS Viya. SAS
Visual Analytics is one component of SAS’s end-to-end visual and augmented data preparation, ABI,
data science, ML and AI solution. SAS’s extensive Viya-based industry, forecasting, text analytics,
intelligent decisioning, edge analytics and risk management solutions use SAS Visual Analytics on
In 2020, SAS introduced a unique market capability for report reviewing that analyzes reports and
suggests good visual design, performance and accessibility practices. It also released SAS
Conversation Designer (included with SAS Visual Analytics) for building customized chatbots
through a low/no-code visual interface. From a go-to-market perspective, SAS and Microsoft formed a
technology and go-to-market partnership, with Azure becoming a cloud provider for SAS Cloud and
plans for future SAS integration with Microsoft’s cloud portfolio. SAS also introduced new
competitive, revenue-capped pricing for SAS Visual Analytics.
End-to-end platform vision: SAS offers a compelling product vision for customers to prepare their
data, analyze it visually, and build, operationalize and manage data science, ML and AI models in a
single, integrated visual and augmented design experience (with progressive licensing). Moreover,
with Visual Analytics, SAS is the only vendor in this Magic Quadrant to support text analytics
natively in a core product.
Augmented analytics: SAS is investing heavily in infusing augmented analytics across its entire
platform. This includes investment in automated suggestions for relevant factors, and in insights
and measures and forecasts expressed using visualizations and natural language
explanations. Automated predictions with key drivers and “what if?” analysis are supported in SAS
Visual Analytics. The platform also features AI-driven data preparation suggestions, voice
integration with user devices, chatbot integration and NLG capabilities developed by SAS, rather
than an OEM.
Global reach with industry solutions: SAS is one of the largest privately held software vendors,
with a physical presence in 47 countries and a global ecosystem of system integrators. SAS Visual
Analytics forms the foundation for most of SAS’s extensive portfolio of industry solutions, which
includes predefined content, models and workflows.

Market perception as outmoded: Although SAS now supports the open-source data science and
ML ecosystem and has introduced a new SDK for SAS Visual Analytics, there remains a perception
that SAS is expensive and proprietary. This perception has obstructed consideration of SAS in this
market, beyond SAS’s installed base. It also impacts the number of new data science and machine
learning students that choose to learn SAS, as most focus their studies on open-source platforms.
Inflexibility at contract renewal: Despite new capability-based and metered pricing options
introduced in 2019, and new, attractive pricing of SAS Viya in 2020, most SAS customers are on
older contracts. Gartner inquiries suggest that these customers often perceive SAS contracts as
being high-cost and inflexible and as involving difficult renewal negotiations.
Migration challenges: SAS Viya provided a new open architecture and brought modernization to
SAS 9 customers, and it is still evolving. However, although SAS has continued to improve its
utilities to make migration from earlier releases easier, Gartner inquiries suggest that customers
continue to view migration as a challenging undertaking.
Sisense is a Visionary in this Magic Quadrant, one best known for its success with embedded
analytics. It has a comprehensive partner program and a strategic partnership with AWS.
Sisense provides an end-to-end analytics platform that supports complex data projects by offering
data preparation and visual exploration capabilities and augmented analytics. Over half of Sisense’s
ABI platform customers use the product in an OEM form.
Sisense 8.2 was released in September 2020 with NLQ capabilities powered by a knowledge graph
and Sisense Notebook, which provides code-first augmented Insights.
Composable architecture: Sisense has a microservices-based architecture that is fully extensible.
Sisense is commonly used to embed analytics capabilities such as interactive visualization and
NLQ within a composed analytic application experience to enable better decision making.
Comprehensive product capability: Sisense’s platform is functionally comprehensive, enabling
both business users and expert developers with different skill levels. Cloud and NLQ capabilities
are particular strengths.
Open platform: Sisense is cloud-agnostic and multicloud-capable. It has deep partnerships with
AWS, Google (GCP) and Microsoft, along with strong cross-cloud analytics orchestration. A robust
cataloging capability supports other analytics vendor assets via APIs. Sisense also offers
extensible connectivity to other reporting tools. An analytics marketplace in which to publish and
build third-party analytics capability is on Sisense’s roadmap.

Lower market momentum outside core use case: Sisense has built a successful OEM business
with its strong partner program. This helps it avoid direct competition with Microsoft (Power BI)
and Tableau, which are dominant in self-service analytics use cases. However, this strategy meansit
has less momentum in the wider ABI market. Organizations choosing Sisense for nonembedded
use may therefore need to work hard to present its platform to their user communities as an
attractive alternative to better-known platforms.
Product packaging complexity: Sisense offers a broad set of capabilities, but in three different
product packages: Sisense for Product Teams, Sisense for Cloud Data Teams, and Sisense
Business Intelligence and Analytics Teams. While indicating the width of Sisense’s overall offering,
this approach entails complexity for organizations considering the vendor. Sisense plans to
simplify its product packaging in 2021.
Less consumer-focused: Sisense’s new knowledge-graph-enabled NLQ feature offers new
consumer capability, but the platform is generally focused more on the development ecosystem
and personas. Sisense for Product Teams, an API-first platform, is its best-selling product. A new
Sisense DevX Portal is intended to empower developers to build analytics applications. This vision
aligns with Sisense’s overall OEM strategy but may not resonate with potential adopters looking to
address the needs of ABI consumers first.
Tableau is a Leader in this Magic Quadrant. It offers a visual-based exploration experience that
enables business users to access, prepare, analyze and present findings in their data. It has powerful
marketing and expanded enterprise product capabilities, but the integration of Salesforce Einstein
Analytics, now renamed Tableau CRM, remains a work in progress.
In 2020, Tableau enhanced its data preparation and data management capabilities. For data
preparation, it released enhanced data modeling capabilities, which make it easier to analyze data
across multiple tables at different levels of detail by building relationships between tables with a
simple in-browser visual experience. For data management, Tableau Prep Conductor and Tableau
Catalog offer a cohesive experience for operating and automating data management and
understanding data lineage. An Einstein Discovery dashboard extension, the first integrated product
to bring the predictive modeling capabilities of Salesforce Einstein Analytics to the Tableau platform,is
scheduled for release in March 2021.
Analytics user experience: Although Tableau keeps adding new capabilities, it always maintains a
sleek experience for users, so they can perform analysis seamlessly. Although visual-based
exploration is highly commoditized in today’s market, Tableau can still differentiate itself by

offering an intuitive analytics experience with richer capabilities based on its patented VizQL
Customer enthusiasm: Many customers demonstrate a fanlike attitude toward Tableau, as
evidenced by the more than 145,000 people who attended its 2020 online user conference.
Tableau Public, a free platform on which to publicly share and explore data visualizations online,
has over 3 million interactive visualizations. A user-experience-focused design means that,
particularly for users in analyst roles, Tableau’s offering is compelling and even enjoyable to use.
Salesforce opportunity: The Tableau Viz Lightning web component offers a low-code experience
to simplify the task of integrating Tableau visualizations into Salesforce., Salesforce’s
cloud offering to help organizations reopen workplaces safely and efficiently, uses the Tableau Viz
Lightning web component to add a global COVID-19 tracker dashboard to the Workplace
Command Center. The deeper integration of the MuleSoft data connector capabilities and a newly
acquired Slack collaboration tool means that Salesforce clients have a strengthening set of
reasons to consider Tableau.
Not cloud-native: Tableau offers cloud-hosted solutions (Tableau Online and Tableau CRM), but
the company’s heritage is in on-premises deployments, for which it has a massive installed base.
Tableau does not have a cloud-native architecture for on-premises customers to embrace the
cloud’s full benefits. Deployment of Tableau Server in a containerized infrastructure is not currently
supported (but is planned for 2021). As such, beyond Tableau Online, it cannot utilize the cloud’s
elasticity to automatically scale out in order to handle dynamic workloads.
Premium pricing: Tableau’s pricing is an issue raised by users of Gartner’s client inquiry service.
Compared with some of the cloud vendors in this market, Tableau is expensive. The addition of
Tableau CRM for a list price of up to $150 per user per month may well increase the concern of
customers who are considering scaling their deployments or acquiring new functions.
Integration challenges: As is to be expected, the integration of Salesforce’s ABI capabilities with
those of Tableau is taking time. Currently, users face a fragmented experience if they want to take
advantage of the augmented analytics functions of the former Einstein Analytics while using the
Tableau platform. Einstein Analytics’ strengths in automated data stories, key driver analysis,
custom automation and explainable AI are not yet integrated into the Tableau user experience.
ThoughtSpot is a Visionary in this Magic Quadrant. Its innovative search-first approach to analysis isbeing
widely emulated by competitors. Its appeal in the ABI platform market is primarily to buyers looking to
add NLP and augmented analytics in order to reach a wider range of users.

ThoughtSpot is defined by its search-driven user experience, its ability to answer analytically complex
questions with personalized and relevant answers, and its deployment of augmented analytics at scale.
In 2020, ThoughtSpot released its SaaS Cloud offering with automated personal onboarding and a
new search engine and personalized experience, ThoughtSpot One. It also added monitoring
capabilities to SpotIQ, which automatically tracks, proactively alerts and explains changes to key
business metrics for business users. Additionally, it added ThoughtSpot Modeling Language and an
in-product integrated development environment, as well as ThoughtSpot DataFlow for faster no-code
data ingestion when bringing data into memory.
Search and AI at scale: Given ThoughtSpot’s use of search and NLP as the primary interface for
querying data, questions can be posed by typing or speaking. ThoughtSpot supports analytically
complex questioning of extremely large amounts of data (often billions of rows). SpotIQ,
ThoughtSpot’s augmented analytics capability, discovers anomalies and correlations and
performs comparative analysis of data points without the need for coding.
Consumer-centric vision: ThoughtSpot’s vision is to drive adoption by giving business users the
power and ease of use of consumer search and social applications. Its technology learns from
collective behavior, intelligence, social signals and networked, cataloged insights to provide users
with the most relevant search suggestions, answers, and and autogenerated insights.
Market recognition as a search specialist: Despite ThoughtSpot’s relatively small size, awareness
of its search-based value proposition is high. This vendor is shortlisted by most of the customers
who use Gartner’s client inquiry service when prioritizing search, NLP and augmented analytics
Complementary cost barrier: ThoughtSpot’s software typically complements other ABI platform
products initially, as it does not cover the full spectrum of requirements at a level that enables it to
fully replace visually driven ABI platforms. In a market where value for money is often prioritized,
organizations are increasingly willing to accept “good enough” (but improving) search, NLP and
augmented analytics from their enterprise-standard ABI vendor, rather than add another platform
from a different vendor.
Limited global reach, ecosystem and user community: ThoughtSpot has substantially expanded
its global ecosystem of system integrators and grown its solution marketplace. However, relative to
the Leaders in this Magic Quadrant, ThoughtSpot has a limited international presence, a limited
number of partner-implemented deployments and a limited, but growing, user community.

Requirement for IT setup: Successful implementation of ThoughtSpot’s software requires data
preparation and mapping of data in advance. This typically demands IT skills and effort. However,
ThoughtSpot’s new SaaS offering, along with some automation of modeling when connecting
directly to cloud databases, has the potential to reduce this requirement. Also, some prebuilt
models for common applications from vendors such as Salesforce and Workday are on its
TIBCO Software
TIBCO Software is a Visionary in this Magic Quadrant, one with mature product capabilities. Its
TIBCO Spotfire offering has a strong presence in the life sciences, high-tech manufacturing, transport
and logistics, and energy sectors, but less momentum outside its installed base, relative to other
TIBCO Spotfire offers strong capabilities for analytics in dashboards, interactive visualization, data
preparation and workflow. The Spotfire A(X) Experience represents an augmented, focused approach
that enables Spotfire users to use data science techniques, geoanalytics and real-time streaming
analysis in easily consumable forms, such as NLQ, NLG and automatically suggested visualizations.
TIBCO has recognized the collision of capabilities and roles across data science and analytics, and
this recognition is driving its vision for “hyperconverged analytics.” TIBCO continues to fulfill this
vision by improving its support for Python data functions and streaming data sources directly within
the platform. Spotfire Mods is a new development framework that enables rapid creation of
lightweight add-ins, bringing new interactive visualization and user interface capabilities to the
Spotfire analysis environment. Mods look and feel like native Spotfire functionality to all users. Mods
work in any environment and can be easily shared across teams and organizations.
Note: During our research for this Magic Quadrant, TIBCO Software announced that it had entered into
an agreement to acquire Information Builders. The acquisition was due to complete in the first quarter of
2021. As a result, product and company integration plans were not developed and available to share with
Gartner in time for consideration in this Magic Quadrant. Consequently, representing the two as
one entity is not warranted, nor would it be useful to readers at this point. TIBCO Software and
Information Builders are therefore represented separately in this Magic Quadrant.
Product capabilities for advanced analytics: TIBCO Spotfire features ML-based data preparation
capabilities for building complex data models. An end-to-end workflow is accomplished in a unified
design environment for interactive visualization and for building analytic dashboards. Analysts and
citizen data scientists have access to an extensive library of drag-and-drop advancedanalytic
functions, with some automated insight features. Capabilities from Statistica are fully integrated
with Spotfire, along with the existing TIBCO Enterprise Runtime for R (TERR) engine andthe
embedded Python engine.

Scalability and enterprise readiness: TIBCO Spotfire analytics have been optimized for scaled,
secure deployment by very large, geographically distributed organizations. The Spotfire platform
has modern automated administration capabilities and the same service-oriented architecture is
used in the cloud, on-premises and in hybrid deployments.
Vision for data and analytics market convergence: TIBCO’s hyperconverged analytics efforts
focus on utilizing its product strengths across data visualization, data science, streaming analytics
and new Spotfire Mods in order to deliver more real-time and tailored insights. The benefits are
realized via strong purpose-built vertical analytics applications in, for example, the pharmaceuticaland
energy industries.
Limited market momentum: TIBCO has less momentum than many competitors in this market.
The Spotfire product was one of the original disruptors of the traditional BI sector, along with
products from Qlik and Tableau, but it now accounts for only a small fraction of inquiries from
users of Gartner’s client inquiry service. Data from Gartner Peer Insights suggests that Spotfire is
evaluated less often than competing offerings.
Market presence: There is relatively little perception of TIBCO as a significant player in the modern
ABI platform market. In Gartner’s opinion, outside certain fields (notably oil and gas and
pharmaceuticals), Spotfire is seldom a standard platform for organizations. This means there is a
smaller user community and fewer experienced staff available to hire who have skills in deploying
and using Spotfire.
Perceived high cost of software: TIBCO’s customers continue to identify pricing and contract
flexibility as a concern with the platform, according to Gartner Peer Insights reviewers. In this
regard, they score TIBCO below the average.
Yellowfin is a Visionary in this Magic Quadrant. Although it is a small and geographically limited
vendor, its product innovation is among the strongest in the market.
Yellowfin began as a vendor of a web-based BI platform for reporting and data visualization, but has
since expanded to offer data preparation and augmented analytics.
In 2020, Yellowfin continued investment in its dashboard canvas approach by adding Code Mode for
developers. It also enhanced its augmented analytics to provide contextualized insights, and
enhanced its API features to enable citizen developers to compose analytics capabilities.

Innovative product vision: Yellowfin’s product vision is both expansive and innovative. Yellowfin
offers automated alerting based on improved ML algorithms in its Signals module, which also
provides contextual analytics. Its microservices-based architecture and Code Mode enable
composability with other applications, enabling it to turn analytic insights into operational actions.
Openness: Yellowfin offers a cloud-agnostic architecture, and much of its usage is by independent
software vendor partners embedding Yellowfin in order to deliver analytics in their applications. As
such, openness is key to its offering. This is also true for the nonembedded use case. Yellowfin’s
data preparation outputs are nonproprietary and can be used with other analytic tools. In addition,
Yellowfin Stories can integrate Microsoft Power BI, Tableau and Qlik reports, dashboards and apps
into long-form data story content.
Comprehensive product functionality: Overall, Yellowfin offers one of the top-scoring products in
terms of functionality. Its capabilities span data preparation, Mode 1 reporting with scheduled
distributions, Mode 2 visual exploration and augmented analytics. Its data transformation module
provides connections to data science models. All capabilities are accessed via a browser-based
Weak natural language support: Yellowfin’s NLQ capability is still only a roadmap item (planned
for 2021). Users’ control of NLG is limited. Automated insights lack explainability (except for driver
analysis) and cannot consume R or Python, which is a shortcoming when drawing on the work of
data science teams.
Low market momentum: Despite its visionary approach, Yellowfin has little market traction,
compared with its competitors. It rarely appears on the vendor shortlists of users of Gartner’s
client inquiry service, and it is less searched for on The size of an ABI platform’s user
community greatly influences its likelihood of selection — and in Yellowfin’s case, the community is
Minimal geographic presence: Although its product supports nine languages and is used
internationally, Yellowfin is little known outside Asia/Pacific. The company has fewer than 200
staff, with only four countries having over 10 full-time Yellowfin employees. The company did,
however, slowly increase its number of employees during 2020, unlike some of its competitors.
Vendors Added and Dropped
We review and adjust our inclusion criteria for Magic Quadrants as markets change. As a result of
these adjustments, the mix of vendors in any Magic Quadrant may change over time. A vendor’s
appearance in a Magic Quadrant one year and not the next does not necessarily indicate that we
have changed our opinion of that vendor. It may be a reflection of a change in the market and,
therefore, changed evaluation criteria, or of a change of focus by that vendor.

Amazon Web Services
Google (Looker)
Looker — now included as Google (Looker)
Birst — now included as an Infor offering
Logi Analytics
Inclusion and Exclusion Criteria
To qualify for inclusion in this Magic Quadrant, vendors had to meet both of the following criteria:
Offer a generally available software product that met Gartner’s definition of an ABI platform:
ABI platform software supports IT-enabled analytic content development. It is defined by a selfcontained architecture that enables nontechnical users to autonomously execute full-spectrum
analytic workflows, from data access, ingestion and preparation, to interactive analysis and
collaborative sharing of insights. ABI platforms are increasingly differentiated by augmented
analytics capabilities.
Rank among the top 20 organizations in the market momentum index defined by Gartner for this
Magic Quadrant. Data inputs used to calculate ABI platform market momentum included a
balanced set of measures:
Gartner customer search and inquiry volume and trend data.
Volume of job listings specifying the ABI platform on TalentNeuron and on a range of
employment websites in the U.S., Europe and China.
Frequency of mentions as a competitor to other ABI platform vendors in reviews on Gartner’s
Peer Insights forum during the year ending July 2020.

In line with Gartner’s Magic Quadrant methodology, the number of vendors covered was limited to 20
However, there are many more ABI platform vendors that are not covered in this research.
Honorable Mentions
The five vendors mentioned below either featured in the 2020 edition of this Magic Quadrant (which
included 22 vendors) or have momentum that may make them of interest to organizations looking
beyond the vendors covered in the present Magic Quadrant. The following list, which does not
include all the notable vendors absent from this Magic Quadrant, is in alphabetical order:
AnswerRocket. AnswerRocket offers an augmented data discovery platform with native
capabilities for NLQ, NLG and automated generation of insights. The platform features
prepackaged and composable analytic applications, called RocketBots, that automate business
analysis use cases. It also supports the ability to plug in third-party AI and ML frameworks such as
scikit-learn and TensorFlow. AnswerRocket’s platform can be run on-premises or in the cloud via
major public cloud providers and a number of supported data management solutions.
AnswerRocket did not achieve the top 20 ranking in Gartner’s market momentum index that was
required for full coverage in this Magic Quadrant.
Dundas. The Dundas BI platform enables users to visualize data, build and share dashboards and
pixel-perfect reports, and embed and customize analytics content. Dundas sells to large
enterprises, but specializes in embedded BI, with the bulk of its revenue coming from OEMs that
extend, integrate, customize and embed Dundas BI in their applications. Dundas did not achieve
the top 20 ranking in Gartner’s market momentum index that was required for full coverage in this
Magic Quadrant.
FanRuan. This is one of the largest ABI vendors in China, where its traditional, report-centric BI
product, FineReport, is widely used. Its new FineBI product offers self-service, visually driven BI via
an on-premises deployment model. FanRuan is adding cloud deployment and augmented
capabilities, and plans to enter the North American and European markets. FanRuan did not
achieve the top 20 ranking in Gartner’s market momentum index that was required for full
coverage in this Magic Quadrant.
Incorta. When it launched in 2013, Incorta was primarily positioned as a provider of software
complementary to the ABI platforms covered in this Magic Quadrant. It used its patented Direct
Data Mapping functionality as a performance optimization layer, primarily enabling data analysis of
complex ERP and CRP systems. However, Incorta’s offering is increasingly being used as a full
ABI platform in its own right, and thus becoming a competitive alternative. Incorta’s addition of a
cloud-based service in 2020 widened its reach to businesses of all sizes. Incorta did not achieve
the top 20 ranking in Gartner’s market momentum index that was required for full coverage in this
Magic Quadrant.

Table 1: Ability to Execute Evaluation Criteria
Evaluation Criteria Weighting
Logi Analytics. This vendor focuses solely on embedded analytics and application development
teams. It offers a suite of embedded analytics development environments that includes Logi
Composer for out-of-the-box embedded analytics, Logi Report for pixel-perfect and embedded
operational reports, and Logi Info for building customizable analytics applications. Its Logi
Composer product represents an evolution of its embedded self-service offerings, one that takes
advantage of no-code and low-code approaches within a microservices architecture. Logi
Analytics did not achieve the top 20 ranking in Gartner’s market momentum index that was
required for full coverage in this Magic Quadrant.
Evaluation Criteria
The Ability to Execute criteria used in this Magic Quadrant are as follows (for the sources of
information that informed Gartner’s evaluations using these criteria, see the Evidence section):
Product or Service: This criterion assesses how competitive and successful a vendor’s ABI platform
product is with regard to the critical capability areas, in light of the vendor’s RFP response and video
Overall Viability: This criterion concerns the organization’s financial status and model as it relates to
ABI. It also takes account of existing and prospective customers’ views about the vendor’s likely
future relevance.
Sales Execution/Pricing: This criterion covers the vendor’s capabilities in sales activities. It includes
sales experience, the ability to understand buyers’ needs, and pricing and contract flexibility.
Market Responsiveness/Record: This criterion addresses the extent to which a vendor has
momentum and success in the worldwide market using a balanced set of measures.
Customer Experience: This criterion concerns customers’ experience of working with a vendor aftera
purchase. Factors include the availability of quality third-party resources (such as integrators and
service providers), the quality and availability of end-user training, and the quality of the peer user
Operations: This criterion concerns how well a vendor supports its customers, and how trouble-free
its software is.
Ability to Execute
The Completeness of Vision criteria used in this Magic Quadrant are as follows (for the sources of
information that informed Gartner’s evaluations using these criteria, see the Evidence section):
Market Understanding: This criterion concerns how closely aligned a vendor is with the shifting
needs of analytic buyers and how widely its customers use recent and emerging capabilities.
Marketing Strategy: This criterion considers whether a vendor has a clear set of messages that
communicate its value and differentiation in the ABI platform market, and whether that vendor is
generating awareness of its differentiation.
Sales Strategy: This criterion concerns the extent to which a vendor’s sales approach benefits from a
range of options and drivers that encourage customers to evaluate its ABI platform.
Offering (Product) Strategy: Gartner evaluates a vendor’s ability to support key trends that will create
business value in future. Existing and planned products and functions that contribute to these trends
are factored into each vendor’s score for this criterion, based on its presented roadmap.
Vertical/Industry Strategy: This criterion assesses how well a vendor can meet the needs of various
industries through templates or packaged analytic content.
Evaluation Criteria Weighting
Product or Service High
Overall Viability High
Sales Execution/Pricing Medium
Market Responsiveness/Record High
Marketing Execution NotRated
Customer Experience High
Operations High
Source: Gartner (February

Table 2: Completeness of Vision Evaluation Criteria
Evaluation Criteria Weighting
Market Understanding High
Marketing Strategy High
Sales Strategy High
Offering (Product) Strategy High
Business Model NotRated
Vertical/Industry Strategy Low
Innovation High
Geographic Strategy Medium
Source: Gartner (February
Innovation: This criterion gauges the extent to which a vendor is investing in, and delivering, unique
capabilities. It considers whether a vendor is setting standards for innovation that others are
Geographic Strategy: This criterion considers how well-represented a vendor is around the world.
Completeness of Vision
Quadrant Descriptions
Leaders demonstrate a solid understanding of the key product capabilities and the commitment to
customer success that buyers in this market demand. They couple this understanding and
commitment with an easily comprehensible and attractive pricing model that supports proof of value,
incremental purchases and enterprise scale. In the modern ABI platform market, buying decisions
are made, or at least heavily influenced, by business users who demand products that are easy to buy

and use. They require these products to deliver clear business value and enable the use of powerful
analytics by those with limited technical expertise and without upfront involvement from the IT
department or technical experts. In a rapidly evolving market featuring constant innovation, Leaders
do not focus solely on current execution. Each also ensures it has a robust roadmap to solidify its
position as a market leader and thus helps protect buyers’ investments.
Challengers are well-positioned to succeed in this market. However, they may be limited to specific
use cases, technical environments or application domains. Their vision may be hampered by the lack
of a coordinated strategy across various products in their portfolio. Alternatively, they may fall shortof
the Leaders in terms of effective marketing, sales channels, geographic presence, industry-specific
content and innovation.
Visionaries have a strong or differentiated vision for delivering a modern ABI platform. They offer
deep functionality in the areas they address. However, they may have gaps when it comes to fulfilling
broader functionality requirements or they may have lower scores for customer experience,
operations and sales execution. Visionaries are thought leaders and innovators, but they may be
lacking in scale, or there may be concerns about their ability to grow and still execute consistently.
Niche Players
Niche Players do well in a specific market segment (such as financially oriented BI), or are good at
meeting the ABI needs of organizations using a particular cloud stack. But may have limited ability to
surpass other vendors in terms of innovation or performance. They may focus on a specific domain
or aspect of the ABI platform market, but lack deep functionality elsewhere. Alternatively, they may
have a reasonably broad ABI platform but limited implementation and support capabilities or
relatively limited customer bases (in only a specific region or industry, for example).
This Magic Quadrant assesses vendors’ capabilities on the basis of their execution in 2020 and
future development plans. As vendors and the market are evolving, the assessments may be valid for
only one point in time.
Readers should not use this Magic Quadrant in isolation as a tool for selecting vendors and products.
They should treat it as one reference point among the many required to identify the most suitable
vendor and product. When selecting a platform, they should use this Magic Quadrant in combination
Critical Capabilities for Analytics and Business Intelligence Platforms. We also recommend using
Gartner’s client inquiry service.

Readers should not ascribe their own definitions of Completeness of Vision or Ability to Execute to
this Magic Quadrant (they often incorrectly equate these with product vision and market share,
respectively). The Magic Quadrant methodology uses a range of criteria to determine a vendor’s
position, as shown by the Evaluation Criteria section above.
Market Overview
According to Gartner’s market share analysis, revenue in the modern BI platform market grew by 19%
in 2019, compared with 22% in 2018, to reach just over $6 billion. Pricing pressure and strong
competition were broadly responsible for this small deceleration (see
Market Share Analysis:
Analytics and BI Software, Worldwide, 2019
). As reported last year, although spending on ABI is
growing more slowly than in the 2010s, the number of people using ABI platforms is accelerating
massively into the millions. This huge increase in user numbers is because the price per user is a
fraction of what it was a decade ago.
Cloud ecosystems now dominate spending. For the first time, all but one of the seven hyperscale
cloud infrastructure and platform service vendors have an offering in the ABI platform market either
directly or via an acquired subsidiary (see
Magic Quadrant for Cloud Infrastructure and Platform
). The exception, Chinese vendor Tencent Cloud, has invested in Yonghong Tech and
offersits Yonghong BI platform on an OEM basis. The presence of the major cloud ERP and CRM
application providers is also an influencer of ABI platform selection considerations. On the one hand,
cloud-led sourcing creates inevitable concerns about lock-in and unforeseen costs. On the other, the
cloud vendors accept the importance of openness in their software stacks and the growing
importance of “multicloud” approaches, whereby organizations run applications in, and across,
multiple cloud offerings.
Currently, one vendor — Microsoft — dominates the market in terms of user adoption. The massive
growth of the Microsoft Power BI cloud service has continued, fueled in part by the bundling of this
product with Office 365 (at E5 license level) at a greatly reduced price. The increasing integration of
Power BI with Microsoft Teams will fuel further growth, given the importance of remote working.
The dedicated, specialist analytics vendors in the ABI platform market are using their independence
from the big cloud providers as competitive differentiators against the large cloud players, playing on
customers’ lock-in concerns. One flanking approach is to open previously closed products in order to
minimize competition with ubiquitous ABI tools. Another is to focus on finding specific market
segments and matching offerings to their needs.
The proliferation of augmented analytics capabilities is putting the ABI and data science and ML
platform markets on a collision course. ABI platforms increasingly include functionality to perform
augmented data science and ML tasks, with predictive models being executed “behind the scenes,”
and insights “surfaced” within the ABI process flow. Data science and ML platforms, for their part,
increasingly feature enhanced data transformation and discovery capabilities, such as data

visualization, that are traditionally more characteristic of ABI platforms. Currently, the two remain
discrete markets with different buyers, but that situation is likely to change.
The submarket for embedded ABI serves a different set of key buyers: software developers and
product managers. Embedded ABI is applied when organizations want to create analytic extranet
applications, monetize data or provide ABI within operational business applications. Additionally,
independent software vendors use embedded ABI when they want to offer ABI capabilities within
their software without developing services themselves. The market for embedded ABI is evolving as
more self-service approaches (such as no code and low code) are applied. These are enabling
noncoding citizen developers to extend the reach and connectedness of ABI (for example, to trigger
workflow processes in operational apps) and even to self-publish composable applications (see
Composable Analytics Shapes the Future of Analytics Applications).
Gartner’s analysis in this Magic Quadrant is based on sources including:
Gartner analysts’ opinions of vendors.
Customers’ perceptions of vendors’ strengths and challenges, drawn from ABI- inquiries
received by Gartner.
Gartner Peer Insights data (see below).
A questionnaire completed by vendors about their business.
Vendor briefings covering differentiation, customer use cases and product roadmaps.
An extensive RFP questionnaire inquiring how each vendor delivers the specific features that make
up the 12 critical capabilities defined for this market.
Video demonstrations of how vendors’ ABI platform products address the 12 critical capabilities.
Externally sourced data on market momentum (job postings, videos on the web and so on).
Gartner Peer Insights
Gartner Peer Insights reviews were considered for metrics relating to operations (service and
support, and quality of technical support), sales experience (pricing and contract flexibility) and
market responsiveness (value received). We considered reviews for modern ABI platform products
posted from December 2019 to September 2020.
Evaluation Criteria Definitions
Ability to Execute
Product/Service: Core goods and services offered by the vendor for the defined market. This
includes current product/service capabilities, quality, feature sets, skills and so on, whether offered
natively or through OEM agreements/partnerships as defined in the market definition and detailed in
the subcriteria.
Overall Viability: Viability includes an assessment of the overall organization’s financial health, the
financial and practical success of the business unit, and the likelihood that the individual business
unit will continue investing in the product, will continue offering the product and will advance the
state of the art within the organization’s portfolio of products.
Sales Execution/Pricing: The vendor’s capabilities in all presales activities and the structure that
supports them. This includes deal management, pricing and negotiation, presales support, and the
overall effectiveness of the sales channel.
Market Responsiveness/Record: Ability to respond, change direction, be flexible and achieve
competitive success as opportunities develop, competitors act, customer needs evolve and market
dynamics change. This criterion also considers the vendor’s history of responsiveness.
Marketing Execution: The clarity, quality, creativity and efficacy of programs designed to deliver the
organization’s message to influence the market, promote the brand and business, increase
awareness of the products, and establish a positive identification with the product/brand and
organization in the minds of buyers. This “mind share” can be driven by a combination of publicity,
promotional initiatives, thought leadership, word of mouth and sales activities.
Customer Experience: Relationships, products and services/programs that enable clients to be
successful with the products evaluated. Specifically, this includes the ways customers receive
technical support or account support. This can also include ancillary tools, customer support
programs (and the quality thereof), availability of user groups, service-level agreements and so on.
Operations: The ability of the organization to meet its goals and commitments. Factors include the
quality of the organizational structure, including skills, experiences, programs, systems and other
vehicles that enable the organization to operate effectively and efficiently on an ongoing basis.
Completeness of Vision
Market Understanding: Ability of the vendor to understand buyers’ wants and needs and to translate
those into products and services. Vendors that show the highest degree of vision listen to and
understand buyers’ wants and needs, and can shape or enhance those with their added vision.
Marketing Strategy: A clear, differentiated set of messages consistently communicated throughout
the organization and externalized through the website, advertising, customer programs and
positioning statements.

Sales Strategy: The strategy for selling products that uses the appropriate network of direct and
indirect sales, marketing, service, and communication affiliates that extend the scope and depth of
market reach, skills, expertise, technologies, services and the customer base.
Offering (Product) Strategy: The vendor’s approach to product development and delivery that
emphasizes differentiation, functionality, methodology and feature sets as they map to current and
future requirements.
Business Model: The soundness and logic of the vendor’s underlying business proposition.
Vertical/Industry Strategy: The vendor’s strategy to direct resources, skills and offerings to meet the
specific needs of individual market segments, including vertical markets.
Innovation: Direct, , complementary and synergistic layouts of resources, expertise or capital
for investment, consolidation, defensive or pre-emptive purposes.
Geographic Strategy: The vendor’s strategy to direct resources, skills and offerings to meet the
specific needs of geographies outside the “home” or native geography, either directly or through
partners, channels and subsidiaries as appropriate for that geography and market.
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