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2/20/2020 Gartner Reprint
https://www.gartner.com/doc/reprints?id=1-1Y7VEZB3&ct=200128&st=sb 1/50
Licensed for Distribution
Magic Quadrant for Analytics and Business
Intelligence Platforms
Published 11 February 2020 - ID G00386610 - 69 min read
By Analysts James Richardson, Rita Sallam, Kurt Schlegel, Austin Kronz, Julian
Sun
Augmented capabilities are becoming key differentiators for analytics and BI
platforms, at a time when cloud ecosystems are also influencing selection
decisions. This Magic Quadrant will help data and analytics leaders evolve their
analytics and BI technology portfolios in light of these changes.
Strategic Planning Assumptions
By 2022, augmented analytics technology will be ubiquitous, but only 10% of
analysts will use its full potential.
By 2022, 40% of machine learning model development and scoring will be done in
products that do not have machine learning as their primary goal.
By 2023, 90% the world’s top 500 companies will have converged analytics
governance into broader data and analytics governance initiatives.
By 2025, 80% of consumer or industrial products containing electronics will
incorporate on-device analytics.
By 2025, data stories will be the most widespread way of consuming analytics,
and 75% of stories will be automatically generated using augmented analytics
techniques.
Market Definition/Description
Modern analytics and business intelligence (ABI) platforms are characterized by
easy-to-use functionality that supports a full analytic workflow — from data
preparation to visual exploration and insight generation — with an emphasis on
2/20/2020 Gartner Reprint
https://www.gartner.com/doc/reprints?id=1-1Y7VEZB3&ct=200128&st=sb 2/50
self-service and augmentation. For a full definition of what these platforms
comprise and how they differ from older BI technologies, see “Technology Insight
for Ongoing Modernization of Analytics and Business Intelligence Platforms.”
Vendors in the ABI market range from long-standing large technology firms to
startups backed by venture capital funds. The larger vendors are associated with
wider offerings that includes data management features. Most new spending in
this market is on cloud deployments.
ABI platforms are no longer differentiated by their data visualization capabilities,
which are becoming commodities. Instead, differentiation is shifting to:
ABI platform functionality includes the following 15 critical capability areas (these
have been substantially updated to reflect the refocus on enterprise reporting and
the increased importance of augmentation):
Integrated support for enterprise reporting capabilities. Organizations are
interested in how these platforms, known for their agile data visualization
capabilities, can now help them modernize their enterprise reporting needs. At
present, these needs are commonly met by older BI products from vendors like
SAP (BusinessObjects), Oracle (Business Intelligence Suite Enterprise Edition)
and IBM (Cognos, pre-version 11).
■
Augmented analytics. Machine learning (ML) and artificial intelligence (AI)-
assisted data preparation, insight generation and insight explanation — to
augment how business people and analysts explore and analyze data — are fast
becoming key sources of competitive differentiation, and therefore core
investments, for vendors (see “Augmented Analytics Is the Future of Analytics”).
■
Security: Capabilities that enable platform security, administering of users,
auditing of platform access and authentication.
■
Manageability: Capabilities to track usage, manage how information is shared
and by whom, perform impact analysis and work with third-party applications.
■
Cloud: The ability to support building, deploying and managing analytics and
analytic applications in the cloud, based on data both in the cloud and on-
premises, and across multicloud deployments.
■
2/20/2020 Gartner Reprint
https://www.gartner.com/doc/reprints?id=1-1Y7VEZB3&ct=200128&st=sb 3/50
Data source connectivity: Capabilities that enable users to connect to, and
ingest, structured and unstructured data contained in various types of storage
platforms, both on-premises and in the cloud.
■
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 hierarchies).
■
Model complexity: Support for complex data models, including the ability to
handle multiple fact tables, interoperate with other analytic platforms and
support knowledge graph deployments.
■
Catalog: The ability to automatically generate and curate a searchable catalog
of the artefacts created and used by the platform and their dependencies
■
Automated insights: A core attribute of augmented analytics, this is the ability
to apply ML techniques to automatically generate insights for end users (for
example, by identifying the most important attributes in a dataset).
■
Advanced analytics: Advanced analytical capabilities that are easily accessed
by users, being either contained within the ABI platform itself or usable through
the import and integration of externally developed models.
■
Data visualization: Support for highly interactive dashboards and the
exploration of data through the manipulation of chart images. Included are an
array of visualization options that go beyond those of pie, bar and line charts,
such as heat and tree maps, geographic maps, scatter plots and other special-
purpose visuals.
■
Natural language query: This enables users to query data using business terms
that are either typed into a search box or spoken.
■
Data storytelling: The ability to combine interactive data visualization with
narrative techniques in order to package and deliver insights in a compelling,
easily understood form for presentation to decision makers.
■
Embedded analytics: Capabilities include an SDK with APIs and support for
open standards in order to embed analytic content into a business process, an
application or a portal.
■
2/20/2020 Gartner Reprint
https://www.gartner.com/doc/reprints?id=1-1Y7VEZB3&ct=200128&st=sb 4/50
Magic Quadrant
Figure 1. Magic Quadrant for Analytics and Business Intelligence Platforms
Source: Gartner (February 2020)
Natural language generation (NLG): The automatic creation of linguistically rich
descriptions of insights found in data. 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”) to consumers grid-
layout, multipage, pixel-perfect reports on a scheduled basis.
■
2/20/2020 Gartner Reprint
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Vendor Strengths and Cautions
Alibaba Cloud
Alibaba Cloud, a new entrant to this Magic Quadrant, is a Niche Player. As yet, it
competes only in Greater China, but it has global potential.
Alibaba Cloud is the largest public cloud platform provider in China. It offers data
preparation, visual-based data discovery and interactive dashboards as part of its
Quick BI platform. It is available as a SaaS option running on Alibaba Cloud’s own
infrastructure or as an on-premises option on Apsara Stack Enterprise.
With release 3.4, Quick BI broadened its enterprise reporting functionality, thus
reinforcing its strong focus on the needs of its local market.
Strengths
Support for Mode 1 (centralized) and Mode 2 (decentralized): In addition to
Mode 2, self-service, visual-based data discovery capabilities, Quick BI provides
Mode 1 capabilities such as Microsoft Excel-like reporting and write-back with
form-based submission. Many of the organizations attracted to Quick BI are
first-time customers with low levels of maturity in analytics. As a ABI platform
that can meet both traditional and modern needs, Quick BI is suitable for them.
■
Operations: According to the reference customers Gartner surveyed, Alibaba
Cloud is operating well. They were very positive about the overall experience,
service and support, and the migration experience delivered by Alibaba Cloud.
Most would recommend Quick BI to others.
■
Wider data offering: Quick BI is a core product within the Alibaba Data Middle
Office offering, which is a productized version of the data and analytics
technology built by Alibaba for its e-commerce business. This is driving market
traction — Alibaba Data Middle Office is the most frequent topic raised by users
of Gartner’s client inquiry service who are interested in deploying a data and
analytics platform in Greater China. Alibaba sees Quick BI as key to its plan to
execute its overall business strategy to develop its ecosystem and win new
business for other Alibaba Cloud products, such as Dataphin (for data
management) and Quick Audience (for customer insights and marketing
automation).
■
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