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The Buyer’s Guide to Business Intelligence (BI)

Software 2019 edition

Table of Contents Why Read This Guide?

About TrustRadius

Why BI Matters

Trends in BI

Buying Insights from BI Software Users

Spotlight on 10 Leading BI Products

Product Comparison Tables

Qlik Sense

TIBCO Spotfire

Alteryx Platform

Tableau Desktop & Server

Microsoft Power BI

Birst

TIBCO Jaspersoft

Sisense

Domo

Looker

3

4

5

8

12

16

18

20

21

22

23

24

25

26

27

28

29

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Why Read This Guide? Business Intelligence (BI) is designed to make sense of the vast quantities of data that corporations generate to help make informed decisions and increase profitability. BI platforms include a wide variety of tools that assist organizations in collecting data from a broad range of internal and external sources.

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These platforms can then prepare that data for analysis, run queries, create reports, dashboards, and data visualizations. Many tools also enable data analysts and scientists to explore data sets to discover outliers or data patterns that provide valuable insight into a company’s operations or strategy. This is a crowded space with a broad range of vendors offering products that superficially seem similar. But different products have different strengths and weaknesses. This guide is designed to cut through the marketing hype and help buyers choose a product that meets their specific needs.

The primary goal of this guide is to help you differentiate between 10 BI products, and find the one that will serve your business needs best.

This guide is based on detailed information from over 4,500 reviews and ratings published on TrustRadius. Each review and rating has been individually vetted by a TrustRadius researcher to ensure we’re providing authentic feedback from real users of the software. Along with detailed reviewer feedback and survey responses, this guide is informed by interviews with domain experts and vendors. We also collected 178 survey responses from end-users about the buying process.

In the guide, readers will find:> BI technology trends and what they mean for buyers.> Buying insights from BI users.> At-a-glance summaries of 10 BI products highlighting reviewer demographics, pros and cons, and end-user feedback.

Improving revenues using BI is now

the most popular objective pursued by enterprises in 2019.

About TrustRadiusTrustRadius is the most-trusted review site for business technology, bringing transparency to the $3.7 trillion B2B market. We help buyers make confident decisions with in-depth reviews and ratings from real software users. Every reviewer on TrustRadius is authenticated and every review vetted by our Research Team before publication. We also help vendors engage and convert buyers by putting their customer’s voice to work, authentically and at scale. Headquartered in Austin, TX, TrustRadius was founded by successful entrepreneurs and is backed by the Mayfield Fund and LiveOak Venture Partners.

To learn more, visit www.trustradius.com.

200,000 reviews from 100%

verified users

©2019 TrustRadius. All rights reserved. Reproduction or sharing of this publication in any form without TrustRadius’ prior written permission is strictly prohibited.For information on reprints, please contact marketing@trustradius.com. TrustRadius is a trademark of T-Radius Holdings, Inc. All other trademarks are the property of their respective owners. The information contained in this publication has been obtained from sources believed to be reliable. TrustRadius disclaims all warranties as to the accuracy, completeness or adequacy of such information and shall have no liability for errors, omissions or inadequacies in such information. This publication consists of the opinions of TrustRadius’ research organization and should not be construed as statements of fact. The opinions expressed herein are subject to change without notice.

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Why BI Matters Enterprises are drowning in data. The sheer volume of data about customers, suppliers, products, and business partners has never been so extensive and yet so crucially important. BI tools matter precisely because they promise to make sense of the tsunami of data and uncover the insights within that are vital to winning in the marketplace. The development of BI technology has largely been focused on fulfilling that elusive goal.

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The Evolution of BI

The first generation of BI technology was largely managed by the IT department as the central guardian of all enterprise data. Data was housed in a common store called a data warehouse. Here, it was normalized—removing redundancy and duplication - to make it easier to run queries and retrieve data for reporting. IT staff would run queries on behalf of business users who did not necessarily have expertise in query languages.

Ultimately, the IT organization would deliver the static report to the business owner. The entire process could take days or even weeks due to dependence on skilled IT staff. In addition, answers provided often provoked additional questions which had to go through the same inefficient process.

The drawbacks of this approach were severe enough that it was gradually eclipsed by a more agile approach that favored self-service capabilities. A new set of BI products emerged which eliminated the technical stack designed for IT users and focused on providing data discovery and visualization tools to business users. Business users were empowered to use BI tools to get their own data, even without advanced technical skills. The focus of these tools is to provide business analysts the ability to conduct ad-hoc analysis of multiple data sources. These tools provide data analysts with an intuitive way to sift through large volumes of disparate data to expose hidden patterns and outliers. They replace the traditional rows and columns of traditional data presentations with graphical pictures and charts.

The emergence of data discovery and visualization tools, and the emancipation of business analysts from old-school Extract, Transform, Load (ETL) processes and data modeling, was highly successful. This revolution democratized data and greatly accelerated the speed of data analysis to help companies make data-driven decisions in competitive environments. As a result, IT-centric BI vendors quickly started to build these data discovery and visualization capabilities into their product portfolios. Conversely, data discovery and visualization vendors were quickly pushed by customers to provide enterprise licensing and features, like data governance, security, data preparation and even report generation.

As data sets become increasingly large, capabilities like data preparation require users with some technical skills to make sense of potentially huge datasets generated by, for example, IoT devices. After all, data preparation and integration were typically handled by IT specialists as part of the ETL process. This, along with a shortage of data scientists with advanced analytics skills, resulted in an evolution of the self-service approach towards automation. This evolution is referred to as Augmented Analytics.

Most companies only analyze 12% of the data they have.1

The term augmented analytics is was coined by Gartner in 2017 to

describe the use of machine learning to enhance data analytics and BI.

1Exciting facts and findings about Big Data you should know

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Business Intelligence Trends

BI tools have evolved from pixel-perfect reporting engines to analytics platforms powered by powerful artificial intelligence technology.

Traditional BI

> Owned and managed by IT

> No business user autonomy over performing data analysis

> Limited self-service capabilities

> Focus on reporting and KPIs

Self-service BI

> Business user-driven

> Some data analysis autonomy for business users

> Focus on ease-of use for benefit of broad non-technical user base

> User-driven insights to aid business decision-making

Augmented Analytics

> Business users don’t require IT assistance to define data models

> Data preparation and analysis aided by artificial intelligence

> Ease-of-use greatly increased by AI automation

> Data correlation and insights automatically generated to drive decision-making

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Trends in BI In order to understand buying trends in this market, we talked to several experts, analyzed review content, conducted research, and surveyed BI software buyers. This section uncovers the main market technology trends likely to influence buyer behavior. These include: augmented analytics, and the automation of processes to simplify product usage, embedded analytics, and big data and IoT.

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Augmented Analytics

The term Augmented Analytics was introduced by Gartner in 2017 to denote the use of machine learning and natural language processing to automate insights into datasets generated by analytics and BI tools. It’s helpful to clearly understand the problem that this approach is trying to solve. The hard truth is that using BI tools to provide actionable insight into the business is difficult.The scale and complexity of corporate data has become more than humans can handle. Therefore, machine learning and AI are helping to automate data analysis. BI tools begin by gathering data from multiple different sources. The next step is to remove duplicates and bad data, normalizing the data so that it can be effectively analyzed. The result of this analysis is actionable insight into the business. Data analysts and scientists spend a large portion of their time working on data preparation tasks, cleaning large data sets, and preparing them for analysis. This is not easy to do, and data analysts and scientists are scarce. Augmented analytics removes some of the dependence on data scientists by using machine learning and artificial intelligence algorithms to automate tasks like data preparation. Today, many well-known BI platforms have built-in capabilities to automate data preparation and detect some correlations and anomalies in large data sets. Some platforms are capable of analysis and even isight generation and action plan generation, but there is still a long way to go.

Global augmented analytics market

was valued at $ 1.6 billion in 2018

and is forecast to grow at a CAGR

of over 39% to cross $ 11 billion by 2024.1

Tip for Buyers

While the traditional BI focus on reports and dashboards is not going away any time soon, companies should start looking at vendor capabilities in the area of augmented analytics. Most BI vendors are building augmented analytics into their platforms, but it is easy to get swept up in the hype. For this reason, it’s important to include analysts and data scientists in the product selection or validation process to help verify vendor claims and ask the right questions.

Begin with the BI investments you have already made. Talk to your vendors about their roadmaps to understand their approach to incorporating augmented analytics and timing. It is also a good idea to look at newer, more innovative vendors to see if switching platforms to benefit from faster access to these capabilities makes sense.

1Global Augmented Analytics Market by Deployment, Organization Size, End-user, and Region—ResearchAndMarkets.com

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Embedded Analytics

Embedded analytics is the integration of BI capabilities directly into a business application or portal. The idea is to provide analytics that are contextual. In other words, analytics tools are available directly within whatever business application is being used at a given moment an ERP system, or a marketing automation tool.

Reports, dashboards, and data visualizations are part of the daily workflow of business users. This means that the people who most need data insights—those making decisions— have immediate access to analytics tools without leaving the application they are working in. Analytics are delivered in context of an application that someone uses every day.

Tip for Buyers

Embedded BI is becoming the predominant model in the BI space and many vendors offer an embedded version of their product. But buying a standalone product and an embedded analytics solution are quite different. These are different use cases with different buyers and buyer concerns.

It is of paramount importance that embedded solutions should be flexible and easy to use for non-technical users. After all, the users of these systems may have limited skills in the data analytics.

Furthermore, the ease of embedding into the host application is an important criterion. Ask vendors to explain in detail how the embedding process works, and how exactly the BI capabilities co-exist within the host application. It is important to have your technical staff understand how open the product is. Products should provide both a software developer’s kit (SDK) and support for open standards, so that the capabilities of the product can be extended if required.

IoT devices alone are projected to

balloon to around 75 billion devices

by 2025.1

1IoT Statistics: Number of Enabled Devices & Growth.

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Big Data and the Internet of Things (IoT)

Most large companies are trying to figure out how to harness the terabytes of unstructured data, like streaming data, video data, machine data, etc., to improve business decision-making and business outcomes. Business data is no longer only collected from operational and transactional internal systems, but from physical devices like IoT sensors and machines, and from human sources like social media, image designers, etc.The relational data warehouse was designed for highly-structured data stored in tables. It cannot comprehend this kind of unstructured data, or this volume of data. Hence, the rapid ascension of Hadoop and so-called data lakes, or vast repositories of raw data stored in its native format until needed. This used to be the province of highly-trained data scientists, but the focus today is on democratizing this data so that business users have access. These business users need the ability to integrate data from multiple sources. Data in Hadoop repositories, but also data warehouses, databases, and spreadsheets must be available to users a single analytics layer. In other words, analytics tools should be capable of desegregating and connecting siloed data so it can be managed from a single place.It’s important to understand the IoT data is not the same as Big Data. While Big Data is crucial for organizational decision-making, there is usually a lag between data collection and data analysis. IoT data is different. Data is collected in real-time to detect security breaches, correct malfunctioning equipment, and more. IoT data analytics therefore depend on managing real-time streaming data.Big Data and IoT processing usually require specialized applications that are beyond the purview of standard BI capabilities. For example:> Data Lakes: These are unstructured data stores, often created using data from the open-source Hadoop ecosystem> NoSQL databases: Used for storing large volumes of rapidly changing data, or unstructured data that is not a good fit for a relational database> Predictive Analytics: Applications that can determine the likelihood of future scenarios> In-Memory Database: Very fast data stores for analyzing time-sensitive or streaming data (like IoT data)> Streaming Analytics: Tools for analyzing rapidly-changing data, like social media data, in real time

Tip for Buyers

Integration to Big Data and IoT technologies is crucial to creating the single analytics layer in BI tools. BI tools should be able to access data from multiple different technologies, and integrate that data so that a common set of data is available for analysis at the point of decision. Ask vendors about how this works in practice. Some key questions:> Does the vendor have pre-built connectors to these key technologies? > Does the vendor offer a high-performance in-memory data engine to help users analyze data faster? > What kind of data ingestion and data preparation tools are available for preparing data for analysis? Are these data preparation tools proprietary and built into the products, or does the vendor partner with data preparation specialists to provide this important capability?

IoT devices alone are projected to

balloon to around 75 billion devices

by 2025.1

1IoT Statistics: Number of Enabled Devices & Growth.

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Buying Insights from BI Software UsersA survey of BI users on TrustRadius provides data on overall user priorities, use of AI and machine learning technologies, and the extent to which DataOps best practices are being used to encourage data specialists and business users work together more effectively.

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BI User Priorities

BI users’ top analytics priorities make up a fairly heterogeneous list of initiatives. Nevertheless, two themes recur frequently:Data GovernanceOne frequently mentioned theme is an increased focus on data governance. As data discovery and visualization tools have become common, the problem of centralized governance is an inevitable concern. A common scenario is that multiple groups within an organization purchase more and more seats for their own instance of a data discovery and visualization tool. This inevitably leads to calls for enterprise licenses and, once a product becomes a standard offering deployed across the enterprise, the IT organization is inevitably involved again to ensure that corporate data is consistent and trustworthy. Over 65% of survey respondents indicated that data management/data governance is either currently in place or planned for the near future. Data DemocratizationAbout 10% of BI priorities fit into the bucket of evangelizing the importance of data for driving the decision-making process across the organization. The overall goal is to extend the internal BI user base to a much larger number of employees.When asked whether IT or business users are the primary users of BI tools in the organization, 19% of survey respondents indicate that IT is still in the driver’s seat. A healthy 80% of respondents, however, indicate that usage has shifted to business users. These business users do include employees with advanced analytics skills, even if they exercise those skills within business functions like marketing and sales. Total self-service by unskilled business users is probably still an aspiration rather than reality.There is a strong desire for tools that are simple enough that business users can leverage them to make data-driven decisions. Here’s what survey respondents said about their BI data democratization priorities:

80% of respondents indicate that BI tool

usage has shifted to business users.

Give our users the data they need to make better business decisions.

Broader adoption of analytics within the organization.

Sell more employees on the value of analytics for our organization.

Get more users actively using BI. Engage people across organization to use [our BI tool] more frequently to

analyse their activities.

1IoT Statistics: Number of Enabled Devices & Growth.

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Reviewers of BI tools on TrustRadius show a bit of a different breakdown. About half (51%) of the reviews and ratings of BI products featured in this guide are contributed by business users, and the other half (49%) are contributed by technical users (those in IT or Engineering departments). There is a great deal of variance by product, however, indicating that some products may be better suited for the business-user use case:

Based on reviewer department: IT and Engineering vs all others.

Percentage of Business Users vs Technical Users

73%

71%

64%

55%

48%

47%

44%

43%

40%

39%

33%

27%

29%

36%

45%

52%

53%

56%

57%

60%

61%

67%

Technical User %Business User %

Alteryx Platform

Tableau Desktop

Tableau Server

Microsoft Power BI

TIBCO Spotfire

TIBCO Jaspersoft

Qlik Sense

Birst

Looker

Sisense

Domo

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AI and Machine Learning

Almost 70% of respondents have already deployed AI or machine learning capabilities as part of their analytics tool set. Some firms are testing the waters with small-scale test projects. Typical use cases where respondents are deploying this technology today include: > real-time forecasting/predictive analytics> automatically detecting data anomalies as part of the data preparation stage> identifying patterns in very large data sets> making automated decisions with limited human interaction> fraud detection> natural language processing

DataOps

DataOps is an emerging approach to managing data across the enterprise and is used by just over 20% of survey respondents. DevOps is a set of best practices that allows companies to reduce their software release cycle time through continuous delivery and integration. DataOps is inspired by the DevOps agile movement, and the goal is to reduce the end-to-end cycle time of data analytics from ideation to creation of charts and visualizations that create value. Essentially, DataOps introduces agile methodologies into data analytics so that data specialists and business users work together more effectively to solve big data challenges. Based on the sheer number of articles in the technical press on this topic, we can expect increasing adoption of this approach to managing data across the enterprise.

Almost 70% of respondents have already deployed

AI or machine learning capabilities

as part of their analytics tool set.

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Spotlight on 10 Leading BI ToolsThis guide aims to give you a snapshot of 10 of the most reviewed BI products on TrustRadius. An exhaustive list of platforms can be found on the BI category on our website. The product profiles in this report showcase aggregate data from reviews collected on TrustRadius, including the most commonly listed pros and cons, exemplary quotes from reviewers, and customer demographic information.

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BI technology has been in existence for over 30 years and has seen several technology shifts as the focus has shifted from IT users to business users. The market is still vast and growing. Market Watch predicts that the global BI market is projected to reach close to USD 150 billion by 2025, and is growing at a CAGR of almost 30%. This is a very crowded and fast growing market with a broad range of products available that are not always clearly differentiated one from another. Almost all products include capabilities such as data source connectivity, data ingestion, data cleansing, and data visualization. For this reason, new trends like augmented analytics that using machine learning technologies to automate some of the more onerous BI tasks such as data cleansing, tend to have outsize importance.

In order to be included in this guide, products must have met the following requirements:

> The product must be in our BI software category.

> The product must have at least 40 reviews.

> The product must be ‘customer verified’—indicating there have been at least 10 new or updated reviews within the past year.

Products are presented in order of percentage of users in enterprises.

57% of buyers evaluating BI products on

TrustRadius are looking at these

10 products.

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Overall Satisfaction

RatingComparisonInformationProduct Metrics Reviewer Company Size

7.9

8.5

8.5

8.2

8.3

8.2

6.9

7.9

8.4

8.3

8.2

114

284

43

104

14

84

96

107

54

58

53

4.0%

4.4%

10.9%

2.5%

12.7%

15.9%

1.5%

2.2%

1.7%

1.4%

1.9%

10%

16%

10%

12%

14%

16%

13%

19%

26%

13%

19%

29%

25%

32%

36%

35%

38%

43%

46%

46%

59%

56%

61%

59%

58%

52%

51%

46%

44%

35%

29%

28%

25%

Share of Traffic Small# of ReviewstrScore Midsize Entreprise Most

Compared To

Tableau OnlineThoughtSpotMicrosoft BI

Microsoft Power BIAmazon QuickSight

Microsoft BI

Microsoft Power BIGoogle Data Studio

SAP Business Objects BI

Microsoft Power BIJMP Statistical Discovery

TIBCO Jaspersoft

Microsoft BIMicrosoft Power BI

Tableau Desktop

Microsoft Power BISSIS

UiPath Enterprise RPA Platform

Microsoft BITableau DesktopTIBCO Spotfire

Amazon QuickSightIBM SPSSDatorama

Microsoft Power BIDatorama

Amazon QuickSight

Google Data StudioAlteryx Analytics

Amazon QuickSight

Amazon QuickSightGoogle Data StudioMicrosoft Power BI

TIBCO Spotfire

Tableau Server

Domo

Tableau Desktop

Looker

Microsoft Power BI

TIBCO Jaspersoft

Birst

Alteryx Platform

Sisense

Qlik Sense

Product Comparison Tables

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Overall Satisfaction

Rating

7.9

8.5

8.5

8.2

8.3

8.2

6.9

7.9

8.4

8.3

8.2

7.1

7.7

5.3

8.2

8.4

8.2

6.9

7.7

8.5

7.5

7.5

7.3

7.7

5.3

8.2

8.4

8.2

6.9

7.7

8.5

7.5

7.5

6.7

8.0

7.1

7.7

8.4

6.9

7.7

7.7

8.8

6.7

7.9

6.7

8.2

7.1

7.7

8.2

7.9

6.6

6.6

8.5

7.4

6.7

7.8

8.3

8.0

8.1

8.2

8.5

8.1

8.1

8.5

7.4

8.5

6.3

6.4

4.0

7.7

7.9

8.7

7.3

6.9

7.7

7.8

6.1

Ad-Hoc Reporting

BI Standard ReportingtrScore

TIBCO Spotfire

Tableau Server

Domo

Tableau Desktop

Looker

Microsoft Power BI

TIBCO Jaspersoft

Birst

Alteryx Platform

Sisense

Qlik Sense

Product Comparison Tables

Data Discovery & Visualization

Mobile Capabilities

Report Output & Scheduling

Access Control & Security

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Easy to produce quality visualizations and reporting easily, but it still has the ability to perform more complex functions for more

experienced users.

Wayne A. | Database Manager Hospital & Health Care

Easy for non-technical users (mentioned in 22 reviews)

Most of the basic stuff was pretty easy, but others still require looking on the net for help. Set analysis is a prime example. It’s a very powerful tool that has provided good solutions, but the

syntax is not exactly intuitive.

Verified User | Manager Industrial Engineering

Learning curve (mentioned in 17 reviews)

Bringing together data from multiple sources: We have multiple servers that each house many

databases along with secure file share folders. Qlik makes it so easy to connect to all of these

sources and choose which data I want to bring in to the app within the snap of a finger!

Mitch S. | BI Analyst Financial Services

Connects to multiple data sources (mentioned in 20 reviews)

More detailed control of the layout of KPIs, charts, and graphs (labels, legends, size of fonts,

positioning of titles, etc).

Lauri S. | Manager of Informatics Hospital & Healthcare

Chart customization (mentioned in 31 reviews)

Score 7.9 out of 10

73% agree that Qlik Sense delivers good value for the price.

100% are happy with Qlik Sense’s feature set.

86% said would buy Qlik Sense again.

54% agree that Qlik Sense lives up to sales

and marketing promises (30% were not involved).

53% thought that implementation

went as expected (20% were not involved).

Based on a survey of 15 Qlik Sense users.

Qlik Sense began life as a self-service visualization tool designed for business users rather than developers. Now, it is the lead product in Qlik’s product portfolio. Qlik Sense provides a governed, multi-cloud architecture and the Qlik Associative Engine, which allows users to explore data freely using interactive selection and search. The company acquired Podium Data in 2018 for data management capabilities, and Attunity earlier this year for data integration and big data management. Qlik has also added augmented analytics capabilities, notably the Cognitive Engine that provides context-aware suggestions to help users identify insights.

Interested in learning more about Qlik Sense? Read reviews on TrustRadius.

Qlik Sense

PROS CONS

Great Fit For: Qlik Sense was designed for business users to explore data without assistance from IT and is a true self-service tool. It is particularly appropriate for data analysts in larger companies, since it also has extensive data governance and security capabilities.

Small

Midsize

Enterprise

Reviewer Company Size Distribution Top 5 Reviewer Industries

Information Technology | 19

Financial Services | 16

Computer Software | 13

Hospital & Health Care |13

Government Admin | 12

10%

61% 29%

21

Visualizing and displaying the data. We can analyze and compare quickly, then summarize

and show visually concise graphs to management so we can make good decisions.

Brian L. | Design Engineer Electronic Manufacturing

Complex data visualization capabilities (mentioned in 31 reviews)

Lack of thorough documentation: particularly when comparing Spotfire documentation to

the documentation for Python, JMP, or Matlab, Spotfire’s documentation is lacking.

Josh S. | Engineer Electronic Manufacturing

Inadequate documentation/support materials (mentioned in 9 reviews)

For tech savvy users, there is an ability in Spotfire to input IronPython, and for statistical analysis using R.

Verified User | Consultant Management Consultancy

Scripting language support (mentioned in 10 reviews)

UI design a little different to most products, increases learning time.

Verified User | Analyst Finance and Accounting

User Interface (mentioned in 14 reviews)

Score 8.5 out of 10

87% agree that TIBCO Spotfire delivers good

value for the price.

94% are happy with TIBCO Spotfire’s

feature set.

94% said would buy TIBCO Spotfire again.

62% agree that TIBCO Spotfire lives up to sales and marketing promises (37% were not involved).

56% thought that implementation

went as expected (25% were not involved).

Based on a survey of 16 TIBCO Spotfire users.

TIBCO Spotfire is a data discovery and visualization tool that is capable of processing very large data volumes. It includes a built-in predictive analytics runtime engine along with location analytics. Spotfire provides dashboards, interactive visualization, and data preparation in a single design tool and workflow, and can be deployed either in-memory or in-database. Spotfire X, released late in 2018, includes Natural Language Queries (NQL), automated data wrangling, and support for streaming data analytics.

Interested in learning more about TIBCO Spotfire? Read reviews on TrustRadius.

TIBCO Spotfire

PROS CONS

Great Fit For: Organizations in data-intensive industries like retail, energy, life sciences, or financial services where advanced analytics capabilities are paramount. Its broad set of capabilities also makes it a good choice for large organizations with multiple use cases.

Small

Midsize

Enterprise

Reviewer Company Size Distribution Top 5 Reviewer Industries

Oil & Energy | 122

Information Technology | 36

Pharmeceuticals | 29

Financial Services | 25

Computer Software | 24

16%

59%

25%

22

We use Alteryx for data prep, cleaning and analytics, to funnel data into Tableau. Used

solely in our analytics department...Data prep becomes a fun activity with Alteryx.

Clinton S. | Health Information Analytics Hospital & Healthcare

Data preparation (mentioned in 10 reviews)

The mapping and graph/charting tools continue to be a drawback for Alteryx. We’d love to use Alteryx as the all in one stop shop solution for analysis, mapping, and reporting but it’s maps

and charts can’t hold a candle to Excel Charts or Mapinfo/ESRI maps.

Adrian H. | VP Analytics Market Research

Mapping capabilities (mentioned in 6 reviews)

Alteryx is capable of connecting with a multitude of data sources from SQL databases to cloud-hosted

solutions like Salesforce, Tableau, or Google Analytics. Also supports OleDB & ODBC connections.

Joe R. | Marketing Intelligence Computer Networking

Data ingestion and merging (mentioned in 9 reviews)

Though it provides good data blending functionality, the visualization aspect is missing.

For visualizing data you need Tableau or Power BI.

Karan G. | Marketing Analyst Financial Services

Data visualization capabilities (mentioned in 8 reviews)

Score 8.5 out of 10

Alteryx Analytics is best known as a self-service data preparation and blending platform. However, Alteryx also provides a drag and drop environment that automates the workflow, from data discovery through preparation, modeling, and presentation. The company is establishing itself as a data science platform, but its reputation in the market is as a solid self-service data preparation tool. It is capable of accessing data from multiple sources and preparing it for analysis by a number of visualization tools like Tableau, QlikView, Microsoft Power BI.

Interested in learning more about Alteryx? Read reviews on TrustRadius.

Alteryx Analytics

PROS CONS

Great Fit For: Companies with a need for data blending and data modeling before staging the data for visualization in specialized tool.

Small

Midsize

Enterprise

Reviewer Company Size Distribution Top 5 Reviewer Industries

IT & Services | 9

Financial Services | 8

Marketing and Advertising | 8

Retail | 6

Computer Software | 5

10%

58% 32%

23

Tableau is perhaps the most aesthetically pleasing software to create beautiful visualizations that get updated in real-time. I wish this software had been

around 20 years ago.

Thomas Y. | CEO Financial Services

Data visualization (mentioned in 21 reviews)

Tableau Desktop lacks the basic functionality of cleaning and prepping the data to be imported

in Tableau Desktop. This is one of the major features which it lacks, you need additional

support from Tableau to get these things added.

Rahul M. | Team Lead IT & Services

Data preparation (mentioned in 12 reviews)

Tableau’s interface allows data analysts of all experience levels to quickly begin

producing compelling and useful analysis and visualizations. As a user gains experience,

Tableau’s feature set continues to keep pace.

Benjamin P. | Technical Project Manager Hospital & Healthcare

Usability (mentioned in 20 reviews)

The licensing model was very expensive and required us to continually buy more seats...It’s best for very small teams or when you get over the ~100 user

mark. You still need to purchase the Tableau Desktop license for each of your data scientists/engineers.

Verified User | VP Product Management Internet

Expensive at scale (mentioned in 19 reviews)

78% agree that Tableau delivers good value

for the price.

81% are happy with Tableau’s feature set.

91% said would buy Tableau again.

62% agree that Tableau lives up to sales and marketing promises

(27% were not involved).

62% thought that implementation

went as expected (16% were not involved).

Based on a survey of 37 Tableau users.

Tableau is, in many ways, the blueprint for visual analytics.It is an exceptionally easy-to-use platform for exploring and visualizing large volumes of data from multiple sources. The product was acquired by Salesforce earlier this year. Tableau Desktop is used to visualize and analyze data, create workbooks, visualizations, dashboards, and stories. Tableau Server allows users to publish workbooks so that they are available in a central location for viewing by others. The product was designed with business users in mind. Tableau introduced an in-memory data engine, called Hyper, in 2018. Hyper can boost query speed by 5X and extract large data sets faster.

Interested in learning more about Tableau? Read reviews on TrustRadius.

Tableau Desktop and Server

PROS CONS

Great Fit For: Companies with business analysts doing discovery and visualization on data from a large number of data sources, and then sharing the data with others across the organization. Tableau is not designed for scheduled reporting.

Score 8.2 out of 10Tableau Server

Score 8.3 out of 10Tableau Desktop

Small

Midsize

Enterprise

Reviewer Company Size Distribution Top 5 Reviewer Industries

Information Technology | 476

Finance & Accounting | 80

Research & Development | 67

Engineering | 60

Marketing | 10813%

51%

36%

24

The visualizations are top notch, and available without additional cost. Everything is in vibrant colors and crisp, clear lines that whatever your produce is clearly pleasing to

the eye. From mapping down to pie charts, the visuals are well done.

Verified User | Finance Manager Consumer Goods

Powerful visualization capabilities (mentioned in 29 reviews)

Greater flexibility in formatting the look and feel or reports and dashboards.

Verified User | Project Manager Computer Software

Difficult to customize charts and graphs (mentioned in 10 reviews)

Similar to office 365 applications such as Excel and Access, that is, it facilitates the usability of those who already have

advanced knowledge of these tools.

Gabriel O. | Programmer Mining and Metals

Integrated with other Microsoft products (mentioned in 23 reviews)

Not all features are available in the online version: If you want to use more advanced

features, you will probably need to download the desktop version of Power BI.

Gabriel C. | Marketing Consultant Consumer Electronics

Feature differences in online version (mentioned in 11 reviews)

Score 8.2 out of 10

95% agree that Microsoft Power BI delivers good

value for the price.

95% are happy with Microsoft Power BI’s

feature set.

91% said would buy Microsoft Power BI

again.

70% agree that Microsoft Power BI lives up to sales and marketing promises (17% were not involved).

74% thought that implementation

went as expected (17% were not involved).

Based on a survey of 23 Microsoft Power BI users.

Power BI is a separate visualization and data discovery product providing data preparation, data discovery, and interactive dashboards as a standalone web-based tool running in the Azure cloud. It is also available as an on-premise version called Power BI Report Server. The product is capable of combining data from multiple sources, regardless of where it resides. Augmented analytics capabilities are a development focus, with capabilities being built for users with limited technical skills. Power BI is a relatively inexpensive tool and has seen rapid uptake by users of the Microsoft BI SQL Server-based reporting product.

Interested in learning more about Microsoft Power BI? Read reviews on TrustRadius.

Microsoft Power BI

PROS CONS

Great Fit For: Price-sensitive organizations with staff familiar with other Microsoft products. The free version is fully functional, although somewhat restricted. The product is ideal for organizations with many Excel power users.

Small

Midsize

Enterprise

Reviewer Company Size Distribution Top 5 Reviewer Industries

Computer Software | 33

Financial Services | 13

Construction | 10

Higher Education | 8

Hospital & Healthcare | 8

16%

48%

38%

25

End-to-end solution, from raw source data, ETL, warehousing and reporting, Birst is able

to do everything we need in one package instead of needing to develop and maintain

multiple technologies.

Verified User | IT Mining and Metals

End-to-end tool with broad set of capabilities (mentioned in 9 reviews)

User administration could be greatly improved. Most user administration tasks (creation, security

assignments, etc.) are singular i.e., one action at a time. If you want to batch these activities up, it is up to you to

have scripts or create your own mini app to do this.

Tim W. | Database Admin Market Research

User administration (mentioned in 6 reviews)

The drilling and filtering capabilities are outstanding! We’ve really been able to get to the lower level of detail that we needed, but could not get, before we started using Birst.

Scott G. | Business Analyst Hospital & Healthcare

Drill-down and data filtering in dashboards (mentioned in 8 reviews)

Documentation of the product in the help files is outdated and incomplete in many cases. Several features are only documented in the community

because enough people have asked questions about a topic. The search functionality in both

help and community is really bad.

Lee Ann B. | BI Architect Information Services

Documentation and training (mentioned in 10 reviews)

Score 6.9 out of 10

Birst provides an end-to-end BI platform that combines data exploration and visualizations with scheduled reporting capabilities. The company provides an in-memory columnar data store and a BI layer comprising a reporting engine, predictive analytics tools, mobile native apps, dashboards, and discovery tools. Following the acquisition by Infor in 2017, Birst is now the de-facto analytics engine embedded in Infor business applications. Birst developed the concept of “networked BI” which constitutes a shared virtual analytics layer, creating a fabric of data for centralized teams and decentralized end users to collaborate. Birst 7 was released earlier this year.

Interested in learning more about Birst? Read reviews on TrustRadius.

Birst

PROS CONS

Great Fit For: Organizations looking for a multi-tenant cloud-based BI platform that can help operationalize analytics across decentralized or centralized teams. It is a good choice for organizations that require a broad range of BI capabilities in a single platform.

Small

Midsize

Enterprise

Reviewer Company Size Distribution Top 5 Reviewer Industries

Computer Software | 20

Financial Services | 5

Hospital & Healthcare | 5

Information Services | 5

IT & Services | 1313%

44% 43%

26

We had many client requests for pixel perfect reporting as well as ad hoc use of their own

data and not enough resources to expend on the effort; Jaspersoft fulfills both of these requests.

Ashley L. | Project Manager Computer Software

Easy to create pixel-perfect scheduled reports (mentioned in 23 reviews)

The JasperSoft Studio (Eclipse + plugins) is not always a joy to use. The choice of using Eclipse as an IDE base is understandable, but it comes with

the issues and difficulty of Eclipse. The Eclipse integration lacks any useful debugging features

and the error messages are vague.

Brian B. | Business Analyst IT & Services

Jaspersoft Studio report builder (mentioned in 8 reviews)

Very easily integrated reporting engine into Java applications. Very easy to integrate with

external applications using the API for the server component and libraries for standalone.

Verified User | Research and Development Legal Services

Embedding in 3rd-party applications (mentioned in 16 reviews)

If a domain is extremely large and contains several derived tables, slowness is a big issue.

Not only is there slowness in the domain designer, but there is slowness when using said domain as the data source of an ad-hoc view.

Verified User | Engineer Computer Software

Performance (mentioned in 8 reviews)

Score 7.9 out of 10

81% agree that TIBCO Jaspersoft delivers good

value for the price.

81% are happy with TIBCO Jaspersoft’s

feature set.

81% said would buy TIBCO Jaspersoft

again.

36% agree that TIBCO Jaspersoft lives up to sales and marketing promises

(36% were not involved).

82% thought that implementation

went as expected (9% were not involved).

Based on a survey of 11 TIBCO Jaspersoft users.

TIBCO Jaspersoft is an open-source reporting and analytics tool that was acquired by TIBCO Software in 2014 to round out its product portfolio with a true pixel-perfect reporting solution. It has a modern user interface and is thus relatively easy to use by end users. It is now most often used in an OEM or embedded scenario where it is integrated as the reporting engine for business applications, improving context and usability of data.

Interested in learning more about TIBCO Jaspersoft? Read reviews on TrustRadius.

TIBCO Jaspersoft

PROS CONS

Great Fit For: Companies that wish to embed a self-service reporting and dashboard engine in a commercial software application or customer-facing applications. This product should be on the shortlist for an embedded BI tool that can integrate easily with a JavaScript front end.

Small

Midsize

Enterprise

Reviewer Company Size Distribution Top 5 Reviewer Industries

Computer Software | 33

Financial Services | 13

Construction | 10

Higher Education | 8

Hospital & Healthcare | 8

19%35%

46%

27

Sisense allows anyone to build their own dashboard. The interface is “uber” intuitive, and “uber” efficient : no need to declare filters, data hierarchy or anything.

Once your data are loaded, just build dashboards.

David H. | Founder Retail

Easy to create dashboards quickly (mentioned in 12 reviews)

Mobile system is wonky and not as intuitive as the desktop version. I would recommend against

viewing the dashboards on a mobile device as well.

Joseph S. | Senior Analyst Information Services

Mobile capabilities (mentioned in 5 reviews)Data source connectivity is one of Sisense best

capabilities. It connects to all of your available sources and creates a virtual Data warehouse, which not only prevents from disconnecting,

but it prevents...data errors.

Verified User | C-Level Executive Management Consulting

Combining data from different sources (mentioned in 12 reviews)

Admins currently cannot edit each others’ dashboards. This gets very cumbersome once

admins change - it takes duplicating and republishing dashboards to let them take over.

Gudrun B. | Product Management Computer Software

Admin permissions (mentioned in 7 reviews)

Score 8.4 out of 10

Sisense is a fully-featured product with a columnar data store, an ETL layer and a set of front- end tools for data cleansing for constructing dashboards and visualizations. Sisense has abandoned the familiar in-memory technology common to visualization tools for an “in-chip” solution which uses the data storage provided by the chip set, in addition to RAM and disk storage. This platform can process very large volumes of data in the columnar database, and the in-chip memory is efficient. The company released Hunch in 2018, which provides machine learning analytics. Sisense merged with data science vendor Periscope in May of this year.

Interested in learning more about Sisense? Read reviews on TrustRadius.

Sisense

PROS CONS

Great Fit For: Companies needing to process large volumes of data from disparate sources without heavy reliance on an IT organization. Sisense also provides self-service analytics tools that are designed for the business user rather than the IT organization.

Small

Midsize

Enterprise

Reviewer Company Size Distribution Top 5 Reviewer Industries

Computer Software | 10

Higher Education | 5

Internet | 5

Financial Services | 4

IT & Services | 825%

29%

45%

28

Score 8.3 out of 10

80% agree that Domo delivers good value for the price.

100% are happy with Domo’s feature set.

80% said would buy Domo again.

54% agree that Domo lives up to sales and marketing promises

(36% were not involved).

54% thought that implementation

went as expected (36% were not involved).

Based on a survey of 11 Domo users.

Domo is a web-based BI tool that provides a suite of capabilities from data warehouse and ETL to visualizations, scheduled reports, and alerts. The product has a much-praised user interface. Domo is capable of ingesting and integrating data from many different sources and producing high-quality visualizations of that data. Integration with enterprise applications is facilitated by a large library of pre-built connectors. Domo also incorporates strong collaboration tools, facilitating real-time discussions around data. Domo’s go-to-market approach is somewhat different to its competitors in that Domo primarily targets business decision-makers.

Interested in learning more about Domo? Read reviews on TrustRadius.

Domo

Visualization layer is clean and is very business presentable. It’s simple but includes depth.

Visuals can be created/used by all roles throughout our firm, it’s not limited to analysts.

Kyle T. | Business Operations Engineer Financial Services

Strong visualization capabilities (mentioned in 22 reviews)

Not for the faint of heart! Even if someone else implements for you and builds out initial

data tables, you’ll likely need SQL skills to build complex visualizations or to make any changes

to your data structure.

Katie N. | Business Process Manager Computer Software

Steep learning curve (mentioned in 5 reviews)

As a tool it’s comfortable, approachable and forgiving. If you have a tool that end users can

dive into without fear, and create and think and collaborate, without training, without

hand-holding, you quickly realize how rare and important that is.

Jesse M. | VP Data Analytics Insurance

Elegant user interface (mentioned in 16 reviews)

The training was very lacking and should have been sold up front with much more options than

after the fact.

John S. | VP of IT Plastics

Product training is inadequate (mentioned in 5 reviews)

PROS CONS

Great Fit For: Companies that want a modern line of business and executive dashboard tool with strong collaboration capabilities will benefit from Domo’s integrated chat and data sharing capabilities.

Small

Midsize

Enterprise

Reviewer Company Size Distribution Top 5 Reviewer Industries

Computer Software | 13

IT & Services | 6

Marketing & Advertising | 6

Retail | 6

Construction | 1013%

28%

59%

29

Score 8.3 out of 10

70% agree that Looker delivers good value for the price.

100% are happy with Looker’s feature set.

100% said would buy Looker again.

70% agree that Looker lives up to sales and marketing promises

(30% were not involved).

90% thought that implementation

went as expected (10% were not involved).

Based on a survey of 10 Looker users.

Looker provides a cloud-native BI platform that connects directly to modern, high-speed analytics databases like Amazon.com’s RedShift, Snowflake, Google’s BigQuery, or Microsoft’s SQL. Looker also created its own modeling language called LookML that allows developers to define the relationships in their database. This means non-technical business users can explore, save, and download data without needing to know SQL. These data models make it possible for non-technical users to build dashboards, drill into row-level detail, and access metrics. Looker also provides support for data science including Google BigQuery ML and Action Hub. In June of 2019, Google announced that it is in the process of acquiring Looker.

Interested in learning more about Looker? Read reviews on TrustRadius.

Looker

The LookML language for creating data models is extremely intuitive. It blurs the line between

the data integration aspect of BI and the visualization or self-service BI component.

Ian H. | Manager Business Analytics Oil & Energy

Building data models (mentioned in 9 reviews)

The calculation field can be incredibly useful but, again, requires technical expertise which not all

of our users have.

Verified User | Product Management Computer Software

Calculations can be difficult to run (mentioned in 6 reviews)

Support is AMAZING. Our live chat support team are always super friendly and go above and beyond to help us. I even had their CTO

Lloyd respond to me once.

Michael S. | Head of Online Advertising Photography

Customer Support (mentioned in 9 reviews)

Looker needs some improvement in the visualization aspects of the product, they are

very good at getting the data, but the choices for visualizing that data are somewhat limited, there are workarounds for some of the weaknesses but

they tend to be labor intensive and fragile.

Verified User | Information Technology Consumer Electronics

Visualization capabilities could be richer (mentioned in 7 reviews)

PROS CONS

Great Fit For: Organizations that wish to empower business users to build their own dashboards and reports, and have skilled BI developers that can leverage LookML to build data models on behalf of business users.

Small

Midsize

Enterprise

Reviewer Company Size Distribution Top 5 Reviewer Industries

Internet | 28

Financial Services | 8

IT & Services | 8

Consumer Goods | 3

Computer Software | 2319%25%

56%

30

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Buyer’s Guide or its contents, please contact us anytime.

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