Visual DiscoveryTools

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Visual Discovery Tools:Market Segmentationand Product Positioning

BY WAYNE ECKERSON

Director, BI Leadership Research

TECHTARGET, MARCH 2013

A TECHNOLOGY GUIDE


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VISUAL DISCOVERY TOOLS: MARKET SEGMENTATION AND PRODUCT POSITIONING 2

BACKGROUND

KEY

CHARACTERISTICS

MARKET TRENDS

TYPES OF VISUAL

DISCOVERY TOOLS

EVALUATION

CRITERIA

VENDOR

ASSESSMENTS

ECOMMENDATIONS

APPENDIX

Background

What are visual discovery tools? They are sel-service, in-memory analysis

tools that enable business users to access and analyze data visually at the

speed o thought with minimal or no IT assistance and then share the results

o their discoveries with colleagues, usually in the orm o an interactive dash-

board.Who are the target users? Visual discovery tools are used by (1) power

users to explore and analyze data in a variety o systems, (2) superusers and

BI specialists to create interactive dashboards or colleagues and (3) casual

users to view and work with those dashboards.

MARKET POSITIONING

As depicted in Figure 1, visual discovery tools

straddle top-down and bottom-up approach-es to business intelligence and are typically

deployed more quickly and at a lower cost

than enterprise-centric BI tools. Visual dis-

covery tools oten replace desktop analysis

tools like Excel and share most characteris-

tics with multidimensional online analytical

processing (OLAP) tools except write-back

capabilities.

Visual discovery tools are now starting tobleed into other BI segments, particularly

ad hoc and operational reporting, relational

OLAP and big data analytics platorms.

Today, visual discovery tools are primarily

geared toward departmental applications,

not enterprise ones. But as unctionality

DEFINITIONS

Power Users

Business users who are paid to

crunch data on a daily basis,

including business analysts,

statisticians and data scientists

Superusers

Business users who master the

use o a BI tool and quickly

become the go-to people in each

department to create hoc reports

and dashboards

Casual Users

Business users who use inorma-

tion to do their jobs, including

executives, managers, ront-line

workers, customers and suppliers


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BACKGROUND

KEY

CHARACTERISTICS

MARKET TRENDS

TYPES OF VISUAL

DISCOVERY TOOLS

EVALUATION

CRITERIA

VENDOR

ASSESSMENTS

ECOMMENDATIONS

APPENDIX

fgure 1. BI Tools Framework

SOURCE: WAYNE ECKERSON, B I LEADERSHIP RESEARCH

BIfunctionality

EnterpriseDepartment

Scope of deployment

Top-down

Bottom-up

Reporting

Dashboards

An

alysis

Mining

Pixel-perfect reporting

Ad hoc

Reports and

dashboards

Analyst

Data mining workbench

Operational

reports and

dashboards

Big data

analytics platforms

Relational OLAP

Multi-

dimensional

OLAP

Desktop

analysis(e.g.,

Excel)

Visual

discovery

Powerusers

Casualusers

evolves in the next year or two, visual dis-

covery tools will compete more directly with

relational OLAP and big data analytics plat-

orms or dominance in enterprise deploy-

ments (See Appendix 1 or a detailed break-

down o the BI market). n

BACKGROUND

TYPES OF INTELLIGENCE

Top-downintelligence consists

o reports and dashboards that

answer predened questions

or monitor business processes

using metrics aligned with

strategic objectives

Bottom-upintelligence consists

o ad hoc analysis and mining

tools that answer unanticipated

questions arising rom new and

changing business conditions


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BACKGROUND

KEY

CHARACTERISTICS

MARKET TRENDS

TYPES OF VISUAL

DISCOVERY TOOLS

EVALUATION

CRITERIA

VENDOR

ASSESSMENTS

ECOMMENDATIONS

APPENDIX

KEY CHARACTERISTICS

Connectivity drivers. Many also connect directly to applicationsor example,

SAP Business Suite and Salesorce.com, XML and Hadoopusually through

separately priced adapters. Once connected to a data source, visual discovery

tools download predened sets o data into local memory, although some alsolet users query the data directly in source systems.

4. In-memory processing. To deliver speed-o-thought analysis, most visual

discovery tools store data in an in-memory database or cache to avoid the

perormance bottleneck associated with accessing data rom a rotating disk.

The tools aggregate or calculate base-level data dynamically and scale linear-

ly as customers add more RAM and CPU. But since RAM is quickly consumed

by data, users and visual controls, most visual discovery tools are designed to

manage workgroup and departmental applications, not enterprise onesorexample, data marts instead o data warehouses. But the memory ootprint

o servers is expanding quickly, making it possible or companies to store

ever-larger volumes o data in memory, even terabyte-scale data warehouses

VISUALIZATION TECHNIQUES

Auto-suggest.Suggests chart types based on the shape o the data

Linking.Links charts to each other or to a data source so changes in one are automatically

applied to the others

Brushing.Highlights a variable in a chart when a user selects a variable in another chart

Lassoing.Lets users select data values in a chart by circling them with a cursor. Oten used

with brushing and linking

Keyword search.Lets users quickly nd one or more attributes in a long list or drop-down box

Mouseovers.Displays tips, metadata or available unctions when users move the cursor over

an object on the screen

Context-sensitive icons.Associated with data objects that indicate available unctions

Animations.Show how data values change over time

Snapshots.Save the current view as a live link that users can email to others

Custom objects.Let users create custom hierarchies, groups, calculations and metrics or use

in a visual display


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BACKGROUND

KEY

CHARACTERISTICS

MARKET TRENDS

TYPES OF VISUAL

DISCOVERY TOOLS

EVALUATION

CRITERIA

VENDOR

ASSESSMENTS

ECOMMENDATIONS

APPENDIX

in the near uture. Moreover, visual discovery tools are ollowing the lead o

Tableau and augmenting their in-memory databases with direct query con-

nectivity, so there is no longer a nite limit to the amount o data the tools

can access.

5. Simple and exible data design. As sel-service tools, most visual dis-

covery products model data on the fy, minimizing the amount o up-ront

design required to implement and use the sotware. Unlike top-down BI tools,

visual discovery tools dont require a specic data model like a star schema

or semantic layer o user-riendly data objects. Most visual discovery prod-

ucts ocus on one data set at a time and automatically link tables using shared

keys, making a best guess at data relationships,

which users can override i desired. O course,the tradeo or this design simplicity and fex-

ibility is complexity. Most visual discovery tools

dont deal well with complex enterprise applica-

tions with thousands o tables and dirty data.

Like most BI tools, they work best when run

directly against a data warehouse or another

clean set o integrated data.

6. Visual design and exploration. Visualdiscovery tools query data visually rather than

visualize query results, that is, numeric tables.

As a result, analysis and design is one and the

same and thus highly interactive and iterative.

Their WYSIWYG interace enables users to

drag and drop metrics, dimensions and attributes onto a visualization canvas

and instantaneously view results in graphical orm using a variety o chart

types that the tools automatically select or suggest based on the shape o

the data.While most BI tools constrain the number o available metrics or attributes

that users can view, visual discovery tools do not. Users can easily associate

any metric or attribute with an axis, color, size or shape to create a multidi-

mensional display that makes it easy to visualize patterns and outliers in the

data. (See Visualization Techniques or a list o some o the more com-

monly used approaches used by visual discovery tools.)

Most visual discovery

tools dont deal well

with complex enterprise

apps. Like most BI tools,

they work best when

run directly against

a data warehouseor another clean set

o integrated data.

KEY CHARACTERISTICS


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BACKGROUND

KEY

CHARACTERISTICS

MARKET TRENDS

TYPES OF VISUAL

DISCOVERY TOOLS

EVALUATION

CRITERIA

VENDOR

ASSESSMENTS

ECOMMENDATIONS

APPENDIX

7. Aordable pricing. Analysts can purchase desktop tools or between

$500 and $2,000, or in some cases, download them or ree with limited data

connectivity (Excel and CSV les only). Since most organizations have only a

handul o analysts, department heads can buy the tools using their operat-ing budgets. For a small workgroup o 10 to 20 users with a publishing server,

organizations can get started or about $50,000, not including training and

proessional services. Enterprise deployments can easily exceed six gures

because o the costs o managing clusters o high-perormance servers with

lots o RAM and CPU. Interestingly, enterprise BI vendors oten bundle visual

discovery tools into their BI suites at no extra cost. This is great or existing

customers but an expensive proposition or prospects. n

KEY CHARACTERISTICS


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BACKGROUND

KEY

CHARACTERISTICS

MARKET TRENDS

TYPES OF VISUAL

DISCOVERY TOOLS

EVALUATION

CRITERIA

VENDOR

ASSESSMENTS

ECOMMENDATIONS

APPENDIX

Market Trends

THE MARKET FOR visual discovery tools is evolving quickly. This section looks

at market trends in user organizations and the vendor marketplace.

USER ORGANIZATIONSOrganizations are guring out how to integrate visual discovery tools in an

enterprise BI environmentor not. Here are the key trends:

1. Dashboard development. Department heads are increasingly using visual

discovery tools as dashboard development platorms instead o treating them

as sel-service tools or business analysts. In most cases, department heads

who are dissatised with the cost, delay and qual-

ity o analytics applications developed by the

corporate BI team buy the tools and hire super-users or consultants to build custom interactive

dashboards or their departmental users.

2. IT backlash. Until recently, corporate BI

teams have viewed visual discovery tools as

renegade BI environments, since department

heads oten build dashboards without consult-

ing with the BI team or adhering to corporate

data standards. Increasingly, however, cor-porate BI teams are getting in ront o these

departmental initiatives beore they hit a scal-

ability or complexity wall. They convince department heads to point relevant

queries at data warehouses and data marts to ensure consistent use o enter-

prise data.

3. Big data analytics. With the advent o big data, analysts want to use visu-

Increasingly, corp-

orate BI teams are

getting in ront o

these departmental

initiatives beore they

hit a scalability or

complexity wall.


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BACKGROUND

KEY

CHARACTERISTICS

MARKET TRENDS

TYPES OF VISUAL

DISCOVERY TOOLS

EVALUATION

CRITERIA

VENDOR

ASSESSMENTS

ECOMMENDATIONS

APPENDIX

al discovery tools to explore large volumes o data in an iterative ashion. Con-

sequently, visual discovery vendors are improving the scalability o their tools

by letting users directly query Hadoop and large analytical databases and

download subsets o that data into memory or deeper analysis. Most are alsoexpanding the scalability o their in-memory capabilities, running their server

sotware in clustered environments and adding statistical and other analytical

unctions to support predictive analysis. As such, the line between visual dis-

covery tools and big data analytics tools and platorms is starting to blur.

VENDOR ORGANIZATIONS

Vendors are rapidly evolving their tool sets to capitalize on market opportuni-

ties and prevent new competitors rom making inroads into their customeraccounts. Here are the key trends:

1. Market encroachment and rear-guard actions. Visual discovery tools

shipped by startups or small BI vendors threaten enterprise vendors that

oer predominantly top-down BI tool sets. As

a result, leading enterprise BI playersIBM

Cognos, SAP BusinessObjects, MicroStrategy,

Microsot and SAS Institutehave all shipped

their own visual discovery tools in the past 12months. Besides oering new sel-service and

visualization unctionality to customers, they

desperately need to protect their fanks rom

incursion by pure-play visual discovery vendors

whose land and expand strategies o selling to

departmental business users have eroded their

account control. To maintain it, enterprise BI

players are largely giving away their visual dis-

covery tools by bundling them into enterprise suites. Most dont have plans tosell their visual discovery tools to net new customers, at least until their prod-

ucts are mature enough to compete head on with pure-play vendors.

2. Scalability wall. Although organizations can store ever-larger amounts

o data in memory thanks to compression and larger RAM ootprints, visual

discovery vendors must deal with the perormance cli that orms when

MARKET TRENDS

Most visual discovery

products were designedor individual users and

then scaled to support

workgroup and depart-

mental applications.


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BACKGROUND

KEY

CHARACTERISTICS

MARKET TRENDS

TYPES OF VISUAL

DISCOVERY TOOLS

EVALUATION

CRITERIA

VENDOR

ASSESSMENTS

ECOMMENDATIONS

APPENDIX

customers consume all addressable memory and begin paging data to disk.

To help customers avoid this problem, more vendors now let users run que-

ries directly against remote databasesa kind o drill through to detailed

data that reduces the amount o data that customers must cram into RAM,ultimately reducing costs and improving scalability. Some vendors, such as

Tableau, are moving in the reverse direction, adding in-memory capabilities

to complement their direct query architectures.

As such, vendors are now debating whose

dynamic query capabilities are most fexible

and useul.

As mentioned, many visual discovery vendors

are also taking steps to improve the scalabil-

ity o their embedded databases. Some, suchas SAP and SAS, are running their in-memory

databases on extensible massively parallel

processing (MPP) grids, while others, such as

SiSense and Platora, are deploying specializing

analytical databases that tightly align memory

and disk to optimize perormance.

3. Enterprise wall. As more customers deploy

visual discovery tools across multiple depart-ments, many will want to consolidate these operations to achieve economies

o scale and standardize their BI environments. The only problem is that

most pure-play visual discovery tools were not designed to handle enterprise

deployments. Besides scalability, most vendors havent invested signicantly

in ailover, load balancing, disaster recovery, clustering, monitoring and

administration utilities. They also havent suciently addressed enterprise

data management issues, such as data quality, data proling, data transor-

mation, metadata management and team-based development. This is where

enterprise BI players oer an advantageat least or customers who havealready implemented their enterprise BI architectures. So the race is on: Pure-

play vendors are busy developing enterprise capabilities, while enterprise ven-

dors are quickly adding visualization and design unctionality to catch up.n

MARKET TRENDS

So the race is on:

Pure-play vendors

are busy developing

enterprise capabilities,while enterprise

vendors are quickly

adding visualization

and design unctional-

ity to catch up.


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BACKGROUND

KEY

CHARACTERISTICS

MARKET TRENDS

TYPES OF VISUAL

DISCOVERY TOOLS

EVALUATION

CRITERIA

VENDOR

ASSESSMENTS

ECOMMENDATIONS

APPENDIX

Types o Visual Discovery Tools

SO FAR I HAVE dened visual discovery tools, described their common char-

acteristics and explained how the market is evolving. Now I will dierentiate

among visual discovery products so you can create a short list o vendors that

make sense or your organization.

There are three types o visual discovery tools: visual exploration, visualanalysis and visual mining. Vendors in each category exhibit specic traits and

share similar architectures, although these distinctions are starting to ade

as vendors expand their unctionalities to match the competition. These seg-

ments are depicted in Figure 2.

Analys

isfunctionality

Data scalability

Visual miningTibco Spotfire, Actuate BIRT Analytics,

SAS JMP, Alteryx, Advizor, Information

Builders

Visual analysisQlikView, MicroStrategy Visual Insight,

IBM Cognos Insight, Microsoft Power

View

Visual explorationTableau, SAS Visual Analytics, SiSense,

Platfora, SAP Visual Intelligence

Low High

High

fgure 2. Segments o the Visual Discovery Market

There are three segments of the visual discovery market, based on analytical functionality

and scalability. These distinctions are blurring fast but will never fully disappear.

SOURCE: WAYNE ECKERSON, BI LEADERSHIP RESEARCH


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BACKGROUND

KEY

CHARACTERISTICS

MARKET TRENDS

TYPES OF VISUAL

DISCOVERY TOOLS

EVALUATION

CRITERIA

VENDOR

ASSESSMENTS

ECOMMENDATIONS

APPENDIX

1. Visual exploration. Visual exploration tools are geared toward business

analysts and data scientists who need to explore large data sets. These tools

excel at connecting to various source systems. They enable business analysts

to model data on the fy without scripting, making them easy to deploy as longas source data is not overly complex. Tableau, one o the most visible vendors

in this segment, uses an innovative visual

query language, VizQL, to connect directly to

most data sources using native ODBC con-

nections to more than 30 data sources. Other

exploration tools embed specialized analyti-

cal databases that support high-perormance

queries against large volumes o data. These

products straddle the big data analyticsplatorm market depicted in the BI Tools

Framework in Figure 1.

2. Visual analysis. Visual analysis tools are

geared toward superusers and BI specialists

who want to build interactive dashboards

or departmental users. In eect, they are

dashboard development platorms that excel

at visual design, interactive analysis, collabo-ration and mobile delivery. Their dominant

ocus is to make it easier or casual users to

analyze data using interactive visualizations.

These tools turn superusers into authors and

analysts into developers who build elegant

interactive dashboards or departmental use.

Most visual analysis tools use in-memory

databases as their primary data management

architecture.

3. Visual mining. Visual mining tools are

geared toward sophisticated business analysts or statisticians who need to

create analytical models or dig deeply into the patterns o underlying data.

These tools contain libraries o statistical, data manipulation and analytical

unctions that enable analysts to prepare data or modeling operations. Most

VISUAL DISCOVERY TOOLS

Visual Exploration

Best known: Tableau

Comparable: SAS Visual

Analytics, SiSense, PlatoraTarget users: Business analysts

and data scientists

Visual Analysis

Best known: QlikView

Comparable: MicroStrategy

Visual Insight, Cognos Insight,

Microsot Power View

Target users: Superusers and

business users

Visual Mining

Best known: Spotre

Comparable: Advizor Visual

Discovery (same as Inormation

Builders Visual Discovery), SAS

JMP, Alteryx, Actuate BIRT

Analytics

Target users: Statisticians and

sophisticated business analysts

TYPES OF VISUAL DISCOVERY TOOLS


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BACKGROUND

KEY

CHARACTERISTICS

MARKET TRENDS

TYPES OF VISUAL

DISCOVERY TOOLS

EVALUATION

CRITERIA

VENDOR

ASSESSMENTS

ECOMMENDATIONS

APPENDIX


also embed data mining sotware or integrate with third-party tools, such as

R, or spatial analysis packages, such as Esris ArcGIS, to support the ull ana-

lytical modeling liecycle. Most use an in-memory database as their primary

data architecture.

MAPPING VENDORS TO MARKET SEGMENTS

To be honest, vendors dont all neatly into any one o the three market seg-

ments. Most span all three segments to one degree or another. The designa-

tions simply indicate clusters o strengths dierent vendors have.

For example, Tibco Spotre embeds its own data mining tools and inte-

grates with the R statistical analysis package. As such, its classied as a

visual mining vendor. But Spotre can be used to build interactive dashboardsas well as explore data. So its capabilities span all three areas but its core

strength is in visual mining. Figure 3 demonstrates how Spotre maps to the

three segments o the visual discovery market.

Analysisfunctionality

Data scalability

Visual mining

Visual exploration

Low High

High

Visual analysisSpotfire

fgure 3. Mapping Spotfre to Market Segments

Most visual discovery vendors span all three subsegments of the visual

discovery market but exhibit core strength in one of the three.



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BACKGROUND

KEY

CHARACTERISTICS

MARKET TRENDS

TYPES OF VISUAL

DISCOVERY TOOLS

EVALUATION

CRITERIA

VENDOR

ASSESSMENTS

ECOMMENDATIONS

APPENDIX

O course, vendors in the visual discovery market are acutely aware o the

competition and how they stack up. Consequently, most are aggressively

closing the unctionality gap with their closest

rivals. For example, visual exploration vendors,such as Tableau, have added in-memory capa-

bilities to oer comparable perormance to

QlikView and Spotre. Conversely, QlikView

and Spotre have recently added direct query

capability to compete better with Tableau as

visual exploration tools.

In a ew years, these three segments will vir-

tually disappear as vendors oer comparable

unctionality, at least on paper. Every vendorwill report nearly identical capabilities on a

unctional matrix in a request or proposal. But

ater 20 years o evaluating commercial sot-

ware, Ive discovered that vendors never stray

ar rom their origins. They will shine in their areas o initial strength and all

short o the competition in their areas o initial weakness. For example, Tab-

leau will likely always oer superior data exploration capabilities, while it may

never catch up with QlikView in publishing or Spotre in mining.

PURE-PLAY VERSUS ENTERPRISE VENDORS

The market or visual discovery tools is neatly divided into pure-play and

enterprise vendors. Pure-play vendors only sell visual discovery tools, while

enterprise vendors also oer a comprehensive BI stack, and in some cases,

other enterprise sotware as well (see Tables 1 and 2).

With two exceptionsPlatora and SAS JMPpure-play vendors are also

the most mature; they have been shipping visual discovery sotware much

longer than enterprise vendors, most o which entered the market just lastyear. In contrast, QlikView shipped in 1993 and Spotre in 1996. Curiously,

these two tools were both invented in Sweden, where visual design is a cul-

tural hallmark (think Scandinavian design).

Mature pure-play vendors oer superior visualization and analytical unc-

tionality, while enterprise vendors oer better enterprise management capa-

bilities. Enterprise vendors built their visual discovery tools on an existing BI


Two o the oldest

visual discovery tools,

QlikView and Spotfre,

were both invented in

Sweden, where visual

design is a cultural

hallmark (think Scan-dinavian design).


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BACKGROUND

KEY

CHARACTERISTICS

MARKET TRENDS

TYPES OF VISUAL

DISCOVERY TOOLS

EVALUATION

CRITERIA

VENDOR

ASSESSMENTS

ECOMMENDATIONS

APPENDIX

vendor product launch date primary architecture primary type

SAS Institute JMP 1992 Local disk Mining

Actuate BIRT Analytics2 2003 Hybrid/in-memorycolumnar DBMS

Mining

InormationBuilders3

Visual Discov-ery1

2006 In-memory DBMS Mining

MicroStrategy Visual Insight 2011 ROLAP cubes Analysis

IBM Cognos Cognos Insight 2012 In-memory DBMS Analysis

SAS Institute Visual Analytics 2012 MPP Analytic DBMS Exploration

SAP Visual Intelli-gence

2012 MPP in-memory DBMS Exploration

Microsot Power View 2012 SQL Server/SharePoint Analysis

table 2.

Enterprise Vendors Sorted by Initial Product Launch Date

(2) Actuate purchased Quiterian Analytics in 2012 and relaunched the product as BIRT Analytics inFebruary 2013.

(3) Inormation Builders embeds Advizor Solutions Visual Discovery product.

vendor product launch date primary architecture primary type

QlikTech QlikView 1993 In-memory DBMS Analysis

Tibco1 Spotre 1996 In-memory DBMS Mining

Alteryx Alteryx 1997 Direct Connect Mining

Tableau Tableau 2003 Direct Connect Exploration

Advizor Solutions Visual Discovery 2003 In-memory DBMS Mining

SiSense Prism 2004 MPP Analytic DBMS Exploration

Platora Platora 2012 Hadoop Exploration

table 1.

Pure-Play Vendors Sorted by Founding Date

(1) Tibco, an enterprise middleware vendor, acquired pure-play vendor Spotre in 2007 but does not haveany other BI tools.



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BACKGROUND

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CHARACTERISTICS

MARKET TRENDS

TYPES OF VISUAL

DISCOVERY TOOLS

EVALUATION

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VENDOR

ASSESSMENTS

ECOMMENDATIONS

APPENDIX

stack that oers rich metadata and data management eatures, a scalable

platorm, and mature administration and monitoring utilities. As a conse-

quence, enterprise visual discovery tools have many sotware dependencies;

that is, customers have to purchase the vendors enterprise BI edition to getaccess to the ull power o the visual discovery tool (with the exception o

Cognos Insight). These sotware dependencies eectively increase the cost

and complexity o deploying enterprise visual

discovery tools, except or existing custom-

ers who have already deployed the vendors

BI stack.

THE FUNCTIONALITY GAPThis gap in unctionality between pure-play

and enterprise vendors will close rapidly in

the next three years as pure-play vendors

build or acquire enterprise capabilities and

enterprise vendors add better analytical and

visual unctionality. For example, QlikTech recently purchased Expressor, an

enterprise extract, transorm and load (ETL) tool to oer graphical data man-

agement capabilities to customers that are deploying QlikView on an enter-

prise scale. n


ORACLE

Oracle is the only major enter-

prise vendor without a true visua

discovery tool, Oracle Exalytics

and Endeca notwithstanding.So I expect Oracle to purchase

one o the pure-play vendors in

the next year or two.


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CHARACTERISTICS

MARKET TRENDS

TYPES OF VISUAL

DISCOVERY TOOLS

EVALUATION

CRITERIA

VENDOR

ASSESSMENTS

ECOMMENDATIONS

APPENDIX

Evaluation Criteria

BEFORE CHOOSING A VISUAL discovery vendor, there is a host o criteria you

should evaluate. Ive dened seven major criteria below that can serve as a

starting point or a more thorough evaluation:

1. Maturity. How many years has the product been deployed? How manyactive customers and partners does the product have? Does the vendor sup-

port an active marketplace o third-party add-on products and an active gal-

lery o user-generated applications? Does the vendor oer an application pro-

gramming interace or third parties to develop product extensions or embed

the product in other applications, such as portals?

2. Analysis. To what degree does the tool support the ull spectrum o BI

analysis capabilities, rom dashboarding to analysis to data mining? How easy

is it to create custom metrics, calculations, hierarchies and groups and usethese in comparative visualizations? Does the tool support orecasting, histo-

grams, variance charts, correlation matrices, descriptive statistics and other

calculations and unctions?

3. Visualization. Is the graphical interace unctional enough or power

users and easy enough or casual users? Which chart typesincluding text

are available? How eectively does the tool suggest which chart types to

apply based on the shape o the data? How easy is it to nd and apply attri-

butes to existing visualizations?

4. Design and publishing. How easy is it to design and publish interactive

dashboards? To what degree can you and your team control layouts and man-

age user access to pages, objects and data values? How easy is it to create a

presentation or brieng book and tell a story with the data? To what degree

can dashboard users share content with one another and collaborate? How


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DISCOVERY TOOLS

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VENDOR

ASSESSMENTS

ECOMMENDATIONS

APPENDIX

EVALUATION CRITERIA

much unctionality is available through mobile devices, Web browsers, portals

and Microsot Oce?

5. Scalability. Does the tool deliver consistently ast perormance as moreusers and data are added to the system? Can it scale linearly as more CPU

and memory are added? Does the tool connect directly to remote databases

to support end-user queries? Can users toggle between in-memory and direct

query modes? Can users drill rom an in-memory database to data in a remote

database? What level o data compression is available? Does the in-memory

database run on an MPP inrastructure?

6. Data management. Does the tool oer data quality, cleansing, proling,

transormation, load and metadata management tools? Does the tool supportaect analysis and lineage, that is, metadata, and enable reuse and team-

based development when constructing data sets and packages?

7. Enterprise-ready. What administrative capabilities are available to sup-

port clustering, ailover, recovery, load balancing, monitoring, security and

administration? How well does the tool integrate with third-party utilities and

applications, including other BI tools, event-driven dashboards, portals and

enterprise sotware? How well does the product work in corporate data cen-

ters and with private cloud inrastructure? n


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CHARACTERISTICS

MARKET TRENDS

TYPES OF VISUAL

DISCOVERY TOOLS

EVALUATION

CRITERIA

VENDOR

ASSESSMENTS

ECOMMENDATIONS

APPENDIX

Vendor Assessments

THE FOLLOWING OFFERS a brie synopsis o vendor discovery products, with an

emphasis on the best-known and most widely deployed products, QlikView,

Tableau and Spotre.

BEST KNOWN

n QlikView. QlikTech popularized the visual discovery segment by growing

aster than any other BI vendor, culminating in a successul public oering in

2010. The company, which sells a single product, QlikView, generated $320

million in sales in 2011. With an eective departmental land and expand

strategy and a vast partner network, QlikTech now has its sights set on domi-

nating the even more lucrative enterprise BI market (see Figure 4).

fgure 4.QlikTech Evaluation

Analysis

Maturity

Scalability

Enterprise

capable

Data

management

Visualization

Publishing



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VENDOR ASSESSMENTS

QlikViews most distinctive eature is its associative graphical user inter-

ace, which displays relationships among the values o all displayed objects on

a screen without excluding any o them. Conceptually, this is like an outer join

o all tables in a data model, allowing any eld to act as a lter or aggregationdimension. Unlike most BI tools, which narrow users views as they drill into a

data set, this associative interace enables users to view all relationships and

patternsas well as those that dont existat once and has resonated with

business users. To cement its attractive graphical user interace, QlikTech

now delivers the same interactive visual display on mobile devices and sup-

ports state-o-the-art collaboration capabilities.

To better support enterprise deployments, QlikTech recently shipped a util-

ity that scans and catalogs all QlikView data elements and tasks in a network,

improving an administrators ability to perorm impact analyses, track datalineage and design enterprise architectures. It also recently acquired Expres-

sor, a modern ETL tool that will minimize the need or developers to write

scripts and better manage back-end data acquisition and governance pro-

cesses. It also has recently expanded its in-memory architecture to support

dynamic queries o remote databases, improving the overall scalability and

fexibility o the environment.

n Tableau. By having the right product at the right time, Tableau has dou-

bled in size and now exceeds $100 million in revenue. Many insiders expectit to go public shortly. Tableaus direct query architectureenabled by its

unique and patented VizQL query languagemakes it better suited to explo-

ration o big data sets compared with in-memory competitors that are limited

by the amount o data they can hold in memory. Tableau is also suited to

exploration because it lets analysts model data on the fy without scripting

(see Figure 5).

To round out its portolio, Tableau shipped an in-memory columnar data-

base in 2011 to optimize perormance when source systems are bogged down

or access is constrained. It also recently shipped a new data managementeature that lets desktop users query predened workbooks on the server

instead o on remote databases. This eature in eect enables administrators

to implement a standard metadata layer to improve inormation consistency

in enterprise deployments.

Tableau is perhaps best known or its visualization capabilities and graphi-

cal interace that make it easy or business analysts to explore data by


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dragging and dropping attributes and metrics onto chart dimensions. The

tools ease o use and versatility is on display at Tableau Public, a ree hosted

website where more than 30,000 Tableau users have published workbooks

that others can comment on, share or tag.

n Tibco Spotfre. Ater its acquisition by Tibco in 2007, Spotre lay dor-

mant, losing ground to QlikTech and Tableau just as the visual discovery mar-

ket shited into overdrive. But now, Spotre is gaining momentum, reasserting

itsel as a mature visual discovery tool that supports sophisticated analytics

and enterprise deployments. IDC projected its 2010 revenues to be $68 mil-

lion.

Because o its support or advanced statistics and data mining, Spotre is

heavily used by nancial services rms and pharmaceutical companies. Itsstatistical serverwhich is an optional add onprovides S-Plus data mining

tools, a runtime engine or R, and access to SAS data mining objects without

scripting (see Figure 6).

Already well represented in the Fortune 1000 with Tibco products, Spotre

is capitalizing on its association with Tibco to urther cement its enterprise

credentials. For example, Spotre integrates with Tibco Silver Fabric, a private

fgure 5.Tableau Evaluation

Analysis

Maturity

Scalability

Enterprise

capable

Data

management

Visualization

Publishing


VENDOR ASSESSMENTS


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cloud inrastructure that supports load balancing, ailover and dynamic pro-

visioning o new Spotre servers. Spotre also integrates with Tibco tibbr, an

enterprise collaboration platorm, and Tibco Business Events, a complex eventprocessing environment in which Spotre displays real-time events and alerts.

Finally, Spotre expanded the scalability o its in-memory engine and added

dynamic query capabilities to access remote data and connectivity to Hadoop

and NoSQL data environments, including Attivios Active Intelligence plat-

orm.

OTHERS

n Actuate BIRT Analytics. Actuate Corp. jumped into the visual discovery

market last month with a gem o a product that it unearthed in Barcelona,Spain. Called BIRT Analytics, the product is a rebranded version o Quiterian

Analytics, which rst shipped in 2003. As such, BIRT Analytics is a airly

mature visual discovery tool that runs on a super-ast and highly scalable

columnar database that leverages both memory and disk storage. As a visual

mining tool, it also oers some very nice visualizations and analytical unc-

tions, including Venn diagrams, orecasting, statistical and machine learning

VENDOR ASSESSMENTS

fgure 6.Spotfre Evaluation

Analysis

Maturity

Scalability

Enterprisecapable

Datamanagement

Visualization

Publishing



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algorithms, and the ability to easily create derived elds and use any eld or

combination o elds as a lter.

n Advizor. Leveraging data visualization and in-memory data technologydeveloped at Bell Labs, Advizor Solutions Inc. oers a ull-eatured visual dis-

covery tool that oers some o the most unique visualizations on the market

and blends them with multivariant regression algorithms to support predic-

tive analytics. The product, which comes in desktop, Web and iPad versions,

is used extensively in higher education to support undraising activities and

orms the basis o Inormation Builders Visual Discovery product.

n Alteryx. For many years, Alteryx Inc. specialized in spatial analytics but

recently extended its platorm with data mining, ETL, data quality, in-memoryprocessing, and business and industry content, and made it available via both

private and public clouds. The company is now working to raise its market

prole and gain traction or its new and innovative all-in-one strategy.

n IBM Cognos Insight. Shipped in February 2012, Cognos Insight is both a

low-cost standalone Windows desktop tool with an embedded, writable in-

memory database, Cognos TM1, as well as a client to Cognos Express, Cognos

Enterprise and Cognos Planning. Users o the standalone version can publish

their designs without alteration to a Cognos BI server edition. This tight inte-gration enables Cognos to seed the market with the low-cost Cognos Insight

product ($500) and then up-sell those customers to one o its more compre-

hensive BI suites.

n Microsot Power View. Shipped in April 2012, Power View extends Micro-

sots SQL Server Reporting Services product with interactive visualization

eatures. The tool requires Silverlight-compliant browsers and SQL Server

2012. Users query PowerPivot models or SQL Server Analysis Services tabu-

lar models stored in SharePoint. The tools exquisite desktop publishing ea-turesthink PowerPointmake Power View ideal or creating visual interac-

tive brieng books.

MicroStrategy Visual Insight. Shipped in 2011, MicroStrategy Visual Insight

is a Flash-based client that is bundled or ree in MicroStrategy Report Servic-

es 9.2 or later, and runs both on-premises and in the cloud. Customers use the

tool to perorm ree-orm analysis, create ad hoc dashboards and prototype

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pixel-perect dashboards. It runs against predened MicroStrategy in-memo-

ry cubes created with MicroStrategy design tools.

n Platora. Launched in the all o 2012, Platora enables data scientists toanalyze Hadoop data, which it loads into an in-memory cache that sits on or

adjacent to a Hadoop cluster. Platora is one o the rst pure-play visual dis-

covery tools that run against Hadoop and big data.

n SAP Visual Intelligence. Shipped in May 2012, Visual Intelligence is a

Windows desktop tool that runs against SAPs new HANA in-memory data-

base. It is similar to SAP BusinessObjects Explorer, which has caused conu-

sion among customers, except that it provides more ree-orm data access,

transormation and charting capabilities than Explorer, which is a server-based tool that IT staersnot business analystsset up, design and main-

tain.

n SAS JMP. Unveiled in 1993, SAS JMP helps statisticians better understand

the shape o their data by applying a vast array o statistical unctions and

visualizations to desktop data sets. The company oers an advanced version

that enables statisticians to create and compare predictive models. It also

oers two versions or the lie sciences industry, one to analyze drug develop-

ment and another to analyze genomics data.

n SAS Visual Analytics. Shipped in May 2012, SAS Visual Analytics can be

considered a server-based version o JMP that runs on a high-perormance

in-memory database which sits on MPP grids rom Dell and Hewlett-Packard

or MPP database appliances rom EMC Greenplum and Teradata. It also sup-

ports various statistical unctions and report design eatures and is geared

toward creating models against big data.

n SiSense Prism. Founded in Israel in 2004, SiSense has ramped up its pres-ence in the U.S. during the past year, claiming its the worlds smallest big

data solution. It recently showed how to analyze 1 TB o data at the speed o

thought using a $750 laptop with 8 GB o RAM. The tool uses a vectorwise,

columnar, in-memory database, the D3 visual library, and lightweight ETL

tools. It is oered on a subscription basis via the cloud or on-premises. n

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Recommendations

BEFORE PURCHASING A VISUAL discovery tool, there are several things you should

consider.

I you are a departmental buyer:

1. Identiy the types o users you want to empower. I you are trying toempower business analysts and data scientists, choose a visual explora-

tion tool, like Tableau; i you want to empower casual users, choose a visual

analysis tool, like QlikView, that makes it easy to create polished interactive

dashboards; i you want to empower statisticians and sophisticated analysts,

choose a visual mining tool, like Spotre, that embeds advanced analytical

unctions.

2. Investigate whether other departments already use a visual discovery

tool. I so, you might as well join the parade and enjoy volume discounts,unless you can prove that the de acto visual discovery tool wont support

your departments requirements.

3. Be ready to explain why a corporate BI standard doesnt meet your

visual discovery requirements. I your corporate BI standard doesnt include

visual discovery, you will need to explain why you need to deploy a nonstan-

dard tool set. Use this report, especially Figure 1, to make your case that an

organization needs many dierent types o BI tools to address user require-

ments, and that visual discovery is a distinct market segment best addressedwith a dedicated tool.

4. Point the visual discovery tool at the data warehouse. Although your

corporate data warehouse probably doesnt contain all the data you need,

you dont want to risk creating duplicate data and denitions. So leverage the

data warehouse and load unique local data into your visual discovery tool to


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RECOMMENDATIONS

deliver all the data you need to support your requirements. This approach will

appease IT staers and make them more aware o your data requirements or

the data warehouse. It will also make them better allies i your visual discov-

ery tool hits the scalability or complexity wall and you need them to bail youout.

I you are a corporate buyer:

1. Remember that a visual discovery tool wont meet all your BI needs. A

visual discovery tool does a nice job o addressing a large percentage o BI

requirements, including both top-down (reporting, dashboarding) and bot-

tom-up (ad hoc exploration and analysis) tasks. But it doesnt support ad hoc,

operational and pixel-perect reporting, relational OLAP, data mining and bigdata analytics. So dont expect a visual discovery tool to be your only BI pur-

chase.

2. Evaluate the enterprise capabilities o your short list o visual discov-

ery tools. Most pure-play visual discovery tools started lie as desktop tools

designed or analysts and later evolved to support workgroup and departmen-

tal applications. Most dont yet support the data management, administra-

tion and inrastructure required to support risk-ree enterprise deployments.

Inquire about the enterprise capabilities o your short-listed vendors.

3. Dont outlaw nonstandard visual discovery tools. First, you cant win

this battle unless your corporate BI team has consistently proven that it can

deliver exquisite applications on time and under budget and has no backlog

o requests. Second, you should ocus your teams eorts on managing data

consistency rather than arbitrating tool standards. Sure, having multiple tools

requires more support, but as long as users are running the majority o queries

against your corporate standard data, youll have plenty o time to support

their tool requirements. n


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Appendix: BI Tools Framework

CREATED BY BI LEADERSHIP RESEARCH, the BI Tools Framework (see Figure 1)

positions categories o BI tools by unctionality, scope o deployment and type

o intelligence (top-down versus bottom-up). To deliver a comprehensive set

o BI capabilities, organizations need to implement BI products or suites that

span all dimensions o the BI Tools Framework.

fgure 1. BI Tools Framework

SOURCE: WAYNE ECKERSON, B I LEADERSHIP RESEARCH

BIfunctionality

EnterpriseDepartment

Scope of deployment

Top-down

Bottom-up

Reporting

Dashboards

Analysis

Mining

Pixel-perfect reporting

Ad hocReports and

dashboards

Analyst

Data mining workbench

Operationalreports and

dashboards

Big data

analytics platforms

Relational OLAP

Multi-

dimensional

OLAP

Desktop

analysis(e.g.,

Excel)

Visual

discovery

Powe

rusers

Casu

alusers


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APPENDIX: BI TOOLS FRAMEWORK

BI TOOLS FRAMEWORK

Here are denitions o the key terms in the ramework:

n Top-down intelligence: This type o intelligence consists o reports anddashboards that answer predened questions or monitor business processes

using metrics aligned with strategic objectives. Top-down tools query data

sets with predened schema, including data warehouses and data marts. Top-

down tools are installed, designed and maintained by the IT department but

operated by power users and superusers.

n Bottom-up intelligence: The term reers to ad hoc analysis and mining

tools that answer unanticipated questions arising rom new and changing

business conditions. Bottom-up tools query any type o data in any applica-tion or system and use fexible schema and rich visualizations to eectively

manipulate and analyze data. Bottom-up tools are used by business analysts,

statisticians and data scientists with minimal or no IT assistance.

TOP-DOWN BUSINESS INTELLIGENCE TOOLS

n Pixel-perect reporting: Also called production or managed reporting tools,

pixel-perect reporting tools enable proessional report developers to create

pixel-perect and complex reports and distribute them to large numbers ousers on a scheduled basis.

n Relational OLAP (ROLAP): Such tools provide a dimensionalized view o

data held in relational databases, using a combination o specialized schema

and end-user metadata, that is, a semantic layer. IT developers use ROLAP

tools to create interactive reports and dashboards that run against large data

warehouses. Because ROLAP tools span reporting, dashboarding and analy-

sis, they are ideal or delivering layered enterprise dashboards that provide

three-click access to any data rom a top-level view, assuming adequate per-ormance.

n Operational reports and dashboards: Unlike ROLAP tools, which connect

to data via specialized schema and architected metadata, operational reports

and dashboards tools connect directly to source systems that run the busi-

ness. Also called data mashups, these tools are used to create operational or


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executive dashboards that monitor near-real-time business activity across

multiple applications and systems. Some higher-end operational dashboards

monitor real-time event streams.

n Ad hoc reports and dashboards: Set up by IT proessionals, these tools

enable motivated business users, that is, superusers, to create ad hoc reports

and dashboards. Superusers create reports by selecting data elements (met-

rics, dimensions, attributes) rom a scrubbed list o data objects (semantic

layer) and then ormat the result set. They create ad hoc dashboards

mashboardsby dragging and dropping predened content (charts, tables,

controls) rom a library onto a dashboard canvas. Ad hoc tools are oten

extensions o ROLAP or pixel-perect reporting tools.

BOTTOM-UP BUSINESS INTELLIGENCE TOOLS

n Desktop analysis: Epitomized by Microsot Excel (and now PowerPivot),

desktop analysis tools are designed or business analysts who want ree-orm

SQL access to any data and the fexibility o a desktop tool to merge, integrate

and model data to answer any question they need to answer.

n Visual discovery: Such tools are sel-service, in-memory analysis com-

ponents that enable business users to access and analyze data visually at thespeed o thought with minimal or no IT assistance and then share the results

o their discoveries with colleagues, usually in the orm o an interactive dash-

board.

n Multidimensional OLAP: These tools store data in a specialized multi-

dimensional ormat and either precalculate or dynamically calculate data

values at the intersection o every level in the dimension hierarchy, providing

ast query perormance. Designed or business analysts, the tools require IT

proessionals to set up, design, load and manage the databases. They are con-strained in size because o the explosion o data caused by the calculation o

dimensional values.

n Data mining workbench: These tools provide a drag-and-drop develop-

ment environment or statisticians who need to design, build and manage

analytical models, either individually or in a team.


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n Big data analytics platorms: These are highly scalable analytical systems

that run on grids o computers, including Hadoop and MPP database appli-

ances, and contain libraries o analytical unctions that enable statisticians

and data scientists to explore, manipulate and model large volumes o datawithout having to move the data to a workstation or departmental server.

They also support analytical sandboxes that enable analysts to upload private

data and mix it with corporate data to conduct analyses. n

ABOUT BI LEADERSHIP RESEARCHBI Leadership Research is an education and research service run by TechTarget Inc. that provides objec-

tive, vendor-neutral content to business intelligence (BI) proessionals worldwide. Its mission is to help

organizations make smarter decisions through the judicious use o data-driven technologies, such as

data warehousing, BI, analytics and big data. Its team o experienced industry analysts conduct in-depth

research on industry trends, evaluate commercially available sotware and host conerence events,

webcasts and seminars.

ABOUT TECHNOLOGY GUIDESTechnology Guides rom BI Leadership Research are designed to help BI leaders crat a short list o ven-

dors to evaluate a product in an emerging BI market segment beore purchasing it. Each report defnes

the segment, describes the salient market and technology trends, defnes criteria or evaluating products

and positions products against one another. Each report evaluates between six and 12 vendors and com-pares their market strategies, architectural approaches and product dierentiators. Although Technology

Guides can help narrow down product choices, they are no substitute or a proo o concept that assesses

vendor products using an organizations internal data.

ABOUT THE AUTHORWayne Eckerson is the director o BI Leadership Research and president o BI Leader Consulting, which

provides strategic planning, architectural reviews, internal workshops and long-term mentoring to user

and vendor organizations. He has conducted numerous research studies and is a noted speaker, blogger

and consultant. He is the author o two widely read books, Performance Dashboards: Measuring, Monitor-

ing, and Managing Your Business and The Secrets of Analytical Leaders: Insights from Information Insiders.

Documents

Visual DiscoveryTools