Sector RoadMap : data discovery in 2014€¦ · Sector RoadMap: data discovery in 2014! 3 Executive summary In the contemporary analytics market, business users want streamlined applications

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Sector RoadMapTM: data discovery in 2014 Andrew Brust March 19, 2014

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Sector RoadMap: data discovery in 2014! 2

TABLE OF CONTENTS Executive!summary!....................................................................................................................................................................!3!Introduction!..................................................................................................................................................................................!5!Methodology!............................................................................................................................................................................!6!

Disruption!Vectors!.....................................................................................................................................................................!6!Self@service!................................................................................................................................................................................!7!Mobile!.........................................................................................................................................................................................!7!Data!blending!...........................................................................................................................................................................!7!Non@relational!DB!connectivity!.......................................................................................................................................!8!The!cloud!...................................................................................................................................................................................!8!Storytelling!via!data!presentation!..................................................................................................................................!8!Flash@survey!results!..............................................................................................................................................................!9!

Company!analysis!....................................................................................................................................................................!11!Datameer!................................................................................................................................................................................!12!Tableau!....................................................................................................................................................................................!15!Splunk!......................................................................................................................................................................................!17!MicroStrategy!.......................................................................................................................................................................!18!SiSense!.....................................................................................................................................................................................!20!Roambi!and!MeLLmo!........................................................................................................................................................!22!Additional!data@discovery!vendors!.............................................................................................................................!25!Actuate!.....................................................................................................................................................................................!25!Birst!...........................................................................................................................................................................................!25!IBM!............................................................................................................................................................................................!25!Jaspersoft!................................................................................................................................................................................!26!Logi!Analytics!........................................................................................................................................................................!26!Microsoft!.................................................................................................................................................................................!27!Oracle!.......................................................................................................................................................................................!27!Pentaho!....................................................................................................................................................................................!28!QlikTech!..................................................................................................................................................................................!28!SAP!.............................................................................................................................................................................................!29!SAS!.............................................................................................................................................................................................!29!Tibco!.........................................................................................................................................................................................!29!

Key!takeaways!...........................................................................................................................................................................!31!About!Andrew!Brust!...............................................................................................................................................................!33!About!Gigaom!Research!........................................................................................................................................................!33!

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Executive summary In the contemporary analytics market, business users want streamlined applications that allow them to

query and visualize data, with incremental sophistication, until that data is truly understood. Such

products, known as data-discovery tools, are the focus of this Sector RoadMap.

Our analysis identifies six Disruption Vectors that companies can drive or harness to gain revenue and

market share. Tech buyers can also use the Disruption Vector analysis to aid them in picking products

that best suit their own situation.

Key findings in our analysis include:

• Self-service will be the most powerful market force for the next 12 to 24 months, with mobile

support almost as critical. Data blending and connectivity to non-relational databases are also

important vectors. Cloud offerings and data-storytelling capabilities play less-critical roles.

• Mobile-device support is a competitive necessity. Native viewing apps for mobile platforms are

acceptable (some would even say better), but an HTML5-based authoring tool with full

functionality for tablets defines the state of the art.

• Most products still have a distance to travel when it comes to cloud offerings. While cloud BI

continues to go mainstream, providers exclusive to that space rarely focus on data discovery.

• Connectivity to CRM data is prevalent among data-discovery products. So while analytics is

applicable in almost every part of business, it is sales automation and revenue forecasting that

drive a lot of activity, especially in the self-service space.

• Currently Datameer and Tableau are the best-positioned data-discovery suppliers. Splunk,

MicroStrategy, and SiSense are also strong, and Roambi is on the radar.

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!Key:!

• Number indicates company’s relative strength across all vectors

• Size of ball indicates company’s relative strength along individual vector

Source: Gigaom Research

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Introduction The data-discovery category can be tricky to define, because to varying degrees all BI and analytics tools

are data-discovery tools, as they provide the building blocks for data-discovery work. But data-discovery

products move beyond mere building blocks. For the purposes of this Sector RoadMap, we define a data-

discovery product as one that provides most or all of the following functionalities:

• Self-service operation, or an interface for acquiring, shaping, and analyzing data that is aimed at

business users rather than BI specialists, data scientists, or IT personnel.

• An HTML5 reactive interface that offers a solid mobile experience. Some vendors provide a native

desktop interface instead and then provide separate native mobile apps for consumption of

analyses as well.

• Connectivity to a broad variety of data sources, including transactional databases; massively

parallel processing (MPP) data-warehouse platforms (many of them appliance-based); NoSQL

databases; multiple Hadoop distributions; and cloud-based data sources, most often including

Salesforce.com, Facebook, and Twitter, to name just a few.

• A large variety of data visualizations consisting of the following, at a bare minimum: bar chart,

column chart, line chart, and area chart. Scatter and bubble charts as well as pie and doughnut

charts are usually included as well, as is some type of combo chart or double-axis chart. Also

useful are tree maps and geospatial map visualizations. Some products provide special

visualization types in addition to all of the above.

• Data blending, or the ability to pull in data from a variety of data sources and analyze all the data

together, without requiring complex data integration inside or outside the data-discovery

application.

• A fast, in-memory database engine, typically featuring a column store architecture for data storage.

It may sound precious to speak of achieving intimacy with data, but that is precisely what data-discovery

tools facilitate. And that, in turn, is what allows business users and data scientists alike to derive the most

knowledge from their data. Expressed in vendor parlance, data-discovery tools deliver actionable insights

to business users.

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Methodology

For our analysis, we have identified and assessed the relative importance of six Disruption Vectors. These

are the key technologies and markets in which players will strive to gain advantage in the sector. Tech

buyers can also use the Disruption Vector analysis to aid them in picking products that best suit their own

situation.

The section below features a visualization of the relative importance of each of the key Disruption Vectors

that Gigaom Research has identified for the data-discovery marketplace. We have weighted the

Disruption Vectors in terms of their relative importance to one another.

Gigaom Research’s analysis process also assigns a 1 to 5 score to each company for each vector. The

combination of those scores and the relative weighting and importance of the vectors drives the company

index across all vectors. That produces the Sector RoadMap chart in the company-analysis section.

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Disruption Vectors

! Source: Gigaom Research

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Self-service

It has long been the bane of BI’s existence that only a small percentage of customers’ employees actually

use it. In the traditional model, the development process has often involved expensive specialist

consultants doing significant data modeling and extract, transform, and load (ETL) work before any

analysis by businesspeople can begin. This has resulted in failed projects some of the time and frustration

almost all of the time.

Self-service BI tools work to disrupt this waterfall workflow and its reliance on specialists, instead

empowering users to do more work on their own in an iterative fashion. Many BI vendors have jumped

on this trend, as initially it helped with license penetration within organizations and recently has simply

become a competitive necessity.

Mobile

Like self-service, the ability to do analytics work on mobile devices is another capability that started as a

differentiator and is now a competitive necessity. Virtually all platforms now offer at least some mobile

capabilities, but important differences among the solutions remain. Are the mobile interfaces HTML- and

browser-based, or are they implemented as native applications? Do the mobile interfaces facilitate only

consumption of analyses (with some ability to filter and drill down), or do they provide full authoring

environments? Different vendors offer different solutions and capabilities, and customers will have

differing needs.

Data blending

Data blending is the term used to describe the performance of analytics on a collection of data sets, each

emanating from a different data source. Rather than first writing ETL scripts or developing visual data

transformations and then building out a complex star schema data model, data-discovery products

capable of data blending let users pull in data, then quickly mash it up and analyze it.

Tools that are capable of data blending tend to be good at inferring schema and determining how the

different data sets relate to one another. This eliminates a lot of friction in the analytics workflow,

especially for self-service tools and business users.

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Non-relational DB connectivity

Data-discovery tools inherit their conventional BI forebears’ ability to connect to the major commercial

and open-source transactional database systems like Oracle, Microsoft SQL Server, and MySQL as well as

MPP data-warehouse platforms like Teradata, IBM Netezza, and HP Vertica. But there are now many

other important data sources to connect to: NoSQL databases, including MongoDB and Cassandra; big

data kingpin Hadoop; and cloud services and Software-as-a-Service (SaaS) offerings such as

Salesforce.com. Different data-discovery products achieve this connectivity to varying degrees.

The cloud

Another impediment to BI adoption has been the complexity of building out the infrastructure. As with

the self-service vector, the cloud empowers businesspeople to implement BI on their own, without the

support of overburdened IT organizations and without the politicized capital-budget commitments that

on-premises BI implementations may involve.

But cloud BI is about more than streamlining infrastructure. Many cloud BI offerings also make short

work of common data-acquisition requirements, especially from various cloud-based SaaS offerings,

Salesforce.com key among them.

Additionally, cloud offerings tend to integrate an entire BI stack’s worth of functionality (including

storage, ETL, data visualization, and deployment to mobile devices).

Storytelling via data presentation

Some data-discovery tools are beginning to offer storytelling capabilities that mash up their drill-down,

scorecard, and visualization capabilities with presentation features. The idea here is to avoid the

sometimes-glib provision of data and tools to users and instead provide observations and a presentation

that explicates root causes.

The ability to add annotations to dashboards and reports was an early, crude form of storytelling. Now

the idea is to provide something with the polish of a PowerPoint presentation that sometimes even allows

interactive analytics at various points in the presentation, should the viewer wish to go beyond the

analyses supplied.

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Flash-survey results

Gigaom Research polled readers of the Gigaom.com blog regarding disruptive trends in the data-

discovery category and name recognition of both emerging and established vendors in the space. This

type of flash survey is directionally indicative of what’s on the mind of technology-industry participants, if

not statistically representative of any particular audience.

Quick takeaways from the poll include the following:

• Far and away, readers see self-service and the cloud to be the most disruptive trends in the space.

Gigaom Research agrees with a top ranking for self-service. Meanwhile we see current cloud

offerings as relatively immature thus far, and we have assigned higher weightings to the mobile

and non-relational data connectivity Disruption Vectors than we did to the cloud.

• Tableau and MicroStrategy, respectively, have the highest first-to-mind showings among

emerging and established BI vendors.

• Of all named vendors, Splunk and IBM come in second place in their first-to-mind showings, with

IBM coming in especially close behind MicroStrategy (24 percent for IBM versus 27 percent for

MicroStrategy).

• The “other” option is significant for first-to-mind showings in both the emerging and established

vendor categories, indicating the immaturity of this sector.

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Company analysis The companies we examined most closely provide a representative cross section of the data-discovery

space. We started by selecting the survey’s winners for top-of-mind emerging and established vendors

(Tableau and MicroStrategy, respectively). Then, for the other four, we selected vendors whose products

each have a different hook. Datameer focuses on Hadoop as the primary data repository. Roambi targets

the iPad as its exclusive platform. Of the remaining two, SiSense offers a sophisticated in-memory engine

that takes advantage of in-chip analytics (vector processing and use of CPU cache for storage), and

Splunk focuses on analysis of unstructured machine data sources as its bread and butter. Detailed

descriptions of our six scored vendors follow.

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Key:!• Number indicates company’s relative strength across all vectors

• Size of ball indicates company’s relative strength along individual vector

Source: Gigaom Research

Datameer

Datameer offers an eponymous discovery tool that is unique in many ways, some of them obvious and

others more nuanced. In the former category is Datameer's user interface, which closely resembles a

spreadsheet application, and its repository, which is Hadoop.

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Profiling and transforming data in Datameer

Source: Datameer

Datameer's spreadsheet interface allows inspection of the data in its raw form and then allows iterative

transformation of that data in place. Each transformation manifests as an additional spreadsheet.

Datameer provides an intelligent data-sampling mechanism for fast, interactive work during this iterative

data-discovery phase and then compiles the query into Hadoop MapReduce jobs that it executes against

the entire data set.

Datameer also ships with more than 50 connectors that facilitate the ingestion of data from a variety of

sources. Datameer provides a vast array of data visualizations and even allows for the authoring of full

infographics rather than just charts and dashboards. The product also provides for predictive analytics to

be performed on the data using a visual interface.

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A Datameer infographic

Source: Datameer

Datameer exposes developer extensibility points and offers an Analytics App Market for developers in its

ecosystem. The product is also a query-able data source (via an ODBC driver), allowing other query and

data visualization clients to be used on top of it.

The comprehensive nature of Datameer’s capabilities is impressive. Its use of Hadoop as a repository,

while unusual, may be a key enabler of this comprehensiveness and a shrewd design decision by the

company.

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Tableau

Analysis of DC crime data in Tableau

Source: Tableau

Tableau is a darling of the BI industry and in 2013 conducted a successful IPO on the New York Stock

Exchange, nabbing "DATA" as its ticker symbol. The company's employees and customers are loyal and

enthusiastic, almost to the point of lifting the product to cult status.

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Analysis of web traffic trend data in Tableau

Source: Tableau

Unlike many other BI tools, which force a workflow involving significant data-acquisition and modeling

work as a prerequisite to visualizing the data, Tableau introduces surprisingly few barriers to moving into

the visual plane. Tableau offers perhaps the broadest range of data visualizations in the industry, and it

also offers native connectors for a huge array of database, BI, data warehouse, NoSQL, and big data

platforms.

Tableau Server offers a web-browser-based user interface that is compatible with a range of mobile

browsers in addition to those on the desktop. Tableau also offers native, touch-optimized mobile

applications for the iOS and Android platforms and is exemplary in addressing the mobile vector.

Tableau practically defines self-service BI, our most important Disruption Vector. Tableau Online does

likewise for the cloud vector, and Tableau’s wide array of data connectors align it with the non-relational

DB connectivity vector.

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Splunk

Splunk is best known for its ability to perform analytics on large machine-generated data. However, the

highly successful company does in fact provide technology that can be used on any data; it also provides

an excellent data-profiling experience that works especially well with semi-structured data.

Splunk Enterprise allows for data discovery and analytics on data of all shapes and sizes. Splunk’s Hunk

product provides similar functionality, specifically for data in Apache Hadoop.

Splunk provides an interactive data-exploration interface that displays data in a column-wise format

(somewhat similar to Datameer’s) and allows for the construction of informal indexes that describe what

data lies where. The latter can be based on members of a collection of known source types.

In the case of semi-structured data, which may be hierarchical in nature, Splunk can be used to discover

and map the parent-child relationships and then facilitate visualization of the data based on these models.

Because the indexes are personal and ephemeral, there is no need to create a formal, monolithic data

model, nor is there a reliance on such a model in order to perform analyses.

In fact, almost immediately after an index is defined, the visualization and creation of sophisticated alerts

can begin.

Splunk also provides a search interface to data and an application platform, accessible from a variety of

languages including Java, JavaScript, PHP, and Python, allowing Splunk’s engine and visualization

capabilities to be leveraged in custom line-of-business applications.

Splunk gets high marks on the non-relational DB connectivity vector, given its ability to map semi-

structured data and the Hadoop savviness of the Hunk product.

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MicroStrategy

A MicroStrategy dashboard, including a map-based image layout visualization

Source: MicroStrategy

MicroStrategy is an established BI vendor and offers a comprehensive BI stack. MicroStrategy Analytics

Desktop (MSAD), which is actually a free product, is perhaps the most vector-compliant of

MicroStrategy’s offerings.

MSAD offers an experience decidedly reminiscent of Tableau Desktop, with all the self-service and data-

discovery accouterments that would imply. It features connectivity to a wide array of data sources, a large

variety of data visualizations, and the ability to publish and share analyses that were built in the product.

MSAD can be combined with MicroStrategy Analytics Enterprise (free for up to 10 users) for server-based

enterprise implementations and/or with MicroStrategy Analytics Express, a cloud-hosted solution that is

free for the first year and supports the same functionality as MSAD, as well as reports, dashboards, and

mobile apps.

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A MicroStrategy dashboard, including Network visualization

Source: MicroStrategy

MicroStrategy Cloud provides a hosted version of the MicroStrategy Analytics Platform, and the

company’s newly announced PRIME (Parallel Relational In-Memory Engine) product, another cloud

service, provides a turnkey analytics backend, including an MPP, in-memory columnstore database.

Put all this together and you have a comprehensive self-service data-discovery platform that runs locally,

server-side, in the cloud, and on mobile devices. On the downside, each of MicroStrategy’s products is

somewhat separate rather than being a collection of features in a single, unified application.

Still, MicroStrategy offers a rather unique combination of enterprise-hardened BI, self-service analytics,

cloud, and mobile solutions.

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SiSense

A dashboard in SiSense Prism

Source: SiSense

SiSense, an Israel-based analytics company, offers an eponymous product that is soon to be updated.

SiSense also includes an especially advanced database engine, which makes use of in-CPU cache, in

addition to memory and disk, for data storage.

SiSense also provides vector-processing capabilities, making use of modern CPUs’ SIMD (single

instruction, multiple data) instructions. All of this allows SiSense to run on a local machine or a single-

server node and yet deliver fast performance, and SiSense’s caching scheme works in such a way that

performance actually increases as more users are added.

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A preview of SiSense 5

Source: SiSense

SiSense offers a wide array of visualizations, and the upcoming new version will offer greater capabilities

still. In general, while the product may appear to be a me-too offering, the sophistication of its database-

engine technology makes it a unique and compelling product.

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Roambi and MeLLmo

A Roambi Elements visualization

Source: Roambi

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Roambi offers data-discovery and reporting tools implemented exclusively as native iPhone and iPad

apps. For many people, Roambi has defined — or, at the least, set the bar — for mobile BI. Roambi on the

iPad is manifested in a pair of apps: Roambi Analytics and Roambi Flow. The former is a typical

analytics-consumption client; the latter is a viewer for data presentations in the form of Flipboard-like

magazine publications.

Authoring is done in the web browser, whether on the iPad or a desktop or laptop computer. Business

users can select a data set, then configure one of the 10 Roambi Analytics views to connect to that data by

selecting which measures and dimensions should be displayed where. When this configuration is

complete, the document can be published and subsequently viewed in Roambi Analytics or embedded in

a Roambi Flow publication.

Roambi Analytics is a native application that stores data locally. As such, it delivers mobility while

retaining the offline functionality of a desktop application. On the other hand, Roambi authoring and

consumption must be done in separate clients, and while its 10 preconfigured views are convenient, they

do not allow the same free-form data-discovery experience that other tools do.

Roambi Business, the cloud-based version of Roambi, cannot access data sources other than spreadsheets,

web pages, and sales force data via direct connection. Roambi ES (Enterprise Server), the on-premises

version of the product, can connect directly to a broad array of data sources, however. These include the

major transactional relational databases, several MPP data-warehouse platforms (including the cloud-

based Amazon Redshift), Hadoop (via Hive), SAP HANA, Google spreadsheets, and a comprehensive set

of OLAP and reporting systems.

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A Roambi CataList visualization - detail view

Source: Roambi

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Additional data-discovery vendors

Gigaom Research Sector RoadMaps are meant to cover representative vendors in a particular sector

rather than serve as an exhaustive collection of all such vendors. Many BI vendors have good data-

discovery offerings, and virtually all of them have products that at least marginally fit in the category.

Below is a list, in alphabetical order, of vendors in the data-discovery space beyond the six covered in this

report, along with commentary on their data-discovery offerings.

Actuate

Actuate’s BIRT Analytics provides self-service data visualization and discovery, which are usable against

conventional databases and data warehouses, as well as against big data repositories like Hadoop. The

product also includes data-mining capabilities, facilitating predictive analytics in place with descriptive

analytics and data discovery. Simple data-blending capabilities are included as well.

BIRT Analytics’ visualization types are relatively limited, and while the product’s heritage in BIRT (a

popular open-source reporting technology) is significant, the product nonetheless faces robust

competition.

Birst

Birst (not to be confused with BIRT) is a BI offering available exclusively as an appliance or a cloud

service. Birst also has tight integration with Amazon’s Redshift cloud-based data-warehouse platform,

built on technology from the former ParAccel (now Actian Matrix) product.

Birst provides competitive data-visualization capabilities and can be used against on-premises data,

despite its own frequent deployment in the cloud. Like many newer BI offerings, Birst makes self-service

capabilities one of its strong suits, and its messaging to the market is that it is a tool for agile BI.

Birst essentially hosts data-discovery technology inside of its dashboards. This is a pleasant surprise for

dashboard users, but it may be somewhat obfuscated for analysts and business users.

IBM

Much like Tableau and QlikView (covered in the QlikTech section, below), IBM’s Cognos Insight provides

interactive data visualization powered by an in-memory database engine (specifically a desktop version of

TM1, the in-memory OLAP database engine Cognos acquired from Applix in 2007, just months before

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IBM acquired Cognos itself). Cognos Insight offers more-limited visualizations, however, and while it

does offer an array of analysis functionality, including sophisticated drill-down and filtering capabilities,

it is less of a free-form exploration and discovery tool than Tableau and Qlik.

Cognos Insight is essentially a powerful, writeback-capable, desktop OLAP analysis and dashboard tool.

It does provide bona fide self-service capabilities, because it imposes minimal preparation of the data

before visualizing it. For some users, this may work well. But for those hoping for a modern data-

discovery tool that runs in the browser and connects out to a variety of data sources including NoSQL,

Hadoop, and cloud services, Cognos Insight will fall short.

Jaspersoft

Jaspersoft provides connectivity to relational databases (including most transactional and MPP data-

warehouse platforms), big data and NoSQL databases, and Microsoft’s Analysis Services OLAP store, as

well as to Jaspersoft’s own OLAP engine.

Basic data-modeling capabilities, a standard array of data visualizations, and an easy-to use canvas for

selecting columns to be visualized are provided. Jaspersoft allows for the typical array of slicing, dicing,

and filtering, and it offers a user-friendly UI element for quickly traversing dimensional hierarchies. The

application’s user interface is browser-based, enabling it to render on mobile devices in addition to

desktop and laptop computers.

Jaspersoft rides somewhere in between an OLAP analysis tool of the last decade’s vintage and a data-

discovery tool from this decade. Unlike many of its competitors, however, Jaspersoft is embeddable in

other applications, giving it strong appeal to independent software vendors (ISVs).

Logi Analytics

Logi Analytics (formerly Logi XML) offers a new product called Logi Vision, which supports an

impressive array of data-discovery technology. Similarly to Datameer and Splunk, Logi Vision starts by

displaying data in its raw form. From that view, simply dragging and dropping one column onto another

immediately visualizes those two columns, in bar chart form, one against the other.

This informal visualization capability is provided as an aide to early profiling of data, and Logi Vision

provides more-sophisticated visualizations as well. In construction of the latter, Logi Vision garners its

DataSmart and ThinkSpace features to show a top recommended visualization immediately and then

provides a list of several more.

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Like other newly developed tools, Logi Vision features an HTML-based interface. When rendered on

mobile devices, however, the web-based UI is more usable for consumption than for authoring

functionality, as the latter is not especially touch-friendly. This first version of Logi Analytics also lacks

connectivity to Hadoop, NoSQL databases, or even MPP data-warehouse platforms. The product is

available for on-premises installation only, under a conventional software license; it is not available in the

cloud nor under subscription pricing.

Microsoft

While Microsoft has long excelled in BI server technology, its client-side offerings during the prior decade

were paltry, and a mobile offering has been nonexistent until recently. But the 2010 release of the

PowerPivot columnstore analytics engine, built as an Excel add-in, lays the foundation for the company to

address these gaps.

After a multi-month beta period, the Redmond, Wash.-based software company has now released to

general availability its Power BI suite, a cloud offering that is procured as an accessory to more-basic

Office 365 subscriptions. Power BI consists firstly of four Excel add-ins: the aforementioned Power Pivot

engine; the Power Query tool for data acquisition, profiling, and shaping; the Power View data-

visualization and discovery tool; and the Power Map geospatial-analysis facility.

Additionally, the Power BI subscription permits Excel workbooks that contain data models and

visualizations built with the add-ins to be uploaded to SharePoint. From there, visualizations can be

consumed in a web browser or in the Power BI Windows 8 application that can run on Windows PCs or

tablets.

Power View in Excel provides a significant number of visualization types, and each visualization serves as

an interactive filter for data displayed in other visualizations on the same page. Slicers and filters can be

added as well. On the server side, Microsoft’s Q&A product provides an English-language query interface

to Power View that makes intelligent choices about visualization types and also allows them to be

specified in the English language query itself.

Oracle

Oracle Endeca Information Discovery sports highly sophisticated data-discovery features. Interactive

visualizations, connectivity to Oracle BI server as well as external data sources, templated dashboard

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layouts, and self-service orientation combine to provide a competitive product. Endeca features a

browser-based UI more typical of data-discovery products from the independent vendors than those of

the megavendors. Endeca also offers data-mashup (essentially data-blending) capabilities as well as

facilities for the analysis of text, visualized as tag clouds.

Oracle Endeca’s independent flair can be explained by its previous existence as a product called Latitude,

offered by independent company Endeca Technologies, which focused on enterprise search and BI,

particularly for online retail. Oracle acquired Endeca in late 2011, allowing the vertical-specific product to

become more generalized and integrated with one of the leading enterprise BI stacks.

Pentaho

Pentaho Analytics 5.0 is an integrated BI platform that includes impressive data-discovery capabilities.

An extended array of data visualizations (with a pluggable add-in architecture to allow even more),

connectivity to Hadoop and MongoDB, and sophisticated data-blending capabilities powered by Pentaho

Data Integration (PDI) are all baked into the platform.

But while Pentaho 5.0 includes these impressive capabilities, it does not offer them in a stand-alone data-

discovery tool. Instead, users must elect to work with the full Pentaho Analytics platform. It should be

noted, though, that this is not the same commitment or undertaking as it would be with a BI stack from

Oracle, SAP Business Objects, or IBM Cognos.

QlikTech

QlikTech’s QlikView is one of the leaders and pioneers in the self-service BI and data-discovery arena. As

a leader, QlikView offers extensive data visualizations (and can accommodate custom or third-party

visualizations), plus connectivity to transactional databases and data-warehouse platforms, spreadsheets,

and CRM.

QlikTech offers associative filtering, wherein all visualizations and grids in a dashboard function as filters

on the other dashboard components. QlikView facilitates the creation of simple, stand-alone analytics

applications, and it offers a native iPad user interface as well. QlikView also features direct discovery, a

data-blending facility that provides for querying big data stores’ contents in place, circumventing the

need for traditional ETL processes while still allowing for caching of data in QlikView’s in-memory engine.

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In general, QlikView offers a compelling data-discovery platform and value proposition. While Tableau

leads Qlik in name recognition and influence, the two should be considered peers, and customers

interested in either should likely evaluate both.

SAP

SAP Lumira (formerly SAP Visual Intelligence) is a data-discovery component meant to complement

various components of SAP’s BusinessObjects BI stack. As its competitors do, Lumira provides a low-

friction, self-service interface for data analysis. It offers a broader-than-average array of data

visualizations within a browser-based user interface.

Lumira is available in a standard edition that offers connectivity to numerous data sources and can share

data sets with BusinessObjects Explorer. There is also a free personal edition that cannot share data sets

in that fashion and connects only to data in CSV and spreadsheet format or in SAP’s HANA in-memory

database.

SAS

SAS Visual Data Discovery provides point-and-click interfaces to the advanced analytic capabilities

present within the Base SAS, SAS/STAT,® and SAS/GRAPH components of SAS’ BI stack. These

interfaces are actually provided by a combination of two products: SAS Enterprise Guide and JMP, both

of which are desktop applications. SAS Visual Data Discovery, then, is a bundle of these five products.

As a SAS product, Visual Data Discovery provides powerful features, including deep statistical analysis

(and integration with the open-source R statistical-programming language); numerous visualization

types, including 3D visualizations; a scripting language; and the ability to export and publish analyses in

Flash format.

Tibco

Spotfire Analytics, a desktop application, is Tibco’s own data-visualization and data-discovery tool, added

to the company’s portfolio via acquisition. Along with Tableau and QlikView, it is one of the founding

fathers of the data-discovery category; it offers a good range of visualizations and associative and

contextual filtering.

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Spotfire Analytics also integrates with other Tibco properties, including StreamBase for CEP (complex

event processing, or streaming data processing) and the Tibco Enterprise Runtime for R (TERR), which

enables predictive analytics.

Tibco offers a web-based UI for authoring dashboards called Spotfire Business Author and another for

consumption called Spotfire Consumer. Spotfire Cloud is available in personal, work group, and

enterprise editions. On the mobile front, Tibco offers Spotfire Mobile Metrics and Spotfire Analytics.

Mobile Analytics is available on Apple iOS devices; Mobile Metrics, which is a key-performance-

indicator-focused app resulting from Tibco’s 2013 acquisition of Extended Results, is available on iOS,

Android, Windows Phone, and Windows 8.

Native connectivity to NoSQL and cloud data sources is scarce, but all in all, Tibco Spotfire provides most

of the other typical data-discovery functionality discussed in this report, even if through a combination of

tools rather than a single application.

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!


Key takeaways When we compared products and strategies against the weighted Disruption Vectors, Datameer came out

just ahead of Tableau with the highest index score. This may come as a surprise, as Datameer gets much

less limelight than some of its competitors (especially Tableau), but Datameer’s product and our

Disruption Vectors are tightly correlated: Datameer has been a self-service tool from the get-go; its

newest version uses a pure HTML5 interface, making it highly useable from tablet devices; its data-source

connectivity is provided by more than 50 different data connectors that ship with the product; its unique

infographic capability makes it a useful tool in the data presentation and storytelling arena; and its

spreadsheet-like interface is designed to make data blending easy and pleasurable.

Runners-up Tableau and Splunk are similarly well-correlated. Splunk’s data-profiling interface is in fact

rather similar to Datameer’s, but while Splunk's visualizations are good, it lacks a presentation mode like

Datameer’s infographic feature. Tableau did well too, but it lost points for its expensive cloud offering and

the lack of a presentation mode in its current version.

SiSense and Roambi came in at fifth and sixth place, respectively, but that is not to say that they are less

than good products. Having seen a preview of the next version of SiSense, Gigaom Research expects it to

do well. As for the current version, its distinguishing feature is its database engine, which, while

important, did not figure among our Disruption Vectors. Roambi’s visualizations are beautiful, and its

native iOS deisgn makes it an excellent BI tool for the iPad. However, the product just doesn’t prioritize

functionality corresponding to data discovery per se, nor to our Disruption Vectors, thus accounting for

its relatively low score.

Other takeaways

• Most products still have a distance to travel when it comes to cloud offerings. While cloud BI is

becoming mainstream, providers exclusive to that space, like Domo and GoodData, don’t focus on

data discovery as we have defined it in our report. Birst, which also focuses on the cloud, does

provide data-discovery facilities, but they are limited.

• Mobile is everywhere. While a good delivery of data visualization can still garner ooohs and ahhhs,

the inclusion of mobile devices in the data-discovery game is now a competitive necessity. And

while separate, native-consumption apps for mobile platforms are acceptable (some would even

!


say better), an HTML5-based authoring tool that can run with full functionality on tablets would

seem to be the state of the art.

• Connectivity to CRM data, especially in Salesforce.com, is a prevalent feature among data-

discovery products. So while analytics is applicable in almost every part of business, it is

nonetheless sales automation and revenue forecasting that drive a lot of activity, especially in the

self-service space.

• MicroStrategy makes a good showing and beats out the megavendors (Microsoft, IBM, SAP, and

Oracle) as the only established vendor to be among the six that we scored. MicroStrategy’s veteran

status gives it enterprise bona fides, and its independence gives it greater agility than its

megavendor competitors. While its products still carry the hallmarks of a large BI stack, it has

nonetheless modernized its core offerings quite admirably.

• There are a lot of vendors in this space. Nonetheless, not all BI vendors are giving data-discovery

tools the priority that they perhaps should.

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About Andrew Brust Andrew Brust is the founder and CEO of Blue Badge Insights, which provides analyst, strategy, and

advisory services for Microsoft customers and partners. He covers big data topics for ZDNet, is a

columnist for Visual Studio magazine, and is very involved with the Microsoft developer community.

About Gigaom Research Gigaom Research gives you insider access to expert industry insights on emerging markets. Focused on

delivering highly relevant and timely research to the people who need it most, our analysis, reports, and

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Visit!us!at:!research.gigaom.com. © 2014 Giga Omni Media, Inc. All Rights Reserved. This publication may be used only as expressly permitted by license from Gigaom and may not be accessed, used, copied, distributed, published, sold, publicly displayed, or otherwise exploited without the express prior written permission of Gigaom. For licensing information, please contact us.

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Sector RoadMap : data discovery in 2014€¦ · Sector RoadMap: data discovery in 2014! 3 Executive summary In the contemporary analytics market, business users want streamlined applications