BARC Score Data Discovery

Transcription

1 BARC Score Data Discovery Data Preparation, Visual Analysis and Guided Advanced Analytics for Business Analysts Authors: Robert Tischler, Larissa Seidler, Julia Henneberger Publication: June 13 th, 2018 Abstract This document is the second edition of BARC s data discovery vendor evaluation using our proven ranking methodology, called BARC Score. This BARC Score evaluates data discovery tools that properly support business users through all steps of the data discovery process. Based on countless data points from The BI Survey, many analyst interactions and detailed test case demonstrations, vendors are rated on a variety of criteria, from product capabilities and architecture to sales and marketing strategy, financial performance and customer feedback.

2 Table of Contents Overview...3 Inclusion Criteria...5 Evaluation Criteria...6 Portfolio Capabilities... 6 Market Execution... 8 Score Score Regions Evaluated Products Vendor Evaluations Dundas IBM Microsoft MicroStrategy Oracle Qlik SAP SAS Sisense Tableau TIBCO Yellowfin Other Vendors Related Research Documents BARC Score Consulting Services BARC Score Data Discovery Data Preparation, Visual Analysis and Guided Advanced Analytics for Business Analysts 2

3 Overview The market for software supporting data discovery, while already a significant portion of the BI market, is still emerging. Demand for tools allowing business users to efficiently analyze and explore data is continually increasing, leading to a fast-growing market. Vendors are reacting by trying to address the needs of the market. They are doing this by striving to improve the flexibility and ease of use of their suites, as well as by building a sound platform around isolated tools to provide enterprise readiness. This report analyzes the strengths and challenges of the leading vendors that offer sophisticated and integrated data discovery features. With all the buzz around data discovery, the perception of the term has become inconsistent. To allow for a fair comparison of vendors and tools, BARC applies its own distinct definition of data discovery: Today s data discovery offering not only has to encompass broad functionality, ranging from data preparation through visual analysis to guided advanced analytics (Figure 1). It must also be tightly integrated, user-friendly and supported by a robust platform (i.e., connectivity and security). These are the key issues enabling the increased adoption, use and usefulness of exploratory analysis in business departments. Data discovery software is a key element in the information landscapes of today s organizations, enabling quick reaction to changing requirements driven by the market. Figure 1: The three pillars of data discovery for business analysts As data discovery is becoming increasingly relevant for a variety of businesses and departments, tools must support an extensive range of use cases. The field of application for data discovery tools lies somewhere between traditional self-service BI and highly complicated data science and advanced analytics use cases. To enable business users to deliver trusted and high-quality results in challenging use cases and in an efficient manner, data discovery tools must provide more than just excellent usability. Active user advice and guidance is required too. With the increasing incorporation of artificial intelligence (AI), this intelligent aspect is becoming an integral part of more and more solutions. BARC expects that AI will continue to increase the productivity of business analysts by allowing them to focus on value-added work rather than repetitive tasks. A key area for increasing the productivity of business analysts through AI, and especially machine learning (ML), in data discovery is automated discovery. With automated discovery, users are not only guided while wading through data, they are actively presented with possibly relevant patterns, outliers and correlations in data sets often explained to the user by natural language generated comments (NLG). While vendors have made a huge fanfare around automated discovery, test cases have shown that only a few have been able to produce or suggest results beyond the obvious. Despite their ability to provide end-to-end analytics (i.e., from data source to communicating and BARC Score Data Discovery Data Preparation, Visual Analysis and Guided Advanced Analytics for Business Analysts 3

4 presenting results) in an independent and self-reliant manner, data discovery tools benefit from wellorganized and integrated data sources. Nevertheless, relying on ad hoc and single purpose-oriented data preparation is not an alternative to a thoroughly modeled and maintained data warehouse to support the requirements of standardized information distribution. Therefore, organizations must have a proper BI and data strategy, defining how the enterprise-wide usage of data should be organized and specifying the role and goals for data discovery. In many organizations data discovery tools are introduced to add flexibility to existing enterprise BI and analytics solutions, traditionally focusing on traditional BI use cases such as standard reporting and dashboarding. The tools analyzed do not, and most do not even aim to, cover all the functions and features provided by BI suites. Data discovery has already become a crucial means for modern organizations to democratize the use of data and analytics throughout the organization. To align the efforts of different business departments, it is becoming increasingly important to select the proper tools for the desired architecture quickly but with an elevated level of certainty. This document helps to focus the selection process by evaluating the most commonly used product sets from major vendors. BARC Score Data Discovery Data Preparation, Visual Analysis and Guided Advanced Analytics for Business Analysts 4

5 Inclusion Criteria There are two separate sets of inclusion criteria for this BARC Score: the first is associated with the vendors products and the other is linked to their financial results and references. To be listed in this BARC Score, a vendor needs to have a strong focus on providing data discovery functionality in three functional areas in a business user friendly and tightly integrated manner: Data preparation Visual analysis Guided advanced analytics In addition, the vendor must generate revenues of at least 10 million EUR, spread across at least two separate geographies. We consider the following as individual geographic regions: Europe, Middle East and Africa North America Latin America Asia and Pacific BARC Score Data Discovery Data Preparation, Visual Analysis and Guided Advanced Analytics for Business Analysts 5

6 Evaluation Criteria Every vendor is evaluated on two dimensions: portfolio capabilities and market execution. Each dimension represents one axis on the BARC Score and considers the following sub-criteria. Portfolio Capabilities In this BARC Score, vendors portfolio capabilities are rated on four major areas. Each consists of several different sub-criteria. At all levels of our portfolio capabilities evaluation, the corresponding weighting for each of the criteria is used (see Table 1). Category Criteria Weighting Data preparation Connectivity and data manipulation capabilities, user advisory High Visual analysis Level of interactivity and user advisory Medium Guided advanced analytics Scope and end-user-friendly access to advanced analytics functions Low Platform Integration of components, performance, usability, governance and collaboration features Low Table 1: Portfolio capabilities criteria and weighting Functional Evaluation In our functional evaluation, we include the following four functional sub-areas. As there is significant overlap between the three core areas data preparation, visual analysis and guided advanced analytics, the lines are blurred. Data manipulation is not exclusive to data preparation, it often happens during analysis. Enabling the user to apply data preparation or analysis at every stage of the discovery process requires tightly integrated solutions but is key to a truly iterative approach to data discovery. Data Preparation Data preparation is the process of preparing and providing data for data discovery and advanced analytics. The goal of data preparation is to support business analysts and data scientists by preparing distinct types of data for their analytical purposes. Data preparation is a sub-domain of data integration. The data preparation capabilities of all the vendors included in this report deliver a higher level of integration with analysis functions than most purpose-built data preparation tools. While the preparation of data can generally take place either in business departments or be performed centrally by IT. For this BARC Score we focus on the needs of business users. Ease of use for business users and guidance throughout the process of profiling and preparing data is rated along with the core functionalities around connectivity, connection options and data manipulation functions. Highly structured sources such as data warehouses still play a key role in many data discovery use cases. The need to quickly access external, unstructured or cloud source data is rising, so we look for connectivity way beyond traditional, structured sources. BARC Score Data Discovery Data Preparation, Visual Analysis and Guided Advanced Analytics for Business Analysts 6

7 Visual Analysis Interactive visualizations allow users to quickly scan significant amounts of data for patterns or to understand the meaning of a dataset at hand. Diverse types of analysis require different visual representations. Therefore, we evaluate how well tools can perform common analysis and how well the visualizations help to gain insight from data. The recommendation of suitable visualizations to users (user advisory) is currently on the rise and helps business users to focus on the analysis itself, while the software takes care of repetitive tasks. To enable quick and thorough investigation of data, it is important that data drives visualization and not the other way around as is typically seen in dashboard-oriented solutions. In traditional BI solutions, visualizations are used as a medium to communicate data, but data discovery uses the interactive graphical representation of data to facilitate its understanding by making use of the human ability to detect patterns. Processing spoken and written language to interactively query data and shape the visual representation (natural language processing, natural language querying) are becoming increasingly important. Users can type or speak questions in natural language to instantly receive answers from the system and build visualizations and dashboards. Natural language generation assists users in understanding patterns behind data by explaining them in an easy to understand manner. Many vendors have already implemented or adopted dedicated functionality. Guided Advanced Analytics Advanced analytics (or data mining) represents non-directed, hypothesis-free data analysis. Statistical algorithms are used to scan data sets searching for patterns used for segmentation, classification or association of data. These methods cover statistical data analysis, neural networks, decision trees, time series and many other algorithms. Guided advanced analytics describes the business user friendly provision of advanced analytics functions. Simple parameterization of predefined and robust algorithms without the need to write code is among the most important aspects to putting advanced analytics capabilities in the hands of business users. User advisory leveraging AI can power automated discovery capabilities, finally putting the intelligent into data discovery. Platform Data discovery is a major trend in bringing analytics and business intelligence to users. One of its major benefits is the way it increases flexibility for data workers (e.g., business analysts) and provides them with analysis capabilities to gain insight into different data sources. However, in many companies, individual and disconnected data preparation and the distribution of inconsistent results have led to a situation where trust in data has been lost and the replication of efforts in data preparation and analysis is blatantly inefficient. Therefore, we evaluate each product s ability to enable central as well as local governance and foster collaboration around data while delivering a high degree of flexibility to business users. Criteria Weighting We do not consider all categories and sub-categories to be equally important in this Score. Our weightings are based on BARC s own view of current user focus and buying patterns. BARC Score Data Discovery Data Preparation, Visual Analysis and Guided Advanced Analytics for Business Analysts 7

8 Market Execution On the market execution axis, we rate the data discovery vendors in this Score using the following criteria and their corresponding weighting (see Table 2). Criteria Weighting Product Strategy High Customer Satisfaction High Geographical Coverage Medium Financials Low Sales Strategy Low Ecosystem Low Marketing Strategy Low Organizational Strength Low Table 2: Market execution - criteria and weighting Product Strategy Product strategy is the single most important criterion when assessing a vendor s ability to execute. Vendors are rated on their product development record, product roadmap and innovation, as well as the company portfolio s alignment with current market trends and demands. Customer Satisfaction In this BARC Score we included the Customer Satisfaction KPI from BARC s The BI Survey. This combined metric comprises product satisfaction, vendor support and implementer support ratings reported by customers. Geographical Coverage Vendors are evaluated on their global presence. We look at the various geographic regions and major countries in which the company conducts business with both a sales and marketing presence as well as development and support functions. Financials This criterion covers the financial position of the vendor, from market capitalization, cash position and EBITDA to profitability, burn rate and investment rounds. For vendors that are private companies or do not break out the numbers for individual product lines, estimated figures are used. BARC Score Data Discovery Data Preparation, Visual Analysis and Guided Advanced Analytics for Business Analysts 8

9 Sales Strategy To rate a vendor s sales strategy, we look at the various channels through which the company goes to market: with both direct and indirect sales teams, through distributors, value-added resellers (VARs), online channels and OEM relationships. We also evaluate the vendor s product pricing and its various sales models, such as perpetual licensing, support subscription, open source and freemium. Ecosystem In this category, we evaluate the extended ecosystem in which the vendor participates. This includes business partner networks, hardware or cloud infrastructure providers, consulting firms and systems integrators, and other technology alliances. Marketing Strategy A vendor s marketing strategy is evaluated by rating its corporate and product messaging, the company s presence in printed media, advertising and social networks, as well as its ability to run events, such as conferences, seminar roadshows and webinars. Organizational Strength Vendors are rated on their organizational stability, which is influenced by consistency of corporate strategy, continuity of executive leadership, but also staff turnover, reorganization and layoffs. BARC Score Data Discovery Data Preparation, Visual Analysis and Guided Advanced Analytics for Business Analysts 9

10 Score Calculating the individual ratings for all criteria produces two scores per vendor: the portfolio capabilities score and the market execution score, each being plotted on the corresponding axis and thus resulting in the vendor s dot on the following BARC Score graphic (Figure 2). Figure 2: BARC Score Data Discovery BARC Score Data Discovery Data Preparation, Visual Analysis and Guided Advanced Analytics for Business Analysts 10

11 Score Regions Vendors can be positioned in one of five regions, depending on their total score on each of the two axes. Dominators Dominators are vendors that drive both technology and market adoption in a highly influential manner. They possess both a broad portfolio of market-leading and dominating products with a strong brand as well as a robust commercial prowess through best-in-class sales and marketing programs, an extensive ecosystem of business partners and alliances, and a rock-solid financial position. Dominators are considered a contender in virtually every planned implementation. Market Leaders Market Leaders are well established vendors that drive strong market adoption, supported by technology innovation and strategic acquisitions and by leveraging robust account management and a solid track record. Their portfolio enjoys high brand awareness in the market, covers an extensive range of technologies and services with only few gaps. Market Leaders typically have a large market share, making them a viable contender in almost all implementation scenarios. Challengers Challengers come in various shapes and sizes. They can be large vendors tapping into a new market by acquisition and pushing their way in with force, small innovative companies with a promising portfolio but limited sales and marketing resources, or vendors that attempt to disrupt a market with a new technology approach or different business model. Specialists Specialists are usually smaller vendors with a portfolio focused on a specific market segment. They can be either limited in their technical capabilities by concentrating on certain features and functions, or they may only focus on select geographic regions rather than the global marketplace. Entrants Entrants are usually startups that have limited reach and visibility in the market. Their product capabilities are incomplete when compared to competitors, and the vendor s long-term market potential is still unproven. BARC Score Data Discovery Data Preparation, Visual Analysis and Guided Advanced Analytics for Business Analysts 11

12 Evaluated Products The latest versions of the following products are evaluated in this BARC Score: Vendor Dundas IBM Microsoft MicroStrategy Oracle Qlik SAP SAS Sisense Tableau TIBCO Yellowfin Product(s) Dundas BI IBM Watson Analytics Power BI MicroStrategy Desktop and MicroStrategy Visual Insight Oracle Analytics Cloud Qlik Sense SAP Analytics Cloud SAS Visual Analytics, SAS Visual Statistics Sisense Tableau Platform TIBCO Spotfire Platform Yellowfin BI Table 3: Evaluated products Vendor Evaluations In the following section, we discuss each vendor in the BARC Score and highlight their strengths and challenges based on customer surveys and market research by the authors. BARC Score Data Discovery Data Preparation, Visual Analysis and Guided Advanced Analytics for Business Analysts 12

13 Dundas Toronto, ON, Canada Founded in 1992, Dundas began building its reputation as an innovator in visualization software with its Chart product based on Microsoft technology. In 2007, Dundas sold its Chart franchise to Microsoft to embark on the mission of building a comprehensive dashboard product, which was then launched in Further expanding its mission, Dundas released Dundas BI in 2014, a full-featured and modern BI platform that launched the vendor into the market for all-in-one BI platforms. Today, the Dundas BI Platform enables business users to discover insights and create dashboards with distinctively intuitive and clean interfaces. With Dundas BI, the company is gaining momentum with customers who are eager to deliver self-service analytics and data discovery that are equally attractive on desktops, tablets and smart phones, often embedded into other applications. Dundas BI offers a broad set of data connectors as well as sophisticated features for live and cached data access, making it attractive for numerous data discovery and analytics use cases. In Dundas BI the steps for data preparation and visualization are tightly integrated, making it feasible to iteratively analyze and enhance data to gain as much insight as possible. Descriptive statistics provide basic data profiling. Transformations are primarily applied through predefined and extensible steps in the data flow. Visualizations are automatically created upon dragging data to the canvas. Users can even automatically change visualization types when additional fields are added to a data visualization. Calculations and set analyses can be created quickly and directly from within visualizations. Dundas offers some predefined statistical functions such as clustering, correlation and exponential smoothing, which can be extended by external libraries. Due to the integration of R and Python, many advanced analytics scenarios can be covered. However, Dundas BI scores lower for its advanced analytics with guidance and user advisory for business analysts than competing data discovery vendors. Strengths Data preparation module offers intuitive transformation capabilities to business users and at the same time powerful functions for developers Simple deployment through a fully browser-based interface with a uniform user experience, clean and intuitive interfaces enabling iterative, efficient and effective data discovery Recommendations for visualizations on selecting data ranging from simple graph to correlation matrix and box plot with re-visualize function Common and optional semantic model is available and can be easily extended for flexible discovery Diverse use cases can be supported through providing live and cached data access Challenges User guidance is the Achilles heel of data discovery in Dundas with room for improvement in most areas Advanced analytics functions mostly available through external libraries with little support Dundas is a comparably small vendor with limited geographical presence and a restricted partner network Awareness and interest in Dundas BI as a viable data discovery product is low outside core markets BARC Score Data Discovery Data Preparation, Visual Analysis and Guided Advanced Analytics for Business Analysts 13

14 IBM Armonk, NY, USA As one of the world s largest vendors of IT hardware, software and services, IBM has a comprehensive portfolio of business intelligence and analytics solutions including offerings for data discovery. For this BARC Score we evaluated IBM Watson Analytics, which is currently IBM s strategic offering for business user-oriented data discovery. Watson Analytics is a cloud-based product for analytics and visualization that aims to assist business user discover insight in data by providing active guidance for analysis. The solution addresses business users and offers data preparation and user advisory through IBM s cognitive capabilities. Data preparation can be done in a pre-load and a post-load module, both with their distinct advantages. With Watson Analytics, IBM wants to make analytics available to a broad audience by supplying oneclick data discovery by using cognitive capabilities and natural language processing to automate pattern detection behind the scenes and guide the user through analysis. The solution simplifies analysis and the generation of visualizations by applying natural language queries. Based on natural language questions, interactive visualizations are created and can be adjusted and assembled as dashboards or storybooks. Watson Analytics not only tries to answer the questions the user raises, but also identifies potentially relevant findings around the current analysis in its Discoveries panel, pointing at patterns and outliers to enable users to quickly dig deeper to identify root causes. Guided advanced analytics is made available with simple to apply but feature-rich one-click predictive modeling powered by machine learning. Drivers for measures can quickly be identified without requiring users to write code or select and parameterize the desired algorithms. Decision trees and rules can easily be retrieved by business users, with Watson Analytics running all preparation steps such as binning and the calculation of the best tree behind the scenes while delivering detailed feedback about the models generated. It remains to be seen how Watson Analytics will evolve alongside the numerous related offerings in IBM s portfolio such as Data Refinery as a part of Data Science Experience (DSX) and Cognos Analytics. Strengths Cognitive capabilities are used throughout the solution to simplify analysis towards automated discovery for business users Natural language queries as a quick way to explore data, with search results providing users with recommended starting points Guidance throughout the solution with data profiling, data quality indicators, automated creation of bins and interference terms as well as additional insights on the Discoveries panel One-click predictive modeling in Watson Analytics automatically generates a model by running a series of algorithms against the data and ranking the results Decision tree with decision rules to identify drivers for the measure to analyze with simple capabilities to exclude irrelevant features Challenges Distinct data preparation modules with varying functionality require data to pass multiple steps before analysis is possible on the cloud platform Limited integration with IBM Cognos Analytics and Planning Analytics BARC Score Data Discovery Data Preparation, Visual Analysis and Guided Advanced Analytics for Business Analysts 14

15 No integration for external statistical libraries (e.g., R) offered to extend the built-in functionality for advanced and predictive analytics Low adoption of Watson Analytics compared to competing products (besides the free version) and only minor improvements compared to last year BARC Score Data Discovery Data Preparation, Visual Analysis and Guided Advanced Analytics for Business Analysts 15

16 Microsoft Redmond, WA, USA Microsoft, the world s largest software company, was founded in 1975 and has become a household name primarily due to its Windows operating system and Office suite. The vendor has a broad enterprise offering too ranging from cloud over database to its ERP offering. Microsoft spread the BI and analytics capabilities offered across the Office, SharePoint, SQL Server, and Azure product lines. Microsoft currently focuses its data discovery and analytics efforts on the Power BI brand, which is evaluated in this BARC Score. It is a cloud-based analytics product consisting of Microsoft Power BI Desktop (a fat client for ad hoc reporting, dashboards, and analysis) and Power BI Service (a web client for publishing and sharing content). The complete capabilities of Power BI are currently only available through the cloud service. A pure on-premises solution that provides a subset of the features was released in Power BI is marketed as an interactive tool for data visualization geared at enabling business users to analyze data and share insights predominantly via dashboards. Its pricing and the dominance of Microsoft products on office computers give Power BI huge traction in the market. Data preparation relies on DAX for advanced functions, making it difficult to use for business users. All preparation steps are tracked and can be undone or altered at any time, making the process not only more flexible but also a lot more transparent. Data visualization in Power BI delivers rich visualizations but is limited in flexibility by its dashboard-oriented approach. Natural language query (NLQ) in Power BI Service makes it easy for users to retrieve relevant visualizations. Guided advanced analytics is mostly covered by Quick Insights, a feature that automatically analyzes data sets for patterns and outliers and provides the user with suggestions about relevant findings. The Insight feature can also be used to receive information about interesting patterns in the data a specific visualization is based on. Both features failed to deliver relevant insight on the test case data sets. While R integration is available, advanced analytics is not a focus of Power BI. Custom visualizations provide the possibility to leverage R functions without having to code but the quality of the components available varies. Updates for Power BI are released frequently, constantly delivering new features. This may contribute to the fact that customer satisfaction improved significantly compared to last year s results. Strengths Tracking of all data preparation steps in a recipe with the possibility to trace transformations or to undo and alter specific steps Good guidance available in data preparation through Add Column from Examples to derive rules and functions from examples based on a single column or multiple columns Integration into MS Office, Teams, and especially MS SQL Server and Azure Natural language query functions for visualizations in Q&A feature Challenges Data sets for imported data are restricted in size and live access is available only for selected data sources Data preparation and modeling are only available in desktop client making client setups mandatory for power users Data preparation does not cover all functions via context sensitive actions or wizards Automated insight functions are only available in the cloud and failed to identify significant patterns in the test case data sets BARC Score Data Discovery Data Preparation, Visual Analysis and Guided Advanced Analytics for Business Analysts 16

17 MicroStrategy Tysons Corner, VA, USA MicroStrategy, founded in 1989, is one of the best-known vendors in the analytics market worldwide. The vendor offers MicroStrategy Desktop and a web client with identical functionally for data discovery to provide flexibility to business users to extend the reach of its core enterprise offering. The data discovery solution extends and leverages highly scalable functionality for governance of the fully integrated analytics suite. By extending its proven architecture, MicroStrategy allows business users to take advantage of semantic models while not confining the user to them. This combination provides a high level of flexibility but at the same time supports governance and collaboration by using shared and common definitions and by making use of existing data preparation efforts. The way MicroStrategy supports the user during data preparation is the sweet spot of the solution. Within a spreadsheet-like preview the user is offered only functions that are relevant in the current context. The possible outcome of available options for these functions (e.g., when splitting data) is shown immediately alongside existing columns. No functions must be keyed in to prepare data and the history script created in the background provides the necessary transparency. MicroStrategy provides solid visual analysis functions on top of its in-memory cached data sets or live access from a vast number of different data sources. For advanced analytics in data discovery MicroStrategy offers a native library of functions and bundles R with its solution. Strengths Data discovery solution tightly integrated in the BI and analytics platform for collaboration, governance, and dissemination of insight Intuitive, comprehensive data preparation capabilities including generation of change script with extensive connectivity Data discovery and data preparation equally available on cached in-memory data sets and live connections Access for 3 rd party analytics tools to data sets and semantic models to get the most out of data assets generated Newly released MicroStrategy Dossier adds capabilities to certify data sources, assets, and content created to increase support for bottom-up governance and collaboration Challenges Visual analysis is dashboard-oriented instead of focusing on ad hoc navigation through data, resulting in longer time to insight Data preparation lacks visual profiling to give uses a quick overview on new data sets Weak guidance and user advisory for suggesting appropriate advanced analytics functions for selected data Commenting currently limited, no association of comments to data points Complicated solution if deployed with MicroStrategy enterprise edition, not easy to maintain for smaller customers BARC Score Data Discovery Data Preparation, Visual Analysis and Guided Advanced Analytics for Business Analysts 17

18 Oracle Redwood Shores, CA, USA Oracle is one of the world s largest software and cloud companies and is the leading provider of data management solutions for analytics in addition to a major supplier of enterprise SaaS applications. Oracle offers its flagship product Oracle Analytics Cloud (OAC) which incorporates data preparation, data visualization, enterprise business intelligence and scenario management capabilities. Data visualization functionality is available in the cloud, on-premises and as a standalone desktop tool. Oracle Analytics Cloud provides users with a modern and intuitive interface, allowing them to become familiar with the tool quickly. Data profiling, usually the first step of data preparation, is supported by the Inspect feature. Profiling provides a quick overview of the distribution of data in the source to allow for efficient data preparation. The number of connectors available for OAC, despite significant improvements, compared to other solutions scrutinized is lower in some areas. Most preparation functions can be applied through context-aware functions directly in the preview providing immediate results, but some functions, e.g., filters for files, must be applied in the data flow step resulting in an extra hop for the users. Suggested enrichments during data preparation to enhance the value of refinement are on Oracle s roadmap for the upcoming release. Visual explorations are as quick to build as they are flexible. Suitable visualizations are suggested based on the type of data drawn on the canvas and can be selected but can also be generated automatically. All filters applied are shown as breadcrumbs on the top, allowing to grasp the analysis workflow at a glance. Quick time to insight is provided through BI Ask (Oracle s solution for natural language querying), offering quick entry points to deeper analysis. Narrate offers the possibility to compile analyses to stories and to present directly out of the application. Trends and forecasts as well as clustering and outlier detection are available as built-in advanced analytics features available in data preparation and visual analysis. The Explain feature brings easy access to automated advanced analytics functionality (e.g., decision rules) to identify relevant drivers and anomalies in a data set. Further prebuilt advanced analysis functions are offered via the Oracle Analytics Library. Strengths Easy to use and integrated interface with decent capabilities in all three major rating areas Good support and extensibility for advanced and predictive analytics including machine learning Integration with broad Oracle BI suite and semantic layer possible to reuse corporate data models Natural language queries and automated analysis for quick insights available via BI Ask and Explain Challenges Not all preparation functions can be applied in spreadsheet environment, adding additional steps to data refining Connectivity to data sources lower than average but exceptionally good on Oracle products with the possibility to extend connectivity through Oracle BI Standalone version lacks features for governed data discovery and enhancing enterprise models with local flexibility is not a one stop shop Some data preparation functions are not available as context sensitive functions but must be scripted BARC Score Data Discovery Data Preparation, Visual Analysis and Guided Advanced Analytics for Business Analysts 18

19 Qlik Radnor, PA, USA Qlik, founded in 1993 in Sweden, moved its headquarters to the United States in 2005 after raising funds from several venture capital firms. QlikView, at that time the company s sole product, had been promoted aggressively after the VC investment. This created enormous attention and traction, and in 2010 Qlik went public on NASDAQ. In 2016, Qlik was acquired by the private equity company Thoma Bravo and got unlisted from the stock exchange. Until the general availability of Qlik Sense in 2014, Qlik was a one-product company. Today, the vendor provides a portfolio of visual analytics offerings: Qlik Sense Enterprise and Qlik Sense Cloud, as well as the Qlik Analytics Platform for developers, QlikView, and Qlik NPrinting. In 2017, Qlik acquired its Swedish partner Idevio to provide advanced features for spatial analysis and now sells this solution as Qlik GeoAnalytics. Qlik Sense is positioned as a self-service data visualization and data discovery solution providing immediate analysis results. Qlik Sense is powered by Qlik s associative engine QIX, providing flexible access to data sets stored in-memory. Qlik significantly improved the business user-friendliness of its data preparation module by adding visual preparation and profiling where scripting used to be mandatory until recently. These improvements enhance the productivity of data preparation for developers and they also make data preparation accessible for less technically oriented users. Augmenting the user experience with helpful and guiding functions is at the core of Qlik s vision for data discovery. During data preparation joins and default aggregations are suggested for measures. Qlik Sense offers further assistance for data visualization by actively recommending suitable visualizations to users based on fields selected and their metadata with the April 2018 release. Qlik Sense currently only offers integration to statistical libraries, such as R and Python, to support advanced analytics. Strengths Intuitive, fast, and flexible associative and set-based navigation in data with capabilities to search in content and apply dynamic filters Performance of data loading and querying was excellent during all steps of test case demonstration Suggesting join candidates based on contents of all tables and columns in a model speeds up joining data sets with unknown content or quality Ecosystem of partners providing solutions to build Qlik data sets directly from data integration or data warehouse automation tools Ease-of-use for preparing data improved significantly with latest releases Challenges Limited out-of-the-box advanced analytics functionality but R and Python integration available in visual analysis and data preparation Limited built-in functionality for data governance to align the definitions for KPIs across distinct applications Focus on dashboard creation resulting in visual analysis being not as flexible and easy to read compared to competitors; recommendations for visualizations recently added Scripting is still required for certain data preparation functions, reducing ease of use for business users but introduction of visual preparation enhanced ease of use significantly BARC Score Data Discovery Data Preparation, Visual Analysis and Guided Advanced Analytics for Business Analysts 19

20 SAP Walldorf, Germany SAP was founded in 1972 as a business applications company. It significantly increased its footprint in the business intelligence and analytics market with the acquisition of BusinessObjects in 2007 (now called SAP BusinessObjects Business Intelligence Platform). SAP Analytics is one of the broadest BI portfolios on the market. In early 2018, SAP announced that it plans to focus its data discovery offering on SAP Analytics Cloud. SAP Lumira Discovery will receive functional updates throughout 2018 and will be supported at least until As its name suggests, Analytics Cloud is designed specifically for the cloud and combines capabilities for data preparation and modeling, planning, reporting, visualization, and advanced analytics into one integrated solution aimed at business users. It addresses all three pillars of data discovery consisting of data preparation, visual analysis, and guided advanced analytics. Furthermore, Analytics Cloud is the only offering in this evaluation that supports planning and budgeting too. Vital aspects of user guidance and automated discovery features are present in all modules. Analytics Cloud is currently not as mature and feature-rich in certain areas as competing data discovery products, resulting in a rather low rating on the portfolio capabilities axis. Connectors for various connection types are missing in Analytics Cloud. It does not offer connectors to as many distinct data sources as competitive products and falls short in data preparation capabilities. Basic data profiling is available but the tile view available neither delivers a quick and good overview on all columns nor a deeper look into a single column. Visual explorations are created in a dashboard-like style that does not offer the level of user guidance and flexibility needed for quick, iterative, and efficient data analysis. Still it offers decent capabilities to visualize data and to collaborate on insight gleaned with good commenting and data storytelling features. Advanced analytics is an important pillar for data discovery in Analytics Cloud. The smart capabilities allow business users to leverage advanced analytics without coding, enabling deep insight into data. During the test case-based evaluation SAP Analytics Cloud, despite advertising its automated discovery features, was not able to identify the patterns in the data sets provided. Strengths Broad BI and analytics functionality integrated in a single cloud offering from planning to data discovery Possibility to create ad hoc data models as well as governed central data models to be used throughout the solution Guidance for advanced analytics available through the Smart Discovery feature Connectivity and pre-built content available for SAP s own data sources and applications Challenges SAP Analytics Cloud is available in the cloud only and is still a young solution with limited features in most areas relevant for data discovery Data source connectors currently focused on SAP data sources with only a limited number of connectors available R integration is only available during data visualization but not during data preparation, so results cannot easily be reused Analytics Cloud was the only solution tested for this BARC Score failing to provide results in multiple areas Little awareness and traction on the market besides SAP s customer base BARC Score Data Discovery Data Preparation, Visual Analysis and Guided Advanced Analytics for Business Analysts 20

21 SAS Cary, NC, USA SAS, founded in 1976, is a privately held company and a well-known brand in the business intelligence and analytics market. The vendor has been a specialist in business intelligence, data management, and advanced analytics for decades. In 2012, SAS released SAS Visual Analytics (VA), a new product line that provides an integrated platform for data discovery, dashboarding, and analysis. SAS s greatest strength lies in its advanced analytics, predictive modeling, and statistics solutions that are increasingly targeted at skilled business users. In 2016, the vendor introduced SAS Viya, a new common platform for analytics. In 2017, SAS started to offer Visual Analytics based on Viya and its in-memory engine, effectively combining exploration, analytics, and reporting with data preparation in a single web frontend. Data preparation in SAS used to be the domain of SAS Enterprise Guide. Visual Analytics today offers an integrated and much easier to use data preparation module (SAS Data Preparation) to load data into the internal Viya in-memory engine (CAS) for fast and scalable analysis. SAS Data Preparation aims to enable business analysts to prepare data and is therefore more suitable for data discovery in business units than Enterprise Guide but does not offer the same level of usability enjoyed in competing solutions. Nevertheless, Data Preparation delivers functions, e.g., for data quality or analytical bins, not available in many other data discovery tools. Visual Analytics does an increasingly decent job in offering flexible analysis functions to business users to efficiently navigate through data. SAS really shines when advanced analytics is required to find all relevant patterns in large data sets. A huge library of functions ready to use without scripting and equipped with self-optimizing features provides business analysts with mighty tools to dig deep for nuggets in corporate data. Strengths Leading advanced analytics capabilities for data discovery through seamless integration with SAS Visual Statistics and SAS Visual Data Mining and Machine Learning Advanced analytics functions offered are comparably easy to employ for business users through guidance and interfaces offered Viya s in-memory engine CAS as a scalable architecture for substantial amounts of data and large numbers of concurrent users Data profiling provides at the same time a good overview of the data set as well as detailed descriptions for selected columns Challenges Still limited but significantly evolved business user-friendliness in SAS Data Preparation Visual analysis lacks the required level of usability and interactivity for efficient and effective discovery Less support for enforcing common definitions than can be expected from an enterprise solution Not a lightweight solution compared to competitors due to resource-demanding in-memory engine BARC Score Data Discovery Data Preparation, Visual Analysis and Guided Advanced Analytics for Business Analysts 21

22 Sisense New York, NY, USA Headquartered in New York City, Sisense is a growing BI and analytics vendor offering a modern BI and analytics product suited for dashboards and analysis as well as more explorative use cases on a single platform. The company sells to medium and large enterprises across the globe. Marketed as a single-stack product that tries to simplify analytics for complex data, Sisense provides an easy-to-use dashboard environment where users have the option to start with predefined dashboards for selected data sources or data modeling and integration tools for querying disparate data sources. Sisense can consume data from spreadsheets, Hadoop, web applications, and relational databases and loads it into its integrated ElastiCube data stores without pre-aggregation or pre-calculation. ElastiCube is based on a columnar MonetDB and, despite its name, not a cube engine. Query performance is enhanced in Sisense by increasingly leveraging CPU caches and vector instructions for calculations. Although mandatory, upfront modeling for building ElastiCubes is simple and very flexible as it is intentionally reduced to a required minimum. Business users are offered good connectivity options and most data transformations can be made while visually analyzing data. The ElastiCube Manager, Sisense s modeling environment, has recently been migrated to a modern web client that currently is lacking the full connectivity of the fat client it is inclined to supersede. Intuitiveness and data profiling during data preparation and modeling are limited by the lack of user guidance (i.e., suggesting functions for shaping data in a context aware manner). Visual analysis unfolds the heritage of a dashboard-based approach with slightly more effort required to produce informative visualizations to reveal insight in data. Sisense offers only mediocre advanced analytics capabilities beyond an obligatory R integration. Plug-ins for, e.g., clustering and forecasting are available that give code free access to the algorithms to business users. The introduction of natural language queries in the BI Bot shows Sisense s aspiration to be recognized as an innovative vendor in the BI and analytics market with a core focus on pervasive analytics by lowering the entry barrier for users. Strengths Integrated product for data discovery and dashboarding use cases on an integrated stack easy to use for business users High customer satisfaction in BARC BI Survey for self-service and visual discovery-oriented tools Internal columnar data store combined with CPU optimized calculations delivers fast query results Wide selection of visualization types for visualizing all sorts of data and good connectivity to big data sources Challenges The new web-based ElastiCube Manager does not yet support all connectors that are available in the fat client The appeal to casual business users is modest due to a lack of context-sensitive actions available during data preparation and visual analysis Local presence limited outside the US and Israel, although the vendor is currently expanding into other territories Limited functions for guided advanced analytics with little improvements in the last year BARC Score Data Discovery Data Preparation, Visual Analysis and Guided Advanced Analytics for Business Analysts 22

23 Tableau Seattle, WA, USA Tableau Software emerged from scientific research at Stanford University. Since its foundation in 2003, Tableau has enjoyed substantial growth. The vendor follows a strategy of delivering software that requires little training and allows business users to interpret their own data mostly by means of interactive visualization. Starting from 2018 Tableau calls its products Tableau Platform to underline its stronger positioning as a platform vendor. All core modules of Tableau Platform are based on one common technology: Desktop, Prep, Server (on-premises), Online (cloud), and Public (cloud, free but limited). Tableau is a user-friendly visual analysis and data discovery platform that provides a lean architecture consisting of a desktop client used for development and authoring and a server for central deployment. Tableau Desktop is the heart of the offering used to build the Workbooks, which are then mostly distributed by the Tableau Server for easy consumption on the web and on mobile devices. The intuitive user interface, built-in intelligence, and memory utilization to optimize performance contribute to the popularity of this solution for visual analysis, dashboarding, and data discovery scenarios. Tableau s openness to different data sources is one of its focus areas. The solution allows users to combine and analyze data from over 60 different data sources. Data preparation in Tableau Desktop can be quick as many manipulations (except, e.g., joining data sets) can be made directly while analyzing data, enabling a true iterative approach to data discovery. This not only applies to basic calculations but also to advanced functions such as binning. With the release of Tableau Prep in version data preparation has been enhanced by providing deeper functionality and a more visual approach with recommendations for data shaping, profiling, and enhanced traceability. Tableau introduced Hyper in version 10.5 as a new data engine to speed up queries and the creation of data sets. The user guidance available during visual analysis is an important reason for Tableau s dissemination among business users, as it allows them to produce meaningful, interactive visualizations in very little time. Visualizations can be annotated and compiled in dashboards or storyboards for content distribution and collaboration. Basic analytics functions can be applied to visualizations by dragging relevant functions, e.g., for forecasting or clustering, on the canvas. Strengths Easy-to-use user interface for visual analysis and data preparation leading to good user acceptance and high productivity Visual analysis with built-in intelligence producing clean visualizations with high interactivity Data preparation in Tableau Prep offers profiling, a revision history, and recommendations for data shaping, but currently fewer connectors than in Tableau Desktop Good annotation and storytelling functions to actively support discussions and collaboration about insight gleaned from analysis Ecosystems of especially data integration and preparation specialists, enhancing Tableau s value for customers Challenges Limited user guidance in advanced analytics to support users in exploring large, unknown, and diverse data sets Limited built-in functionality besides data source certification to support data governance in distributed environments, e.g., for common KPI BARC Score Data Discovery Data Preparation, Visual Analysis and Guided Advanced Analytics for Business Analysts 23