Categories   >   Data Discovery Tools
Last Reviewed: November 12th, 2024

Best Data Discovery Tools Of 2024

What are Data Discovery Tools?

Data discovery tools are like search engines for your data warehouse. Businesses often hoard information in hidden corners – spreadsheets, databases, legacy systems. These tools act as a map, helping users find, explore, and understand relevant data. Imagine a marketing team uncovering a hidden dataset on customer purchase history, revealing a surge in online orders on mobile devices. This solves the problem of data silos, where valuable insights get locked away. Their importance lies in democratizing data analysis. By making data accessible to a wider range of users, not just data scientists, these tools foster data-driven decision making across the organization. Key functionalities include search capabilities, data visualization tools, and the ability to filter and analyze data from various sources. Emerging features include natural language processing for intuitive querying and automated data lineage tracking for better data understanding. Data discovery benefits a wide range of users, from business analysts to marketing managers, and industries like finance, retail, and healthcare. However, limitations exist – data quality and user expertise can still impact the effectiveness of these tools. Overall, data discovery tools empower businesses to unlock the hidden gems within their data, driving better decision-making and innovation.

What Are The Key Benefits of Data Discovery Tools?

  • Find Hidden Insights
  • Self-Service Analytics
  • Break Down Silos
  • Improved Data Accessibility
  • Faster Decision Making
  • Empower Business Users
  • Data Exploration
  • Identify Trends
  • Root Cause Analysis
Read more

Overall

Based on the latest available data collected by SelectHub for 169 solutions, we determined the following solutions are the best Data Discovery Tools overall:

Tableau
Tableau
Start Price
$15
Per User, Monthly
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Why We Picked Tableau

Tableau Desktop is a BI solution for data visualization, dashboarding and location analysis. In online reviews, users said they found its drag-and-drop charting a boon for creating charts and maps. Regarding customization, many users praised the platform for its various labeling and design options.

I recently tried the Tableau Desktop 2024.1.3 version. The trial is only for 14 days and is enough for a sneak peek into Tableau’s dashboarding and data storytelling capabilities. For more straightforward use cases, Tableau is incredibly user-friendly and fast. Creating a new sheet gives you a canvas to create a visualization. Once you have the required sheets, combining them into a dashboard view is straightforward — select and add.

My dataset included healthcare data, including details of patients, their hospital visits and insurance payer details. One use case was to find the total claim settlement amount. I dragged the Total Claims Cost and Payer fields to the column and row shelves, and Tableau gave me a bar graph. The toolbar had single-click options for sorting data from increasing to decreasing values or the other way around.

To view the number of encounters by payer, I dragged the Payer field to the row shelf and used the SUM(ROW_COUNT()) function on the column shelf. The chart popped up with more visualization and layout options.

I wanted an interactive filter to view the average claim cost by birthdate. I dragged the Birthdate field to the Filters shelf and right-clicked on it to set the end date as October 22, 1961. Selecting Show Filter added a slider conveniently to the right of my visualization. I could see the data for people born before October 22, 1961, and if required, I could change the end date.

Another use case would be viewing the data by the type of hospital visits — how many people were inpatients, outpatients or those who needed emergency care. I dragged and dropped the Total Claims Cost and Payer fields into columns and rows, respectively. Similarly, I dropped Encounterclass into the Filters shelf and clicked on Show Filter to enable a checkbox on the screen. It had all the categories of visits, giving users the option to select the desired views.

One-fourth of the users discussing adoption said there was a steep learning curve. Tableau relies on Python and R scripts for statistics in its visualizations. It's where the named licenses can prove to be a blessing, as you can opt to train upcoming Creators and Explorers. We recommend factoring in training if you want to hit the ground running.

Some reviewers felt discounted packages for business editions should be available, similar to the free student licenses. At $70 per user, the Creator license can seem costly when compared to Power BI ($9.99 per user) and Qlik Sense ($30 per user).

Here's the good news, though. Its built-in user management acts as a permissions layer for your organization - users can only access the relevant content. Plus, an organization will have very few Creators and a greater number of Viewers and Explorers, and the license fee reduces from Creator to Explorer to Viewer.

We recommend opting for a wise license combination to get the most out of the product.

On the upside, the vendor constantly releases new features, the latest one being Einstein CoPilot in beta.

Overall, Tableau is a competitive BI solution, but if the pricing seems inflexible, quite a few other solutions offer live insights and advanced analytics out of the box.

Pros & Cons

  • Data Visualization: Almost 98% of users who reviewed its visual capabilities praised the platform for its dashboards and the freedom to play around with data and modify charts as desired.
  • User-Friendly: According to 93% of users who mentioned ease of use, it makes data accessible with its easy user actions and handy tooltips.
  • Data Connectivity: About 92% of users who discussed data sourcing praised its ability to pull data from disparate systems.
  • Pricing: Around 90% of the users citing cost found it expensive.
  • Speed: About 71% of the users who discussed performance found it slow when processing large data volumes.
  • Onboarding Woes: Approximately 67% of the users who reviewed the platform's adoption said there was a steep learning curve.

Key Features

  • Connectors: Combine data from various sources by choosing from a wide range of connectors — no need to spend on expensive third-party data integration tools. Tableau Bridge connects private networks to live data sources via Tableau Cloud.
  • AI: Tableau now offers AI capabilities thanks to Einstein Analytics.
    • Tableau Pulse: Explore data independently and ask questions with AI analytics. Tableau Pulse is available with Tableau Cloud and Embedded Analytics.
    • Explain Data: Understand the displayed insights with natural language explanations of data points.
    • Einstein CoPilot (Beta): Close the gap in understanding data with AI insights. Discover hidden trends by asking follow-up questions without losing context, thanks to generative AI. Einstein CoPilot is available with a Tableau Cloud subscription.
  • Tableau Prep: Clean and transform data of all types, including survey results, feedback data and social media posts. Shape and combine it with Tableau Prep, which is available with the paid edition only.
  • Data Stories: Convey your message with compelling narratives to get stakeholder buy-in. Drag and drop sheets onto the storyboard to show the growth, decline or stability of critical metrics.
  • Animations: Explain how data changes over time with animated charts and customize them to include graphics, labels and colors.
  • Filtering: Focus on the data that matters; it’s as easy as dragging and dropping desired fields to the Filter shelf. Specify a value range, set a condition or choose the top values to display.
  • User-Based Licenses: Explore cost-effective license combinations that work for your team.
    • Creators can build dashboards, permissions, and governance rules, and establish connections to new sources. They’re content authors who transform and analyze data. This license is available at $70 per user monthly, billed annually. However, they can’t control the Tableau Server or Desktop environment.
    • Explorer licenses are suitable for line-of-business users whose role requires independent data exploration. They can author content but within a governed ecosystem. Each Explorer license costs $42 monthly, billed annually. They can’t connect to new sources, modify data, or use the Tableau desktop or custom SQL.
    • Viewers can interact with data, apply filters and follow pre-decided workflows. This license is available for $15 per user monthly, billed annually. Viewers have limited rights and can’t create and edit visualizations and the underlying data.
Power BI
Power BI
Start Price
$10
Per User, Monthly
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Why We Picked Power BI

Our researchers ranked products on a whole bunch of features. They include data management, querying and visualization, advanced and embedded analytics, mobile BI, and IoT and location analytics.

In our rankings, Power BI scores 87 for connectivity, leaving behind Tableau, Oracle Analytics and Dundas BI. Robust Microsoft technology is one reason, for sure. Besides, intelligent techniques like DirectQuery and easy data modeling make it popular among users.

In product reviews, some users mentioned a lag when sharing reports from the desktop to the cloud. For me, the platform was a tad slow to start, but otherwise, it stayed performant for my average-sized dataset.

When dealing with sales data, total sales, the top-performing products, seasonality and period trends are common queries. Creating a sales KPI report in Power BI was an excellent way for me to answer them. My CSV files included sales, calendar, products and store data.

Connecting to sources is straightforward with Get Data on the home screen and toolbar. Once I had pulled in the data, I clicked on Transform Data and opened the Power Query editor. It automatically detects the data type for strings and numbers but can get confused with dates and currency, which it marks as text. It involved some manual wrangling, but I had it sorted in no time. Read my article on KPI Reports to learn how I did it.

But I wouldn’t call it a deal-breaker as it’s not a tedious task. I had the same experience with Qlik Sense, but Tableau was way better as it recognizes seven data types — string, number, date, date and time, boolean, geographic and cluster values.

Tracking sales over periods required a greater level of detail, so I added new columns to the calendar data — start of month and start of week. Column statistics were immensely helpful in identifying unique, distinct and null values and correcting incomplete records. Clicking on the number of products selling at a particular price allowed me to see which toys sold at that price.

Creating a relational data model by defining primary keys is a manual process and seems dated once you’ve used Qlik Sense. Adding calculated measures is where DAX shows its magic. For data workers well-versed with SQL, DAX is a ready-to-go tool they’ll be glad to have in their corner.

Creating visualizations wasn’t as intuitive as Tableau as it involved drag-and-drop onto the canvas, and frankly, I felt like I was flying blind. I didn’t feel that way with Tableau, and it’s slicker.

Power BI offers a paintbrush tool that lets you define the layout, the card arrangement and the maximum number of cards. You can define the canvas settings, background and headers and determine the filter pane settings. It took me longer to create a dashboard from scratch than it took in Tableau.

Some users found the pricing structure too complex. While using Azure data in Power BI for basic queries is free, costs can add up when you go for text and sentiment analysis. With Microsoft Fabric, the pricing complexity is set to rise. Though Power BI is available separately too, you’ll need to rely on Fabric to manage users, licenses and other administrative tasks.

About 31% of the users mentioning cost complained about onboarding difficulties, possibly because DAX introduces the complexity of learning syntax. It can daunt non-technical users initially, but guided formulas can make the task easier. That said, I agree with the majority of user reviews that training will speed up onboarding and help your team maximize the investment.

Overall, Power BI has many powerful features and will give you value for your money. If you’re not a Microsoft user yet, it’s worth checking out for the baked-in vendor technologies like Azure and SSAS. If you are an MS user, Power BI might be a no-brainer, though be prepared to shell out a little extra for advanced functionality and additional modules.

Pros & Cons

  • Integrations: Around 95% of users who mentioned data sources said they were satisfied with its flexibility in connecting to sources.
  • Data Visualization: About 93% of the users who discussed visual analysis said they relied on it for daily reporting.
  • Functionality: Over 75% of the users reviewing features said they were impressed with its live queries, DAX calculations and data modeling.
  • Ease of Use: Approximately 72% of the users who mentioned its UI said it was straightforward to use.
  • Speed: About 95% of recent reviews citing performance said the platform lagged when dealing with large data volumes.
  • Adoption: Around 81.5% of the reviewers mentioning adoption said the learning curve was steep.
  • Cost: Approximately 71% of users discussing pricing complained about the platform being expensive.

Key Features

  • Dataflows: Save time with reusable workflows that lock the logic in. While shared datasets are open to interpretation, dataflows will take your users in one direction only, ensuring consistent results. It’s like a written recipe, just follow the steps to get the taste right.
  • Analyze in Excel: Focus on the end game. Give your teams the freedom to analyze their data in Excel and move the results back to Power BI.
  • DAX: Empower your people to go beyond raw data. Derive calculated columns and measures with Data Analysis Expressions. Watch them update as you apply filters and slicers and interact with data in other ways.
  • Data Alerts: Act in time to keep things running smoothly. Stay informed of changes with alerts. Subscribe to receive notifications via email or the Power BI notification center (available only with Power BI Service). Among visualizations, KPI cards, cards and gauges have the alert option. 
  • Data Refreshes: Stay ahead of trends with the latest insight. Update data on demand in Power BI or schedule refreshes with Power Automate. Power BI Pro and Premium allow up to eight and 48 refreshes daily, respectively.
  • Key Influencers Visual: Decide the next steps by spotting the factors affecting a critical metric. As a transporter, does only the terrain impact how consistently your trucks deliver, or is the average age of the fleet vehicles also a factor?
  • Decomposition Tree: Identify which product category or region contributed most to sales increase or decrease. For instance, you can analyze sales trends by channel with the decomposition tree.

Pricing

License/Subscription Cost
  • Based on the number of users for Power BI Pro and capacity-based pricing for Power BI Premium
Maintenance Cost
  • Included in the subscription cost
Installation/Implementation Cost
  • Included in the subscription cost. Additional charges may apply for data migration during implementation of Power BI, maintaining on-premise data sources and building dashboards and reports
Customization Cost
  • Dependent on functional requirements and specific needs of the organization
Data Migration Cost/Change Management/Upfront Switching Cost
  • Dependent on your current software, amount of data to be migrated, availability of migration tools, complexity of data and gaps between the existing system and the new system.
Recurring/Renewal Costs
  • Renewal cost is included in the fees paid monthly or annually
Mathematica
Mathematica
Start Price
$1,800
Annually
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Why We Picked Mathematica

Let's crunch some numbers and see what users have to say about Mathematica!

Mathematica has garnered a reputation as a powerful computational tool, particularly in academic and research settings. Users frequently praise its symbolic computation capabilities, allowing them to manipulate and solve complex mathematical expressions and equations with ease. This strength sets Mathematica apart from competitors like MATLAB, which primarily focuses on numerical computation. Mathematica's notebook interface also receives positive feedback for its ability to combine code, visualizations, and text in a single document, facilitating reproducible research and clear communication of findings. However, Mathematica's steep learning curve and high price point are often cited as drawbacks. Users transitioning from other programming languages may find Mathematica's syntax and functional programming paradigm challenging to grasp initially. Additionally, the cost of a Mathematica license can be prohibitive for individual users or small businesses.

Overall, Mathematica is best suited for researchers, scientists, and engineers who require a comprehensive tool for symbolic and numerical computation, data analysis, and visualization. Its extensive functionality and ability to handle complex mathematical problems make it an invaluable asset in these fields. However, individuals or organizations with limited budgets or those seeking a more user-friendly option may want to explore alternative software solutions. Keep in mind that software is constantly evolving, so it's always a good idea to check for the latest updates and user reviews to make an informed decision.

Pros & Cons

  • Symbolic Computation: Mathematica excels at handling and manipulating symbolic expressions, making it ideal for tasks that involve algebra, calculus, and other forms of mathematical analysis. This can be particularly useful for financial modeling, risk analysis, and other business intelligence applications that require complex calculations.
  • Visualization Capabilities: Mathematica offers a wide range of visualization tools that can be used to create high-quality charts, graphs, and other visual representations of data. These visualizations can be interactive, allowing users to explore data from different perspectives and gain deeper insights. This is essential for effectively communicating complex data to stakeholders in a business setting.
  • Automation and Scripting: Mathematica allows users to automate tasks and create scripts, which can save time and improve efficiency. This can be particularly useful for repetitive tasks, such as data cleaning and analysis. Automating these tasks can free up time for business intelligence professionals to focus on more strategic initiatives.
  • Machine Learning and AI: Mathematica includes a wide range of machine learning and artificial intelligence (AI) tools that can be used for tasks such as predictive modeling, classification, and anomaly detection. These capabilities are becoming increasingly important for business intelligence, as they can help organizations to identify trends, make better decisions, and gain a competitive advantage.
  • Price: Mathematica comes with a hefty price tag, especially for commercial use, which can be a significant barrier for individuals or small businesses.
  • Learning Curve: The software has a steep learning curve due to its vast functionality and unique syntax, requiring a significant time investment to master.
  • Closed Ecosystem: Mathematica operates within a closed ecosystem, making it challenging to integrate with other data analysis tools or programming languages commonly used in business intelligence.
  • Limited Collaboration: Collaboration features are not as robust as those found in other business intelligence platforms, hindering teamwork and knowledge sharing.
  • Visualization Capabilities: While Mathematica offers visualization tools, they may not be as intuitive or user-friendly as dedicated data visualization software, potentially limiting the ability to create compelling and insightful dashboards.

Key Features

  • Wolfram Language: Wolfram’s proprietary computational language allows developers to code with a language that allows both computers and humans to communicate with each other through almost 6,000 built-in functions. Built on a philosophy of knowledge-based programming, it aims to help users automate as much as possible and maximize coherence of design while being universally deployable in any environment.
  • Connect to Everything: Through symbolic expressions, interactions and external connections, the Wolfram Language conveniently connects to a broad spectrum of platforms, languages, databases, protocols, APIs, applications, file formats and devices.
  • Notebook Interface: With structured documents that store text, runnable code, dynamic graphics and more, Wolfram Notebooks provide an environment for technical workflows that supports interactive computation. They empower user literacy in a high-level programming interface through interactive coding, natural language queries and expansive documentation that make the platform accessible to users without coding experience.
  • AlgorithmBase: Not just through industrial-strength algorithms but also meta-algorithms and super functions, which automatically select the optimal algorithms to use in a given situation, users can define their goals or concepts and let the system take over to automatically achieve them, enabling discoveries and experimentation with algorithms. With its robust library of scalable and accurate algorithms, the AlgorithmBase serves as a trustworthy resource for programmers to use to ensure high-quality computations.
  • Data Visualization: Through algorithms, Mathematica can create visually compelling representations of data in the form of 2D and 3D plots, graphs, histograms, word clouds, geographic visualizations and more.
  • Machine Learning: Through highly automated functions that work on many types of data, the platform can carry out a wide range of tasks, including classifying data in categories, predicting values, learning from examples and performing automated time series analysis. 
  • Mathematica Online: Powered by the Wolfram Cloud, users can harness the computational system from directly within their web browsers, with no installation required. Everything automatically saves and stays in the cloud, and users can control who can access their documents through instant sharing, URL links and permissions controls. Seamlessly integrated with the desktop version, it allows users to upload or download notebooks and access the cloud from a computer.
  • Wolfram Knowledgebase: Mathematica and the Wolfram Language has access to the world’s largest and broadest trusted source of computable knowledge, curated by experts and derived from primary sources, including not just the data but also the methods that compute results.
  • Mobile App: The Wolfram Cloud free app for iOS and Android mobile devices allows users to edit, run and deploy programs and access Wolfram notebooks and instant apps through its home-screen-like experience.
Looker
Looker
Start Price
$2,900
Monthly
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Why We Picked Looker

Looker is a forerunner in the business intelligence field for a reason; it generates reports that include easy sharing via link, automatic scheduling and a level of granular detail that allows for deeper analysis below the surface. It excels in its filter and drill-down features and creates unique URLs when users make changes to data, leading to enhanced sharing. However, one of its biggest strengths could also be considered one of its biggest weaknesses: its proprietary programming language, LookML which is used to construct SQL queries in the platform. While a flexible and powerful data querying language, of course, LookML isn’t the most accessible to non-technical users, which means that Looker requires an IT or data team to access its full capabilities and has a steep learning curve. Users also note that its data visualizations, while simple and easy to understand, are quite basic and lacking in customization options, particularly in comparison to competitors. Some users say that it may be more appropriate for internal reporting than presentation to shareholders and end-users because of its bare-bones visualization options. However, Looker truly shines when used by enterprises, with its scalability and data accessibility making it a stellar solution that can align departments and provide thousands of users access to data insights. Its price point reflects this, with its pricing being prohibitive to startups as about 88% of users who comment on its cost remark. Overall, Looker is a solid pick for larger businesses that have a team of power users who can maximize its functionality and set it up to deliver to employees across an entire organization.

Pros & Cons

  • Reporting: Looker features strong reporting features that offer a degree of granularity and scheduling that 100% of users who mention reporting evaluate as a strong benefit.
  • Support: Of the users who say they’ve contacted customer support, 95% say the team’s quick and informative responses are a plus.
  • Data Accessibility: All users who mention accessibility to data say Looker does this well, distributing insights to employees across departments and teams with ease, with 100% of users mentioning this feature believing it is a benefit.
  • Learning Curve: About 74% of users who touch on the platform’s ease of use say that the confusing documentation, lack of training opportunities and difficulty of using programming language make Looker a tough tool to pick up as a beginner.
  • Setup: Of the users who mention implementation, 81% say that setting up the platform is difficult, with integrations not being as plug-and-play as competitors and assistance from IT necessary to the setup process.
  • Speed: Approximately 87% of users who comment on the platform’s speed say that it is slow to render certain queries and often takes a while to load.
  • Functionality: About 78% of users who talk about Looker’s features say that they are left wanting many functions and find the ones that it does have limited in customization or too complex to use easily.

Key Features

  • Automated Modeling: Connects to relational databases and automatically generates models from the database schema.
  • Intuitive Visualizations: Generates visualizations in real time directly from the specified data source. Choose from an expansive library of visualization options like bar graphs, pie charts, Sankey diagrams, spider web charts, sunburst graphs, chord diagrams, heatmaps, funnels, treemaps and many more.
  • Time Zone Handling: Incorporates data seamlessly into the visualization, regardless of what time zone it is coming from.
  • LookML Data Modeling Language: Create scalable, reusable data models through the proprietary SQL-based data modeling language LookML.
  • Pre-Built Analytics Code: Use its Blocks feature as a starting point for building data analytics models with customizable code blocks. Includes optimized SQL patterns, custom visualization options, pre-built data models and more.
Qlik Sense
Qlik Sense
Start Price
$30
Monthly, Quote-based
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
Request Pricing Request Demo

Why We Picked Qlik Sense

Qlik Sense focuses on independent data analysis for enterprises with advanced tools that include AI, natural language processing and automation. User reviews praise it for its associative engine, interactive visualizations and sophisticated analytics.

Its dataset-linking functionality gets my vote as the most significant differentiator since it makes data modeling seamless and saves time. In comparison, manually linking tables in Tableau and Power BI feels like a huge task.

It supports fewer features out of the box (69%) compared to Tableau (72%) and Power BI (74%), but this could be intentional. Qlik has ready-to-go modules for analytics, automation and printing, so keeping it lean is a smart vendor move. Users should be aware that additional modules will cost extra, though.

Qlik Sense SaaS is multi-cloud, so unless the admin assigns separate workspaces, your users won’t be able to create personal dashboards — everything is shared otherwise. Some users said the platform slowed when processing large workloads, which is a common issue with many other platforms. Assess your need for speed before committing to a purchase.

If upgrading from QlikView, you’ll need to create new objects initially, as both platforms have different architectures. However, the vendor assists in seamless migration with the Qlik Analytics Modernization program.

Overall, Qlik Sense is an efficient platform that offers many analysis capabilities worth considering. We recommend checking it out if you’re looking for an alternative to Power BI, entrenched in Microsoft technology, or Tableau, with its emphasis on visualization.

Pros & Cons

  • Integrations: Approximately 86% of users reviewing data sources were satisfied with its wide connectivity.
  • Ease of Use: About 84% of users who cited usability praised the platform for self-service BI.
  • Functionality: Around 80% of the reviews that mentioned features praised it for ETL and data visualization.
  • Data Visualization: About 66% of the users discussing dashboards were satisfied with its interactive displays that allowed them to dig deep.
  • Cost: About 87% of users who mentioned pricing found the tool expensive.
  • Performance: Around 86% of users citing speed said it lagged when processing large and complex datasets.
  • Training: Approximately 69% of users who discussed adoption said there was a significant learning curve.
  • Customization: Around 65% of users who mentioned the freedom to design dashboards said the tool offered limited options.

Key Features

  • AI Integration: Ask and answer questions in natural language and automate processes using OpenAI and H2O.ai. Feed massive datasets to the LLM and watch as it summarizes the insight for you. Move beyond traditional analysis by working with the IBM Watson API for natural language.
  • Qlik Sense Management Console: Develop apps, manage tasks and connections, and track performance. With QMC, create content and consume data insights.
  • Reporting Service: Keep partners and clients on the same page by sending reports to everyone involved, even non-Qlik users. Download reports, subscribe to charts and sheets, or automate report delivery with its Reporting Service, available with Qlik Sense Enterprise SaaS.
  • Apps: Create interactive dashboards and visualizations for separate tasks within Qlik Sense. An organization can use hundreds of Qlik Sense apps in its tech stack. 
  • Associative Recommendations: Save time defining how data tables relate with its intelligent suggestions, something Tableau and Power BI lack. Bubbles represent data tables and color-coded rings — green, orange and red — inside them indicate the possibility of links between the tables.

Pricing

License/Subscription Cost
On-Premise:
  • License fees include an upfront fee to own the software, plus IP for a fixed term, installation, customization and integration costs
  • Enterprise Edition is offered on-premise and is based on a token system
  • Based on a combination of server, user, document and application-based licensing
Cloud-Based/SaaS:
  • Based on recurring subscription-based model: $X per user, per month
Cost may vary depending on the Qlik Sense Pricing plan selected:
  • Cloud Basic, Cloud Business, Desktop, Enterprise Edition or Personal Edition
Maintenance Cost
On-Premise: Maintenance cost is over and above the upfront fee
Cloud-Based/SaaS: Maintenance cost is included in the service fees charged at the time of purchase
Installation/Implementation Cost
On-Premise: Included in the upfront cost/subscription cost
Cloud-Based/SaaS: None
Customization Cost
For both on-premise and cloud-based/SaaS, customization costs vary depending on the product and pricing tier chosen, and the level of customization requiredCosts will vary depending on the package selected
Recurring/Renewal Costs
On-Premise: Annual recurring fees to be paid over and above the upfront cost include annual renewal, upgrades and ongoing support
Cloud-Based/SaaS: A recurring monthly fee is charged, which typically includes maintenance, monitoring, upgrades, training and support
Spark
Spark
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Key Features

  • Standalone Mode: Standalone mode is a web-based cluster manager for creating and distributing clusters on local machines, without using YARN or Apache Mesos. It can be used for local data processing or testing on a smaller scale. 
  • GraphX: A series of API that enable graph-parallel computation and graph generation within the system. It can accomplish ETL, iterative graphing and exploratory analysis. 
  • Machine Learning: The MLlib library enables machine learning at a big data level. It works with Python, R and Scala, and features machine learning pipeline construction and a community-supported set of algorithms. 
  • Distributed Datasets: Datasets are partitioned into smaller segments for distributed processing, called Resilient Distributed Datasets. RDDs are created by parallelizing a set or referencing an external one. 
  • Data Streaming: Spark Streaming is an extension that allows for a continuous data flow, enabling real-time analytics. It receives live data in a stream that it partitions into batches before sending it to the Spark Engine for processing through high-level abstraction called discretized stream.  
  • Integrations: Because it is open source, a vast community is constantly adding extensions and API to the core software. Spark can connect to virtually every mainstream data source, big data solution, warehouse/lake or visualization program. If the connector does not already exist, it could likely be developed. 
Funnel
Funnel
Start Price
$149
Monthly, Quote-based
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
Request Pricing Request Demo

Key Features

  • Custom Rules: Create rules to segment data based on specific needs, mitigating the need to write code. Leverage a point-and-click data prep toolbox to combine data or define custom metrics to answer business-specific questions. 
  • Currency Conversion: Automate currency conversion to facilitate side-by-side comparison across platforms and markets. 
  • Standard Rules: Use pre-built rules and logic to prepare data for analysis. Define standard naming conventions for dimensions and metrics. Combine traffic sources and segment data by the market or product sold. 
  • Data Explorer: Use the data explorer to explore data, perform ad hoc analysis, create reports and export them. 
  • KPI Reporting: Combine sales with advertising data to report on KPIs, including return on ad spend, revenue, conversion rate and more. Get a complete performance view at a deeper level to ensure profitability. 
QlikView
QlikView
Start Price
$2,500
Per User, Annual
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
Request Pricing Request Demo

Why We Picked QlikView

QlikView is one of the foremost BI solutions in the market today, mainly due to the power of its associative query engine to link data from multiple sources that drives its visually impressive dashboards. With its strong data visualization capabilities, users can perform search and filter through data on-the-fly and conduct deep-dives to glean insights that matter to them. With a fast setup, users can have their first data model up and running in very little time. The software resides in-memory and houses data in RAM for quicker retrieval. With multi-tier access permissions for in-organization users, it enables users to view executive summaries at a glance, while allowing them to drill-down into data to find out more.
Sadly, Qlik is now scaling back on improvements and updates for QlikView and focusing on promoting QlikSense instead, a possible reason why its filter and search functions, ad-hoc reporting and graphics are lagging in terms of quality, as mentioned in many user reviews. Also, this platform can prove to be resource-heavy for databases housed on local machines, especially when performing batch update jobs. In addition to inflexible pricing plans and the cost of licensing, quite a few necessary add-ons are paid.
In summary, QlikView is one of the leading in-memory BI tools available in the market today and rates excellently with users in terms of data aggregation and visualization capabilities; however, buyers should factor in its pricing plans and other limitations when searching for the perfect BI solution for their enterprise.

Pros & Cons

  • Data Visualization: Approximately 80% of users who review its data visualization capabilities are satisfied with its intuitive drag-and-drop feature, rich libraries and its range of aesthetically appealing data representation options.
  • Data Preparation: Of users who mention data processing, 83% appreciate the platform’s seemingly limitless data transformation capabilities that help them deep-dive into all possible data relationships to glean actionable insights.
  • Functionality: Among users who share their views on this platform, around 68% say that they are satisfied with the power of its associative query engine that enables faster on-the-fly calculations and analytics aggregation at the speed of thought.
  • Sharing and Collaboration: About 83% of users who comment on sharing capabilities appreciate its multi-tier permissions capabilities and easy sharing of reports with clients via external sharing options.
  • Setup: Around 66% of users who mention ease of setup say that QlikView has a fast implementation cycle.
  • Cost: Pricing plans are inflexible and can be cost-prohibitive for small organizations and startups, though large organizations may find that it offers high value, as stated by 93% of users who mention its cost.
  • Performance: Approximately 42% of users say that performance-wise, this platform is resource-hungry and liable to slow down when crunching large amounts of data on local machines.
  • User Interface and Graphics: Of users who mention user interface, around 44% say that it needs improvement in deep-dive capabilities, as well as its quality of graphics.
  • Reporting: Of users who mention reporting, approximately 46% say that it lacks ad-hoc reporting and built-in reporting capabilities, requiring paid plugins to enhance the graphics quality of reports.

Key Features

  • Direct Data Source Connection: Connect to almost any data source, including cloud, big data, file-based and on-premise data. Pull information from many services (Salesforce, Hive, Teradata) and combine intel seamlessly into unique and intuitive dashboards.
  • Intelligent Visualization: Offer interactive displays and represent data in multiple ways for better data analysis. Flexible visualizations allow users to change and adjust graphics according to screen size.
  • Enterprise Collaboration: Facilitate collaboration for users to share the same dashboard, look at the same view or track one another as they navigate the application.
  • Strong Associations: Leverage the strength of the platform’s built-in association engine to conduct direct and indirect searches across data or within a single field. Identify data that is related and not associated.
  • Self-Service App Building: Build apps and files via the drag-and-drop function. Create individual lists with their visualization while managing and sharing across organizations.
  • Associative Indexing: Combine, transform and ingest data from multiple sources. Gathers data and indexes it to find logical associations. Explore and search big data repositories freely while keeping data intact.
  • Interactive Dashboards: Provide visualization capabilities and improve interaction using tooltip, lasso selection, filtering and drill-down functions. Encourage viewers to explore data by creating smart dashboards and distributing them using interactive elements.
  • In-Memory Application: House the software in memory, so conversions, queries and searches happen quicker and more efficiently. Eliminate problems that traditionally plague slow, on-disk applications. Locate all data in RAM.
  • Web Connectors: Extract data from multiple social networking sites and web-based sources using web APIs. Built-in connectors easily connect to any URL and fetch data.
  • Robust Data Controls: Enable meaningful data manipulation within the application by leveraging unique dashboards, reports and filter views.
  • Data Alerts: Spot anomalies and outliers by requesting context-aware alerts. Monitor and manage data without limitations.

Pricing

License/Subscription Cost Based on a combination of server, user, document and application-based licensing
Maintenance Cost
  • For On-Premise solution, maintenance cost is over and above the upfront fee
  • Standard support services are charged at 20% of the license cost
  • Premium (24X7) support services are charged at 23% of the license cost
    		•
    Installation/Implementation Cost Implementation services are provided by QlikView Consulting or through an implementation partner at an additional cost
    Customization Cost Will vary depending on the functional requirements or services chosen
    Data Migration Cost/Change Management/Upfront Switching Cost Dependent on your current software, amount of data to be migrated, availability of migration tools, complexity of data and gaps between the existing system and the new system.
    Training Cost
    • E-learning or self-learn modules are available free of cost on QlikView.com
    • All other trainings are charged based on volume. Live classroom training or online (virtual classroom) training is charged at $700 per person per day or $3,500 for a dedicated course (1 company) for up to 10 people
    Recurring/Renewal Costs Renewal costs includes software update license and support cost
    Spotfire
    Spotfire
    Start Price
    $0.99
    Monthly, Quote-based
    Company Size
    Small Medium Large
    Deployment
    Cloud On-Premise
    Platform
    Mac Windows Linux Chromebook Android
    Request Pricing Request Demo

    Why We Picked Spotfire

    In online reviews, Spotfire emerges as a user-friendly big data platform. Most users found data exploration easy with a drag-and-drop interface. Some users said the UI was dated, though, and said it could use a revamp. Most users praised its interactive visualizations and dashboards, saying they helped them interpret data better. But, a few said they would love to have more visuals to choose from.

    A user mentioned they did the calculations in Excel and imported them into Spotfire for visualization. It's a common scenario when a steep learning curve slows down adoption, and teams fall back on Excel. Most users said Spotfire takes time to learn. You might have to opt for a balance of multiple platforms to balance your departmental and enterprise needs.

    Spotfire surpasses Excel in data management, especially data prep. Customizable visualizations and custom Mods give you enough freedom to work within the platform.

    Though 72% of reviewers were happy with the integrations, Spotfire lacks some standard connectors, such as for Apache Kafka, forcing users to rely on workarounds.

    A majority of users found its pricing structure complex, especially as users increased. In such cases, organizations often tend to opt for a cheaper alternative for less advanced use cases while using the pricier platform for the critical ones. We advise doing a deep dive into the vendor's pricing plans to avoid making your tech stack top-heavy.

    Ultimately, Spotfire's appeal lies in its balance. It's visually captivating and user-friendly for casual users while offering enough depth for seasoned analysts. However, its pricing and learning curve might deter organizations on a tight budget.

    Pros & Cons

    • Data Visualization: About 86% of reviewers were satisfied with the available options when designing dashboards.
    • Support: Around 74% of users praised vendor support for their timely response and helpful attitude.
    • Integration: Almost 72% of users were satisfied that it integrates with their preferred systems.
    • Friendly Interface: Around 68% of reviewers said the platform was easy to use.
    • Functionality: About 64% of users said it had a rich feature set.
    • Cost: Around 96% of the user reviews said it the price was high and licensing complex.
    • Adoption: 90% of reviewers said there was a significant learning curve and users would need specialized knowledge of data science and statistics.

    Key Features

    • Spotfire Actions: Decide what to do with and act instantly — no need to switch to your procurement application to pause new orders. This powerful feature allows you to run scripts within analytics workflows. You can also trigger actions in your external system through visualization. Spotfire can set up over 200 commercial connections and has 1800 community connectors.
    • Mods: Build reusable workflows and visualization components, much like apps in Power BI and Qlik Sense. They allow your users to tailor their analytical processes so they don’t have to start from scratch every time. Based on code, they run in a sandbox with limited access to system resources for security. Users can share them through the Spotfire library. Mods improve efficiency and collaboration.
    • Batch Edits: Make similar changes to multiple files in one go. Write custom scripts to call the Spotfire API that’ll make changes to the files. Update the IronPython version to the latest one or embed the Spotfire JQueryUI library instead of its references.
    • Recurring Jobs: Simplify event scheduling to better manage your time and tasks. Improve efficiency and deliver reports at the same time on the same day of the week or month. The latest Spotfire version allows you to set recurring automation jobs to occur every X hours, days, weeks or months.
    • Web Player REST API: Share insight with clients and partners without them needing to sign up for a paid Spotfire account. Engage them via data visualizations on the web browser, thanks to Spotfire Web Player. Update analyses on the web with real-time data in the latest Spotfire version.
    • Roles: Invest wisely — opt for licenses that align with user roles. Choose Spotfire Analyst for data analysts, scientists and power users who need deep-dive analysis. Get the Business Author license for enterprise users, analysts and power users to create and consume insights without deep expertise. Choose consumer licenses for users who’ll interact with and consume data. They include the C-suite and non-technical users within the organization.
    • Information Designer: Prepare fully governed data sources for business users in a dedicated wizard. Set up their preferred data sources and define in advance how Spotfire will query and import data into storage. Specify which columns to load and which filters, joins and aggregations to apply.
    • Audio and Image Processing: Add user feedback from customer calls and videos. Interpret public sentiment about your product by analyzing social media pictures and videos. Spotfire enables writing code to extract text from audio and image files. You can then import the data into the platform for analysis.
    • IoT Analytics: Gain insight at lightning speed; build microservices and deploy them at the edge. With Spotfire, you can add IoT data to your regular data for the complete picture.
    Oracle Analytics Cloud
    Oracle Analytics Cloud
    Start Price
    $16
    Per User, Monthly
    Company Size
    Small Medium Large
    Deployment
    Cloud On-Premise
    Platform
    Mac Windows Linux Chromebook Android
    Request Pricing Request Demo

    Why We Picked Oracle Analytics Cloud

    Oracle Analytics Cloud is among the vendor’s many data services, including a business intelligence suite and a data intelligence platform. Besides, Oracle offers bespoke solutions for HCM, supply chain and customer experience. What differentiates Oracle Analytics is that extra dash of augmented capabilities.

    Embedded BI is where it truly shines, giving you natural language insights with a single click. This feature extends to its mobile app, and it outperforms many leading platforms with natural language queries and podcasts on mobile.

    According to our researchers, Oracle Analytics Cloud has fewer out-of-the-box features than its competitors, such as Power BI and Qlik Sense. Plus, licensing becomes complex when combining the database, middleware and analytics applications.

    It’s common for large vendors to offer specialized platforms, but the downside is that they can be out of reach of small organizations. But there’s a silver lining. Many vendors offer customized solutions, so we advise reaching out to the vendor for quotes.

    Users appreciate its regular updates, but some report initial bugs due to its relative newness. Despite a positive user experience, the learning curve can be steep. Some users found technical support slow and inadequate, as did I. They took two business days to get back to me when I needed assistance with my account.

    Oracle Analytics, though a robust platform, is suitable for mid- and large organizations. If you seek a powerful, scalable platform, consider opting for a trial, but be prepared for sticker shock, especially if you’re new to the Oracle ecosystem.

    Pros & Cons

    • User-Friendly: Citing its interface, about 91% of users agreed that a drag-and-drop UI makes it easy to use.
    • Machine Learning: Around 86% of users who discussed augmented analytics were impressed with its ML capabilities.
    • Integrations: Approximately 84% of users who mentioned connectivity said the platform worked well with other systems, especially Oracle products.
    • Functionality: According to 83% of users who reviewed capabilities, it has all the required features to support data tasks.
    • Data Visualization: Around 73% of users who mentioned visualization praised the platform for its storytelling features.
    • Price: About 88% of reviews citing pricing said that it’s too expensive.
    • Adoption: Approximately 87% of users who discussed onboarding said there’s a significant learning curve.

    Key Features

    • Deployment: Install and run anywhere, including as a hybrid solution. Scale the instance depending on your workload — deploy OCPUs in multiples of two, extending up to 52. Or pause it when idle. Though identity management is available, there is the option to use one’s own SSO provider. Admins can set user, group and role-based permissions.
    • Connectivity: Make decisions based on data; connect to social media feeds, data lakes and IoT sources. Store and process data at scale, irrespective of its volume, velocity and variety. Get started as soon as you log in with over 40 readymade connectors.
    • Direct Query: Oracle Analytics Cloud uses live queries and data caching to fetch responses. Each has its downside. Live connections are heavier on the system, and you might have to compromise on data freshness with data caching. A combination of both might be best. Consider live queries for critical KPIs and data caching for less frequent queries.
    • Data Preparation: Enrich data from the interface — get data quality insights as you work. Remove the grunt work — create reusable flows for transforming data you can test, share and schedule. Add custom calculations or write regular expressions in the dataset editor.
    • Semantic Data Modeling: Engage business, dev and data teams in meaningful discussions. Give them data views with a presentation layer that simplifies metrics. Hide the physical data structure with a logical one that speaks the business language. Give stakeholders the power to explore data independently.
    • AI/ML: Boost productivity with embedded machine learning and natural language insights every step of the way. Display quick forecasts, trend lines and clusters from a popup menu with one click. View the basic facts, key drivers and anomalies with the Explain option. Hit the ground running with recommendations on dimensions, measures and attributes to use when you don’t know where to start.
    • Oracle Analytics Publisher: Generate reports from any dataset or semantic model. Create formatted documents unique to your business, be it shipping labels, checks, letters or PDF forms.
    • Data Visualizations: Put your best foot forward with suitable charts and graphs that convey your message effectively. Modify them to answer users’ questions better. Choose from over 45 visualization types, or build your own using extensions from its vast library.
    • Embedded AI On Mobile: Get real-time alerts and intelligent recommendations on mobile. The Oracle Analytics mobile app captures your preferences and location. Upload datasets just like on the desktop or create a workbook from existing data. Powerful searches enable access to your favorite worksheets; add them to your home screen for a quick view. Use voice-enabled searches and listen to the results as a podcast.

    COMPARE THE BEST Data Discovery Tools

    Select up to 2 Products from the list below to compare

     
    Product
    Score
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    SelectHub’s Analyst Rating is based on data from our 400+ point analysis of Data Discovery Tools, user reviews, and our own crowd-sourced data from our free software selection platform. Scores are periodically updated via ongoing selection projects and user activity within our platform.
    Start Price
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    Tableau
    $15
    Per User, Monthly
    Yes
    Small Medium Large
    Cloud On-Premise
    Mac Windows Linux Chromebook Android
    Tableau
    Power BI
    $10
    Per User, Monthly
    Yes
    Small Medium Large
    Cloud On-Premise
    Mac Windows Linux Chromebook Android
    Power BI
    Mathematica
    $1,800
    Annually
    Yes
    Small Medium Large
    Cloud On-Premise
    Mac Windows Linux Chromebook Android
    Mathematica
    Looker
    $2,900
    Monthly
    Yes
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    Cloud On-Premise
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    Looker
    Qlik Sense
    $30
    Monthly, Quote-based
    Yes
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    Cloud On-Premise
    Mac Windows Linux Chromebook Android
    Qlik Sense
    Spark
    Still gathering data
    No
    Small Medium Large
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    Mac Windows Linux Chromebook Android
    Spark
    Funnel
    $149
    Monthly, Quote-based
    Yes
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    Cloud On-Premise
    Mac Windows Linux Chromebook Android
    Funnel
    QlikView
    $2,500
    Per User, Annual
    No
    Small Medium Large
    Cloud On-Premise
    Mac Windows Linux Chromebook Android
    QlikView
    Spotfire
    $0.99
    Monthly, Quote-based
    Yes
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    Cloud On-Premise
    Mac Windows Linux Chromebook Android
    Spotfire
    Oracle Analytics Cloud
    $16
    Per User, Monthly
    Yes
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    Oracle Analytics Cloud

    All Data Discovery Tools (169 found)

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    Style Scope

    by Inetsoft
    Style Scope
    Style Scope is inetSoft’s analytics component of its overarching Style Intelligence solution. It allows in-depth data harvesting and processing with some reporting tools, though the bulk of that weight is carried by Style Report. It’s web-based, allowing for real-time data processing and viewing from any web-accessible device. It offers live visualizations that allow for continuous monitoring and automatic notifications based on certain triggers.
    Cost Breakdown
    $100 - $500
    Company Size
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    Mac Windows Linux Chromebook Android
    Request with no obligation:
    Demo Pricing

    Actionable Intelligence Platform

    by Mortgage Technology, Inc.
    Actionable Intelligence Platform
    The Actionable Intelligence Platform (AIP) by Mortgage Technology, Inc. is a business intelligence (BI) tool designed for mortgage lenders. It helps them analyze loan data to improve decision-making, streamline operations, and mitigate risks. AIP offers pre-built dashboards and analytics that provide lenders with insights into loan performance, customer trends, and potential inefficiencies. Users can also configure custom rules to identify areas for improvement and automate tasks based on real-time data. This platform is most suited for mid-sized to large mortgage lenders who need a comprehensive BI solution. Its key benefits include improved operational efficiency, better risk management, and enhanced customer retention through data-driven strategies. While pricing information isn't publicly available, similar BI solutions typically follow a subscription model with costs based on the number of users and data volume.
    Cost Breakdown
    $500 - $1,000
    Company Size
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    Mac Windows Linux Chromebook Android
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    Demo Pricing

    White Cup BI

    by Management Information Tools (MITS)
    White Cup BI
    White Cup BI is a sophisticated software solution designed to streamline business intelligence tasks, offering robust data analytics, reporting, and visualization capabilities. It is particularly well-suited for mid-sized to large enterprises that require comprehensive insights into their operations to drive strategic decision-making. The software excels in transforming raw data into actionable intelligence, making it invaluable for companies aiming to enhance their competitive edge. Key benefits of White Cup BI include its user-friendly interface, which simplifies complex data analysis, and its ability to integrate seamlessly with various data sources. Popular features encompass advanced reporting tools, customizable dashboards, and real-time data monitoring, all of which contribute to more informed business strategies. Users often praise its intuitive design and the depth of insights it provides compared to similar products. Pricing for White Cup BI typically falls within a mid-to-high range, often based on the number of users and the specific features required, with options for monthly or annual payments. This flexibility allows businesses to choose a plan that aligns with their budget and needs.
    Cost Breakdown
    $10 - $100
    Company Size
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    Deployment
    Cloud On-Premise
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    Mac Windows Linux Chromebook Android
    Request with no obligation:
    Demo Pricing

    Altair Monarch

    by Altair Engineering
    Altair Monarch
    Monarch from Altair is a data preparation solution that helps pull proprietary information from multiple sources for efficient analysis and predictions powered by machine learning. Out of the box, it can work with a wide range of report formats including PDF, XML, HTML, text, spool and ASCII files. Enterprises can access data from invoices, sales reports, balance sheets, customer lists, inventory, logs and more.With instant data export for visual analysis and sharing, it provides a wide range of built-in functionality to automate workflows. Businesses can opt for the standard Classic license, or the Complete version with Data Prep Studio for faster data needs. Licensing is on a per desktop basis. For larger deployments, the vendor provides centralized management of licenses through its License Server.
    User Sentiment User satisfaction level icon: great
    Cost Breakdown
    $10 or less
    Company Size
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    Mac Windows Linux Chromebook Android
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    Demo Pricing

    Reveal BI

    by INFRAGISTICS
    Reveal BI
    Reveal is a purpose-built data analytics solution that empowers everyone within an organization with sophisticated insights they can use to make smarter business decisions. It allows users to connect to their data and create visualizations and interactive dashboards to monitor KPIs and trends in real-time. Through a self-service model, it encourages users to view, explore and share meaningful business insights and drive data-informed decision making. It is available for both on-premise and cloud-based SaaS deployment. It offers a seamless experience across web, Windows, iOS and Android. Pricing varies, with a fixed annual rate subscription model for embedded offerings and monthly to annual subscriptions billed on a per-user basis for individual licenses.
    User Sentiment User satisfaction level icon: great
    Cost Breakdown
    $10 or less
    Company Size
    Small Medium Large
    Deployment
    Cloud On-Premise
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    Mac Windows Linux Chromebook Android
    Request with no obligation:
    Demo Pricing

    SAP Business Analytics

    by SAP, Inc.
    SAP Business Analytics
    SAP Analytics is a collection of data warehousing and analytics platforms that help businesses discover, share and act on meaningful patterns in proprietary data. It includes SAP HANA, SAP BI and SAP Analytics Cloud, SAP BW/4HANA and SAP Data Warehouse Cloud. SAP Analytics offerings analyze business data in order to describe trends, make predictions, improve performance, recommend best actions and guide decision-making at various levels throughout an enterprise.
    Cost Breakdown
    $1,000 or more
    Company Size
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    Deployment
    Cloud On-Premise
    Platform
    Mac Windows Linux Chromebook Android
    Request with no obligation:
    Demo Pricing

    Sigma Computing

    by Sigma Computing
    Sigma Computing
    Sigma Computing is an analytics solution that directly connects to cloud warehouses and delivers real-time insights that drive data-informed decision making. With a spreadsheet-like interface, it provides a familiar experience for business users and analysts to utilize formulas to analyze and visualize their data. Through a self-service model, users can explore their data and ask and answer questions for themselves. It streamlines complex data analysis processes and promotes teamwork throughout a business with a balance of collaboration and control. It is entirely cloud-based and enables data access without the risk of extracting, moving and storing data. Suitable for companies of all sizes, this SaaS runs on Windows, Mac and Linux. Pros User-friendly interface Affordable pricing Scalable for business growth Cloud-based accessibility Responsive customer support Cons Limited data analysis features Less powerful than competitors Occasional performance issues Restricted data storage capacity Lower customization options
    Cost Breakdown
    $100 - $500
    Company Size
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    Cloud On-Premise
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    Mac Windows Linux Chromebook Android
    Request with no obligation:
    Demo Pricing

    Power BI

    by CCG Analytics Solutions & Services
    Power BI
    Microsoft Power BI by CCG Analytics is a one-stop-shop for all your analysis needs. Users can make data-informed decisions quickly, accurately and based on the organization’s proprietary data drawn from wherever it lives. Using this data, they can create visual reports and share or collaborate with teammates to explore them.
    Cost Breakdown
    $500 - $1,000
    Company Size
    Small Medium Large
    Deployment
    Cloud On-Premise
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    Mac Windows Linux Chromebook Android
    Request with no obligation:
    Demo Pricing

    Babelfish BI

    by Babelfish Tech
    Babelfish BI
    Babelfish is designed to automate an organization’s sales and marketing intelligence. It draws data from marketing campaigns, CRMs and commerce records to give users insight into customer behavior and demographics. Its NLP-based search function lets users query data in simple language, which it translates into a data query, offering ad-hoc analytics. It uses machine learning to learn behavior’s patterns and needs the more they use it, providing recommendations and guidance for better, faster decision-making.This product isn’t to be confused with the translation program acquired by Bing in 2012.
    Cost Breakdown
    $100 - $500
    Company Size
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    Deployment
    Cloud On-Premise
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    Mac Windows Linux Chromebook Android
    Request with no obligation:
    Demo Pricing

    Buyer's Guide

    Data Discovery Tools Are All About Identifying Data Relationships for Business 

    By Ritinder Kaur, Market Analyst at SelectHub

    Data Discovery Tools BG Intro

    It’s a tedious task getting separate databases to talk to each other — even more difficult is determining which datasets to combine to gain the desired insight. Data discovery tools and techniques support research and enterprise by identifying correlations, trends and patterns. All analytics and business intelligence tools have information discovery techniques in their feature set — which software will fit your business?

    Data discovery platforms have the usual BI and analytics capabilities, but placing analytics at the front and center of all information-centric activities is their differentiating attribute.

    Independent software vendors (ISVs) and value-added resellers (VARs) offer integrated solutions in partnership with analytics platforms. Salesforce partners with BeyondCore to extend smart discovery and advanced analytics capabilities in its Analytics Cloud.

    This buyer’s guide gives you knowledge, tips and resources for your data discovery software research.

    Executive Summary

    • Data discovery software keeps analytics at the forefront of information-related activities.
    • It combines engagement metrics with transactional information to derive meaningful insight that drives business decisions.
    • There is a concentrated push for user autonomy with visual analytics and AI-driven information discovery.
    • Following our lean software selection methodology will make the search process straightforward.
    • Ask the right questions to capture your business requirements and prepare for vendor discussions.
    What This Guide Covers:

    What Are Data Discovery Tools?

    Data discovery tools are software with the techniques and tools to derive meaningful data results from multiple sources. Data preparation, modeling, visual analytics and advanced statistical analysis constitute data discovery, a subset of business intelligence.

    Engagement touchpoints like POS systems, mobile devices, web applications and navigation systems capture new types of contextual information that traditional systems can’t process. Combining this information with systems of record gives you valuable customer insight, which helps improve your sales approach the next time they log into your app or walk into your store.

    Information discovery software is one of the fastest-growing segments in the BI software industry. Verified Market Research predicts the data discovery software market will grow to $21.5 billion by 2028. The need for fast insight with increasing information volumes is a primary driver of this industry-wide trend.

    Data Discovery Tools Market Stat

    Data Discovery Tools Categories

    Data discovery involves three steps.

    • Integrating, cleansing and preparing information for analysis.
    • Visualizing it as charts, graphs, dashboards and maps.
    • Organizing it into reports or dashboards to support business decisions.

    Three categories of information discovery tools are presently available.

    1. Text-based search tools have a search engine-like interface to enable keyword searches of surveys, documents, presentations and product literature. They serve organizations that seek to incorporate unstructured information into their metrics.
    2. Visual discovery tools with direct querying, interactivity and manipulation capabilities support self-service analytics. Clicking on elements and dragging and dropping dimensions and measures into visualizations triggers SQL queries at the back end. Changing the visualization type refreshes information automatically.
    3. AI-based tools perform pattern identification with machine learning, so you don’t have to. But, the system might flag one-off occurrences as trends, and human context is indispensable in such cases. A tourist event in a city might drive up sales temporarily, which you can tell the system to ignore to avoid false positives.
      Examples of AI-based information discovery tools include IBM Watson and BeyondCore. But it’s early days, and small and mid-sized businesses (SMBs) might find visual tools better suited to their needs.

    Data virtualization and direct query methods make information discovery systems ideal for small organizations that can’t afford data warehouses. Mid and large-sized enterprises will likely prefer visual discovery tools, though software with artificial intelligence (AI) might be in the higher cost bracket.

    What Is Smart Data Discovery?

    BI systems were always user-focused — earlier, end users needed experts to get the desired information. With the launch of enterprise resource planning systems (ERPs) and data warehouse tools, organizational information became more accessible to BI vendors. It encouraged them to innovate and develop software that could query a single consolidated repository.

    This method was faster than pulling information from distributed systems and networks and sped up downstream processes like statistical modeling, enterprise reporting and big data analytics.

    But the gap between independent insight and end users persisted. The process wasn’t intuitive — you needed to know which metrics you wanted and how to source them. Information discovery required modeling and statistical analysis skills. Additionally, it could introduce human bias into the results.

    Smart discovery tools autonomously perform data prep, modeling visualization and advanced statistical analysis. Plus, computers and software programs are better at asking questions than humans. Using this premise, vendors ventured into the smart data discovery domain, removing the hypothesis phase and, consequently, human bias from the analytics process.

    Smart discovery or autonomous analytics isn’t about “which metrics do I want?”. Instead, it adopts a business approach, asking questions like “why did our campaign fail?” or “why did sales fall in the north region?”

    Thanks to AI-driven smart data discovery, you can connect to the preferred source with point-and-click actions, give natural-language text instructions to view the desired results and watch the software work its magic.

    No matter the technical skills, smart information discovery helps you better understand key business trends by using advanced analytics without needing complicated formulas.

    BI platforms that integrate information discovery with reporting and dashboarding capabilities are worth considering when looking to buy software solutions.

    Primary Benefits

    Data discovery is a BI pipeline component with the same benefits. It helps you visualize datasets, trends and outliers, manage risk and compliance, and get a high-level overview of critical metrics and how they impact your business.

    Data Discovery Tools Primary Benefits

    Gain Actionable Insight

    Modern data discovery software shows the desired information in everyday language on a user-friendly interface while deriving KPIs, business trends and patterns behind the scenes. It empowers users unfamiliar with your system, like clients, to view the metrics independently.

    Manually exploring information and gleaning the required metrics is a thing of the past. Connect to the right sources and ask the right business questions, relying on the system to do the rest.

    Save Time and Money

    Customer preferences and market trends change every day. Data discovery systems give you the latest results by continually analyzing incoming data while combining it with existing information. They automatically find and explain insight from voluminous datasets of varying complexity, presenting the outcomes with automated recommendations at the click of a button.

    Embeddability drives productivity by bringing visualizations and data discovery platforms where you work. It saves time switching between applications, telling you what you need to know at the exact moment in your regular business workflows.

    Incorporate Big Data

    Websites and social media platforms significantly contribute to business insight, but semantic layers in traditional BI systems are often hard-coded and inflexible. Tweaking them every time to incorporate new information types isn’t practical in a dynamic data environment.

    Data discovery tools address this shortcoming by connecting directly to operational databases, removing the need to go through the semantic layer. They perform data preparation on the fly, assigning terminology and mapping it to the relevant datasets as they go.

    It saves your IT team the task of standardizing business terms across your organization. Additionally, you have greater flexibility to perform sophisticated queries and create models instead of being restricted to the models coded into the semantic layer.

    Classify Data Automatically

    Associative engines in discovery tools help you uncover correlations, trends and outliers. Their primary role is metadata scanning and indexing in BI tools, extract, load and transform (ETL) tools, and third-party metadata catalogs.

    Outliers and Trends

    Identifying outliers and trends with Smart Data Discovery. Source

    They identify and profile information sets, providing their lineage from high-level system views to granular column-level perspectives. These tools are intelligent enough to scan static and dynamic code in SQL scripts and stored procedures.

    Advanced dependency tracking helps you understand each transformation and why it happened. AI-driven domain discovery, information commonality, business term associations and recommendation technologies automatically curate extracted metrics.

    Using BI vs. Data Discovery

    Information discovery and BI have a common purpose — maximizing information use for your business by consolidating insight siloed in disparate sources. There’s a fundamental difference, however. BI is a consistent, continuous process of information flow for everyday operations and decision-making, while data discovery is the on-demand analysis of business-critical information and trends.

    Your BI tool will provide the following functionality.

    • Identify short and long-term trends.
    • Generate live information for routine operations.
    • Provide enterprise reports.
    • Share insight via collaborative interfaces.

    Use a data discovery tool to address these needs.

    • Enable tactical analysis and planning.
    • Get answers to specific business queries.
    • Perform in-depth analysis.
    • Incorporate unstructured information into reporting and analytics.

    In summary, BI provides comprehensive information, while data discovery enables specific analysis with deep-dive exploration and investigation.

    Key Features & Functionality

    Visual Interface

    Intuitive visualizations streamline information access, manipulation and pattern identification. Dig deep into performance metrics and market trends with point-and-click actions.

    Create dashboards and visualizations without expert help. Highlight contextual information by drilling into selections to create datasets for in-depth analysis. Spot discrepancies and potential pitfalls.
    In-memory Processing

    Storing information in random access memory (RAM) enables the local blending of large volumes. It reduces bandwidth requirements and processing overload.
    Big Data Connectivity

    Your preferred software should have the capability to incorporate unstructured information, including audio, video, images and text from files, websites and social media feeds.

    Beyond standard formats like XML, PDF and TXT, the software should have the flexibility to connect to your sources. Direct connections to databases complement source access via the semantic layer.
    Data Discovery and Preparation

    Your preferred system should process information efficiently, no matter where it resides. Data quality management tasks like parsing, correction, profiling, master data management and security and governance are business-critical.

    The software should support normalization, removing trailing spaces and testing the accuracy of joins on the fly. Built-in cleansing and information prep are essential for direct source connections since they don’t go through the semantic layer.
    Advanced Analytics

    Your users should be able to build models and perform statistical analysis without coding or performing SQL queries. A visual interface tool abstracts the technical details while performing the statistical analysis for you.
    Data Visualization

    Ask the vendor if they offer your preferred visualization types and customization options. A visual toolkit lets you interact with the metrics and manipulate them as needed.

    White-labeling the software should enable branding the interface with your company’s logo, themes and colors.

    Embeddability into websites, web applications, email clients, and BI and analytics systems help maximize your business information.
    Collaboration

    A good information discovery platform lets you collaborate within visualizations with clients and other team members. Record observations and conduct meaningful team discussions with built-in chat, comment and annotation options.
    Predictive Analytics

    A good information discovery platform lets you collaborate within visualizations with clients and other team members. Record observations and conduct meaningful team discussions with built-in chat, comment and annotation options.
    Metadata Management

    Compliant classification and metadata assignation are must-have features in all data software.

    A well-designed metadata system will support analysis by answering questions like “Where can I find this information?”, “How was it used earlier?” and “Can I use better data for my models?
    Scalability

    The software should scale with changing user roles so you don’t have to learn a new piece of technology whenever you need information beyond your regular responsibilities.

    It should let you transition from viewing information to interacting with it and discarding it to build your own.

    Software Comparison Strategy

    To select a best-fit software, create a requirements checklist to assess your business needs internally. Our pre-populated, customizable requirements template can get you started in no time.

    With requirements locked down, convert these into questions to ask potential vendors. Refer to our Key Features section to fine-tune this list.

    Which sources should the software connect to out of the box? Is collaboration a deal-breaker? Which governance features do you want?

    Distribute these questions to potential vendors with a request for proposal (RFP), a request for information (RFI), a requisition for quotation (RFQ) or a combination of all three.

    Once you hear back, review how the vendors score on a scale of 1 to 100 in addressing your business needs.

    Cost & Pricing Considerations

    Product cost can make or break your decision to purchase data discovery software. Budget limitations can weigh heavily on your mind when you know the decision will impact your business for years. Check the pricing tiers from the vendor’s website or contact them directly.

    Get your hands on our pricing guide to learn how much a data discovery solution will likely cost you.

    Pricing will vary depending on the deployment method, user licenses, modules and add-ons purchased. Bare-bones support is often part of the package but is available only during business hours. You might have to opt for a paid plan for round-the-clock support and quicker turnaround.

    Factor in the total cost of ownership (TCO) and look into any hidden charges. The better a vendor’s score, the more likely you’ll want to reach out to them to ask for demos and proof-of-concept (POC).

    The Most Popular Data Discovery Tools

    SelectHub analysts ranked data discovery software based on how well they deliver enterprise requirements. We discuss the three most popular products here.

    Tableau

    Tableau is a flexible, scalable and secure information discovery and visualization software that connects to spreadsheets, cubes and relational databases. It performs information storage and processing, preparation and transformation, cataloging and metadata management with querying.

    Dashboards are embeddable, customizable and reusable — assign permissions to the new users for information viewing and access.

    Tableau

    Dynamic predictions in Tableau with the Einstein Discovery dashboard extension. Source

    Power BI

    Microsoft provides the data hub for dataset discovery and permission-based access in the Power BI service and Power BI app in Teams. Besides report generation and permissions management, derived insight supports the Analyze module in Excel. Managing data items by viewing usage metrics, refresh status, related reports and lineage is possible.

    Power BI

    The data items list in Microsoft Power BI’s Data hub.

    BOARD

    It’s a BI and corporate performance management (CPM) suite that enables app building with its library of code-free functions. The software allows point-and-click integration and drag-and-drop visual analysis. Available in cloud-based and software-as-a-service (SaaS) versions, the solution integrates with MS Office and supports reports from email and Excel.

    It’s scalable with full read and write support and node balancing, thanks to in-memory processing.

    BOARD

    A sales intelligence and planning dashboard.

     

     

    Questions To Ask

    Ask these questions as a starting point in the context of your organization and its needs.

    • What’s not working that you hope to address with data discovery software?
    • What’s your budget?
    • Which deployment method will you prefer?
    • Is scalability a deal-breaker?
    • Is AI-driven information discovery a must-have feature?

    Data Discovery Tools Key Questions To Ask

    Preparing a list of questions to ask vendors about the software and their services saves time and helps you check off the boxes in your functional and technical requirements list.

    • Is a mobile version available?
    • Is the information discovery process intuitive for all users?
    • Is the learning curve steep? Is training included in the product cost or paid?
    • How often do they release updates?
    • What is the support turnaround time?

    In Conclusion

    There are no shortcuts to success, and every step of the software selection process requires due diligence. In-depth market research of leading data discovery software and techniques is essential to defining clear-cut requirements. Asking the right questions of vendors through well-documented RFPs is critical to your search.

    Finalize vendors by reviewing their proposals in discussion with your stakeholders and sign on the dotted line. Set up an implementation plan and decide when to go live with the software.

    Product Comparisons

    About The Contributors

    The following expert team members are responsible for creating, reviewing, and fact checking the accuracy of this content.

    Ritinder
    By Ritinder Kaur
    Technical Content Writer
    Ritinder Kaur is a Senior Technical Content Writer at SelectHub and has eight years of experience writing about B2B software and quality assurance. She has a Masters degree in English language and literature and writes about Business Intelligence and Data Science. Her articles on software testing have been published on Stickyminds.
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    Sagardeep
    Technical Research By Sagardeep Roy
    Senior Analyst
    Sagardeep is a Senior Research Analyst at SelectHub, specializing in diverse technical categories. His expertise spans Business Intelligence, Analytics, Big Data, ETL, Cybersecurity, artificial intelligence and machine learning, with additional proficiency in EHR and Medical Billing. Holding a Master of Technology in Data Science from Amity University, Noida, and a Bachelor of Technology in Computer Science from West Bengal University of Technology, his experience across technology, healthcare, and market research extends back to 2016. As a certified Data Science and Business Analytics professional, he approaches complex projects with a results-oriented mindset, prioritizing individual excellence and collaborative success.
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    Manan
    Technical Review By Manan Roy
    Principal Analyst
    Manan is a native of Tezpur, Assam (India), who currently lives in Kolkata, West Bengal (India). At SelectHub, he works on categories like CRM, HR, PPM, BI, and EHR. He has a Bachelor of Technology in CSE from The Gandhi Institute of Engineering and Technology, a Master of Technology from The Institute of Engineering and Management IT, and an MBA in Finance from St. Xavier's College. He's published two research papers, one in a conference and the other in a journal, during his Master of Technology.
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    Hunter
    Edited By Hunter Lowe
    Content Editor
    Hunter Lowe is a Content Editor, Writer and Market Analyst at SelectHub. His team covers categories that range from ERP and business intelligence to transportation and supply chain management. Hunter is an avid reader and Dungeons and Dragons addict who studied English and Creative Writing through college. In his free time, you'll likely find him devising new dungeons for his players to explore, checking out the latest video games, writing his next horror story or running around with his daughter.
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