Best Data Discovery Tools
Buyer's Guide
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 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.
- Automated Reporting Tools
- Big Data Analytics
- Business Analytics
- Business Intelligence
- Cloud BI
- Data Analytics
- Data Cleaning
- Data Integration
- Data Management
- Data Preparation
- Data Warehouse
- Ecommerce Analytics
- Embedded Analytics
- Enterprise Reporting
- ETL
- HR Analytics
- OLAP Tools
- Predictive Analytics
- Qualitative Data Analysis
- Self-Service BI Tools
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.
- 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.
- 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.
-
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.
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.
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.
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.
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.
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?
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
Narrow Down Your Solution Options Easily
closeApplying filters...
Search by what Product or Type or Software are you looking for