So you’re in the market for a BI software solution? You may be tempted to purchase the most expensive system you see, but hold on! Preparing a list of your business intelligence requirements can save significant heartache and guide you toward the perfect system.

This article is a ready reckoner for creating a requirements checklist. Feel free to add or drop any features you see fit, depending on your unique business intelligence needs. We also answer some common questions you should consider.

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Article Roadmap

• Key Requirements
  • Dashboarding and Data Visualization
  • Data Management
  • Data Querying
  • Reporting
  • Embedded Analytics
  • Advanced Analytics
  • Augmented Analytics
  • Geospatial Visualizations and Analysis
  • Internet of Things (IoT) Analytics
  • Mobile BI
• Considerations
• Building a Requirements Document
• Next Steps

Stakeholder approval gives authenticity and authority to the requirements checklist, making it a collective responsibility.

A business intelligence requirements gathering template is handy during software selection and implementation as a constant point of reference for you and your team.

Key Requirements

Though not exhaustive, this list is a good starting place when deciding which business intelligence requirements you should look for.

1. Dashboarding and Data Visualization

The solution should allow single-screen views of metrics divided into panels to show related data points.

Dashboards must be web-accessible and conform to Web Content Accessibility Guidelines (WCAG 2.0 AA) and US Section 508 requirements.

Complex data analysis and visualization require integration with Python and R libraries and third-party packages like D3.

Among BI requirements, interactive visualizations are primary, enabling selection, linking, and drilling up and through data points. Adding animations to them help depict changes across groups or time periods, while automatic refresh shows the latest data.

Branding is a crucial BI requirement. The ability to use custom font styles, logos and colors helps establish your company’s identity.

• Accessible Dashboards
• Advanced Python/R Visualizations
• Animations
• Auto-Charting
• Auto-Refresh
• Dashboard Rebranding
• Web Embeddability
• Interactive Data Visualizations
• Third-Party Charts Packages
• Drill-Down and Drill-Up Capabilities

2. Data Management

Statistical and visualization techniques in BI solutions help uncover useful insights by blending structured and unstructured datasets.

Preprocessing data — profiling, cleansing and transformation — before analysis gives you clean data for your calculations. Data modeling is a key BI requirement and helps map how the information will move between sources and the host application.

Metadata management and data catalogs make it easy to find stored datasets. A BI tool supporting OLAP — roll-up, drill-down, and slicing and dicing — helps analyze multidimensional data.

Implementing data governance enforces business rules compliance, providing a single source of truth for analytics.

• Data Preprocessing
• Data Blending
• Data Exploration
• Data Governance
• Data Modeling
• Data Preparation
• Metadata Management and Data Catalog
• OLAP and Multidimensional Analysis

3. Data Querying

Live connectivity enables direct querying against databases without storing a local copy. Batch updates offer a power-saving alternative by updating the records locally and regularly sending updates to the database.

The solution should support your preferred data sources and provide a robust query editor for creating and modifying simple and complex SQL queries.

Anyone, even non-technical users, should be able to run queries and schedule them to run later using visual drag-and-drop tools.

In-memory analytics gives faster results, while multi-pass SQL drives parallel processing of large reports by splitting them into smaller components.

• Batch Updates
• Complex Queries
• In-Memory Analytics
• Live Connection
• Multi-Pass SQL
• Query Multiple Data Sources
• Readable and Modifiable SQL
• Scheduled Queries
• Visual Querying

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4. Reporting

Ad hoc reporting is a primary BI application. Self-service BI tools close the gap between technology and non-technical users, allowing them to generate on-the-fly and static reports on demand.

Your BI tool should have something for everyone — technical users must be able to create and templatize complex reports.

Scheduled and trigger-based reporting keeps you on top of your tasks and updates.

Conditional formatting saves time by highlighting relevant cells. Report versioning ensures changes aren’t lost, and you can revert to a previous draft if necessary. Exporting reports in HTML, Excel, CSV, and PDF helps share insight with internal teams and clients.

Written text summaries and voice reports using chatbots cover the last mile of analytics, giving you understandable information with a few commands.

Your business intelligence report requirements template should include the following features:

• Ad Hoc Reporting
• Auto-Schedule Reports
• Built-in Alerts
• Canned/Managed Reporting
• Conditional Formatting
• Interactive Reporting
• Reports Exporting
• Reports Versioning
• Text-Based Natural Language Reports
• Voice-Based Natural Language Reports

5. Embedded Analytics

A BI application that embeds into your regular workflows adds value by avoiding unnecessary platform switching. The system should seamlessly run in the background with white labeling, providing data to the host application.

Separate workspaces maintain the integrity of tenant data.

Integrated workflow actions give you your money’s worth by speeding up decisions and follow-up actions from the dashboards. You can pause orders from the inventory dashboard when it says stocks are surplus — no need to log into the ordering system.

Secure write-backs keep your sources synced with the latest data changes.

• Background Processing
• Embedded Analytics
• Embedded Multi-Tenancy
• Integrated Workflow Actions
• Secure Write-Backs
• White Labeling

6. Advanced Analytics

Business intelligence is more than presenting numbers — it involves data analytics using calculations, functions and formulas. Your data could be anything — text, audio, images and videos — and your BI tool should incorporate it all.

Deriving insights enables predictive analytics — forecasting business performance and market trends using regression, time-series and what-if analysis.

PMML (Predictive Model Markup Language) allows the exchange of predictive models between software, irrespective of the platform you use.

Distributing customer data sets into groups with similar attributes — segmentation and clustering — helps derive buyer behavior trends.

Analyzing sophisticated data sets includes calculating mean, median, mode, skew and variance. Text mining and sentiment analysis help incorporate customer analytics into your projections.

Your business intelligence report requirements template should include the following features:

• Calculated Columns or Fields
• Cluster Analysis
• PMML Support
• Regression Analysis
• Scenario and What-If Analysis
• Segmentation and Cohort Analysis
• Sentiment Analysis
• Statistical Functions
• Text Mining
• Time-Series Analysis and Forecasting

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7. Augmented Analytics

A lot of augmented analytics features are automation-driven.

When machine learning (ML) automatically prepares data for you, it’s augmented analytics. Additionally, it drives information quality management, metadata building and cataloging.

Automation sets the stage for programmed segmentation, forecasting, algorithm selection, model tuning, key driver analysis and anomaly alerting. The solution should display contextual insight depending on who’s looking at the numbers.

Many BI tools have hands-free querying in the form of voice-based searches.

8. Geospatial Visualizations and Analysis

Does your business need location-based insights, and of what kind? Some BI tools may allow geographical searches by inputting addresses or area coordinates. And if you don’t have that, map-based searches can be equally effective, provided your BI tool has the requisite integrations.

Support for shapefiles, MapInfo tables, Keyhole Markup Language (KML), TopoJSON and GeoJSON files enables robust visualization. Built-in Mapbox, Google Maps and Bing Maps integrations enhance your BI tool’s geospatial capabilities.

Besides mapping locations, geographical functions allow data transformations for analysis. Not every map suits every data type, so map libraries offer many options for your selected data points.

Examples are proportional symbols, choropleth, dot distribution, flow, spider and dual-axis maps and heatmaps.

• Geocoding
• Geographic Search
• Geospatial Functions and Calculations
• Mapping and Maps API Integration
• Maps and Geospatial Visualizations
• Spatial Files Support
• WMS Servers Integration

9. Internet of Things (IoT) Analytics

The Internet of Things comprises interrelated devices and networks exchanging data without human intervention.

It enables devices on closed private networks to communicate and share information, like sensors on manufacturing machinery, delivery trucks, pipelines, smart meters and home appliances. Automotive, telecom and energy are key industries benefiting from IoT.

Streaming analytics — real-time analysis of streaming data — is one way to get live updates.

Edge analytics takes it a step further by analyzing data at the point of generation, reducing reliance on internet connectivity. Embedded devices, smart sensors and edge computing enable data analysis at the source.

• Edge Analytics
• IoT Analytics
• Streaming Analytics

10. Mobile BI

Mobile-optimized, interactive reports and dashboards with on-demand analysis on Android and iOS devices keep you connected to your business when out of the office.

Your team should be able to collaborate on reports and dashboards — draw freehand shapes, add comments and share the annotated screenshots. They should be able to incorporate geospatial analysis into the dashboards and reports.

The solution must work with the mobile device’s push notification feature to deliver alerts as popups, banners and app badges.

Sophisticated caching algorithms provide offline data access in low or no-network areas. The solution should read and display data from barcodes and QR codes.

• Mobile Collaboration
• Mobile Dashboards and Reports
• Mobile Geospatial Analysis
• Native Mobile Apps
• Offline Mode
• Push Notifications and Alerts
• Responsive Web Design
• Scan Machine Readable Codes

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Considerations

Where do you seek to deploy?

• SaaS (Software-as-a-Service) deployments are cost-effective, lightweight, no-maintenance, cloud-based software delivery models.
• Many cloud-based BI solutions work with legacy systems, making them a viable option for businesses with existing infrastructure.
• Private, public and hybrid cloud deployments are flexible deployment options.
• On-premise solutions enable more control over data security, customization and upgrades.

Who are the users?

Assess who will use the tool and how much training or onboarding time the team might need. Investing in a sophisticated solution without factoring in your team’s skill level might result in low adoption, making it challenging to realize your ROI (return on investment).

Will you need technical support?

SaaS systems deploy with a few clicks, but standing up a cloud server for your BI deployment might require technical assistance.

What are the tradeoffs involved?

They depend on your organization’s unique business needs, priorities and resources. In-depth internal assessment, market research and vendor discussions can help you select suitable software.

• Cost vs. Features: Balancing functionality with the price is a tightrope walk. Anticipating which features you need in the foreseeable future can help you allot a realistic budget for software procurement.
• Ease of Use vs. In-Depth Analysis: Pre-built templates and point-and-click user actions are fine, but what if the tool doesn’t support complex analysis? On the other hand, technical tools may be unintuitive. Are you willing to sacrifice ease of use for performance?
• Customization vs. Built-in Functionality: Ready-to-go metrics and templates help you get started with BI tasks quickly. Others may need customization to work with your systems but give you more flexibility. Which will it be?
• Scalability vs. Implementation Speed: Solutions that can handle increasing data loads without lagging are desirable, but deploying them can be complex and tedious. Would you rather have a simple solution that deploys quickly or is scalable?

• Integrated vs. Standalone Platform: CRM, ERP and warehouse integrations provide comprehensive BI metrics, but their development and upkeep is an extra overhead. Standalone solutions may require manual data integration.
• Security and Privacy vs. Data Accessibility: How much protection is too much? Additional authentication steps or access restrictions can hurt adoption. Information should be easily accessible without compromising data integrity.

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Building a Requirements Document

Here’s how to go about finalizing your BI requirements.

Create the Requirements Checklist

Acquiring an advanced BI platform without considering your business needs is like navigating without a destination.

Evaluate your objectives by assessing current and upcoming goals and your long-term plans.

• Which features are must-haves and nice-to-haves?
• Do you want better customer insights, improved performance or predictive analytics?
• Which features can make it happen?
• What level of customization will you need?
• With which systems should the BI platform integrate?
• What are your foreseeable expansion plans?

You don’t have to do it alone. Get a head start with our free business intelligence requirements gathering template. Once you list them, it’s time to get your stakeholders’ opinions.

Get Stakeholder Input

Get all concerned parties involved in discussing what stays and goes. You can use a collaborative tool like Google Sheets.

Rank Features and Functionality

Ask each stakeholder to rank the features as per their individual needs, say, in a spreadsheet or use a requirements management tool. Having them rank the features by priority or on a preapproved scale, say from zero to 10, incorporates their preferences into the mix.

Team preferences for features and functionality may vary depending on their goals and objectives.

Formalize Business Requirements

Generate the final ranking by consolidating the results. The results might surprise you, resulting in deprioritizing certain features while others make it to the top of the list. Freeze the list into a requirements document after getting everyone’s sign-off.

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Next Steps

Send them to vendors with a request for proposal (RFP).

Review the vendors on your shortlist against your requirements, ranking them from zero to 100. Generate a free comparison scorecard of the top software on your list. The higher a vendor’s score, the more you’ll want to contact them for discussions and demos.

Follow our Lean Software Selection methodology to ace software selection.

Which features are on your company’s business intelligence requirements list? Did we leave one of yours out? Let us know in the comments!