Your organization creates and collects immense amounts of data — probably even more than you realize. What do you do with that data once it’s there? How can it help you? How do you make sense of it? This is where business analytics comes into the picture.

Business analytics tools fall into a subcategory of business intelligence and involve the methodical exploration of data to help businesses make data-driven decisions. We’ve created a handy business analytics requirements checklist to help you determine what your organization needs.

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This Article Covers:

• Factors To Consider While Selecting a Business Analytics Solution
• Key Requirements

Factors to Consider

Let’s look at the factors to consider before choosing a business analytics solution:

Determine Business Objectives

Your business analytics solution should support existing and future business needs. It is vital to document core business objectives and outcomes. Then break down the objectives into measurable analytics goals. Finally, choose a platform that helps you achieve your goals.

Provide an Intuitive User Interface

You will refer to the business analytics platform while making key decisions. So, the solution should provide robust self-service analytics capabilities and a user-friendly interface. Non-technical users should be able to create and understand reports and dashboards easily.

Facilitate Advanced Analytics

The business analytics solution should recognize data patterns and trends while predicting future events and outcomes. Build advanced statistical models to make accurate forecasts.

Offer Mobility

Mobile analytics is an effective way to keep everyone in the organization connected. Global companies need to be able to make data-driven decisions on the go. Before considering solutions, make sure you ponder the following questions:

• What kind of mobile analytics capabilities do you need?
• Do you need to view dashboards and reports or create and edit them on the fly?

Mobile BI facilitates collaboration and boosts user adoption rates.

Obtain Data From Multiple Sources

Modern analytics solutions combine and analyze multiple complex data sources, including structured, semi-structured and unstructured. With the ability to combine data from a variety of sources onto a single dashboard, these solutions provide a complete view of business performance.

Provide Integration

While selecting a solution, decide whether a standalone or an integrated system is apt for your business. Integrated platforms offer access to existing analytics capabilities that users understand. You must gauge how easily an analytics platform can integrate with the existing solution and third-party sources.

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Key Requirements

The following is a checklist of important business analytics requirements:

1. Advanced Analytics

Advanced analytics is an umbrella term for a wide range of analytics techniques that make use of cutting-edge computing techniques such as machine learning. It refers to the autonomous examination of data using techniques beyond those offered by more basic BI. The goal of advanced analytics is to discover insights, make predictions or generate recommendations for improving business.

Some features included involve structuring unstructured data with text and statistical analysis. Explore and analyze vast amounts of unstructured text data to identify concepts, patterns, keywords and other attributes through text analytics capabilities.

Generate a regression model that describes and analyzes relationships between dependent and independent variables and leverage it to make accurate predictions. It supports different types of regression analysis such as linear, logistic, exponential, polynomial and multivariate.

Leverage scenario and what-if analysis to compare different scenarios and their potential outcomes based on dynamic parameters or values. Forecast future trends based on present and past data using forecasting methods like exponential smoothing, moving averages, ARIMA, SARIMA and more.

• Text Analytics
• Statistical Analytics
• Cluster Analysis
• Regression Analysis
• Scenario and What-If Analysis
• Segmentation and Cohort Analysis
• Sentiment Analysis
• Time Series Analysis and Forecasting
• Statistical Functions
• Predictive Modeling Markup Language (PMML) Support
• Calculated Columns or Fields
• Advanced Data Analysis using Python and R

2. Augmented Analytics

Leverage augmented analytics and machine learning automation to augment data profiling, harmonization, modeling, manipulation, metadata development and cataloging. Find new clusters or segments and create forecasts automatically. Adjust algorithm hyperparameters to improve the accuracy of the predictive model. Automate alerts for outliers/anomalies based on data changes.

Identify robust variables for use in predictive modeling. Leverage a text or voice-based search interface to search through data using natural language statements.

• Augmented Data Preparation
• Autogenerated Segments or Clusters
• Autogenerated Forecasts or Predictions
• Automated Algorithm Selection and Model Tuning
• Automated Anomaly Alerting
• Automated Descriptive Insights
• Automated Feature Generation or Selection
• Automated Model Monitoring
• Automated Model Packaging or Deployment
• Contextualized or Relevant Insights
• Key Driver Analysis
• Text-Based Natural Language Search
• Voice-Based Natural Language Search

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3. Dashboarding and Data Visualization

Create advanced visualizations using libraries and packages of Python and R programming languages. Animate data and present it to display changes across multiple groups or time periods. Leverage auto-charting capabilities to select suitable visuals for graphical data representations.

Create dashboards to provide a single-screen view of KPIs, business analytics metrics and critical data points. Facilitate automatic dashboard visualization refreshing at regular intervals. Use drill-down and drill-up capabilities to explore multidimensional and hierarchical data. Interact with charts, graphs and other visuals through scaling, linking, tooltips and more.

• Advanced Visualizations
• Animations
• Auto-Charting
• Auto-Refresh
• Dashboard Rebranding
• Embed Dashboards in Webpages
• Interactive Data Visualizations
• Visualizations with Drill-Down and Drill-Up

4. Data Management

Leverage data blending functions to combine multiple datasets for analysis. Explore large unstructured datasets to uncover patterns, characteristics and points of interest and describe them using visualization tools and techniques.

Ensure data is governed and in sync with the organization’s procedures, policies and objectives to mitigate risks of multiple sources of truth. Facilitate multidimensional analysis by supporting OLAP operations like roll-up, drill-down, slicing and dicing.

Centralize metadata and offer information about each data piece, including location, profile, statistics, summaries and comments.

• Advanced Data Preparation
• Data Blending
• Data Exploration
• Data Governance
• Data Modeling
• Data Preparation
• Metadata Management and Data Catalog
• OLAP and Multi-Dimensional Analysis

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5. Data Querying

Query data from multiple sources, including relational databases, XML, CSV, web services and more. Update records and return them to the data source without maintaining a connection for batch updates.

Create complex queries using filters, set operators and calculated conditions. Execute queries on data residing within the main memory. Create complex database queries using drag-and-drop visual tools to find data across different sources. Run queries at scheduled intervals and events.

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

6. Embedded Analytics

Integrate interactive dashboards, reporting, workflows and data analysis into the embedding application. Ensure multitenancy by creating separate workspaces for tenants and users to maintain data separation. Update multiple data sources in real time by initiating backend processes in the embedding application.

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

7. Geospatial Visualizations and Analysis

Plot geographic locations on the map with forward and reverse geocoding. Find locations on the fly without knowing geographic coordinates with map search functionality. Perform calculations and functions to transform geospatial data and perform analysis. Visualize spatial data with interactive map visuals, including proportional symbols, choropleth, dot distribution, heatmaps, dual-axis maps and more.

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

8. Internet of Things (IoT) Analytics

Process and analyze IoT data at the edge, close to its point of generation without moving it to a centralized location. Explore and analyze data generated by connected IoT devices such as sensors on manufacturing equipment, pipelines, weather stations, delivery trucks, smart meters and more. Facilitate real-time and continuous analysis of time-based data as it generates.

• Edge Analytics
• IoT Analytics
• Streaming Analytics

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9. Mobile BI

Mobile business intelligence (mobile BI) refers to the ability to provide data analytics services to mobile/handheld devices and/or remote users. It enables users with limited computing capacity to use and receive the same or similar features, capabilities and processes as those found in a desktop-based business intelligence software solution.

BI systems with mobile BI give users the ability to access data, content and other features from mobile devices like smartphones and tablets. This feature is becoming increasingly popular as people spend less time at their desks and more time on the move. It can include anything from mobile phone apps to designated web browsers to mobile versions of the site where the software is hosted.

Leverage mobile-optimized dashboards and reports with interactive drill-down/up capabilities. Facilitate geospatial analysis on mobile devices. Use the mobile app in offline mode as it incorporates sophisticated caching algorithms to enable user interactions in areas with limited or no network access. Enable push notifications to take corrective actions immediately.

• 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

10. Reporting

Create and distribute reports on the fly with ad-hoc reporting capabilities. Deliver reports at regular intervals. Create built-in alerts to distribute reports only when a pre-specified condition is met. Leverage conditional formatting to highlight cells with a particular color or font when a specific condition is fulfilled. Design and templatize complex reports for particular user groups. Process, query and generate reports through text-based natural language.

• 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

11. Platform Functions

Collaborate and share visualizations and reports via team messaging and collaboration platforms, emails, discussion threads and more. Store and manage data generated by different departments independently. Create projects or workspaces that contain a collection of visualizations, dashboards and reports. Support globalization standards such as language, fonts, symbols, time zone, currency and more.

• Collaboration and Information Sharing
• Decentralized Analytics Environment
• Globalization Support
• Projects or Workspaces
• Write to Transactional Applications

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Conclusion

It’s important to select the right solution based on objectives, user interface and visualization, advanced analytics, mobility and integration capabilities. The business analytics requirements checklist can help you adopt a suitable solution that caters to business needs. Implementing a business analytics solution with the right set of features can improve processes and overall performance.

Did we miss any requirements? What do you want to see in a business analytics tool? Tell us in the comments!