With data volume expanding exponentially across organizations, data scientists rely on advanced analytics tools to extract insights and inform critical decisions. This guide explores leading solutions, evaluation criteria for selection, implementation best practices and recommendations.
The Analytics Imperative: Growth in Data and Adoption
Powerful trends are driving adoption of analytics and research tools:
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Data growth – By 2025, IDC predicts global data will grow 61% to 163 zettabytes. Wide variety of structured, unstructured and streaming data makes leveraging it more complex.
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Competitive intensity – 70% of executives believe analytics creates competitive advantage, prioritizing it more.
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Cloud scale – IaaS cloud storage capacity grew over 35% in 2021 alone, enabling scalable analytics.
These interlinked trends create enormous potential, but also greater complexity for data science teams. Advanced tools tackle key challenges:
- Taming massive data variety, velocity and volume with accessible warehousing and refined ETL
- Generating valuable insights across consumer, operational and IoT data via dashboards, mining and modeling
- Achieving enterprise scale supporting more data scientists, analysts and use cases
- Driving competitive differentiation with sharper forecasts, algorithms and recommendations
Well selected platforms help convert exponential data growth into extraordinary business value.
Key Capabilities and Components
Sophisticated analytics tools share common architectural elements that tackle modern data intricacy while accelerating measurable outcomes:
Data Connectivity and Preparation
- Hybrid data connectivity – Access diverse data from RDBMS, data lakes, cloud apps, streaming and more
- Secure data access– Role-based, row-level security safeguards sensitive data
- Advanced ETL – Leverage push-down optimization and machine learning to cleanse, enrich and transform
- Self-service data prep – Enable analysts to visually prep data without coding expertise
Analysis and Modeling
- Interactive analysis – Native support for searching, filtering, correlations, projections and pivots
- ML algorithms – Extensive library for supervised and unsupervised techniques
- Notebook integration – Execute external notebooks from integrated development environments
- Automated ML – AutoML to drive model development, tuning, testing and staging
Visualization and Collaboration
- Customizable dashboards – Pixel perfect interactive dashboards with advanced visuals
- Alerts and workflow – Configure alerts on metrics and trigger actions in tools
- Annotations and sharing – Notes, bookmarks and lens with notifications
- Governed self-service – Give analysts access to curate without harming underlying source
Deployment Flexibility
- On-premise support – Install directly on infrastructure with scaling and HA options
- Private/public cloud – Managed SaaS, bring-your-own on IaaS, and hybrid models
- Embedded and OEM – Embed capabilities in apps and partner solutions
These interconnected capabilities work together to enhance productivity for every persona – from analysts to data engineers.
Evaluation Criteria
The wide range of analytics and research tools available, combined with intricate organizational needs, makes narrowing down options complex. A structured selection process centered across 8 key criteria enables methodically shortlisting ideal platforms:
| Criteria | Description |
|---|---|
| Data Connectivity | Support for breadth of structured and unstructured sources |
| Ease of Use | Intuitiveness for users across skill levels |
| Analytics Capabilities | Advanced modeling techniques enabled |
| Visualization & Collaboration | Interactive dashboards, sharing and alerting |
| Enterprise Scalability | Concurrent users, data volumes and oversight |
| Deployment Flexibility | Platform support aligning to infrastructure |
| Skill Set Alignment | Matching organizational resources |
| Total Cost of Ownership | Licensing, training, run costs |
Balancing requirements across these areas will guide tailored platform evaluations for your environment and objectives.
Additionally, hosting options introduce dimensions like latency, admin overhead, and elasticity for factoring:
| Factor | On-Premise | IaaS Cloud | SaaS |
|---|---|---|---|
| Latency | Lowest | Moderate | Highest |
| Admin Effort | Highest | Mixed | Lowest |
| Elasticity | Manual | Auto-Scaling | Built-In |
Now let‘s analyze how leading solutions deliver across these requirements:
Leading Analytics and Research Solutions
Now let‘s explore both widely adopted and emerging analytics solutions against evaluation criteria:
Tableau
Overview: Interactive data visualization tool enabling faster dashboard creation
Key Capabilities:
- Import and prep data for analysis
- Intuitive drag and drop web interface
- Create customized, interactive dashboards
- Flexible markup for narratives and presentations
- Statistical modeling and forecasting built-in
- Available as desktop, server or SaaS
Ideal For:
- Enabling enterprise self-service analytics
- Rapid design of sharable dashboards
- Blending data access with content flexibility
Considerations:
- Requires add-ons for advanced analytics
- Can experience interactivity lags with large volumes
- Limited version control and operationalization
Qlik
Overview: Platform delivering guided analytics apps and dashboards
Key Capabilities:
- Associative data indexing powers high performance analysis
- Centralized hub for search and AI-driven insights
- Advanced Analytics through acquisitions
- Enterprise administration, security and scaling
- Software and SaaS licensing options
Ideal For:
- Scalable, governed self-service analytics
- Guided exploration for business users
- Embedding analytics into workflows
- Large enterprise deployments
Considerations:
- Primarily for analytics apps vs data science
- Add-ons required for data prep and modeling
- Initial learning curve steeper than some
Microsoft Power BI
Overview: Fastest growing platform tightly integrated across Microsoft stack
Key Capabilities:
- Import data from wide sources for transformation
- Industry leading natural language query
- Pre-built templates for embedded analytics
- Tight integration with Microsoft data services
- Rapid deployment with SaaS or on-prem licensing
Ideal For:
- Microsoft-centric organizations
- Accelerating industry-specific analysis
- Embedded and white-labeled analytics
Considerations:
- Interoperability limited outside Microsoft ecosystem
- Primarily self-service vs advanced coding
- DAX learning curve for customization
Sisense
Overview: Specialized for complex data prep and interactive analysis
Key Capabilities:
- Extreme user concurrency and interactivity
- Sophisticated ELT driven data preparation
- Broad connectivity to cloud data sources
- Customizable analytic apps and dashboards
- On-premise and cloud licensing choices
Ideal For:
- Complex analytics across large user groups
- Organizations seeking customization
- Scalable dashboards and applications
- Pixel perfect analytic apps
Considerations:
- Less out-of-box machine learning
- Steep learning curve for new users
- Very large data volumes require add-ons
ThoughtSpot
Overview: AI-driven analytics platform to accelerate search and insight
Key Capabilities:
- Interactive data search across sources
- Conversational natural language interface
- Recommendations for next best actions
- Live data connectivity powered by LiveConnect
- On-prem and cloud enterprise deployment
Ideal For:
- Enabling pervasive, yet governed data access
- Accelerating insights for business users
- Rapid adoption fueled by AI capabilities
- Scaling search and analytics exponentially
Considerations:
- Requires database expertise for managing LiveConnect
- Additional components required for ML workflows
- Careful rollout for change management
Databricks
Overview: Unified analytics platform optimized for Apache Spark
Key Capabilities:
- Managed infrastructure for scalable Spark clusters
- Notebook-driven workflows in Python, R, Scala, SQL
- Integrated experiment tracking and feature engineering
- Streamlined deployment on AWS and Azure
- Operationalization of workflows into production
Ideal For:
- Power users and data engineers
- Developing and deploying Spark-based models
- Building ML pipelines and applications
- Cloud-native implementations on Azure or AWS
Considerations:
- Demands expertise developing Spark environments
- Limited standalone reporting and visualizations
- Steep learning curve outside core personas
Knime Analytics Platform
Overview: Open source workflow platform for production data science
Key Capabilities:
- Code-free workflow builder enables self-service ETL
- Extensive nodes for integration, modeling, analysis
- Works across Python, R, Spark, databases and more
- Server solution centralizes workflows and automation
- Free open source options available
Ideal For:
- Flexible workflows from ETL to deployment
- Citizen data scientists requiring automation
- Operationalizing pipelines into apps and services
- Seeking open source economics
Considerations:
- Open platform demands more talent depth
- More IT infrastructure without cloud option
- Not optimized for ad hoc visualization
H2O Driverless AI
Overview: AutoML platform to automate key machine learning workflows
Key Capabilities:
- Automated feature engineering
- Automatic visualization, tuning and ensembling
- Incorporates latest open source algorithms
- Interoperability with Python, R environments
- Comprehensive model monitoring and deployment
Ideal For:
- Accelerating time-intensive ML workflows
- Getting more models deployed faster
- Making sophisticated ML accessible to all
- Maximizing model value through MLOps
Considerations:
- AutoML limits customization opportunities
- Primarily focused only on ML use cases
- Interperability gaps with broader platforms
This analysis shows both long-standing and modern tools against key selection criteria. While all support extracting insights from data, differences arise in factors like accessibility, integration, governance and TCO.
Use Cases Showcasing Business Impacts
Let’s explore examples of analytics tools driving measurable outcomes across industries:
Retailer Performing Customer Segmentation
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A fast growing retailer sought to identify higher value customer cohorts using transaction, loyalty program and website data
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Using Qlik Sense data architects integrated POS, CRM, and web logs into Associative Store
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Data scientists utilized grouping, cluster analysis and decision tree capabilities to define 6 segments
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Interactive apps allowed marketing teams to analyze segments and launch personalized campaigns
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10% revenue growth in first 6 months fueled by more targeted customer interactions
Bank Detecting Credit Card Fraud
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A bank experienced escalating fraud costs and chargeback risks amid ecommerce growth
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SAS Model Manager was deployed to operationalize fraud models into MLOps workflows
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70+ algorithms were evaluated using AutoML against 1TB of transaction data
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The optimized model accurately flags ~80% of fraudulent transactions for review
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$12M in fraud prevention in 12 months with 30% operational efficiency gain
Factory Optimizing Supply Chain
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A leading CPG company saw excessive waste from demand/supply mismatches
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Alteryx Designer enabled rapidly developing and iterating forecast models
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By combining sell-through, inventory, and supplier data they created a hybrid model
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Dashboards guide just-in-time production planning and raw material orders
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$8M in waste reduction in first year expanding margins by over 2%
These examples showcase analytics and research tools increasing revenue, reducing risk, and optimizing operations through data-driven insights.
Best Practices for Successful Adoption
Getting the most from analytics investments requires focusing equally on technology, people and process:
Phased Rollouts: Take an iterative approach across business units to build skills and trust before enterprise-wide deployment. Start with driven use cases.
Training & Enablement: Combine instructor-led and online learning to transfer knowledge across roles – from developers to business leaders.
Develop Analytics Culture: Enable evidence-based decisions through executive mandates, cross-functional data sharing, and embedding analytics into workflows.
Refine Processes: Evolve experimentation, modeling, testing, validation and automation procedures for efficiency, reuse and integrity.
Scale Governance: Grow oversight of security, data compliance, model explainability and platform interoperability through Center of Excellence teams.
Blending organizational change management with hands-on platform expertise unlocks transformational benefit.
Key Takeaways and Recommendations
With data positioned as an enterprise’s most strategic asset, analytics and research platforms unlocking its value confer enormous advantages. Key recommendations:
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Evaluate against current and emerging requirements – Account for complex and growing data sources, personas and use cases in your environment.
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Assess capability to usage fit – Ensure solution empowers technical and business users based on skills. Overinvesting creates unused features while underinvesting limits growth.
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Model TCO diligently – Factor both licensing and multiyear operational costs across options. The cloud shift warrants revisiting platform choices.
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Prioritize flexibility – Seek integrations across BI, data science, and infrastructure ecosystems to enable combining capabilities.
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Enable expansion – Start focused, yet architect for additional data sources, authors, applications and algorithms.
With exponential data growth poised to intensify, arming your organization with a modern analytics and research platform delivers a foundational advantage.
