Deriving actionable and timely insights from data is crucial yet challenging in modern enterprises. This is where AI data analysis tools step in by automating facets of data prep, query, visualization and modeling using techniques like machine learning. Leading solutions in this space empower businesses to enhance productivity, efficiency and performance.
Through this comprehensive guide, we take a closer look at the technological underpinnings, real-world benefits and key evaluation criteria for AI-powered data analysis software.
The Promise and Business Value
Before diving into available solutions, it is prudent to level-set the value major advancements in this domain offer:
Democratizing Data: By utilizing natural language and search based interfaces instead of SQL coding skills, AI analysis tools open up data access and insights to more business users. This facilitates data driven decision making company-wide.
Accelerating Time to Insights: Automating cumbersome manual tasks involved in preparing, profiling, analyzing and visualizing data reduces effort delivery by over 80% as per Gartner. Rapid insights translate to real business performance gains.
Boosting Analyst Productivity: AI assistance amplifies an analyst‘s ability to process huge datasets, detect hidden patterns and deliver predictive foresights much faster – augmenting their efforts. Better productivity in data science teams has over 300% ROI as IDC estimates.
Increasing Business Agility: By operationalizing analytical models and workloads, enterprises can spot emerging trends, adapt quicker to market changes and address issues proactively. This empowers taking the best data-backed actions sooner.
Enhancing Trust: Advanced techniques provide metrics like accuracy, explainability, transparency for models and analysis. This enhances user trust and drives adoption across the enterprise to migrate from intuition based to data driven planning and execution.
Cost Savings: Easing of personnel constraints by requiring fewer expert data scientists and establishing centralized data science platforms achieves significant cost reductions compared to earlier approaches.
Forrester projects businesses could realize over $1 billion in value through the use of AI for advanced analytics over 3 years. Given these compelling benefits, it is vital to assess solutions fitting your need.
Overview of Leading Solutions
Here we explore the most prominent enterprise-grade AI analytics offerings:
1. Microsoft Power BI
As a leader in business intelligence platforms, Power BI pairs self-service data prep and governance with AI-powered natural language and augmented analysis capabilities for faster insights.
Key Capabilities:
- AutoML assisted modeling
- Power Query data transformations
- Natural language queries through Q&A
- Auto Insights finding trends/patterns
- Azure integration for ML models
- Auto forecasting of KPIs
- Anomaly detection in streams
- Interactive visualizations
Use Cases: Sales analytics, financial reporting, marketing analytics, operational KPI monitoring
2. Tableau
Tableau augments understanding of data relationships through smart AI-based pattern detection, correlations and driver analysis behind chosen dimensions and measures surfaced via intuitive interactive visualizations.
Key Features
- Explain Data for automatic insights
- Trend and outlier identification
- Associative discovery
- Sentiment analysis
- Ask Data natural language
- Einstein Discovery AI assistant
- Enterprise scalability
- Extensive visualization formats
Use Cases: Sales performance, campaign analysis, customer analytics, supply chain analytics, HR analytics
3. ThoughtSpot
ThoughtSpot provides an AI-first approach making enterprise data easily searchable through natural language and voice interfaces to deliver live answers and interactive dashboards rapidly without dependencies on analysts.
Core Capabilities:
- SpotIQ semantic search and NLQ
- Contextual bot conversations
- Liveboards business visualizations
- Continuous learning with usage
- Embrace predictive modeling
- SmartCTO automated insights
- Usage analytics enhancement
Use Cases: Customer intelligence, product performance, digital analytics, inventory analysis, churn reduction
4. Qlik
Qlik Sense applies augmented intelligence across self-service analytics and real-time active intelligence capabilities through its cognitive engine, delivering natural language conversational analytics.
Main Features:
- Natural language search/dialog
- Association & cluster analysis
- Predictive modeling and forecasting
- Pattern detection across live data
- Geoanalytics using native mapping
- Streaming analytics integration
- Enterprise scalability
Use Cases: Campaign management, supply chain analytics, healthcare analytics, telecom analytics
5. Sisense
Sisense Explain leverages AI to accelerate development of analytic apps and deliver insights rapidly without compromising user flexibility via techniques like machine learning, statistical analysis, natural language generation and search.
Leading Capabilities:
- Insight discovery automation
- AI model workflows
- Anomaly detection
- Statistical insight significance
- Natural language interaction
- Cloud Native architecture
- Codeless model building UI
Use Cases: Marketing analytics, sales performance, inventory optimization, network analytics, fraud detection
6. Alteryx
Alteryx augments analyst productivity through automation in data science workflows including assisted modeling powered by machine learning and NLP in the end to end analytic process flow.
Salient Features Include:
- Auto Insights for automated analysis
- In-database, in-memory computation
- Predictive & spatial analysis
- Software development kit for customization
- Conversational analytics
- Drag-drop workflow convenience
- Enterprise-scale
Use Cases: Churn analysis, targeted marketing, predictive maintenance, clinical studies analytics
7. SAS Analytics for IoT
SAS incorporates robust AI and automation technology across its analytics solutions ranging from fraud detection to forecasting and optimizations helping data scientists work smarter and accomplish more in less time.
Notable Capabilities:
- Automated analysis modeling
- Neural network model building
- Text sentiment analysis
- Forecasting with deep learning
- Data mining and visualizations
- Model building comparison
- Scalability to handle complex data engineering
Use Cases: Network analytics, predictive maintenance, campaign optimization, clinical trial analysis, financial crime detection
8. IBM Watson Studio
IBM Watson Studio provides an environment to prepare data, utilize AutoAI to build ML models, run experiments, visualize outcomes and deploy models at scale through automation aimed at enhancing productivity.
Core Features Include:
- Automated pipeline creation
- Data refinery for cleaning and shaping
- AutoML to generate suitable models
- Neural network support
- Integrations with Watson Assistant, NLU
- Trusted AI toolkit
- MLOps deployment pipelines
Use Cases: Document analysis, customer intelligence, personalized recommendations, predictive maintenance
9. Oracle Analytics Cloud
Oracle Analytics helps drive business decisions leveraging techniques like natural language query, conversational analytics, beautiful data visualizations and powerful AI/ML capabilities integrated to accelerate insight discovery.
Key Highlights
- Natural language exploration
- Data preparation assist
- Predictive modeling/forecasting
- Anomaly detection algorithms
- Statistical analysis visualization
- Storytelling from insights
- Enterprise scalability
- Flexibility via 230+ data connectors
Use Cases: Supply chain analytics, sales performance, financial data discovery, HR analytics
10. Databricks Unified Analytics Platform
Databricks provides a unified platform allowing data engineers, data analysts and data scientists to collaborate using data science lifecycle workflows automated via ML powered capabilities on open standards based lakehouse architecture.
Core Capabilities
- Automated pipeline ML
- Experiment tracking
- MLOps model management
- Auto visualization
- Notebook based collaborative analysis
- Security and access controls
- Auto Insights natural language
- Integrated lakehouse queries
Use Cases: Log analysis, customer intelligence, predictive analytics, financial crime detection
Key Machine Learning and AI Competencies
Cutting edge techniques adopted by data analysis platforms under the hood to drive automation and enhance augmented analytics include:
Automated Machine Learning (AutoML): Automating rote tasks of ML model development like data preprocessing, feature engineering, model training, hyperparameter tuning and algorithm selection to boost productivity of data scientists through solutions like DataRobot, H2O Driverless AI etc.
Reinforcement Learning: Enabling models to determine ideal actions dynamically in complex environments based on maximizing success through time by learning via trial and error. Helpful in optimization use cases.
Transfer Learning: Leveraging knowledge gained by models trained on one task and applying it to a different but related task. Quickly creates usable models through pre-built starting points.
Transformers and Foundation Models: Powerful neural network based architectures like BERT, GPT-3 etc. that can be pretrained on huge datasets and then fine-tuned by using smaller domain-specific data for downstream tasks providing better context.
MLOps: Applying DevOps style rigor and best practices using ML specialized tooling around the machine learning lifecycle steps – data management, model building, deployment, monitoring – enabling reliability.
GPU Usage: Tapping into massively parallel processing power of graphical processing units and similar accelerators provides 50-100x speedups in computing intensive model building activities lowering latency.
Sophisticated techniques equip analytics tools featured earlier with expanded capabilities to automate deeper insights from complex data. Let us assess some tangible real world benefits being realized from deploying solutions discussed through use cases across domains.
Industry Specific Use Case Examples
Banking:
IDBI Bank fast tracks product offers leveraging analytics with SAS to analyze past trends and campaign performance to identify most relevant customers and offers driving 10% lift in conversion rates.
Santander Bank uses data mining algorithms in SAS to detect insurance claim fraud patterns. Using neural networks, the bank minimizes losses through claim investigations. Less than 1% valid claims are rejected maintaining customer trust.
Telecommunications:
Vodafone Germany tapped capabilities like machine learning, IoT and AI offered on SAP Analytics platform to boost revenue and sales conversions through targeted real time interventions in their call center. This reversed negative revenue trends.
Deutsche Telekom enhanced mobile network quality 7x leveraging geospatial visual data analysis in SAS to streamline network integration, identify deficiencies, strengthen infrastructure, expand reach through predictive modeling and simulations.
Healthcare:
The Center for Data Science at MGH applies data science workflows integrating AI and ML techniques operationalized via Databricks to gather population health insights around complex areas like opioid prescribing by analyzing vast datasets while maintaining highest privacy levels through deidentification.
Beth Israel gains full visibility into patient treatment journeys via advanced event sequence analysis in SAS uncovering bottlenecks in care coordination between departments and predicting at-risk patients enabling targeted interventions and optimized capacity management raising quality standards significantly.
Retail:
Lowe‘s taps into computer vision, AI and ML capabilities within SAS Viya to uncover insights from 1000s of hours of in-store video feeds to improve retail environments, positioning and inventory planning. This boosted sales conversion rates considerably through dat
Starbucks powers recommendations to users of its mobile app leveraging reinforcement learning algorithms in IBM Watson Studio which continuously adapts ordered coffee suggestions based on user clicks and rewards through a virtual barista chatbot enhancing engagement.
Manufacturing:
Daimler optimizes production processes in factories to minimize scrap rates and enhance output quality through self learning analytics algorithms from Siemens which adjust configurations by continuously assessing streaming sensor data from machinery over months.
Dow Chemicals achieved over $7 million in savings using IoT data analytics with SAS real time edge processing combined with Azure AI to perform predictive maintenance and reduce unscheduled downtimes of mission critical equipment by over 20% through failure predictions.
Government:
Los Angeles County Department of Public Social Services accurately estimates impact of policy decisions around areas like food stamps allocation through scenario modeling using IBM SPSS predictive analytics minimizing errors in planning budgets and utilization of funds.
The Australian Taxation Office has used rule based pattern detection capabilities offered in Oracle Analytics to catch tax fraud earlier preventing millions in losses through Benford‘s analysis which assesses if tax filings show unnatural duplicate numbers indicative of false reporting.
Financial Services:
payments platform Adyen protected merchants from >$25 billion in fraud using KNIME machine learning models which adapt through automated periodic retraining as new payment data flows in keeping risk scoring updated continuously.
Standard Chartered bank applies deep learning and NLP offered on the Qlik analytics platform to analyze trades and electronic communications detecting collusion risks accurately during investigations enhancing compliance.
Evaluating Solutions
With increasing breadth of options, a systematic methodology is required by enterprise IT teams to determine solutions fitting their needs, workloads and constraints.
Beyond factors like data connectivity, marketplace adoption and scalability – central aspects to evaluate machine learning competencies for analytics include:
AutoML Capabilities: Assess ease of building models with limited data science skills via automation around task flows like data prep, feature engineering, model creation, comparison, deployment, refresh. Review access and integration to complementary AutoML solutions.
Algorithm Support: Ensure availability of ML algorithms like decision tree, random forest, SVM, kmeans, reinforcement learning, neural networks, NLP and computer vision models to cover broad analytical use cases.
ML Model Building: Analyze drag and drop versus coding options, MLOps pipeline integration, model explainability metrics, reproducibility, experiment tracking and model management features.
Model Monitoring: Check for drift detection, transparent monitoring, deviation alerts and retraining workflows available to maintain ongoing model accuracy after deployment.
Enterprise Responsibility: Review aspects like bias mitigation and fair AI techniques used to build trust, plus data privacy, lineage tracking and model governance capabilities are important.
Cloud Platform Exploitation: Assess ability to tap into specialized hardware like GPUs and TPUs for accelerating model building by exploiting cloud native implementations. Review prebuilt marketplace offerings integration.
Evaluate the above facets through demos using your own test data versus simple canned use cases to effectively test relevance for your needs. Talking to client references in your industry also provides helpful datapoints. After finalizing a shortlist, consider initiating proofs of concept focused on critical projects to confirm selection.
Emerging Trends to Track
We are only scratching the surface of how AI can transform data analysis. Tracking bleeding edge advances can help better prepare your analytics modernization:
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Generative AI: Models like DALL-E enabling automated visualization creation from text prompts and GPT-3 for natural language based analyst augmentation
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Quantum ML: Tapping into quantum computational power once maturity increases for complex ML model development and training
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Blockchain based Analytics: Decentralized models for privacy preserving insight sharing across organizations
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Analysis Automation: End to end ML pipelines analyzing raw data and providing business recommendations without humans in the loop
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Causal ML: Making inferences about cause and effect relationships from observational data to inform decision making
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Responsible AI: Governance frameworks to address transparency, bias mitigation and trust in advanced analytical models
While some emerging technologies carry longer timeframes to percolate into mainstream enterprise products, others like generative AI carry tremendous disruptive potential much sooner. Tracking advances aligned to your industry now allows smarter roadmap planning.
Key Takeaways
With growing data complexities and need for responsiveness, AI-powered analytics augments and multiplies enterprise capabilities for extracting timely insights to outmaneuver competition. When carefully adopted, these leading platforms drive measurable business performance improvement. Consider ensuing your analytics overhaul initiatives assess key differentiators around automation, ML competencies and emerging functionality.
Through our exhaustive examination spanning technological underpinnings, real world case studies and evaluation guidance – data analytics leaders can pursue robust foundational upgrades enabling fact based, analytics driven organizations of the future.
