Hello there! If you‘ve found your way to this guide, you likely already know the massive potential of data analytics in empowering strategic decisions and delivering a competitive advantage. However, you may still rely on manual reporting and dashboarding that provides static snapshots rather than an intelligent, unified view of your business.
This is where artificial intelligence comes in – to automate insights from exponentially growing datasets and place dynamic analytics directly into the hands of decision makers like yourself.
In this guide created just for you, we’ll cut through the hype and explore practical applications of AI in analytics while revealing platforms that align to your unique business needs. You see, not all solutions are created equal, so we’ll equip you to make the right choice for your analytics modernization journey!
The Evolution from BI to AI
Remember the days when data analysts would run batch reports out of data warehouses to build dashboards in business intelligence tools? While this provided value, it required heavy involvement to prepare the data, engineer features, tune models and generate those report snapshots.
Today, AI augments and automates these manual tasks through:
Conversational Interfaces – Ask questions about your business in plain language to instantly receive automated insights through charts, alerts and more.
Automated Machine Learning – AutoML solutions handle data prep, algorithm and model selection, hyperparameter tuning and more to make AI accessible to non-technical employees.
Continuous Intelligence – Identify risks and spot trends as they emerge through real-time analytics rather than static reports. Receive intelligent alerts for quick reaction.
An IBM study found organizations are embracing AI-driven analytics, with 76% of CEOs directing their management teams to incorporate AI to enhance their business decisions. 62% have already adopted AI in their data and analytics strategy.
However, as you evaluate tools – not all live up to the same standards of trust and transparency. We’ll cover how ethical AI empowers your entire organization to make data-informed business decisions confidently and responsibly.
Now – are you ready to explore the future of analytics? Let’s dive in!
AI Analytics in Action
Before reviewing the top vendors, you may wonder how AI-enhanced analytics would actually be applied in the real world. Here are just a few examples across industries:
Smart Manufacturing – A leading CPG manufacturer uses IoT sensor data combined with AI to spot early signals around machine failures, improving uptime and avoiding costly outages.
Personalized Healthcare – Through clinical and claims analysis, AI helps healthcare organizations detect early signs of patient risk and enables proactive intervention while optimizing limited resources.
Dynamic Pricing – An online retailer leverages real-time analytics to adjust pricing based on fluctuations in supply costs, demand and competitor data – applying AI to strike the right balance.
Predictive Maintenance – Industrial companies are combining AI and IoT to better predict equipment issues and only run maintenance when actually needed vs routine servicing.
Customer Churn – Banks use AI-powered analytics to determine each customer’s propensity to churn while identifying the highest value accounts to then direct tailored retention campaigns towards.
Hopefully this sparks ideas around potential applications in your industry! Now let’s explore top solutions to power AI-driven analytics and business intelligence in your organization…
15 Leading AI Analytics Platforms
Today‘s analytics leaders leverage augmented intelligence to empower their entire organization. After evaluating dozens of solutions, these 15 platforms lead in enhancing decisions through automation, natural language BI and continuous intelligence.
1. ThoughtSpot
ThoughtSpot provides AI search & analytics across your company‘s data that easily plug into your existing tech stack and data infrastructure. Leverage conversational analytics via search or Talk to Spot, ThoughtSpot‘s voice-driven analytics assistant.
2. Qlik
Qlik leads in augmented analytics leveraging automation, natural language and embedded real-time BI. Qlik‘s new Cognitive Engine autonomously detects insights and correlations before generating interactive data stories.
See Qlik‘s Augmented Analytics
3. Sisense
Sisense infuses analytics with AI everywhere – from data preparation to investigation to user experiences. Ask questions with natural language and get accurate answers. Embrace true self-service BI leveraging automation.
4. IBM Cognos Analytics
IBM Cognos augments how users discover, interpret & share analytics through AI-assisted modeling & visual recommendations. Leverage guided data exploration & predictive analytics.
5. Microsoft Power BI
Infused with AI across the complete business intelligence platform. Experience natural language queries, AutoML predictive capabilities, personal digital assistant in Power BI mobile, and more.
Learn About Microsoft Power BI
6. Zoho Analytics
Zoho’s conversational Zia AI assistant enables intuitive interaction with data through natural language search, voice commands and more. Receive AI-powered recommendations for accelerated analysis.
7. Databricks
Databricks offers an integrated platform unifying data science, engineering and business. AutoML automates machine learning while SQL analytics lowers barriers to exploring data and collaborating.
8. DataRobot
End-to-end enterprise AI platform centered on automated machine learning, MLOps, and trust. Quickly build and deploy accurate models while monitoring for fairness, bias, explainability.
9. TIBCO Spotfire
Unified analytics environment with automated insights, natural language search and hyper-speed processing of streaming data sources. Recommended next steps guide users through analysis.
10. Alteryx Designer
Alteryx empowers everyone to accelerate analytics automation through a low / no code environment. Rapidly clean, blend, prep data while building repeatable workflows.
11. Trifacta
Trifacta provides an intelligent platform to profile, cleanse and transform complex data for analytics while monitoring data quality. Automated machine learning options too.
Explore Trifacta‘s Data Engineering
12. SAS Viya
SAS delivers AI-driven analytics, visualization and reporting accessible by all employees through natural interfaces. Users of any skill level can uncover insights.
13. Teradata Vantage
Teradata Vantage enables real-time intelligence by unifying analytics, data lakes and data warehouses. SQL, Python and R provide flexibility for coders and non-coders alike.
14. Oracle Analytics Cloud
Oracle Analytics Cloud combines cost-effective infrastructure with trusted analytics for the enterprise leveraging conversational, narrated and automated insights.
15. GoodData
Embedded analytics platform for developers that enables you to integrate analytics workflows, visualization and data processing as turnkey components.
This list provides a starting point based on independent analyst reports from Gartner, Forrester and others coupled with firsthand experience implementing AI analytics initiatives.
When evaluating technology, resist the temptation to solely focus on features. Instead consider the specific analytics challenges and questions you need to empower your users to address. Then examine how potential solutions would fit into existing infrastructure.
Now, let‘s explore some tips for getting the most from AI analytics…
Best Practices for Getting Started
Leveraging the following strategies will help you maximize ROI both during initial rollout and continual optimization:
1. Validate Data Quality
No analytics solution, AI-powered or not, can excel with poor data. As you prepare data pipelines, ensure proper governance through:
- Schema enforcement
- Duplicate detection
- Reference data checking
- Pattern violation identification
- Statistical analysis
Continuously monitoring data quality gives analysts confidence in the insights being generated.
2. Champion Change Management
Any organizational change requires thoughtful change management. When implementing modern analytics, address adoption through:
- Executive alignment
- Clear communications
- Personalized training
- Embedded analytics and KPIs tailored to each department
- Dedicated data ambassadors and AI coaches
- Soliciting and incorporating user feedback
3. Start Small, Scale Seamlessly
I always advise clients to start with limited scope pilots focused on high priority outcomes before broadening analytics access to the entire organization. Take lessons from the pilot outcomes to refine processes and platforms.
4. Plan for Responsible AI
Trust in algorithms that touch decision making requires focus on responsible AI via:
- Reducing unintended bias
- Increasing transparency
- Monitoring ethical outcomes
- Protecting user privacy within dataflows
5. Maintain Connectivity
Leverage platforms capable of leveraging real-time data from apps, IoT devices and transactional systems. Combining streaming and historical analytics paints the full picture.
Through applying these best practices, you‘ll ensure broad user adoption and remain future-proofed as AI continuum and your analytics needs advance.
Now, let‘s examine AI analytics delivering real business outcomes across various industries…
…..
Hopefully this guide has shown the art of the possible with AI transforming analytics, while equipping you to evaluate solutions matching your organization‘s maturity and objectives.
As cutting-edge innovations like natural language BI, augmented intelligence and AutoML continue advancing, the analytics landscape will experience continual disruption. But organizations proactively embracing analytics 3.0 powered by responsible AI will unlock immense opportunities.
Want to see some of these AI analytics platforms in action tailored to your business use cases? Reach out and let‘s explore modernizing your analytics approach!
To your data-driven success!
