How Do Businesses Democratize Analytics With AI in 2024?

Augmented analytics is an emerging approach to data analysis that uses artificial intelligence to assist with data preparation, exploration, and insight generation. As we move into 2023, augmented analytics will play a key role in democratizing access to analytics across organizations, enabling more data-driven decision making.

A Primer: What is Augmented Analytics?

Augmented analytics leverages AI technologies like machine learning, natural language processing (NLP), and data visualization to enhance and automate aspects of the data analysis workflow. The goal is to make working with data accessible to more business users – not just highly skilled data scientists.

With augmented analytics, AI handles many of the repetitive, manual tasks involved in analytics while augmenting human intelligence. Key capabilities include:

  • NLQ – Natural language queries allow users to ask questions in plain language instead of complex query syntax.

  • Automated insights – Algorithms highlight key trends, anomalies, and relationships for further exploration.

  • Data prep automation – Systems combine, cleanse, and transform diverse data sets without human intervention.

  • Advanced visualizations – Users can interactively visualize data in new ways to uncover patterns.

According to Gartner, by 2020 augmented analytics had already reduced time spent on data preparation by up to 80%, empowering users to focus on higher-value work.

The AI Behind Augmented Analytics

Several AI technologies work together to enable augmented analytics:

Machine Learning

ML algorithms can process huge volumes of data to identify complex patterns and relationships impossible for humans to surface manually. As they ingest more training data, ML models continuously improve their analytical capabilities.

Natural Language Processing

NLP techniques like semantic parsing allow users to query data using conversational language. The system understands the true intent and meaning behind natural language questions.

Computer Vision

Computer vision enables systems to process and gain insights from visual data like video, images, and handwriting. For example, reading license plate numbers or identifying defects in manufacturing.

Knowledge Graphs

Knowledge graphs capture relationships between various data entities, improving contextual insights. Connecting data together in this graph representation helps uncover correlations.

According to IDC, by applying these AI technologies to analytics, businesses can accelerate time to insight by up to 650% compared to traditional methods.

Real-World Use Cases and Examples

Augmented analytics applies across functions and verticals. Here are some real-world examples:

Retail

Leading retailers like Amazon and Walmart apply augmented analytics to transaction data, inventory metrics, and third-party data to optimize pricing, promotions, supply chain, and fulfillment. Walmart saw a 10-15% sales lift from optimizing promotions with data science.

Manufacturing

Manufacturers like Airbus are utilizing visual analytics solutions to identify defects and anomalies in production early. Combining computer vision and ML on imagery from the assembly line reduces quality issues.

Healthcare

Augmented analytics helps healthcare providers gain insights from vast amounts of patient, clinical, and operational data. These insights improve clinical decision making and patient outcomes while reducing costs. Partners Healthcare saw a 59% drop in readmission rates after an analytics initiative.

Financial Services

Banks apply augmented analytics to combat fraud and manage risk more effectively. According to SAS, ML helped ING Bank slashed false positives by 50% in anti-money laundering efforts.

Marketing

Marketers like Unilever are using augmented analytics to optimize campaigns, better attribute leads to channels, model the customer journey, and personalize experiences.

These examples showcase how augmented analytics expands access to data-driven decision making beyond just highly skilled analysts. According to survey data from Dresner Advisory Services, 86% of augmented analytics users are business analysts or managers vs. only 14% data scientists.

The Benefits of Democratizing Analytics with AI

There are several compelling benefits to democratizing analytics access with augmented intelligence:

  • Increased adoption – By making analytics tools more intuitive and user-friendly, augmented analytics expands regular usage beyond just trained experts. According to Gartner, analytics adoption increases by at least 50% when enabled by AI augmentation.

  • Faster insights – Automating cumbersome manual tasks accelerates the insight-to-action cycle. Time spent on data prep alone falls by up to 80%, per Gartner.

  • Deeper analysis – Users can invest more time exploring root causes and SIMULATIONS aided by algorithms highlighting areas warranting more attention.

  • Improved productivity – Automating repetitive, low-value tasks allows skilled analysts to focus on high-value work and guidance. Augmented analytics users have seen productivity gains of up to 50%, says Forrester.

  • Cost savings – Reducing reliance on highly paid scarce data science talent generates significant cost savings. Citizen data scientists using augmented tools can fill the gap.

In essence, by breaking down barriers to access and usage, augmented analytics unlocks the next level of democratization of data-driven decision making.

Implementation Best Practices

For companies starting their augmented analytics journey, here are some best practices to drive adoption and success:

  • Understand your talent. Audit employees‘ data literacy and interest in analytics as a baseline. Look for analysis bottlenecks and skills gaps.

  • Start small, think big. Pilot augmented analytics on focused high-value use cases with specific KPIs before scaling across the organization.

  • Manage change and training. Change management and training in augmented tools are crucial to drive adoption. Appoint citizen data science ambassadors.

  • Monitor usage and gather feedback. Track usage metrics and user sentiment to fine-tune the rollout. Continue enhancing skills and tools based on insights.

  • Augment people first, replace tasks second. Keep humans in the loop rather than handing full control over to automation. Foster trust in AI.

Evaluating Augmented Analytics Platforms

Leading augmented analytics platforms to evaluate include:

Microsoft Power BI – An end-to-end suite with conversational NLQ via Power BI Q&A, automated ML-powered insights in Power BI key influencers, and compelling visualizations.

Tableau – Tableau augmented analytics uses Explain Data to automatically surface insights. The new Ask Data capability allows NLQ querying. Advanced chart types and analytics integrate seamlessly.

Qlik Sense – Qlik applies NLP and embedded ML across the self-service analytics platform, including conversational search and insight advisor automation features.

ThoughtSpot – ThoughtSpot uses a powerful Google-like NLQ search engine combined with AI-driven insights surfaced as "Thoughts." The cloud platform scales easily.

SAS Viya – SAS augments its advanced analytics platform with automated modeling, NLQ interaction, and search tools enhanced by ML. User guidance features drive actionability.

When evaluating solutions, look for the degree of automation in insight generation, flexibility of NLQ, breadth of ML-powered analytics capabilities, and ease of use for citizen data scientists.

The Road Ahead for Augmented Analytics

According to IDC, global spending on augmented analytics solutions will grow at a 50% CAGR from 2020 to reach over $7 billion in 2024. Adoption will rapidly accelerate as capabilities improve.

As augmented analytics evolves, expect analytics automation to move beyond just preparing data and providing basic insights. AI will play a growing role in recommending actions and contextualizing decisions. According to Gartner, by 2025 over 75% of new analytics projects will be explicitly intended for operationalization of analytics.

Natural language capabilities will also advance to not just query data but engage in complex conversations about findings and perform analytics via voice. Vision capabilities like reading visualizations will unlock new modalities.

The end goal is augmented analytics platforms that can serve almost as virtual data science collaborators – helping to guide non-experts through data-driven decisions using contextual recommendations tailored to users‘ needs and goals.

Key Takeaways

  • Augmented analytics harnesses AI techniques like ML and NLP to automate data preparation and analysis while enhancing human intelligence.

  • Core benefits include increased usage, faster insights, deeper analysis, improved productivity and lower costs.

  • Augmented analytics solutions democratize access to data-driven decision making beyond just highly skilled data scientists.

  • When implementing augmented analytics, focus on change management, use case prioritization, user adoption metrics and continuous enhancement.

  • Look for analytics platforms with robust insight automation, NLQ capabilities, ML integration and ease of use for citizen data scientists.

By enabling more data literate and data-driven organizations, the rise of augmented analytics accelerates data democratization into the future.

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