AI in Analytics: How AI is Shaping Analytics in 2024 in 4 Ways

The integration of artificial intelligence (AI) into analytics is rapidly accelerating, transforming what is possible. As an analytics leader with over a decade of experience, I‘ve observed firsthand how AI is making data analysis more automated, accessible, wide-ranging and predictive. In this comprehensive guide, I‘ll share expert insights into how AI is shaping the world of business analytics as we enter 2023 and beyond.

1. Automating Analytics Tasks with AI

One of the biggest impacts we‘re seeing is that AI allows us to automate many repetitive, manual analytics tasks. According to a McKinsey survey, this could free up 45% of a data scientist‘s workload. For business analysts focused more on intelligence than modeling, that number could be even higher.

Automating Data Prep

Data preparation and cleaning represent up to 80% of an analyst‘s time, according to CrowdFlower. But AI techniques like natural language processing (NLP), image recognition, and optimization algorithms can automate:

  • Data ingestion from APIs, websites, and documents
  • Connecting, merging, and shaping datasets
  • Dealing with missing values and anomalies
  • Parsing unstructured data

This alleviates the heavy data lifting that has traditionally bogged analysts down.

Automated Reporting via NLG

Reporting and visualization used to require extensive manual work to synthesize insights and create presentations. With natural language generation (NLG), reports can be auto-generated from data inputs, saving weeks of analyst time.

Gartner predicts that by 2025, 50% of reporting will be auto-generated using AI techniques like NLG. Analysts can focus less on building charts and more on higher-value analysis.

AI-Driven Analysis and Alerting

Today‘s analytics tools can use machine learning to autonomously analyze data streams, identify key changes, take prescribed actions, and alert analysts. Rather than just simple rule-based alerts, current systems can learn patterns and adapt.

For example, a manufacturing plant could have an AI system monitoring sensor data that learns to detect possible failures and alert engineers to intervene before a breakdown. This enables a level of real-time responsiveness impossible through manual monitoring.

According to Accenture, the addition of AI to analytics can improve forecast accuracy by up to 50%. The automation of rote tasks allows analysts to focus where humans still excel – higher-order thinking.

2. Democratizing Analytics via Natural Language

Historically, deriving insights from data required an understanding of complex analytics tools and query languages. AI advances are changing this by enabling natural language interactions.

With conversational interfaces and natural language processing (NLP), users can get answers and insights through intuitive, conversational interactions. They simply ask questions in plain language rather than writing complex queries.

Vendors like ThoughtSpot and legacy providers like IBM and Oracle now offer NLP-powered analytics platforms. Rather than waiting for reports from analysts, business users across departments can leverage data on their own terms.

According to IDC, 75% of enterprises will use conversational analytics tools by 2025. Democratizing data access in this way can transform organizational analytics maturity. Benefits include:

  • Faster insights – Questions get answered immediately without waiting for analysts. This enables data-driven decision making.

  • Reduced bottlenecks – More employees can derive insights without going through a small analytics team. This scales analytics throughput.

  • Convenience – There‘s no need to learn complex query languages. Users get insights conversationally.

The natural language analytics revolution promises to be as impactful as self-service business intelligence was. But this time, AI is eliminating the tool literacy barrier.

3. Analyzing the 80%: Unstructured Data

Legacy analytics tools like Microsoft Excel are great for analyzing structured data – ordered datasets in fields like sales records, account info, or product catalogs.

But according to IDC, unstructured data accounts for 80% of enterprise information, including formats like documents, emails, social media, support tickets, and media.

Unstructured data represents a hugely underutilized asset. But new AI techniques are making it more analyzable than ever before. For example:

  • Natural language processing – Tools can extract entities, classify documents, and determine sentiment and intent within text. This unlocks analysis of documents, emails, chats, social media, notes and more.

  • Speech recognition – Transcribing call center calls allows searching transcripts for topics and analyzing major pain points. Emotion detection identifies angry customers.

  • Computer vision – Object recognition, facial analysis, and optical character recognition (OCR) extract structured data from images, video, and scans to feed downstream analysis.

According to Pentaho, unstructured data analysis can yield a 42% higher ROI compared to traditional analytics based on structured data alone.

AI gives knowledge workers and analysts superpowers for unlocking insights from the 80% of data that was previously underutilized.

4. Expanding the Scope of Analytics with AI

In addition to unstructured data, AI techniques are expanding analytics scope in two other key ways:

Analyzing Anonymized PII Data

Many organizations collect troves of detailed, personally identifiable customer data. Data privacy regulations often restrict analysis of this PII data, hindering CX improvement efforts.

But using AI, companies can now create anonymized customer data via synthetic modeling. The synthetic data exhibits the same statistical properties as the real PII data.

Analysts can run simulations and deep analytics on synthetic customer data to derive insights while fully protecting individual privacy.

Gartner predicts that 60% of organizations will use synthetic data techniques by 2024. This enables expansive analytics capabilities, even for highly regulated industries like banking, insurance, and healthcare.

More Powerful Predictive Modeling

Traditional analytics relied on simple correlations and regressions. But machine learning algorithms identify non-linear relationships and interdependencies that were previously invisible.

As a result, ML transforms analytics from merely reactive insights to robust forecasting for scenarios like:

  • Customer churn prediction
  • Supply and demand forecasting
  • Predictive maintenance
  • Healthcare risk modeling
  • Fraud detection

According to McKinsey, machine learning can improve forecasting accuracy by 10 to 30 percent, and reduce forecasting costs by up to 40 percent.

The bottom line is that AI expands analytics from just hindsight and description to accurate foresight and prediction. This amplifies the business impact substantially.

Industries Quickly Adopting AI Analytics

While nearly every vertical is applying AI techniques to unlock more value from data, some industries lead the pack. For example:

Manufacturing – AI optimization helps manufacturers improve quality, reduce waste, minimize downtime, and increase output. According to Cerasis, 70% of manufacturers have now adopted AI analytics.

Healthcare – AI powers clinical decision support tools, optimized hospital operations, improved patient experience, and predictive population health models. Accenture estimates the AI health market will reach $52 billion by 2026.

Retail – Retailers use AI across business functions from CX insights to pricing optimization to predicting purchase intent. Customer analytics drives personalized promotions. Per Salesforce, AI-driven recommendations can raise sales by 10% or more.

HR – For chief people officers, AI unlocks talent analytics to predict churn, boost engagement, understand employee sentiment, and highlight diversity gaps. According to Oracle, 61% of HR leaders say AI is very or extremely important to the future of their operations.

Top Analytics Tools With Integrated AI

Many business intelligence, data science, and data visualization platforms now integrate AI capabilities for automating workflows, enabling natural language interactions, building ML models, and more. Here are some of the top solutions:

  • Salesforce Einstein Analytics – Einstein Discovery auto-generates models. There is NLP query capability and auto-visualization.

  • Oracle Analytics Cloud – Offers natural language Q&A, auto-insight generation, data preparation assistants, and native AI/ML integration.

  • Microsoft Power BI – Power BI now includes NL interaction and autoML capabilities for non-experts to leverage machine learning more easily.

  • IBM Watson Studio – A suite of AI-powered analytics tools including Project Debater for natural language-based data discovery.

  • SAP Analytics Cloud – Features computer vision and NLP functions. Also enables workflow automation between SAP products.

  • Qlik Sense – Users can ask natural language questions and receive auto-generated visualizations. Also offers automatic forecasting and associations.

  • SAS Viya – Provides automated data prep, visualization, modeling, and machine learning capabilities accessible through its Visual Analytics interface.

The common thread is leveraging AI and ML to enhance analytics productivity – either by automating rote tasks or by putting insights into the hands of more users through NLP. This expands the analytics accessible to everyday business users.

The Future of Analytics is AI-Powered

Based on all the innovations we‘re seeing, it‘s clear that AI will continue playing a central role in analytics innovation going forward. As someone who‘s been in the analytics space for over 10 years, I expect to see some major strides in the years ahead in areas like:

  • Truly conversational analytics interfaces powered by more advanced NLP that go beyond just querying data to deeper insights.

  • Increased integration of predictive analytics into processes via techniques like deep learning and reinforcement learning.

  • Further automation of data science workflows allowing analysts to focus on higher-order problem solving.

  • Expanded use of synthetic data analytics for safely deriving insights from PII data at scale.

The bottom line is organizations need an enterprise analytics strategy that allows them to take advantage of these innovations. With the speed of progress today, data-driven companies will pull farther ahead of traditional lagging competitors.

Those who strategically adopt AI analytics will drive tangible business returns through sharper predictive intelligence, faster iteration, improved customer experiences, breakthrough innovations, and optimized workflows. Based on the acceleration of development, the future of AI looks very bright for analytics indeed.