How AI Speeds Up BI, Pipelines, and Analytics Modernization

Analytics teams face challenges such as slow dashboards, unstable data pipelines, and increasing volumes of enterprise business intelligence (BI) requests. As organizations continue to capture additional raw data and add users, it is becoming difficult for traditional methods of reporting, optimizing SQL commands, and managing pipelines to keep up. As analytics teams attempt to retrieve actionable data from the multitude of systems, these challenges result in delayed decision making, higher maintenance costs, and dissatisfied business stakeholders.

Organizations and their analytics teams will find that artificial intelligence (AI) is a viable way to accelerate and augment the performance of BI and facilitate data engineering. AI will not replace current BI solutions, nor will it content with current BI solutions; however, AI will complement current BI solutions by optimizing their performance, automating repetitive tasks, improving governance, and modernizing analytics processes. Organizations need to properly understand how and where they can gain measurable benefits from AI today and where traditional optimization methods are still important.

Perceptive’s POV

We at Perceptive Analytics have discovered that the root cause of most BI performance issues is not the reporting tool itself, but rather an inefficient process. When there is an excessive amount of technical debt, dashboards slow down; when pipeline maintenance is performed manually, the pipelines become brittle; and BI teams are often inundated with repeated requests from end users.

The most successful modernization programs make use of AI as a co-pilot for analysts, developers, and data engineers. By using AI for the optimization of SQL workloads, automating repetitive reporting tasks, creating better visibility into data pipelines and strengthening governance controls, BI teams can spend more time generating insights and have less workload associated with maintaining the underlying infrastructure. As enterprise analytics continue to evolve and grow, AI will continue to play an important role in enabling scalable, future-ready BI ecosystems.

Why BI Dashboards and Data Pipelines Are Slow Today

Organisations have to be aware of the causes of performance failures before going ahead with using AI.

Potential dashboard problems are:

• Poor SQL query performance
• Large, inefficiently structured data sets
• High computational demands for dashboard calculations
• Lack of semantic integrity in model design
• Duplicate reporting logic maintained by multiple data manipulation processes

According to Microsoft, problems in Power BI have been caused primarily due to poor data modelling, excessive visuals and poorly designed calculations which will impact latency of a dashboard.

In addition, Tableau identifies complex calculations, overly large joins and inefficient extract processes as significant barriers to performance.

In addition to performance issues on dashboards; there are also issues around how data flows within a pipeline. Issues commonly faced include schema drift, manual monitoring of data quality and lineage, lack of visibility of data lineage and ineffective governance of data; these issues compound over time resulting in increased delays reporting and growing maintenance effort. AI addresses a number of these bottlenecks by providing automated solutions, prediction and intelligent optimisation.

Core AI Techniques That Accelerate SQL and Python Pipelines

AI is being used more frequently in the workflows of data engineering. The use of AI will help you to get better results than ever before. AI can also be used to optimize your current processes and improve upon those processes as well.

• Intelligent Query Optimization

AI systems can analyze execution patterns and assist you with the structure of your queries.

Compared to traditional methods:

• Traditional tuning is done through manual reviews of queries.
• AI continuously looks for opportunities to optimize your queries.

Where it fits best:

• In environments with very large analytical workloads.
• In environments that have complexity to their reporting requirements.

• Automated Code Recommendations

With the use of generative AI, developers can receive automated recommendations for the following types of code:

• Ways to improve SQL code.
• Ways to convert code from Python to another programming language.
• Ideas to refactor your existing code.
• Ways to generate documentation.

Developers will receive code recommendations from the use of AI. Using an AI system that provides recommendations allows a developer to accelerate their production and still allows the developer to maintain oversight of their work.

• Optimizing Parallel Processing

Using Apache Spark and Dask, both of which have scalable architectures and increasingly utilize intelligent workload management.

Apache Spark Performance Optimization Techniques:

Dask best practices:

Where they work best

• For very large sets of data
• In environments with distributed processing

Includes restrictions

• Must have infrastructure to support them
• Cannot make up for poor data architecture.

• Workload Prediction Using Machine Learning

Machine learning can be used to predict spikes of workloads so that resources can be allocated before the spike occurs.

By doing this, you will achieve:

• Better use of resources
• Fewer queries experiencing congestion
• More reliable pipelines

Through the work done by Perceptive Analytics, we continue to find that combining the use of AI-assisted optimization with foundational data architecture improvements will produce a higher return on your investment than using automation alone.

Using AI to Fix Slow BI Dashboards

Many companies think that when they are having performance problems with their dashboards, they need to recreate their reports from scratch. The truth is, with the power of AI, most of the time you can get to the root cause of your issues and have AI recommend specific improvements for your organization.

• Usage Analysis of Dashboards

AI will analyze the user’s behavior on dashboards and provide you with:

• Unused Visualizations
• Unproductive Navigation Paths
• Slow Loading Elements

• Optimization of Semantic Models

AI can also provide you with recommendations for:

• More Effective Aggregation Methods
• Better Relationships Between Data
• An Overall Simplified Data Model

• Queries Performance Monitoring

With machine learning, AI helps identify repetitive bottlenecks and suggest fixes before the user has to deal with any delay.

• Refresh Optimization

AI will assist you in determining the:

• Most Appropriate Time to Refresh
• Best Opportunity for Incremental Processing
• Partitioning Strategies for Data Sets

Benefits of AI-Assisted Over Traditional Methods

Traditional dashboard tuning

• Manual troubleshooting
• Periodic optimization projects
• Reactive performance management

AI-assisted optimization

• Continuous monitoring
• Automated recommendations
• Faster issue resolution

Example – Perceptive Analytics developed a unified Executive Dashboard with campaign performance metrics, acquisition metrics and the ability for Executives to report from a single point of ground. This consolidation of disparate reporting into a unified Analytics Environment allowed Executives to gain faster access to the data they trust while reducing the workload on manual report preparation.

Limitation on AI Performance Improvements

While AI can improve performance, it cannot rectify fundamental data infrastructure and governance issues.

Automating Repetitive BI Work and Backlog With AI

One of the quickest means of obtaining value from AI is to reduce the redundancies that contribute to increasing the size of the BI backlog.

Common ways that companies have automated tasks

• Reporting Generation

AI creates useable reports or summaries regularly from pre-created templates.

• Dashboards Descriptions

Generative AI provides dashboards descriptions, dashboard definitions and dashboard user assistance.

• Data Quality Verification

AI verifies the quality of data on a continual basis instead of only through manual verification.

• BI Request Classifications

AI assists in categorizing and prioritizing incoming BI request.

• Explanatory Insights

Natural language generation automatically produces explanations of any trends, anomalies and business drivers.

Comparing Benefits of Traditional BI Business Operations with AI-Enhanced BI Business Operations

Traditional BI teams have

• High manual effort
• Increasing backlog of report requests
• A substantial amount of maintenance overhead to their product

AI-Enhanced BI teams will have

• Faster delivery cycle times than traditional BI teams
• Less of an increase in their backlogs than traditional BI teams
• More time spent by analysts performing true business analytics than traditional BI teams.

Example: Perceptive Analytics has developed a Automated Data Extraction for Real-Time Review Insights solution that automates data extraction and analysis workflows allowing for quick and easy access to customer sentiment information and improving the timeliness of reporting.

Over the last several years, there have been major improvements to generative AI and embedded co-pilots making it less difficult to leverage these tools than in the past.

AI-Driven Approaches for Fragmented Pipelines and Governance Gaps

Many modern organizations struggle with disparate data ownership, inconsistent policies governing data, and limited visibility into data flow as their analytics environments become increasingly disconnected. In addition to these challenges, using AI can help replace distributed/dysfunctional governance of data with automated lineage mapping, metadata intelligence, governance monitoring, and anomaly detection.

More and more organizations apply AI techniques to:

• Identify pipeline failures
• Seek out schema drift
• Assess compliance-related issues
• Measure data quality degradation

Identify risks

Some risks include:

• Inaccurate recommendations from AI algorithms
• Failure of human oversight
• Conflicting governance policies

NIST’s AI Risk Management Framework provides a strong starting point for organizations seeking to implement AI using a responsible, accountable and transparent approach.

Perceptive Analytics has stated that governance modernization should keep pace with your automation initiatives and not be considered a separate project.

Top AI Techniques Powering Analytics Modernization in Large Enterprises

These are five essential techniques that consistently create value for enterprise analytics efforts:

• Optimizing AI Queries

Allows for automated query performance improvements in SQL and the ability to improve resource utilization.

• Detecting Anomalies

Identifies failures in pipelines, quality control problems, and performance bottlenecks before they reach users.

• Using Metadata Intelligence

Automates the tracking of lineage, cataloging, and governance.

• Generating AI Assistants

Assist in developing SQL code, scripting with Python, creating documentation, and creating reports with a faster speed than would normally be the case.

• Monitoring Power

Identify faults before they occur to minimize operational disruption.

McKinsey’s State of AI indicates that companies that derive the most value from AI increasingly embed these new capabilities directly within their operational workflows instead of treating them as a separate project.

A similar approach is being used on many modernization projects that Perceptive Analytics has undertaken, which integrate AI capabilities into analytics workflows versus placing them on top of separate tools.

Case Study: With Perceptive Analytics’ Unified Business View solution, customers consolidated reporting systems into one centralized solution, providing consistent reporting, improved governance, and increased executive decision-making.

Getting Started: Practical Steps and Guardrails

Practical steps to get started with a modernized BI infrastructure include the following five items:

• Identify High-Friction Areas – This may include slow dashboards, manual reporting or unstable pipelines.
• Prioritize Quick Wins – Focus on repetitive tasks with measurable business value.
• Pilot AI-Assisted Workflows – Select a small use case, conduct a pilot and then expand your project.
• Maintain Governance Oversight – Human reviewers will continue to be needed to maintain quality, compliance, and accountability.
• Measure Outcomes – Monitor performance, productivity, adoption and reliability across business unit functions.

Conclusion

AI continues to serve as an effective means of facilitating BI modernization including, but not limited to: improving dashboard performance; optimizing SQL and Python pipelines; reducing reporting backlogs; increasing governance; and developing modernized analytics workflows. The greatest potential for value from AI-assisted automation will occur when AI-assisted automation is combined with a solid data foundation, effective governance practices and solid alignment to business objectives.

Next Steps: Get your copy of our checklist “10 Quick Wins for Utilizing AI to Accelerate BI & Pipelines” or read our guide on how to prioritize AI use-case(s) within your analytics roadmap. If you’re already investigating opportunities for implementation, Perceptive Analytics can assist in evaluating where AI may be able to achieve the most rapid and sustained value within your organization’s BI ecosystem.