Integrating AI with BI – The Benefits & Data Challenges

Business intelligence (BI) has become a cornerstone of enterprise decision-making. Whether delivered through centralized dashboards developed by IT and BI teams or via self-service applications, BI tools are now indispensable for business users. However, the integration of artificial intelligence (AI) is redefining how decision-makers interact with and derive value from the business data analyzed in BI systems.

AI offers a broad range of potential benefits and applications, some that enhance BI and others that introduce entirely new capabilities. In this article, we’ll explore how AI can elevate BI practices, its current impact, and key data-related considerations you’ll need to make as the trend of combining these technologies evolves.

AI’s Expanding Role in Business Intelligence

BI tools, such as dashboards and interactive reports, have traditionally focused on aggregating and analyzing historical and real-time data to provide an overview of business operations. This approach, referred to as descriptive analytics, focuses on what has happened and what is happening now.

However, modern businesses increasingly seek answers to two critical questions: What is likely to happen next? and What actions should be taken in response? These needs are addressed through predictive analytics, which forecasts future trends, and prescriptive analytics, which recommends actions based on those forecasts.

Historically, deploying such systems posed significant challenges due to their complexity. AI now bridges this gap by delivering advanced analytics capabilities with simplified user experiences, often powered by natural language processing (NLP). By augmenting descriptive analytics with predictive and prescriptive analytics, AI empowers businesses to anticipate trends and take proactive measures.

Integrated AI / BI capabilities also enhance the timeliness of insights by supporting real-time data analysis. Additionally, AI facilitates process automation, enabling businesses to streamline repetitive tasks, reduce manual effort, and improve overall operational efficiency.

Key Benefits of AI-Enhanced BI

The integration of AI with BI unlocks transformative possibilities. Among the emerging trends, four significant benefits stand out:

Increased Automation
AI’s ability to automate processes with minimal human intervention delivers substantial economic benefits. Much of BI involves repetitive tasks like data preparation, which AI can handle efficiently. By automating both data preparation and analysis, AI allows business users to focus on strategic initiatives that leverage their expertise, enhancing productivity and driving better strategic outcomes.

Improved Decision-Making
Machine Learning (ML), a subset of AI, excels at uncovering complex patterns within large datasets. Companies can leverage ML algorithms to conduct extensive analytics experiments, test scenarios, and extract insights from intricate data environments. While traditionally a domain of advanced data science, these algorithms are increasingly integrated into BI, providing more comprehensive and actionable insights for better decision-making.

Democratized Data Analysis
Modern AI tools with NLP interfaces make the BI systems more accessible to non-technical users. Business users can interact with BI data in plain language, bypassing the need for specialized query skills or scripting. This democratization fosters a data-literate culture across organizations, empowering more employees to make data-driven decisions.

Data-Related Challenges of AI / BI Integration

While AI has significantly enhanced BI by enabling advanced analytics, predictive insights, and real-time decision-making, leveraging the powers of both together is not without its hurdles, particularly concerning data management. Organizations face various challenges related to data collection, governance, quality, and infrastructure that can hinder the success of integrated AI / BI initiatives.

Both AI and BI require vast amounts of high-quality data to function effectively. However, organizations often struggle with issues related to data accessibility, integrity, governance, and scalability. Addressing these challenges is crucial to ensuring that the analytical models of BI and machine learning models of AI provide accurate, actionable insights.

Data Silos and Fragmentation

One of the most significant obstacles to BI and AI systems is the existence of data silos. Many organizations store their data in disparate systems, including customer relationship management (CRM) platforms, enterprise resource planning (ERP) systems, external third-party databases and personal spreadsheets. This fragmented approach leads to:

Inconsistencies in Data Formats – Different sources may store data in incompatible formats, making integration challenging.

Limited Data Accessibility – Important business data may be locked within specific departments, preventing models from accessing the full dataset for comprehensive analysis.

Complex Data Integration Needs – More sophisticated ETL (Extract, Transform, Load) capabilities becomes necessary to consolidate and standardize the data, particularly for AI processing.

Data Quality and Completeness

For AI and BI to deliver valuable insights, data quality must be a top priority. Poor-quality data can lead to incorrect predictions, misinformed business strategies, and reduced trust in the technologies for decision-making. Common issues include:

Incomplete or Missing Data – Gaps in datasets can skew models, leading to biased or unreliable results.

Inconsistent or Outdated Data – Information that is not regularly updated can lead to decision-making based on obsolete or inaccurate data.

Data Validation and Cleansing Needs – Organizations must invest in data validation tools, automated cleansing processes, and enrichment techniques to maintain high data integrity.

Building a Trustworthy Data Environment

A trustworthy data environment is needed for BI and AI to work. Data management processes and procedures must be defined to ensure that the data flowing into analytic applications (or for use by AI and machine learning models to make predictions and drive innovation initiatives) is valid and meaningful. As datasets enlarge, the odds of data quality compromising decision-making accuracy may rise.

This is often the case when an organization rushes to pull data in, with the end result being a disorganized mess. A perfect example is a data acquisition for which an organization would want to import and consolidate its customer data right away. However, customer data is often defined differently between systems. A lack of data management standardization can lead teams to move back to their own Excel spreadsheets, leading to different versions of the truth within the organization.

Management of an organization’s master data ensures uniformity, accuracy, semantic consistency and accountability of an organization’s shared assets—delivering a single source of truth. It’s absolutely imperative for businesses with data across multiple systems and business entities.

Here are some tips to help you incorporate better data management within your organization to ensure that integrated AI / BI initiatives deliver accurate, reliable and impactful results.

Data Quality

Data quality is with no question the foundation upon which successful BI and AI models and applications are built. To harness their true potential and ensure their responsible and compliant use, a relentless focus on collecting, transforming, and filtering high-quality data is indispensable.

Data quality is vital to making quality decisions. It’s an essential aspect that must be thoroughly reviewed when implementing a solution for the data consumers of your organization. However, it must also be an on-going process that’s regularly reviewed as your business changes – whether by acquisitions, product restructuring, etc. Business procedures and data transformation rules need to be established and strictly followed to help ensure the highest quality of data.

Some things to consider when examining the quality of data:

Consistency of Aggregated Data – Watch for the consistent treatment of items such as customer and product numbers. For example, when reviewing data by National Accounts – are all Sold-To’s correctly aggregating to the same National Account? Did one of your key customers acquire a competitor (which you previously sold to directly) and are you including those sales dollars? What business procedures need to be set up so that this information is applied to the data?

Building Awareness of What Data Represents – Does everyone understand the definition of ‘Net Sales’? Here again, the data needs to be consistent and understood by all users. A glossary that administrators can customize with descriptions to help business users understand what each object in the data model represents is highly recommended.

Cross-Referencing Across Sources – If you plan to analyze POS data – are the product numbers used by your partners being correctly cross-referenced to your internal product / material numbers? Are transactions being rejected and not included in calculations?

Data Integration

The second step in preparing data for BI and AI is integrating information from multiple sources to create a unified, continuously updated dataset ready for intelligent analysis. Effective data integration allows for greater accessibility, enriches data with additional attributes, and minimizes data silos and inconsistencies. Key methods for integration include data extraction, transformation, loading, and synchronization.

A well-structured analytics layer built on clean data is essential for the success of descriptive, predictive AND prescriptive analytics. The integration process typically involves:

Loading raw data from diverse sources into an operational data hub that includes built-in integration tools.

Transforming data through cleansing, formatting, and applying business rules.

Building an analytics layer with structured tables and dimensional models. Some business intelligence solutions, such as Silvon’s, offer pre-built models that simply require data input and customization to match organizational needs.

Enhancing data with augmentation to improve quantity, diversity and quality – such as supplementing customer and product data with additional attributes that are not currently included in your ERP or other data sources. This process helps mitigate data scarcity, reduce bias, and strengthen model generalization.

Data Access

Ensuring secure data access is crucial. Implementing role-based access controls restricts sensitive information to authorized users, protecting data from unauthorized exposure.

For example, you might want to define security settings at multiple levels:

Hierarchy-based access – E.g., sales representatives can be restricted to viewing only their assigned territory data.

Measure-based access – Certain data points, such as costs, can be hidden from non-financial users.

User group security – Security roles can be defined for groups with shared permissions, ensuring consistency and streamlined access control.

These are just a few key data considerations that should be addressed when integrating AI with BI. While data is the fuel that powers these technologies, you need to ensure that your data is curated, cleaned and crafted with precision for the best outcomes. Fortunately, tools like Silvon’s data hub-based business Intelligence platform can streamline the process.