Traditional enterprise data warehouses (EDWs) have served businesses well for decades, but they come with limitations: high costs, inflexibility, and lengthy implementation times. OLAP knowledge graph technology represents a transformative approach to data integration that addresses these challenges while opening new possibilities for organizations to better leverage their data assets.

Benefits and Limitations of Traditional Data Warehouses

Traditional EDWs are centralized repositories — usually maintained on premises — that store structured data for analysis and reporting applications, often using extract-transform-load (ETL) workflows. They offer reliable, secure, and scalable ways to store and manage large amounts of data. They support high levels of data quality and offer the desirable “single source of truth.” EDWs can efficiently process large volumes of structured data, provide users with consistent, reliable reporting, and enable standardized analysis across the enterprise. 

But enterprises looking at EDWs must also be aware of limitations, including:

• Extensive up-front schema design requirements that can eat up budget and valuable implementation time.
• High implementation costs.
• High risk; many EDW projects fail to achieve their objectives or are cancelled before completion.
• Ongoing demand for experienced and specialized people to maintain the system. These people come at a steep cost.
• Time-consuming and expensive to add new data sources, modify to answer new types of queries, and support new business requirements due to their utilization of rigid data models.
• EDWs can’t effectively integrate unstructured data, which comprises around 80% of enterprise data today – and is rapidly growing due to generative AI.

In short, the EDW approach simply can’t meet global enterprises’ needs in today’s environment.

Why EDW Projects Fail So Frequently

One survey found that 83% of organizations aren’t fully satisfied with the performance and output of their data management and data warehousing initiatives. Various articles and studies suggest the following reasons for the high failure rates of EDW projects:

• Unclear project scope and definition
• Inadequate technical infrastructure and tools
• Lack of user involvement and communication
• Poor data quality
• Treating data projects as purely IT initiatives rather than business endeavors
• Long development cycles
• Failure to deliver business value

Data Lakes: An Inadequate Solution

Data lakes attempt to address the limitations of EDWs with a “schema on read” approach, but this introduces new problems, including:

• Poor performance on complex join operations
• Difficulty in maintaining data quality
• Challenges in finding and accessing relevant data
• Limited ability to represent complex data relationships

The OLAP Knowledge Graph Advantage

OLAP knowledge graphs are a different way to handle data integration compared to data lakes and EDWs. They combine the performance benefits of traditional data warehouses with unprecedented flexibility and intuitive data modeling.

Intuitive Data Representation

OLAP knowledge graph systems model data (things, attributes, and relationships) as it exists in the real world and eliminate the complexity of translating business concepts into technical tables. This makes data much more accessible to users without technical expertise.

Rapid Implementation

OLAP knowledge graphs utilize a “load the data, ask questions later” approach with no need for extensive up-front schema design. This allows enterprises to start small and expand incrementally as needed, which vastly reduces implementation risk and facilitates user acceptance.

Agile and Adaptable

Enterprises using OLAP knowledge graph systems can make changes quickly and without spending enormous amounts of money to answer new business questions and support new requirements. They can add new data sources without disrupting their existing systems and incorporate unstructured data sources – including emails, documents, web pages, and images – alongside structured data.

Richer Insights

OLAP knowledge graphs enable users to explore and correlate data from previously siloed systems, discover unexpected relationships, and get answers to complex questions that span multiple domains.

Lower Total Cost of Ownership

In addition to fast implementation, the OLAP knowledge graph approach reduces the enterprise’s dependence on specialized technical staff and minimizes ongoing maintenance requirements.

How OLAP Knowledge Graphs Work

OLAP knowledge graph systems store data as a network of interconnected entities instead of traditional data tables. This supports important technical advantages:

• Labels instead of tables: OLAP knowledge graph systems organize data using meaningful labels instead of rigid table structures. The labels are defined in ontologies and are chosen using recognizable terms, which creates a semantic (natural language) layer.
• Schema-less design: Data models can evolve over time without disrupting daily operations.
• Instance data and metadata stored together: OLAP knowledge graph systems store business context (metadata) alongside the data itself.
• Flexible integration options: Enterprises can use labels — shared entity descriptions — to combine any number of different data sources.
• Automated query generation: The system can generate complex queries on demand based on the data model and its semantic descriptions
• Multi-graph support: OLAP knowledge graph systems can segment data (structured and unstructured) as needed to support different use cases and security requirements.

Data Integration Options

OLAP knowledge graphs support two major integration approaches to support organizations’ requirements.

Bottom-Up Integration

This is like a traditional ETL approach in that the OLAP knowledge graph system maps source data to a target conceptual model. This is a good choice when working with well-understood, frequently used data sources.

ELT-style Integration

The extract-load-transform (ELT) approach loads all the raw data first and then transforms it within the knowledge graph. This supports unrestricted data exploration and discovery and is ideal for working with less familiar data sources.

Example OLAP Knowledge Graph Use Case: Clinical Trial Data

Let’s use an example to illustrate how this system works. A pharmaceutical company that must manage and analyze thousands of clinical trials can use OLAP knowledge graphs to:

• Load all study metadata into the knowledge graph
• Use analytics to classify tables and columns in the source data across studies
• Generate mappings to standardize target data models
• Transform measurement systems and correct outliers
• Create integrated views across all clinical trials

This enables researchers to quickly analyze patient populations across studies, discover new opportunities for existing drugs, and identify patterns in adverse events data — tasks that are prohibitively complex with traditional data warehousing approaches.

Get Started with OLAP Knowledge Graphs

OLAP knowledge graphs are a major advancement in enterprise data integration. By combining the performance of traditional data warehouses with unprecedented flexibility and an intuitive data model, they enable organizations to derive more value from their data assets while reducing costs and risk.

As businesses face increasing pressure to make data-driven decisions quickly, the ability to integrate and harmonize data from diverse sources becomes a critical competitive advantage. OLAP knowledge graphs provide a foundation for this capability that aligns with the pace and complexity of modern business environments.

Altair has worked with many global enterprises to implement knowledge graph software – these are the critical aspects that foster successful implementation.

Identify High Value Use Cases

Look for scenarios requiring integration across multiple systems and focus on areas where business needs change frequently. Make projects that have struggled with traditional approaches a priority.

Start Small, Then Expand

Start small to demonstrate value to users and stakeholders. From there, add data sources incrementally to allow the model to evolve.

Focus on Business Outcomes

Define clear business questions to answer and measure operational improvements in terms of speed, cost, and insight. Be sure to communicate success to stakeholders in business terms, not technical capabilities.

To learn more about Altair’s knowledge graph capabilities, visit https://altair.com/altair-graph-studio.