What is a Knowledge Graph?

A knowledge graph is a network of interconnected data points that reveals relationships and patterns. Think of it as a map of your information universe, connecting the dots between different pieces of data. Instead of isolated data points, you get a rich tapestry of interconnected information.

Why Knowledge Graphs matter for business

In the age of big data and AI, knowledge graphs are becoming increasingly important for businesses. They make sense of vast amounts of information and extract valuable insights. This drives better decision-making by:

• Uncovering deeper insights: Move beyond surface-level analytics to hidden connections and trends.
• Making smarter decisions: Informed choices based on a holistic understanding of your data landscape.
• Streamlining data access, analysis, & adaptation to change: Gain efficiency and agility.
• Gaining a competitive edge: Leverage AI and machine learning with the ideal structure of knowledge graphs.
• Improving data governance: Manage, control, and audit your data effectively.
• Complying with regulations: Ensure adherence to industry standards and regulations, such as the GDPR and CCPA.

The challenge of data silos & discord

A common challenge shows the true power of knowledge graphs: data silos and data discord.

What are data silos?

Data and information become isolated within specific systems, departments, or formats, forming data silos. This fragmentation hinders collaboration and leads to inconsistencies in how an organization defines and uses data.

The high cost of data silos:

• Inconsistent terminology: Different teams may use different terms to describe the same concepts, leading to confusion and miscommunication.

• Conflicting definitions: Differing calculations of metrics can produce conflicting reports and inaccurate analyses.

• Missed opportunities: Siloed data obscures the big picture, hindering the identification of growth and improvement opportunities.

A common business problem: not being on the same page

Many organizations struggle with these fragmented data sources and inconsistent terminology, leading to confusion, misalignment, and poor decision-making.

Imagine a retail chain trying to define an "active customer." One department might consider five transactions in six months as active—while another might require ten. This discrepancy can lead to inaccurate reporting and misguided marketing efforts.

The solution: A unified view of data

The solution is a unified, consistent view of data across the organization. This is where the true value of knowledge graphs becomes evident.

As MicroStrategy experts Ananya Ojha and Vihao Pham highlighted in their webinar, Unifying Analytics with the MicroStrategy Semantic Graph, achieving analytical harmony is crucial. This allows organizations to scale and make informed decisions. They emphasized that even slight misalignments in definitions can lead to significant discrepancies over time.

"People need to have a common understanding of what they are measuring and how they are measuring it," Ojha stated.

How Knowledge Graphs work

Knowledge graphs work by integrating data from various sources. They extract entities—like customers, products, or locations—and map the relationships between them. This process creates a structured, interconnected network of information for easy querying and analysis, including: 

• Data integration: Pull in data from databases, spreadsheets, cloud applications, and more.

• Entity extraction: Identify key concepts and relationships within your data.

• Relationship mapping: Create a web of connections between entities, building a knowledge network.

• Inference & reasoning: Use AI to derive new insights and predictions from the interconnected data.

Why a model-first approach is key

In the world of analytics, two main approaches prevail:

• Dataset-first: Analysts use SQL to create a dataset, then the application builds a model to interpret the data. This approach can be quick for simple reports but becomes cumbersome as complexity grows.

• Model-first: Create a model that defines terminology, relationships, and business rules. Use this model to guide the creation of datasets and reports. This model then guides the creation of datasets and reports. This approach is more scalable and adaptable to change.

The model-first approach is essential for maintaining consistency and accuracy as your enterprise's data needs evolve. It ensures everyone uses the same blueprint, regardless of who creates or views reports.

MicroStrategy Semantic Layer is a prime example of a model-first approach in action. It addresses the challenges of data silos and inconsistencies. This provides a unified and flexible foundation for your analytics ecosystem.

MicroStrategy Semantic Layer: Your unified data universe

The MicroStrategy Semantic Layer—or Semantic Graph—acts as a central hub for your organization's data. It connects disparate sources and establishes a common language for everyone to use. Its model-first approach means you start by creating a blueprint of your data. Define key terms, relationships, and business rules.

This model serves as the blueprint for all your analytics, ensuring consistency and accuracy across the board. With the MicroStrategy Semantic Graph, analysts can easily access and analyze data without needing extensive technical knowledge.

Noel Cyr, VP of Business Intelligence and Analytics at NBCUniversal, has seen instant results:

"We're already seeing savings in the thousands of hours, by implementing the first couple iterations of [the unified semantic layer]." 

Benefits of MicroStrategy Semantic Graph

• A single version of truth: With the Semantic Graph, everyone in your organization works from the same accurate information. The data is up-to-date—and this eliminates confusion, fosters collaboration, and builds trust in your data.

• Agility & adaptability: As your business evolves, so does your data. The MicroStrategy Semantic Graph makes it easy to update definitions and rules. This ensures your analytics remain relevant and aligned with your changing needs.

• AI-powered insights: The Semantic Graph is not just about organizing data—it's about uncovering hidden potential. By leveraging machine learning algorithms, you can identify patterns, anomalies, and trends that would otherwise go unnoticed. This empowers you to make proactive, data-driven decisions that drive your business forward.

• Accessibility for all users: The Semantic Layer democratizes data insights. It translates complex data structures into easily understandable concepts. This makes data-driven decision-making accessible to everyone, not just data experts.

Noel Cyr further notes, "Our analysts can come in and do this thing on their own, and they can do it on their own because we've organized the data for them."

Real-world applications of the MicroStrategy Semantic Layer

The MicroStrategy Semantic Graph isn't just a theoretical concept—it's a powerful tool. It transforms how businesses operate across various industries, including:

• Retail: Retailers can use the Semantic Graph to gain a 360-degree view of their customers. They  can understand their preferences, purchase history, and interactions across different channels. This enables personalized marketing campaigns, targeted promotions, and improved customer service.

• Finance: Financial institutions can leverage the Semantic Layer to detect fraudulent activity, assess risk, and ensure regulatory compliance. This connects disparate data sources and applies complex business rules—producing a comprehensive understanding of their financial landscape.

• Healthcare: In the healthcare sector, the Semantic Graph integrates patient data from various sources. This can be: electronic health records, lab results, and insurance claims. This unified view of patient information can lead to more accurate diagnoses, personalized treatment plans, and improved patient outcomes.

These are just a few examples of how the MicroStrategy Semantic Layer can help solve real-world business challenges. By breaking down data silos, organizations can establish a common language, and apply AI-powered insights.

From data silos to a harmonious data ecosystem

The Semantic Graph offers a powerful solution to the challenges of data silos and discord. By creating a unified, model-driven view of your data, it empowers your organization to:

• Achieve a single source of truth: Ensure everyone is working with consistent, accurate data, fostering collaboration and trust.

• Gain agility & adaptability: Easily update definitions and rules as your business evolves, keeping your analytics aligned with your goals.

• Empower business users: Democratize data insights by making them accessible to everyone, regardless of technical expertise.

• Unlock AI-powered insights: Leverage machine learning to uncover hidden patterns and drive smarter, data-driven decisions.Ensure data governance & compliance: Maintain control over your data, track changes, and ensure adherence to regulations.

Unlock your data's full potential with MicroStrategy

Don't keep your data trapped in silos. Transform it into a powerful asset that drives your business forward.

To learn more about how the MicroStrategy Semantic Graph creates analytical harmony, you can watch the full webinar: How MicroStrategy's Semantic Graph Creates Analytical Harmony.