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What is a Semantic Data Fabric?

A New Solution To Data Silos

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Semantic Data Fabric is a new solution to data silos that combines the best-of-breed technologies, data catalogs and knowledge graphs based on Semantic AI.

Poor data quality is the unintended consequence of data silos and poor data & analytics governance

Gartner (2019): ‘Think Big, Start Small, Be Prepared — Master Data Management’

The Challenge: Data Silos Everywhere

Business success depends heavily on the ability to make effective use of data. The first step towards this data-driven culture is data access, but many organizations have data silos that hinder this effort. Siloing data has its advantages and disadvantages. While you can maintain full control over the data and establish your own governance processes, data silos reduce speed, accuracy in reporting and data quality. A data silo owner cannot efficiently handle the full range of contexts that are potentially available to enrich his or her data.

The Need To Overcome Data Silos

Data silos present two primary challenges for enterprises:

  • How to deal with existing data silos
  • How to avoid creating new data silos

This is especially true when exploring Artificial Intelligence, Machine Learning and other advanced techniques rooted in data management and analysis. Siloed data often leads to re-creating knowledge that already exists but is not accessible; and worse, it can create conflicting data due to inconsistent data standards or methodologies. This leads to unnecessary costs and potential inaccuracy. Migrating and standardizing data represents primarily cost in the mechanical effort required, but also requires standard definitions and methodologies to be accurate.

A Solution To Silos

The ultimate goal is to unify unstructured, semi-structured and structured data to make all that available as if it were one database. Our approach combines the respective advantages of data lakes, data warehouses, and cloud-native data catalogs and complements them with the advanced linking methods and text mining facilities that Semantic Graph Technologies bring with them.

Implementing data catalogs without a strategic plan to link them to broader metadata management needs will lead to metadata silos, making them difficult to effectively manage and integrate in the longer term.

Gartner (2019): ‘Augmented Data Catalogs: Now an Enterprise Must-Have for Data and Analytics Leaders’

Data.world Cloud-Native Data Catalog + PoolParty Semantic Suite

Driven by a joint passion for knowledge graph technology, data.world and PoolParty, two leading metadata management platforms, have teamed up to provide a powerful solution. Businesses that leverage both products will benefit from a highly scalable, standards-based, and user-friendly business solution that meets the requirements of enterprise data management and metadata management. Both technologies are recognized in the 2019 Gartner Magic Quadrant for Metadata Management Solutions.
data.world makes it easy for everyone—not just the “data people”—to get clear, accurate, fast answers to any business question. The cloud-native enterprise data catalog collects, connects, and aligns data and metadata, creating a unified body of knowledge anyone can find, understand, and use. PoolParty uses text mining (entity extraction) and inference mechanisms to easily create and manage knowledge graphs, which can then be used for sophisticated metadata enrichment and classification tasks within data.world. Both platforms are built on graph standards such as RDF, enabling seamless integration.

The Benefit: Analytics Over Connected Metadata in a Knowledge Graph

Selecting, connecting, and managing the right tools to manage data, metadata, and analysis is a rapidly evolving and often complex task. While no two business’ tech stacks will look alike, most have at least one thing in common: they’re often made up of a variety of point solutions, each designed to do a specific task extremely well. Figuring out which tools to buy is often a daunting task, and the effort to integrate each new addition with the rest of a data ecosystem can quickly become overwhelming.

PoolParty and data.world’s seamless integration reduces some of that complexity, offering a fast route to deploy two critical components of your data management strategy. With pre-built integration points, businesses can get started fast without heavy IT bottlenecks: this standards-based solution immediately works with existing data assets to build a trustworthy, scalable solution. And with both tools offering a strong foundation in knowledge graph technology, data is intuitively connected, aligned, and more usable.

Investing in a semantic data fabric solution also means that new opportunities for modern enterprise data management will arise. These are manifold:

Find, integrate, catalog and share all forms of metadata based on semantic data models

Make use of text mining: deal with structured and unstructured data at the same time

Graph technologies: perform analytics over linked metadata in a knowledge graph

Use machine learning for (semi-) automated data integration use cases

Automate data orchestration based on strong data integration backbone

Content

  1. The Challenge: Data Silos Everywhere
  2. The Need To Overcome Data Silos
  3. A Solution To Silos
  4. Data.world Cloud-Native Data Catalog + PoolParty Semantic Suite
  5. The Benefit: Analytics Over Connected Metadata in a Knowledge Graph
  6. More Value From Data

More Value From Data

With a semantic data fabric, companies can combine text and documents (unstructured) with data residing in relational databases and data warehouses (structured) to create a much more comprehensive and accurate view of their customers, employees, products, and other vital areas of business.

Here are 3 examples of how companies can use a data fabric to extract more value from data:

Enrich your knowledge

You can find valuable unstructured information about the customer on the web, social media, and in other channels, to augment your understanding of the customer and the problem they are experiencing.

Predict accurately

Replacing equipment can cost millions of dollars. Anticipating failure to provide life-extending maintenance and plan for replacement is essential. To accurately predict equipment failure, you need data from different observation, reporting, and maintenance systems. A data solution that understands and links text and other data across different sources can improve predictions and save businesses millions.

Optimize costs

Not seeing the full picture of your employees’ skills and expertise costs you precious time and money. Typically, such information is siloed in HR databases, resume documents, and other disconnected systems. By combining this unstructured and structured data, you can optimize costs and boost productivity by better matching your people to problems and tasks.

Get a deeper look into data fabrics.

Learn more about data silos and how you can enhance your infrastructure with semantic technologies.

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