If you type “data fabric” in a Google search, you will get at least ten different definitions of the data fabric. I will not attempt to extend that list. However, I will focus on the value data fabric does bring to organizations and how Teradata can help your business achieve it.
1. Unlock ALL data with flexible data architecture
In most organizations, important data remains underutilized, unintegrated, and inaccessible by those who need it most. The data can be in various source systems and in various formats. There are many ways to solve this problem. The not-so-smart way is to dump all data in a central place like a data lake for further usage. However, this requires a lot of data movement and is not feasible for large organizations. To make the subject more complex, you can have streaming data as well as batch data.
What you need is a flexible data architecture that can process the data at the source as well as integrate it across the cloud and in on-premise environments. Flexible data architecture is one of the important characteristics which defines a powerful data fabric.
Teradata Querygrid enables flexible data architecture, delivering you a powerful data fabric. It helps you bring all your data together or leverage data where it is, using push-down-processing via our intelligent query fabric. This can also help you in managing costs with low-cost object storage as well as streaming data ingestion in real-time.
2. Enable data democratization by automating data discovery
A few years back, the self-service BI movement put the analysis capabilities directly in to the hands of business users. However, data preparation and curation were still with IT. We are now moving towards the age of data democratization, where business users will have the power to directly access data and process it for consumption. The acceleration towards data democratization has also been fueled by the data-mesh movement, which promotes the case of self-service data platforms.
The key element which makes data democratization a reality is automating the process of data discovery. With intelligent and automated data discovery, business users can easily find the data they are looking for.
Teradata Vantage provides you with various technical capabilities, such as schema discovery as well as data catalog, to make data discovery a reality.
3. Intelligent data exploration enabled using industry data models and knowledge graphs
If you get access to data and discover what the data is about, you are just getting warmed up. You might also be overwhelmed by the sheer amount of data you have discovered. In order to start using the data productively, you will need to explore the data and convert it into useful business insights.
A powerful data fabric facilitates the way you explore your data in an intelligent way. The intelligent way is to integrate the data with well-known industry best practices. In this way, you are assured that your data exploration is not some random combination of data sources, but instead follows some pattern that has a proven way to deliver business value. Meet Teradata Industry Data Models. They offer a proven way to integrate the data necessary to run the business.
Industry data models provide a way to accelerate data integration through semantic mapping. On the other hand, the knowledge graphs provide a way to integrate data by analyzing data consumption patterns. For example, with Teradata Vantage, you can analyze which table fields are frequently joined in a query, and which will help you create a knowledge graph based on data consumption patterns. A knowledge graph can indicate the relationship between a retail store and all events in the place where the store is located.
This ability to connect data so that it facilitates intelligent data exploration is the value derived from a powerful data fabric.
4. Faster time to value with data industrialization
Make no mistake — data fabric is not just about discovering and exploring data. It also needs to have industrialization capabilities so that the process of going from data to value is operationalized in a robust and scalable way.
The way in which this is done is using data industrialization which helps streamline and automate the process which extracts data from source systems, organizes it in a structured data environment, and makes it available on the platform to the end business user. This results in a faster time to value going from data to business insights. Teradata provides various services such as Data-Ops which enable data industrialization.
5. Accelerate AI initiatives with faster data preparation and operationalizing AI at scale
In recent times, AI has been the top strategic initiative with enterprises. Despite heavy investment into AI, many companies have not fully realized the full potential of AI. One of the main challenges which they face is around data preparation for AI as well as operationalizing AI at scale.
Data fabric should also address these challenges as AI is becoming core to any digital transformation initiative. Teradata has been successfully addressing these challenges through robust in-database advanced analytic functions which helps data preparation at scale. As they are in-database functions, all data preparation functions are run directly inside the database. This helps to avoid unnecessary data movements, and thus helps accelerating data preparation required for AI.
Operationalizing AI is another area where Teradata Vantage excels. Operationalizing AI requires the integration of AI model predictive scores with operational data. For example, if you have a model which predicts manufacturing equipment failure, operationalizing the AI model means you will also find out where the equipment is located, as well as the manufactured components that will be potentially impacted by the predicted failure.
Operationalizing AI models is not just about mathematical formulas. It is more about using AI within the context given by operational data. The data fabric should facilitate integration of AI model with operational data. With Teradata Vantage, you can operationalize AI at scale. You can store AI models directly inside the database, thus facilitating seamless integration between AI model scores and operational data.
As you accelerate your AI initiatives, the number of AI models which are developed will exponentially increase. Lots of AI model will need to be stored securely and monitored continously. Teradata ModelOps enables continuous monitoring of model performance. Governance and life-cycle management of analytic models using ModelOps enables reporting on lineage and potential model drift. Thus, you will not be overwhelmed with the exponential increase in number of AI models. Teradata Vantage delivers the promise of a powerful data fabric by accelerating AI initiatives.
6. Extend data fabric capabilities with partner integration
With cloud momentum showing no signs of slowing down, many of the data fabric capabilities will be provided by cloud service providers (CSP). Extending data fabric capabilities to leverage those provided by the CSP is becoming very strategic in the cloud era.
The CSPs have enabled various data fabric capabilities. For example, AWS Glue Data Catalog, or Google Data Catalog, to name a few.
Teradata Vantage provides seamless integration with CSP data fabric capabilities thus extending the capabilities which are natively provided by Vantage.
7. Best price-performance at enterprise-grade security
A powerful data fabric is not just about advanced functionalities. It should also deliver all benefits at low TCO as well as provide best-in class security to the most valuable asset — your data.
Teradata offers the best price-performance in the industry with our adaptive cost-based optimizer, workload management with flexible pricing models and deployment choices.
With Teradata, you also have the advanced data security with proven regulatory compliance for your data.
From unlocking all your data for business value, to enabling faster time-to-value, to extending capabilities in the cloud era, the value provided by a powerful data fabric is key to a successful digital transformation,
