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Improving Data Processes Through the Data Fabric

Given the complex nature of how data is transmitted in utilized within the enterprise, especially with technologies like cloud, AI, and the Internet of Things, it has become a challenge for data managers and personnel to oversee and manage what has become a complex data infrastructure without an architecture that allows for tighter and more effective integration.

This is the purpose of having a data fabric in place.

What is a data fabric?

Data fabric is an architecture that facilitates the end-to-end integration of various data pipelines and cloud environments through the use of intelligent and automated systems. This is certainly beneficial for businesses looking for a more efficient tool to manage their complex data systems.

Data fabrics provide a streamlined experience across all the solutions in the organization’s data ecosystem, creating a bridge between technical and business users. However, such effectiveness will depend largely on how the organization has prepared their systems to accommodate it. Without the preparation needed, the data fabric will not work as effectively as it should,

Maximizing the effectiveness of the data fabric

In order for organizations to maximize the effectiveness of their data fabric, there are three practical steps that need to be done:

1. Build a semantic layer for data context.

To help business users make sense of data, organizations need to have a semantic layer which is an additional set of information that defines the relationships across the dataset. The semantic layer allows users to access the data on their own and use business terms they know, streamlining knowledge management and sharing within the organization, which in turn breaks the data silos and provides valuable context for the data to facilitate sound decision-making.

With such a layer in place, teams no longer need to recreate siloed metrics for every application but only need to define them once and synchronize them across the entire data stack. In addition, it also allows asynchronous communication which provides insight as to the data that exists in the dataset and where it is located. This requires integration with all the relevant tools being used for processing and saving data, which presents challenges related to integration, complexity, and cost.

2. Develop data integration.

Integration is at the heart of any data fabric architecture. It provides results for organizations that have complex data environments across different locations that users can easily access. This entails an expansion in the volume of data sources, something that many businesses are still struggling with, particularly in trying to come up with a single solution that will take into account these different sources. An open-format could be a good system to adopt for organizations that are thinking ahead.

3. Integrate data quality guardrails.

It is critical that the insights the users are getting from the data are of high quality and with the utmost accuracy. Thus, implementing guardrails for data quality not only ensures consistency in data quality and accuracy but also develops a high level of confidence and trust in the data that users can rely on to get timely insights and be able to make sound critical decisions.

Guardrails can be set up through a variety of methods such as the write-audit-publish pattern which helps capture data quality and assess it early on before it’s merged with the rest of the data. It also enables a staging environment for extensive data testing before merging with downstream data. It must be noted however that technologies for performing data checks this way are still immature, as it only started evolving recently.

The secret towards successful implementation

Having a data fabric in place is a must for businesses that extensively utilize data. But it also poses challenges, especially for businesses who do not have the processes and infrastructure in place to handle the powerful capabilities that the data fabric offers.

It is thus critical for business leaders to implement both long-term strategies and short-term deliverables that will make the most out of the data fabric. Examples of these short-term deliverables are connectors and quick quality checks like validating nulls, comparing ingested versus processed records, and adopting open format data, all of which can demonstrate the impact of data fabric on stakeholders without losing sight of the long-term accomplishments that build the competitive advantage for the future.


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