These days, enterprises are struggling to streamline and optimize data management in order to drive better decisions, improve operational efficiencies, ensure compliance and proper data governance, and enable more sophisticated analytics. Unfortunately, siloed or unstructured data, which is all too common at the enterprise level, presents significant challenges and can further enhance inefficiencies, especially for data scientists and analysts who then spend a disproportionate amount of time organizing data.
Luckily, there is an emerging data management design solution that is revolutionizing how businesses connect, manage, and use data; a data fabric. Data fabrics are helping companies optimize data management, thereby improving data governance and allowing them to leverage advanced analytics. Find out what data fabrics are, why they matter, and tips for overcoming common challenges during implementation in the following article.
What Are Data Fabrics?
Data fabrics are an architectural approach designed to address the complexities of managing data across diverse and distributed environments. A data fabric architecture integrates various data management technologies and practices to create a unified, intelligent data management ecosystem. When we talk about data fabrics, it’s important to highlight three main concepts that separate them from other data management techniques.
- Unified data access across multiple sources: Using robust integration tools and data processing capabilities, a data fabric reduces the need to move or copy data manually. A data fabric provides a single layer of data access, allowing users to interact with data across multiple sources and formats without needing to know the underlying complexities.
- Improved data lifecycle management: Data fabrics leverage metadata to automate data integration, preparation, and delivery processes, which makes data management more efficient and scalable, improves governance and compliance, and helps to ensure data quality.
- Enable more sophisticated analytics: By making data more accessible and incorporating machine learning and artificial intelligence to enhance data discovery, classification, and quality management, stakeholders can access advanced analytics to drive better business intelligence.
Why Use a Data Fabric?
A data fabric can be a worthwhile investment for a multitude of reasons, including removing data silos, unstructured data, and enhancing business intelligence. Overall, a data fabric can help to modernize data management practices, giving enterprises more control over their data and allowing them to produce more sophisticated analytics. Since a data fabric is a data management design, it leverages existing infrastructure, including data lakes and enterprise data warehouses, to build a better framework. All of which translate to improved operational efficiency, reduced costs, and added business value. The full benefits include:
Elimination of Data Silos
Isolated pockets of data make it difficult for cross-functional collaboration and can limit an organization’s ability to pull data-driven insights, especially across expansive enterprises. A well established enterprise data fabric breaks down these silos by creating a cohesive data environment where data from disparate sources is integrated and made accessible to relevant stakeholders. This integration not only fosters collaboration but also enhances data visibility by building an efficient data environment, enabling organizations to make more informed and holistic decisions.
Enhanced Business Intelligence Insights
A data fabric employs machine learning algorithms and other AI technologies and tools to analyze data from various sources, uncovering hidden patterns and trends that traditional methods might miss. This enhanced analytical capability allows organizations to gain deeper insights into customer behavior, market trends, and operational efficiencies. With better business intelligence and data visualization, organizations can make decisions based on data that propel growth, improve customer experiences, and create a competitive edge in the market.
Improved Data Governance
Data governance is another pain point of organizations these days, and even more so for those in heavily regulated industries. Getting the right data to the right person while ensuring data security and compliance throughout the business is crucial. A data fabric uses metadata, or the context and meaning that is given to data, to deliver the right data to the right person based on predefined policies, ensuring centralized control and compliance. A data fabric thus acts as a champion of data security and integrity by ensuring the highest standards of data governance and protecting sensitive information.
Simplified Data Structure
Managing complex data structures can be daunting and resource-intensive. It also means that roles responsible for working with data often spend more time liaising across teams to get the data they need and then organizing that data rather than performing valuable analysis. A data fabric simplifies data management by providing a unified architecture that abstracts the underlying complexity. With a simplified data structure, organizations can extract more valuable insights which can in turn speed up the development of new applications and services. The streamlined approach offered by an enterprise data fabric ensures that data management is no longer a bottleneck but a catalyst for innovation.
How Does a Data Fabric Work?
While it’s still an emerging data management design, a data fabric, as described by IBM, essentially covers three different areas: accessing data, managing the lifecycle of data, and exposing data.
When we look at accessing data, this usually involves ingesting data from a variety of sources without having to copy or move anything manually. For example, an enterprise might have data lakes, data warehouses, different data catalogs, and application databases, but the list can be virtually endless. A data fabric uses robust integration tools to virtualize data processing and make sure everything is delivered to the appropriate central repository without having to get your analysts to manually move a bunch of datasets from one place to another.
Managing the lifecycle of data involves using metadata to set protocols and define policies that ensure compliance with industry rules and regulations and data governance and privacy by getting the right data to the right person without any additional information. This can be a complex endeavor in large enterprises with lots of roles and in heavily-regulated industries with high compliance requirements.
Finally, exposing data means giving users access after all of the above protocols and policies have been defined. Users will gain access based on business functions, and depending on their role, they will connect different machine learning and AI tools to build applications, extract insights for BI, or drive different operations.
As with any new data endeavor, you’ll have to ensure that a data fabric and the resulting work makes sense for your enterprise given the challenges it presents in terms of implementation. This is a far from one-size-fits-all approach, and really depends on the knowledge and skills available to your organization as well as your goals.
Best Practices for Implementing Metadata Management and Data Fabrics
To effectively implement a data fabric, you’ll need to have experts available, including data engineers, that are skilled in a number of methodologies and technologies as a complete data fabric is not something you can purchase for implementation. Some best practices for establishing a data fabric include the following.
- Create an outline of what you have and what is lacking – At this stage, you’ll want to build a clear idea of what data you have, where it lives, and all metadata. This can also be a great time to highlight automations, integrations, and what insights you’ll want to gain.
- Improve metadata and clean up loose ends – Metadata, including descriptive, structural, and administrative, is how protocols are established and helps to define governance and relationships between data sets. Review your metadata processes and conventions to ensure they support your industry requirements and robust protocols.
- Define the protocols and compliance requirements – To make any data fabric work, you will need to clearly understand and define the data lifecycle using different protocols and policies to ensure the right data is delivered to the right people and all compliance needs are met.
- Implement robust integration tools – Data tends to be more siloed when it comes from a multitude of unrelated data sources and different teams have different goals. Implementing robust integration tools is vital to the success of your data fabric, and organization as a whole. With comprehensive integration you can easily ingest, consolidate, and unify data without having to manually touch a thing. It’s data sharing at its finest.
- Apply advanced technology (ML and AI) with controls and oversight – Machine learning is the gold standard for getting actionable insights and delivering advanced and predictive analytics. However, as with any AI tools, you should put controls in place to manage biases, fairness, and explainability. Additionally, a data fabric should support multiple vendors and open source technologies.
A data fabric allows organizations to start harnessing the full potential of their data assets and enhance business intelligence, ultimately gaining a competitive edge. If you’re interested in building a data fabric but don’t know where to start, give us a call. We’re experts at building and deploying data management architectures and applying innovative approaches to complex data issues.
With Ceiba’s expertise and miia’s cutting-edge technology, you can transform your data into actionable insights, driving more intelligent decision-making and accelerating your business growth.
