What is a Data Fabric?
By now, you’ve heard the good news: The business world is embracing data-driven decision making and growing their data practices at an unprecedented clip. The pandemic may have forced their hands, but they’ve seen the value of data and will never go back to making decisions based on hunches.
Here is the so-so news: They’re moving so fast that they’ve amassed more data than they can analyze. Organizations, on average manage, 10 times more data than they did five years ago. They are struggling to use their data in a way that is efficient, compliant, intuitive, and secure.
What if the problem isn’t in the volume of data, but rather where it is located—and how hard it is to gather? After all, the average enterprise has 900 applications, but only one-third of them are connected. Nine out of 10 IT leaders report that these disconnects, or data silos, create significant business challenges.* These commonly include cost inefficiencies, data integration errors, missing, or inaccurate data, and culminate in an overall lack of trust in data.
Therein lies the opportunity that businesses have today. If they connect their siloes and harness the power of data they already gather, they can empower everyone to make data-driven business decisions now and in the future. The way to get there is by implementing an emerging data management design called data fabric.
What is a data fabric design?
A data fabric is an emerging data management design that allows companies to seamlessly access, integrate, model, analyze, and provision data. Instead of centralizing data stores, data fabrics establish a federated environment and use artificial intelligence and metadata automation to intelligently secure data management.
As leaders continue to refine strategies to elevate productivity and mature analytics, the data fabric is a single architecture that can address the levels of diversity, distribution, scale, and complexity in an organization’s data assets.
At Tableau, we believe that the best decisions are made when everyone is empowered to put data at the center of every conversation. We’ve infused our values into our platform, which supports data fabric designs with a data management layer right inside our platform, helping you break down silos and streamline support for the entire data and analytics life cycle.
Tableau helps strike the necessary balance to access, improve data quality, and prepare and model data for analytics use cases, while writing-back data to data management sources. Let’s take a quick look at each of those capabilities.
- Analytics data catalog. Review quality and structural information on data and data sources to better monitor and curate for use
- Metadata management. Surface robust metadata where users need it most across their analytics journey, while ensuring bilateral communication with enterprise tooling
- Data quality and lineage. Monitor data sources according to policies you customize to help users know if fresh, quality data is ready for use. Shine a light on who or what is using specific data to speed up collaboration or reduce disruption when changes happen
- Data modeling. Leverage semantic layers and physical layers to give you more options for combining data using schemas to fit your analysis
- Data preparation. Provide a visual and direct way to combine, shape, and clean data in a few clicks
- Data, security, and resource governance: Nurture data across its lifecycle with policies that remain consistent with every use. Ensure the behaves the way you want it to— especially sensitive data and access
- Data integration. Gain useful insights from data stored across different platforms and data sources, such as data warehouses, data lakes, and CRMs
- Virtualization and discovery. Increase understanding of data sets on hand for data integration or data analysis
- Orchestration. Automate coordination of data happenings, like data quality or flow failures, right in the workflow
- Augmented analytics. Nurture and use AI to make analytics processes—like data management, data prep, and analysis—easier to complete in a few clicks
The analytics-first approach
Business leaders have long recognized the importance of data analytics to the future of their organizations. International Data Corporation, a global market intelligence firm, reports that 83% CEOs want their organizations to be more data-driven and are investing in growing their Data Cultures. Those who are leading with data are now 23 times more likely to add customers and 1.5 times more likely to grow revenue by 10%.
As organizations begin their data fabric journey, it’s critical they remain focused on where value is being generated for the business. If this is analytics for you, as it is for most, stay your course. Data fabric implementation will take several years, so it is important to set near-term goals to be able to showcase value and keep stakeholders engaged.
With Tableau as part of your data fabric design, you can overcome some classic problems that pop up during the last mile of data initiatives. For example:
- Lack of adoption by the business. Rev adoption by thousands of users by working where the business is already working, the platform becomes the collaboration zone or federated environment for business users to access data and governance analysts/IT to implement their enterprise projects
- Slow implementation of governance standards. Create trust and verifiability where viewers consume their data. Tableau provides information in context around data freshness, certification status, data quality warnings, field definitions, data sources, and overall usage
- Loss of visibility after data leaves EDW. The beauty of data today is that it can be used in so many different ways—which is also a challenging pitfall for governance. With metrics on who is consuming data and how data consumers are interacting with data, IT can get true insight into what data sources are providing the most value and discover and remediate sensitive data use automatically