How not to let data-trust issues undermine your transformation

Much of that telemetry data from the edge is referenced in place to provide insights on everything from product improvement opportunities to enhanced and predictive diagnostics on equipment failures. Because EY reduced replication, throughout these downstream processes, where the data has come from, how it’s being used, and where it’s going is never in doubt.

The secret of data fabric’s success is that each data type or data domain is left to reside in its original data storage mechanism – as long as that repository is fit for purpose. There is no replication of data and no data entropy as a direct result.

This is the first viable paradigm that unifies data at the compute level instead of the storage level. Instead of laboriously moving data to a warehouse or lake, these repositories are linked using modern lightweight cloud-native federated query or data-visualization tools to something called the semantic layer. This layer enables the consumer to view raw data from potentially hundreds of linked databases quickly and easily via a single pane of glass.

Accessing data faster, cleaner and with more confidence

Until now, report writers, visualization designers and data scientists could spend up to 80% of their time making connections to dozens of data stores, gathering the information they need. Now, however, data fabric makes this heavy lifting obsolete. Building the right infrastructure to store and retrieve data from a data lake can take four to six weeks, but by using data fabric it’s possible to obtain highly trustworthy data from its native state, using the semantic layer and virtualization, in as little as 30 minutes.

Data fabric finally places similar levels of trust and agility within the reach of incumbent organizations nearly a decade later – without the need to painfully rip and replace whole ecosystems of legacy technology.

Four steps to high-trust data

Companies serious about overcoming data-trust issues should first tackle the data replication and lineage challenges. It’s impossible to focus on every single data point within an enterprise. So, the first step is to identify and prioritize critical data elements – what data will provide the outcomes you need and the biggest return on your investment? Isolate and focus on this data above all else.

The next step is to catalog exactly where this critical data is being held – this involves understanding data domains, what data products your organization is producing and how to access it.

Having done this, it’s easier to identify the best system of record for your critical data. Does this repository hold raw data in its native state, or has it been transformed or altered?

The final step is to design a plan for unifying these data sources. A well-designed data lake may be a good starting point for some organizations to tackle a specific tactical problem. Ultimately, however, an organization’s long-term goal should be to break down internal silos with a data fabric design pattern.

Unlocking the data-trust dividend

Businesses that seize the data-fabric opportunity and overcome data-trust issues can expect a compelling return on their investment in the form of agility, enablement and long-term value.

Without the need to spend weeks building data pipelines to integrate databases, businesses are able to access trustworthy insights within minutes. They also don’t need to build functionality – thanks to data fabric, users can go to a data marketplace, view data products, download the data they need and start building their algorithms in record time. There’s also no need to clean data or build scripts so data can be replicated.

The biggest benefit, however, is that thanks to data fabric, C-suites will have the confidence to make high-impact business decisions in near-real time, safe in the knowledge that they are acting on the best data insight available.