As organizations evolve their data architecture to solve for emerging use cases, they’re finding this process to be overwhelming. Many of their challenges can be attributed to a lack of transparent data access, antiquated toolsets that do not present clear lineage of the data or a lack of big data skills.

There are three key components that companies can address to help overcome these challenges.

Data Catalog

A data catalog exists to enable all users to find and understand the data. This is imperative to creating a system that gives users the ability to visualize their data and find relevant insights that are required to stay ahead of the competition.

We recently asked some webinar attendees for their biggest challenges in finding data. Results uncovered a wide range of issues when trying to find data. Some respondents citing all of the above.

Challenges user have finding data
These responses are concerning when you consider how critical data is to thrive in a digitally competitive world. Right-sized governance and access to the data catalog need thoughtful consideration when forming a scalable data strategy that breaks down barriers to insights.

Data Quality

Knowing where your data is, is one thing. But knowing which data is valid and valuable is another. Achieving quality data is more than simply having a policy in place. There are also levels of human intervention that enable the processing of the data to ensure it meets the standards of the policies. As the data architecture evolves and machine learning and AI take over, the level of human intervention must ultimately decrease.

When asked about the top cause for inconsistent data, participants were much more decisive. Nearly half with misaligned standards across data sources.

top cause for inconsistent data

This means that many organizations are not adopting enterprise-wide insights because they can’t be sure of sources and policy consistencies applied to the data that they use.

Self-Service

One of the most overlooked aspects of a modern data architecture is self-service. However, it is critical to scaling and accelerating time to value.

As a data scientist, imagine not having to wait for your data requisition to go through IT. As a data engineer, imagine where you would spend more time if you didn’t have to constantly approve data access. Based on the size of your organization, either type of work might lend itself to a full-time job.

When participants were asked if self-service is a component of their data strategy. The results were surprising. None of the attendees have fully embraced self-service and a near majority have no self-service capabilities.

data strategy self service
Whether organizations aren’t yet ready for self-service, or they’re still struggling with access and governance, is yet to be determined. However, as time to insight becomes a standard of performance for decision making, providing self-service access to the data pipeline will become a key differentiator for IT organizations.

Modern Data Architecture

All three of these components need to be present and operationally sound in a data platform for an organization to achieve a modern data architecture that scales for growth. To learn more about building these components for success, watch the replay of our webinar about platform modernization with the Zaloni Data Platform.

The Top 3 Considerations for Modernizing Your Data Platform