Data silos continue to burden the world of modern enterprises; differing data sources, the complexities of hybrid infrastructures, and the general proliferation of data introduces a myriad of challenges to the business landscape. Solving these challenges, namely data integration and governance, are as critical as they are complex.
Data experts joined DBTA’s webinar, Modernizing Data Integration and Governance for the Hybrid and Multi-Cloud Era, to discuss innovative approaches toward overcoming data silos while simultaneously ensuring both effective governance and access.
According to Susan Laine, global strategist director and data intelligence thought leader at Quest, bad data can have a devastating, long-term impact on an organization—much like plastic in the ocean. How, then, can an organization continuously deliver high-quality, valuable, and trusted data? To begin, they can adhere to Quest’s seven steps toward maximizing data value:
- Model (design the data architecture)
- Catalog (search and find data easily)
- Curate (enrich with business context)
- Govern (apply business rules and policies)
- Observe (raise data visibility to proactively manage)
- Score (automate data profiling and quality scoring)
- Shop (make trusted, governed data widely accessible)
To aid in the good data pursuit, Quest offers a solution that enables enterprises to get the most value from their data—the erwin Data Marketplace, a “one stop shop” for trusted, easily accessible data. Providing trusted datasets, AI models, and data scoring, the erwin Data Marketplace unites technical and non-technical users alike in leveraging governed, observable data.
James Beecham, founder and CEO at ALTR, centered his discussion around data access governance and the data mesh, posing the question: Why does data access governance sometimes fail? According to Beecham, distributed data owners, differing data sources, and disparate tools are large aggressors in the failure of data access governance.
Naturally, enterprises look toward investing in certain strategies and technologies—such as automation, AI, and integrations—to remedy these challenges, yet they are only as good as the people using them.
Beecham pointed to no code as the step in the right direction, where decentralized data access governance leads to a plethora of tangible benefits, including:
- Increased access time to data sets
- Less money spent on contractors
- Centralized audit ability
Additionally, metadata offers a definite advantage when dealing with data security; he urged that viewers marry the pieces of policy with the pieces of metadata that already exist. Meaning, when you know the users, roles, and security requirements within your environment, it is critical to give existing tools enough information to make automatic decisions on how data should be stored and accessed.
Bobbi Caggianelli, manager of sales engineering at Collibra, boiled down the complexities of modernizing data, offering these statistics:
- 63% of enterprises are migrating data to the cloud
- 77% are integrating up to five different types of data in pipelines
- 95% are integrating data across hybrid cloud
- 65% are using, at a minimum, ten different data engineering tools
The implications of these numbers do not go unnoticed; enterprises are struggling to keep up with both the modern demands of data as well as their existing legacy systems.
Caggianelli argued that a data mesh can remediate many of these challenges, emphasizing a few core principles, strategies, and technologies that define its successful implementation:
- Domain ownership of data, where domains act as spheres of influence of knowledge to propel autonomous ownership
- Treat data as a product supported by a longstanding vision, roadmap, and lifecycle
- A self-service data infrastructure platform, removing friction and enabling autonomy
- Federated governance, which will produce consistent, high quality, safe, and usable data through globally relevant policies and standards
He then walked viewers through a detailed demo of Callibra’s various offerings, which range in use cases from data governance to data catalogs, data lineage, data privacy, and data quality and observability.
For an in-depth review and discussion of modern data strategies featuring live demos of various data products, you can view an archived version of the webinar here.