In this article, we’ll talk about the importance of Informatica data governance as well as how Informatica’s newest product may assist businesses in tackling complicated problems in this area. We also go through how Informatica tracks the origins of enterprise data coming from various sources using a graph database solution.
What Exactly Is Data Governance?
Data governance is a system of rules, guidelines, and procedures that guarantees the accuracy and consistency of your data. It also makes sure that your data can be trusted to support digital transformations, corporate activities, and decision-making. You may perform these tasks in a repeatable manner with the aid of robust data governance software. As data volumes—and sources—increase and technologies advance, such software may scale and adapt. Simply put, effective data governance allows you to confidently use your data both now and in the future. If you’re curious about the distinctions between data governance and data management, check out our blog.
Why Is Data Governance Important?
Data governance makes a guarantee that your data is reliable, accurate, consistent, and trustworthy to support data-driven decision-making. In the past, risk and compliance have been the main concerns of data governance. But during the past few years, data governance’s emphasis has changed. An approach to data governance that was formerly inflexible and one-size-fits has evolved into one that is more nimble and focused on providing the data insight needed for data-driven decisions.
How is Informatica Data Governance?
It is necessary to be able to manage a wide variety of entities with complicated relationships that are unknown until runtime in order to store data for data governance and lineage. Because data frequently through numerous hops and changes before it reaches its destination, maintaining and accessing data lineage information from a relational database would necessitate complex joins of tables at multiple levels.
By utilizing a graph database, Informatica was able to have as few constraints as possible on the number of hops for the data or the number of facts for each asset, enabling us to respond to complicated data queries in milliseconds. A graph database is better suited for storing and retrieving relationships than a relational database is. This offers relationship-based dynamic searching. A graph database can be used to search the assets, connections, and facts as nodes and edges.
You can discover, comprehend, control, and trust your data with the help of the cloud-native Informatica Cloud Data Governance and Catalog solution. It creates a single cloud-native solution for data intelligence by combining capabilities for data cataloging, governance, quality, and democratization.
This multi-tenant software-as-a-service (SaaS) solution is designed for businesses with significant investments in cloud data lakes and data warehouses who want to get the most out of those expenditures by converting data into insights. Stakeholder and policy management, business glossary development, discovery, lineage, profiling, and the ability to record and manage AI models and their implementations are all included in the solution.
In this approach, the technology makes it possible for cloud data lakes and data warehouses to support self-service analytics and governance. AWS, Snowflake, Databricks, databases, analytics, and BI (business intelligence) systems, ETL (extract, transform, and load) tools, and other cloud and enterprise systems are just a few of the settings that it connects with to scan hybrid sources including cloud data lakes and data warehouses.
Informatica Data Governance Framework
Framework aids our clients in concentrating on what matters most. Many customers, in our experience, only focus on the technology without taking into account the big picture or all of the data governance elements necessary to accomplish desired business goals.
Yet by employing a top-down strategy, we ensure alignment and attention. Setting the setting for the entire framework by outlining business strategies and drivers is the “Strategy” building block. You cannot truly match data governance to your company until you determine the desired business results.
They must enhance quality and have a 360-degree view of the consumer from a governance standpoint. They must establish their governance structure and make sure their DQ personnel are DQ-skilled in order to attain this goal. In order to make the massive amounts of data accessible in a meaningful and structured fashion, they will also need to develop technology and methods to standardize and clean the data.
Another illustration is that you might need to decide how to categorize customers or improve the quality of your customer data in order to increase customer satisfaction and retention rates. For each of the outcomes, there can be certain difficulties or governance capacities that must be resolved if the organization is to succeed. Focusing on alignment with business results and value is the ultimate objective. They finally take the form of quantifiable use cases and particular governance topics.
Conclusion
A potent graph data store for the metadata knowledge graph is being constructed by the Informatica Data Governance and Catalog solution. That allowed the Informatica team to concentrate on developing the functional capabilities while Amazon Neptune took care of the non-functional needs of performance, scalability, and recoverability. Also, it reduced the complexity of the solution and optimized the time spent on product development. You can attend a future live demo event to learn more about the Informatica data Governance and Catalog solution in-depth. Via Informatica Documentation, you may access the product documentation.
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