When you embark on a cloud journey and you’re looking to modernize to Microsoft Azure, you need to have a unified data governance strategy to make the most of your Azure investments. Assessing the landscape is a crucial first step to ensuring Azure data governance. Discovering relevant data requires an assessment as well, as customers need to find the data of value to migrate first, and mitigate risk by identifying sensitive data, so they can effectively deliver high value, via an iterative approach, to build upon their Azure investment. Informatica’s Enterprise Data Catalog (the “catalog of catalogs”) helps Azure customers automate their current state discovery though a vast number of prebuilt scanners, so you can see if your business rules are spread across analytics, semantic layer, data foundation, integration, and data sources.
Automating your current state assessment helps you to accelerate, prioritize and strategically plan how to best utilize the Azure ecosystem, while also allowing for a multi-cloud, multi-hybrid approach. Leveraging a platform that can transcend ecosystems, while also providing transparency, is imperative to a sustainable, scalable, and maintainable solution. Enterprise Data Catalog helps you with the following key steps:
Semantic Search: With Google-like semantic search, Enterprise Data Catalog can help you convert “tribal” knowledge to the enterprise. For instance, if “grade” is outdated terminology, Enterprise Data Catalog can recommend the enterprise-preferred term “tier” when searching.
Finding Certified Assets: Data architects and developers no longer have to be subject matter experts, as Enterprise Data Catalog allows for an interface where both data stewards and information technology (IT) teams can collaborate. For instance, as a data architect, I can now easily search and filter by application, data types, application names, user ratings, and validated assets.
Assessing Quality: Before building your integration patterns, it is imperative to know your data orphans, outliers, patterns, and types so you can ensure the data is fit for use at the time of end-user consumption.
Similar Domain Discovery: Customer data could lie in multiple source systems and finding the “source of truth” can be a daunting task without artificial intelligence (AI) and machine learning (ML) capabilities to simplify and speed up your efforts. Enterprise Data Catalog can reliably identify similar domains and the percentage of accuracy across all your applications scanned, so you can quickly determine the correct source of record.
Data Lineage & Impact Analysis: Most organizations have a top set of reports they use to run the business, and it is here that automated data lineage can provide tremendous value for the company, showing where and how their data assets are compiled. Impact analysis further provides sustainability, as you work to modernize and sunset applications or redundant integration patterns, so you can adjust your analytics appropriately without impacting the data consumer experience.
In addition, transparent logic provides the ability to see the business and transformation rules applied for a data field to be presented on a report. For instance, Earnings Before Interest, Taxes, Depreciation, & Amortization (EBITDA) is a common metric used to evaluate a company’s operating performance. The EBITDA metric is a complex key performance indicator, that requires multiple variables to calculate, and having the ability to see those calculations visually (versus sifting through code) helps data consumers trust and adopt the data.
Modernizing applications and migrating data to Azure opens up new opportunities to reduce costs and scale out elegantly, but it requires transparency and intelligent insights into your business-critical data that enable trust. With Enterprise Data Catalog and AI-driven metadata intelligence, you can accelerate your journey.
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