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In recent years, enterprise data across multiple industries has led to groundbreaking innovation:
- In the utility industry, energy companies build consumer portals for sharing energy-saving information, supporting the customer by cutting energy costs and protecting the environment.
- Financial institutions build modern applications and analyze client financial records that empower customers to manage funds and investments through online virtual assistants or bots.
- Telemedicine leverages HIPAA compliance technology that allows providers to communicate with patients remotely, track vitals, send and receive patient information, and collaborate to improve patients’ health.
The number of industries using data as a strategic business asset continues to grow at a rapid pace.
These types of state-of-the-art, customer-centric products and services are developed from trustworthy data by lines of business, not IT. However, there still remains a reliance on IT to manage and prepare data for analysis. This process often fails to provide any business context or knowledge for the data user. Instead, IT sends business users reports filled with technical information, because that’s the information they know best. This leaves the business user perplexed and behind in the race to use this information to discover insights that can transform the business, uncover trends, and spark innovation.
Data users need to be self-sufficient to quickly locate data and understand its Data Quality levels. Additionally, data users need to uncover business knowledge around the data, and yet, recently, this task has grown especially difficult with a remote workforce. Data leaders understand having these capabilities helps identify the right customer, make profitable business decisions, and earn customer trust and credibility.
To support finding, understanding, maintaining, and applying business context around data, organizations require a single, unified enterprise data catalog.
Creating a True Data Catalog Requires Technical and Business Knowledge
Delivering reliable, easily understandable data to both business and technical users is one of the most persistent challenges industries face. Yet, data catalog projects are imperative to establish a centralized location of information and knowledge about an organization’s data, processes, and quality levels that produce, manage, and consume data.
However, many data catalogs only organize the technical details about an organization’s data assets, such as its location and technical meaning. But, for a data catalog to enable business users to take full advantage of their information, the catalog must also deliver business-ready data.
A data catalog should help all users identify and understand all available data sets and must provide transparency into metadata, including ownership, data definitions, synonyms, key business attributes, quality scores, and usage. Most importantly, it must include detailed business and technical data lineage perspectives.
Defining Different Data Lineage Functions
Data lineage has vastly different meanings, depending on the user.
Users in IT are interested in where sensitive information lives, how it changed, who has access to it, and how it is shared to assure smooth operations and regulatory compliance.
Identified as technical data lineage, it captures data on a physical level such as schemas, tables, columns, and how it moves across systems. As a result, IT users can uncover elements critical to compliance and operations, narrow down Data Quality issues, and determine upstream and downstream effects of changes to technical metadata.
At the same time, business users need to understand how data fits the business and its impact if the organization alters its use. They also want to know the information’s business context, including data’s specified business meaning, usage, and how it affects the company.
Achieving a 360-degree augmented-view of a company’s data landscape, for both business and technical users, requires enterprise-wide Data Governance that incorporates automated data lineage and metadata ingestion built into its data catalog.
Automated Data Lineage Plays a Key Role in Crafting a Single Source of Knowledge
Despite being based on individual perspectives, creating a data catalog requires organization-wide agreement on data definitions. A Data Governance framework unifies and drives disparate departments to collaborate in the documentation of business knowledge, policies, objectives, metrics, processes, standards, Data Quality rules, and glossaries.
Next, to keep data catalogs from becoming obsolete, organizations need technologies that deliver integrated and automated capabilities. This ensures that data changes are captured and accounted for prior to decisions being made.
For instance, automated metadata and data lineage ingestion profiles discover data patterns and descriptors. As a result, business users can quickly infer relationships between business assets, measure knowledge impact, and bring the information directly into a browsable, curated data catalog.
A searchable, business-ready catalog then provides an extensive, single source of knowledge of all enterprise data, across data siloes, in a centralized location. It delivers a broad view of data lineage around both business and technical characteristics, including where data lives and technical details, along with Data Quality levels, how it’s used, what outcomes it will deliver, and business terminology around the data.
With the right approach and tools, organizations can utilize their business and technical data catalog to quickly and easily derive value from data assets, with precision and speed, while protecting and preserving business knowledge dispersed throughout the company.
What is your organization doing to improve efficiency, quickly build customer-centric products and services, and increase profits?