Do Not Let Critical Business Data Context Fall by the Wayside

Cameron OgdenJuly 15, 2020

e-book: Data Catalog

There was a time when IT was the only line of business with the expertise to manage data. However, as data-smart organizations continue to enable business users to become more self-sufficient with enterprise data, there is still a reliance on IT to manage and prepare data for enterprise use — mostly in three areas:

  1. Finding and resolving data quality issues that support business processes or key reporting insights
  2. Implementing controls and safeguards on various data environments to help meet compliance and regulatory demands
  3. Ensuring enterprise-wide operations run smoothly

IT plays a large role in implementing quality assurance and regulatory compliance programs, following a set of guidelines and best practices to meet various requirements. IT must also find where sensitive or critical data elements live, how they transform, how they join other data sets, who has access to the data and how to share the information. All of this technical information is critical to both establishing compliance and ensuring fluid operations.

While this critical information is captured by IT, what often gets overlooked is how critical business context around data is captured, which improves organizational data literacy, fosters better collaboration between the business and IT, and enables the creation of reliable business intelligence.

The Urgent Need for Business Knowledge Around Data

Business users encompass most employees in an organization. Individuals in marketing, sales, operations, finance, human resources, product management and customer service, to include a few, rely on data to generate intelligence that improves the business. Thus, when business users understand how data fits the organization’s strategic goals, how to use it, what outcomes it delivers and any business terminology around the data, success follows.

For instance, if a business user in procurement utilizes data for analytical insights into organizational spend analysis, they need additional data context about supplier onboarding procedures, vendor payment terms, segmentation in region, country, plant, etc.  This means having visibility into where critical supplier data is used, the vendor management business processes that create and update data, and related data quality metrics dictating the health of the information.

Delivering trustworthy, business-ready data to business users is a common challenge industrywide. Business users must take an active role in data management endeavors and streamline efforts through modern technologies to generate the data they need and insights to grow and innovate.

Providing Business Context for Enterprise Data

Just because we can capture business contextual information about data doesn’t mean that it is “set it and forget it.”  Delivering business-ready data to business users requires organizations to merge data management efforts within a data governance framework so the data remains trustworthy and protected. Data governance promotes enterprise-wide communication and collaboration across business and IT stakeholders who are aligned on common objectives about how to extract the most value from enterprise data. The importance of data governance is directly proportional to the importance an organization puts on its data.

Successfully Implementing Tried and True Technologies

With data governance, businesses can define and agree on standard business glossary definitions, business rules, and enforceable data standards across departments. This information can be used to complement a data catalog that documents and arranges data into defined, relevant and searchable business terms and details about an organization’s data and processes. By providing data definitions, synonyms, business attributes and data usage, both business and technical users can identify and understand available data sets.

Data catalogs also track data lineage to uncover key details about where data came from, where it’s going and its transformation. With visibility and traceability of data, business users can also verify data sources, uncover and resolve data quality issues and trust the information they’re leveraging for business intelligence.

Automated data lineage technologies significantly enhance a data catalog by providing additional business knowledge around data assets. With automated data lineage, companies can create an easily accessible, browsable and trustworthy data catalog that includes both business knowledge and context. This includes data’s business meaning, its usage and how it impacts the organization.

By merging data management efforts to build a data catalog and automating data lineage, organizations deliver both a comprehensive technical and business view of enterprise data assets. As a result, the catalog supplies business context and knowledge around data to provide immediate and accurate information, while enhancing data trust and understanding for all users.

Are you looking for more information on data and capturing business knowledge?  Check out this e-book above or below for more detailed information.

For additional resources on business and technical data lineage, visit: https://www.dataversity.net/what-is-data-lineage/.

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e-book: Data Catalog