Data Catalogs Extend Meaning-Based Collaboration Between Business, Technology And Operational Personnel

Take a look at a day in the life of a data analyst - the persona of a Report Analyst begins with analyzing the data requirements obtained from a business owner. Report owners often receive their reporting needs from an isolated "Just-in-time" conversation with analysts.

An empirical scenario in publishing a report or an artificial intelligence model
According to Tejasvi Addagada, a renowned data catalog specialist, the turnaround time to analyze reporting requirements can be prolonged when the analyst examines the key performance indicators, the business logic, and the semantic meaning of data that needs to be used for reporting. The analyst will also have to inspect systems and databases to seek the correct coverage of data. This may start with an unplanned phone call to the system owners.

Next, with all the unplanned collaboration and lack of standard processes and tools, the time taken to provide a report increases to a few weeks. In the same way, an artificial intelligence analyst models insights to augment a customer journey, such as a mortgage offered to a customer.

What can an ideal scenario be in managing, engineering, and governing data?
Tejasvi further quotes that when an organization has a formal process for eliciting and managing its data requirements, it becomes easier to discover reporting needs. Collaboration with key stakeholders, analyzing the business meaning, or experimenting with planned activities are ways to accomplish this. It may also be necessary to engage data stewards to assist with the analysis after finding the business domains for the data.
Why can data planning be crucial for efficient data consumption?

Aswin James Christy, a data architect, claims that system engineers often fail to design structures like tables with simple names that reflect their contents when implementing systems and data structures. The reporting analyst remains confused about the provisioning source in the absence of simple descriptions and semantic names.

A catalog changes the typical routine to include planning and curating know-how about data and its meaning from system and database engineers; data can also be discovered after implementing a strategy, further enabling crowd-sourced definitions from data providers and consumers. As an ongoing activity, this fosters the culture of sharing and trust in using data created or obtained by others within an organization.

The future of data-based collaboration is through a data catalog 80% of the time to build a model or a report goes into data preparation. And one of the challenging activities is finding the right data that can be used for that coverage. With advancements in a catalog, an intelligent search capability is available that incorporates a semantic name and provides the most relevant sources during the data analysis. This increases the availability of intelligence about data that further brings context to any analysis.

Moreover, democratizing data across an organization requires executive sponsorship from the source and consumption data owners. It requires extensive training of personnel, awareness around curating business metadata, and tools for self-service data sourcing needs.

An organization's democratization of data can also enable its divisions to share and integrate data, thus breaking down silos. Furthermore, native processes can be digitized easily as data is discovered and available for use.

Additionally, because the same data is replicated across numerous sources, knowing the profile of the data to be sourced can provide intelligence into locating the right source. Likewise, a data quality profile can help analysts determine the extent to which bad information needs to be cleaned.

Harmonizing data quality rules across many native and digital channels will bring consistency in sourcing the correct data. When the data quality is shown, it leads to a higher level of trust as it is used for an artificial intelligence model or in a new digital application.
Tejasvi Addagada published the book Data Management and Governance Services: Simple and Effective Approaches and can be reached on LinkedIn and Twitter. Aswin James Christy is a Data Practioner working with Talend and can be reached on LinkedIn