Whenever I’ve examined data governance frameworks, I’ve found that rules and logic are different. The frameworks can vary not only from company to company but even between departments within the same company. Definitions of quality, usable, and secure data differ, depending on the priorities of leadership and each department. Communication is key to data governance.
Data Governance, at its core, is an act of continuous, regular, and unceasing communication and collaboration. It must be done with all stakeholders to ensure that the data is used for reporting, generating insights, and driving the bottom line. Without a framework of communication, data quality eventually degrades, and business insights disappear.
There are three effective ways to improve this communication:
Constantly Document and Verifying Data
A culture of constant maintenance is important. Data governance does not stop once all stakeholders approve definitions, data quality standards, etc. Data quality is constant maintenance: documentation and verification of data quality is an unending process.
Data governance teams must take initiative to discover when business definitions, data quality, and reporting requirements change. They also must communicate changes made to documentation and verify with multiple stakeholders the new standards. Small changes have massive impacts and affect departments in unseen ways.
A good organization strives to make this part of its data culture, which helps make sure data quality remains high.
Make Documentation more Accessible
Accessibility has two parts: a centralized area for documentation and easy-to-read documentation. Governed data documentation is inaccessible if users cannot find it. It is catastrophic when that documentation is written in jargon or complex language.
Centralize your data. Have a SharePoint, google drive, or any secure document storage with data models, logic, and mapping available. If data documentation is going to be effective, people need to know where it is. Centralized data documentation improves data quality. It removes ambiguity, by setting standards for data quality, business logic, and the current reporting. Centralized data documentation is easily accessed by all and allows better collaboration.
Write in simple language. Writing in a complex way uses jargon, or is used for gatekeeping destroys the accessibility of data governance. The best data governance documents can be understood quickly by all end users. If a unique term is used, it must be clearly defined, so all users can understand it. Use simple sentences, break up important points, and use diagrams as much as possible. Simple language helps users understand the data quality standard.
Office Hours: Make your analysts and data stewards available.
Your analysts, data stewards, anddata engineers must have office hours.
It’s a great way to improve communication, clarify governance, request changes, and educate on data quality. It bridges the gap that frequently exists between data stewards, departments, and analytics and engineering teams. It’s a time to ask questions and gives suggestions.
Office hours for data stewards, help answer larger strategic and tactical questions. They answer how quality and governed data fits within the larger goals of the organization. Data steward office hours are a time for individual stakeholders to discover how their efforts drive data quality, as well as suggest ways to govern quality.
Office hours for data analysts and data engineers should answer questions of the day-to-day use, transformation, and ingestion of data. Data engineer office hours should cover ingestion questions, transformation, and acquisition. While data analyst officer hours should cover business logic and explanations. Ideally, both the data analysts and engineers should be in the same office hours. This gives coverage for both technical and business questions.
They also serve another purpose: building credibility with the larger organization. Business intelligence and analytics teams often work in a black box. Departments see the outputs and inputs of the work, but not the processes. Office hours give teams a chance to explain the processes and business definition.
Breaking it down like this helps create transparency. It builds company confidence in BI and analytics teams by having a resource to refer to, not just a document. The credibility you build can go a long way to making your governance and data quality efforts more efficient in the future.
Data Governance is a dynamic process. It is a cycle of continuous integration and development, that evolves alongside technology changes and business direction.
Unless you have clear, regular, and consistent communication, the governance of data will weaken. It’s important to keep clear lines of communication of governance changes, to maintain good, timely, and accurate data quality and processes.
Thank you for reading!