Navigating Data Quality and Management: Insights from a Business Systems Analyst

Business Systems Analyst

In the dynamic landscape of operational data management, ensuring data quality is a crucial yet often overlooked aspect. Businesses commonly implement diverse validations during data entry into systems, coupled with automated checks governed by predefined rules. These rules, varying based on the software in use, can range from SQL queries to other validation mechanisms.

Operational Quality Checks

Operational data quality checks typically involve validating data against set criteria periodically, often on a monthly basis. The frequency of these checks finds a balance between business needs and avoiding excessive strain on telecommunication infrastructure.

Addressing Data Anomalies

When anomalies are detected, the information can be escalated to the Data Governance Board. This body is responsible for identifying the reasons behind deviations from the norm and initiating corrective actions. Owners of the data are then involved in providing explanations for errors or triggering procedures to restore the established data quality level. Depending on the severity and importance of the data type for business operations, corrective actions may be immediate or triggered after surpassing a predefined error threshold.

Tools for Effective Data Management

Specialized IT tools play a pivotal role in data management, particularly from the perspective of a Chief Data Officer. These systems, often equipped with artificial intelligence functionalities, or at least automated capabilities, can be complex and time-consuming to implement. Organizations should assess the functionality of these tools based on their specific expectations and constraints. In some cases, smaller organizations or those with less extensive needs may find cloud-based tools, less advanced but equally effective, more suitable.

The Rise of Data-Driven Companies

The buzz around “data-driven companies” is prominent in today’s business discourse. Yet, the practical implications of embodying this concept are significant. In a true data-driven company, decisions are automated based on data analysis and predefined criteria. This approach is often applied to retail decisions, such as replenishing individual product stocks based on data analysis.

Striving for Data-Driven Excellence

The term “data-driven” is trendy, but its implementation requires a profound shift in organizational decision-making. Becoming a genuine data-driven company entails trusting data, ensuring its reliability, and acknowledging its criticality to business operations. This trust is cultivated through adept data management, with data classification playing a pivotal role. Classifying data allows organizations to maintain control over truly valuable information, saving time and resources that might otherwise be spent on less relevant or insignificant data.

In conclusion, the journey toward data-driven excellence involves not only the use of sophisticated tools but also a cultural shift within the organization. Acknowledging the importance of data, ensuring its quality, and embracing a mindset where decisions are data-informed contribute to the realization of the data-driven ideal. For now, many businesses may be considered “data-informed”, leveraging data to generate optimal models and decision-making insights. The path to becoming a fully data-driven company lies in building confidence in data through effective data management practices.