Introduction
In the contemporary world, data is omnipresent. Organizations have the opportunity to derive maximum competitive advantage from internal and external information thanks to current Business Intelligence and Analytics tools: with these resources, it is truly possible to realize the famous mantra “Get Things Done.” However, to achieve this goal, it is essential for companies to adopt a robust Data Governance strategy, ensuring data control throughout its entire lifecycle. Proper data governance ensures that information quality enables increasingly accurate decision-making. Data Quality is merely the tip of the Data Governance iceberg.
Data governance
Data Governance is a framework that defines the Data Management model to be applied to an organization, regardless of its complexity. Governance represents the rules to be respected, while management consists of the decisions that determine the concrete actions taken to enforce those rules and maintain control over circulating information. Data governance encompasses three key areas: people, processes, and technology. The primary objective is to instill a data-driven culture within the company, creating added value from its information assets. To build a concrete data-driven culture, it is necessary to evaluate the operational and functional aspects of organizational processes in detail.
Management of Information Systems
Through Cloud or on-premises information systems, input data flows are extracted, decoded, reprocessed, and returned as knowledge for the resources that contribute daily to growth, sustainability, and increased process efficiency. The quality of the flows transmitted by the corporate information system is measured based on several criteria:
- Selectivity: The elimination of superfluous and redundant information.
- Timeliness: The time elapsed between an information request and the response.
- Accuracy: Determining how correct and precise a data point is.
- Reliability: The correctness and certainty of the information.
- Flexibility: The ability to collect and transmit data under all circumstances.
- Acceptability and Accessibility: It is crucial that corporate data and information are easy to understand.
Management of Loading Flows and Controls
To guarantee the criteria mentioned above, it is essential that initially raw input data is acquired and directed into automated flows that define its form, structure, logic, and organization. This process results in clear and immediate final information. To achieve this, it is fundamental to use appropriate tools known as ETL (Extract, Transform, Load), designed to perform data loading, transformation, calculation, and processing. These tools, provided by major software vendors, can be customized to meet specific needs. The goal of a loading flow is to translate unstructured input data into a clear, correct, and consistent output, such as a table, a graph, or a performance indicator (KPI). These outputs are then visualized through analysis tools like dashboards, ranging from standard solutions to sophisticated platforms.
Controls to Ensure Data Quality
ETL operations may seem simple at first glance, but they present significant complexities during implementation. Data professionals—specifically Data Engineers and Data Analysts—manage these data flows with intermediate controls, ensuring data consistency and quality at every stage and intercepting common errors like redundancies and duplications upstream. These intermediate control phases, commonly known as “consistency checks” within a structured and governed information system, are supported by messages and notifications sent to Data Owners in the event of errors or anomalies. It is vital to monitor these anomalies properly to avoid compromising the integrity of the system and the data. Detailed logs tracking flow activities allow for the identification of situations that could jeopardize loading processes and, consequently, the updating of information. By intervening promptly at the most appropriate stage of the ETL flow, rules can be added to ensure continuous maintenance and improvement.
A governed flow is fundamental to obtaining correct and reliable data, thereby guaranteeing information quality. To ensure effective data management, it becomes strategic to entrust the governance of continuous flow maintenance to a team of specialized experts. Data Governance, Data Quality, and an excellent AMS (Application Management Service) make the difference.
Data Marts and BI
Data loading flows structure both quantitative and qualitative data, organizing them into final tables that feed vast databases—technically known as Data Warehouses and/or Data Lakes—utilized by organizations in both Cloud and On-Premises environments.
Thanks to the expertise of data specialists, these tables are organized into Data Marts for specific analyses. This serves as the starting point for those who transform raw data into actionable insights. Business Intelligence and Analytics professionals create solutions that enable companies to refine their strategies through data. Above a single data mart, it is possible to build interconnected table structures that form a comprehensive data model, generating storytelling that illustrates the history and performance of a specific department, process, or business area.
Within an organization, it is crucial to effectively manage the development of a robust data warehouse or data lake structured through a Data Governance strategy. Managing massive volumes of data carries the risk of losing complete control over one’s information assets. The most effective strategy is to define clear rules within the IT department and disseminate them throughout the entire organization, often with the support of specialized consultancy firms.
This approach allows for the effective management of data from individual departments and processes, centralizing analysis while distributing ad-hoc tools across the company to improve business performance. Tools such as BI and Analytics are fundamental, as they empower the business to make informed decisions at every level. Supported by the numbers, charts, tables, and Key Performance Indicators (KPIs) provided by these tools, companies can compete effectively, gaining market share and sustaining high-level results.
Data Quality and Business Continuity
Management texts often highlight the “Four Ps”: Product, Price, Place, and Promotion. However, to correctly promote a product, determine a competitive price, and define market positioning, data is fundamental. Data quality for accurate pricing, consumer behavior studies, and market trend analysis provides greater opportunities to increase sales and gain market share over competitors.
Data is the foundation for great results; to achieve them, it is essential to put into practice everything defined so far. An efficient and constantly updated Data Warehouse, combined with performance reports, guarantees efficient, timely, consistent, and reliable data. These are the pillars of Data Quality within Data Governance. Thanks to these elements, a business can continue to thrive, ensuring Business Continuity at all levels.
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