What Is Data Management?
Data management is the process of accessing, capturing, processing, storing, and archiving data. It often falls under the control of a Chief Data Officer who oversees a variety of subsidiary data-related functions such as data quality management and business intelligence. Fintalents’s data management consultants describe Data management in finance as the collection, access, processing, storage, and archiving of information for financial decision-making. The core job of a Chief Data Officer (CDO) is to oversee a variety of subsidiary data-related functions such as data quality management and business intelligence.
The data you collect from your business will form the basis for many different types of decision making. For example, when you engage in a cash-based business model, you must manage and track your financial assets. When you switch over to an asset-based business model, you’ll also need to track your financial assets as well as allocate them between different uses. As explained in the article “What is data management?”, data management is often regarded as an important part of the Chief Data Officer role. Many different aspects comprise data management as highlighted further below.
Data Quality Management
One important part of managing your company’s data is ensuring your internal information remains reliable. This is accomplished through a process called “data quality management”.
Business intelligence is the process of using data to discover new insights and potentially offer you better decisions on what to do next. You can think of BI as an “insight engine” for your business. It turns raw data into meaningful information so that you can gain new insight and make better business decisions.
Data Quality Management, or (DQM), is a structured approach to managing data quality throughout the company so that appropriate actions are taken when issues arise. DQM leads organizations to employ standardized data cleansing procedures, systematic analysis of data and solid testing procedures, in order to verify the quality, accuracy and completeness of their corporate database. The ultimate goal of DQM is to provide accurate, reliable and useful information for decision support and business intelligence analysis.
What does Data Quality Management (DQM) Look Like?
The most common form of data quality management is a systematic framework that consists of four key components: logical controls, physical design, procedural controls and technical controls. It provides a flexible approach that can make change less daunting for managers. The logical controls section consists of data definitions, data dictionary, maintenance control structure and quality assurance factors (QAFs). They establish the overall integrity of your corporate database. The physical design section includes architecture and arrangement parameters that are essential in determining data storage limitations, performance requirements and availability issues. The procedural controls section includes data validation, data profiling and data logging. They enable you to predict problems and take proactive measures to address them. The technical controls section consists of backup and restoration plans, validation and verification procedures, survey plans for defining relationships among data elements in the corporate database and records management policy. In summary, these four core components are designed to ensure that your corporate database is an accurate reflection of what you know about your business.
What Data Management Does Not Include?
Data management was not designed as a complete solution for all of your business needs. You still have to include other vital functions in the field of data management such as budgeting, leadership skills, internal relationships and training. So, although data management is a crucial component of data collection, there are other important tasks that do not fall under the umbrella of data management.
What Is Data Quality?
Data quality is the degree to which your corporate database accurately represents what you already know about your business. The goal of data quality is to ensure that the information contained in your database is consistent and reliable, and it conforms to organizational standards. No two entities in any sector share exactly the same internal structure or external environment. Therefore, thorough planning and an understanding of existing conditions are essential if the content of your corporate database is to be accurate and reliable with respect to those conditions. Often, people will get confused between the two terms “data quality” and “quality data management”, but they are different. Data quality refers simply to the accuracy or usefulness of your database. Quality data management is a process by which you can ensure that your database is accurate, reliable and complete at all times.
Business intelligence (or BI) refers to a variety of analytical techniques used to turn raw data into meaningful information that can be used by companies for better decision-making processes. Your financial data may contain enormous amounts of information. In order to make the most of this data, you must use your analysis skills to find meaning in it. This requires a solid understanding of data governance, changes in business practices, and the ever-changing nature of your industry.
For example, imagine that you run a cash-based business model and have created several cash pools in order to manage your assets and track their movement across different business uses. You create one pool for each month that is used for profit, another one for overhead costs like staff costs or rent and so on. Based on these three pools, you determine the remaining cash available at any given time. In this scenario, you utilize your business intelligence skills to identify your current profit and loss situation.
Conclusion – Data Management is a Lifeline for Any Business
Data management and business intelligence are vital aspects of data collection. If you want to create a better understanding of your business, then you need to equip yourself with data management tools because they are the building blocks of business intelligence and analytical techniques. The more data you have available, the more accurate your analysis will be. Therefore, any decisions that stem from poor analysis or no analysis at all will be bad decisions that could lead to risks and inefficiencies in your business operations.