which is a highly effective tool for managing and displaying data in organized manner
Answers
Answer:
Explanation:
Every business in the world has to contend with data. From a single-person LLC to multinational enterprises, data is everywhere, and it needs to be properly managed to be an effective business tool.
Data isn't just customer records and other externally sourced information, though--employee records, network maps, payroll data, and other forms of external and internal information all fall under the list of data that has to be managed.
It takes a lot of work to turn data into something usable. Without proper management, you can end up with duplicate records, incorrect information, wasted time and storage space, and a host of other problems that come with poor organization. Digital data is a lot more complicated than paper, so it requires specialized skills to organize it.
Enter the world of data management. Here are the essentials about data management, including models, software, implementation, and more. This article is also available as a download, Cheat sheet: Data management (free PDF).
SEE: All of TechRepublic's cheat sheets and smart person's guides
What is data management?
There are as many ways to define data management as there are websites that focus on it. DAMA International, a consortium of data management professionals, defines data management as "the development and execution of architectures, policies, practices, and procedures in order to manage the information lifecycle needs of an enterprise in an effective manner."
In other words, data management is multidisciplinary and keeps data organized in a practical, usable manner. At its most fundamental level, data management works to ensure that an organization's entire body of data is accurate and consistent, readily accessible, and properly secured.
Along with being a way to eliminate duplicates and standardize formats, data management also lays the groundwork for data analytics. Without good data management, analysis is practically impossible at worst and unreliable at best.
Additional resources
Business analytics: The essentials of data-driven decision-making (ZDNet)
How master data management brings order to big data (TechRepublic)
What is involved in a complete data management model?
If the definitions and descriptions of data management make your head spin a bit, it's understandable--there is a lot that goes into the practice of data management.
DAMA International breaks data management down into 11 knowledge areas:
Data governance, which is the planning of all aspects of data management. This commonly includes ensuring availability, usability, consistency, integrity, and security of data managed by an organization.
Data architecture, or the overall structure of an organization's data and how it fits into a broader enterprise architecture.
Data modeling and design, which covers data analytics and the design, building, testing, and maintenance of analytics systems.
Data storage and operations, which is concerned with the physical hardware used to store and manage data.
Data security, which encompasses all elements of protecting data and ensuring only authorized users have access.
Data integration and interoperability, which includes everything to do with the transformation of data into a structured form (i.e., in an organized database) and the work necessary to maintain it.
Documents and content, which includes all forms of unstructured data and the work necessary to make it accessible to, and integrated with, structured databases.
Reference and master data, or the process of managing data in such a way that redundancy and other mistakes are reduced by standardizing data values.
Data warehousing and business intelligence, which involves the management and application of data for analytics and business decision making.
Metadata, which involves all elements of creating, collecting, organizing, and managing metadata (data that references other data, like headers, etc.).
Data quality, which involves the practices of monitoring data and data sources to ensure quality information is being delivered, integrity is being maintained, and poor quality data is being filtered out.
All of these elements have to be included in a total data management model; if even one element is missing, some aspect of managing data is complicated, if not damaged entirely. For instance, if you get rid of metadata management, you lose the ability to easily categorize data. Without data quality being ensured, the entire data structure becomes suspect, and analytics become useless. Eliminating integration and interoperability would make it nearly impossible to combine disparate forms of data into a usable whole.
Answer:
When gathering data, whether qualitative or quantitative, we can use several tools, such as: surveys, focus groups, interviews, and questionnaires. To help organize data, we can use charts and graphs to help visualize what's going on, such as bar graphs, frequency charts, picture graphs, and line graphs
Explanation:
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