Data and Information Management (DIM) refers to the set of people, processes, and technologies supporting the creation, collection, storage, exploitation, and disposal of information assets. It comprises policies, procedures, and best practices to ensure that data information is understandable, trusted, visible, accessible, and interoperable. Hence It aims to promote interdisciplinary data-driven information management research, especially targeting large-scale datasets in scientific/academic, government, and business domains.
Driven by the need for qualitative and reliable data and information to bring their reporting and analytics initiatives to the next level, or pushed by regulatory and compliance rules (like BCBS239, AnaCredit or Basel III for banks and Solvency II for insurance companies), we are noticing an increasing interest for companies to properly manage their data information assets.
WHAT’S IN IT
About data and information management
Data information carries with it the ability to make the organization smarter and more effective. Most organizations pay close attention to the data asset. Usually, Data is very essential to make well-known decisions that guide and measure the achievement of the organizational strategy. The way an organization uses and manages the data is just as important as the mechanisms work to bring it into the environment.
The appropriate data of the right quality permits the organization to determine which processes have the greatest impact on the business.
Most of the data information in organizations ensure that their data assets are accessible to the individuals who need it, usually are of competent quality and timeliness, and are protected against any form of misuse and abuse.
Successfully giving data and information assets does not happen by itself; it requires dynamic data management by applying specific domains, policies, and competencies throughout the life of the data. Similar to systems, data goes through a cycle. As you can see The diagram below presents the key phases of the data life cycle.
Efficient data management through all of the data information cycle phases is the foundation for reliable information. Data may typically have a longer life than the project that creates it. Though the funding period officially defines the lifespan of most projects, the resultant data may be available for many years afterward.
If an organization manages and stores the data properly, the data is available for use for the future, increasing the investment made to generate it by increasing clarity and usefulness. Hence the time invested in planning and implementing effective data management pays results far above its investment costs.
Value of data
Data without any context has no value; data information that consumers never use is worthless, also. The value of data is in the knowledge and uses. Hence, Extracting information and presenting it in an appropriate format may be summarized as data analysis and reporting. However, data analysis and reporting circumscribe several overlapping disciplines, among them statistical analysis, data mining, predictive analysis, artificial intelligence, and business intelligence. The data information and Information Management has an appreciation for these domains and may use the same tools and incorporate some of these domains. The common ground among all of these domains and IDM is making the best use of the data.
This does not come easy and brings new challenges for businesses when it comes to managing their data and information. Dealing with data information should be knowledgeable in at least one of the following environments or disciplines:
Big data describes the high growth and availability of data, both structured and unstructured. Its characteristics are volume, velocity, and variety. It might be difficult to manage with traditional tools, it might move too fast, or it might exceed current enterprise processing capacity. Big data information and applications contributed to the growth of NoSQL databases.
Operational environments provide central transactional capabilities (i.e., processing applications, claims, payments, etc.) that usually work with a DBMS. For structured data, Relational DBMS, or RDBMS, are used mostly.
Organizations use data information exchanges and data exchange standards to share information with internal or external parties. Side by side standardizing exchange formats and metadata minimizes impacts to both the sending and receiving systems and reduces cost and delivery time. A related field is master data information and management (MDM). An example is a vendor list. The U.S. Treasury requires specific information for identifying contractors before the federal government reimburses them. Most federal agencies use this centrally collected list. Exchange, transform, and load tools typically support these types of data trade activities. ETL tools manipulate data and move it from one database environment to another.
The integration of same and disparate data from across organizational, functional, and system boundaries can produce new data assets. The organizations can use the latest data to ensure consistent analysis and reporting, to increase the information needed for decision making. Data information may be structured, unstructured, or both. Business intelligence has become a recognized discipline. Hence, It takes advantage of data warehouses to produce business performance management and reporting.
Data mining and knowledge discovery
Mining applications explore the patterns within data to find new insight and predictive models. An organization should use specialized software that applies high statistics, neural net processing, graphical visualization, and other high analytical techniques against targeted extracts of data. Hence, In addition, tools should evaluate continuously streaming data within operational sources.
Knowledge in this discipline requires specific training related to a specific DBMS and being certified. A certified database management professional (CDMP) is responsible for the installation, configuration, and maintenance of a DBMS (e.g., storage requirements, backup, and recovery), as well as database design, implementation, monitoring, performance, and security of the data in the DBMS (Software that controls the organization, creation, maintenance, retrieval, storage, and security of data in a database. Applications make requests to the DBMS, but they do not manipulate the data directly.)
A data architect is responsible for the complete data requirements of an organization, its data architecture and data information models, and the design of the databases and data integration solutions that support the organization. Usually, The data structure must meet all the business requirements and regulations. A specialized area in data architecture is the role of the data steward. The data steward is usually responsible for a specific area of data such as one or more master data.
An efficient data management program begins with identifying core principles and collaborative activities that form the foundation for providing efficient, effective, and sustainable data. You can see The organization should interlace the following core principles throughout all of the data management activities:
- Firstly, the data collected is apt, timely, relevant, and cost-effective.
- Secondly, data efforts are cost-efficient and they minimize redundancy and respondent burden.
- Thirdly, activities related to the collection and use of data are consistent with applicable confidentiality, privacy, and other laws, regulations, and relevant authorities.
- Fourthly, data activities look for the highest quality of data and data collection methodologies and use.
- Data activities are coordinated within the organization, maximizing the standardization of data and sharing across every medium.
- Partnerships and collaboration with all stakeholders are cultivated to support common goals and objectives around data activities.
- Data activities adhere to appropriate guidance issued by the organization, its advisory bodies, and other relevant authorities.
- Also, we use data to inform, monitor, and improve policies and programs.
In summary, Information and Information Management (DIM) alludes to the arrangement of individuals, procedures, and advancements supporting the creation, assortment, stockpiling, misuse, and removal of data resources. Hence, It includes strategies, systems, and best practices to guarantee that information data is reasonable, genuine, obvious, open, and interoperable.
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