Things You Probably Didn’t Know About Data Management

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What is Data Management?

Data management is the process of collecting, storing, organizing, and securing data generated or received by a company during its activities. The goal of data management is to maximize its potential while also ensuring that data is accessible, trustworthy, and up to date for all workers.

Data management is critical for every organization in order to make better, more informed business decisions and obtain deep insights into consumer behavior, trends, and possibilities.

Effective data management necessitates an enterprise data management strategy as well as dependable techniques for accessing, integrating, governing, storing, and preparing data for analytics.

It compensates for the creation, execution, and management of plans, strategies, and practices that govern, safeguard, deliver, and enhance the value of data and information assets.

Because information is derived from data, the accuracy of the information is determined by the accuracy of the data upon which it is based. Organizations must ensure that the data acquired is of high quality in order to give the management the ability to take business decision informed by data.

Structured and unstructured data are the two basic categories of data. More details may be found in the post below.

Structured vs Unstructured Data: 5 Main Differences (theecmconsultant.com)

It doesn’t only eliminates duplication and standardizes formats, but it also provides the framework for data management for analytics. Analyzing without effective data management is nearly impossible at worst and inaccurate at best.

In short, enterprise data management is the strategy companies use to manage, control, protect, access, store, and maintain data. It starts with complete control of the cycle, from data capture to storage.

Data management strategy

Data Management Knowledge Areas

DAMA International, a group of data management experts, uses a framework comprised of the following 11 knowledge areas:

I’ve described the details in the table below; for more information, acquire your book from

Data Management Body of Knowledge (the DMBoK) 

  1. Data Governance: All parts of enterprise data management must be planned, overseen, and controlled. This generally comprises guaranteeing an organization’s data’s availability, usability, consistency, integrity, and security. The purpose is to ensure that data is managed properly, according to policies and best practices. Governance teams frequently employ a document management system to aid in the administration of policies and procedures.
  2. Data Architecture: Its purpose is to realize the defined vision by gathering requirements and desires, which are then converted into conceptual plans based on models. It specifies how the overall data structure fits into the overarching enterprise architecture and how it will be embedded in standard operations.
  3. Data Modeling and Design: The technique used to define and analyze data requirements,
  4. Data Storage & Operations: Hardware storage, distribution, and management of structured physical data assets.
  5. Data Security: Maintaining confidentiality, privacy, and proper access of data.
  6. Data Integration & Interoperability: Managing the integration of data between different lines of business applications.
  7. Documents & Content: Manage, control, index, and secure access to data and information.
  8. Reference & Master Data: Managing shared data to eliminate redundancy and improve data quality by standardizing the definition and use of data values
  9. Data Warehousing & Business Intelligence: Coordinating the processing of data management for analytics and providing access to decision support data for reporting and analysis.
  10. Metadata: Metadata collection, categorization, maintenance, integration, control, management, and delivery.
  11. Data Quality: Identifying, monitoring, maintaining, and enhancing data integrity and quality.
Knowledge Area GoalsActivitiesDeliverables
Data Governance 1- Define, authorize, communicate, and execute data management policies, procedures, tools, and responsibilities


2- Track and enforce regulatory and internal data policy compliance


3- Policy compliance, data consumption, and management actions are all monitored and guided
1- Define the organization’s data governance


2- Define the strategy, goals, principles, and policies

4- Examine the standards for regulatory compliance

5- Implementation and sponsorship

1- Data governance strategy

2- Operations plan

3- Principals, policies, and processes
Data Architecture1- Determine your data storage and processing needs

2- Create structures and plans to address the enterprise’s current and long-term data needs
1- Create a corporate data architecture

2- Project Management of Enterprise Requirements
1- Data Architecture
Design

2- Data Flows

3- Implementation
Roadmap
Data Modeling and Design1- Validate and record a knowledge of enterprise data requirements that are tightly aligned with current and future business needs

2- Laying the groundwork for master data management and data governance
1- Plan for data modeling

2- Build the conceptual data model

3- Review and manage
1- Conceptual, logical, and physical data model
Data Storage & Operations1- Manage data availability throughout the data lifecycle

2- Ensure the data assets’ integrity

3- Control the performance of data transfers
1- Manage Database technology & operations

1- Database Environments

2- Database Performance
OLA
Data Security1- Prevent unauthorized access

2- Compliance with policies for data privacy

3- Ensure that data privacy is enforced
1- Determine your security needs

2- Create a security policy

3- Establish security guidelines

4- Procedures must be followed
1- Data security architecture

2- Security policies

3- Access control

4- Audit reports
Data Integration and Interoperability1- Data consolidation

2- Data delivery in the format and time frame required by consumers

3- Assist with efforts in corporate intelligence, analytics, master data management, and operational effectiveness
1- Define the needs for data integration and data lifecycle management

2- Conduct data discovery

3- Data orchestration in design
1- Data access agreement

2- Data services

3- Data exchange specifications
Document and Content1- Comply with all legal responsibilities pertaining to records management

2- Ensure that documents and content are stored, retrieved, and used efficiently
1- Plan for records management

2- Develop a content strategy

3- Manage records lifecycle

4- Create and distribute content
1- Strategy for managing content and records

2- Procedures and policies

3- Repository of content

4- Audit trails
Reference & Master Data1- Allow information to be shared across apps

2- Provide a reliable source of quality-assured master data and data references
1- Determine the drivers and requirements

2- Provide data integration services

3- Sources of data should be evaluated and assessed
1- The criteria for master and reference data

2- Reference and master data that may be relied on
Data Warehousing & Business Intelligence1- Assistance with efficient data analysis and decision-making

2- create and manage a business intelligence environment and infrastructure
1- Create and populate a data warehouse, data mart, or data lake.

2- Implement a Portfolio of Business Intelligence

3- Keep track of data products
1- DW and BI architecture

2- Data products

3- Release plan

4- Plan for learning and adoption
Metadata Management1- Provide a grasp of business terms and use within the organization

2- Obtain and include metadata

3- Provide a standardized method of access
1- Define metadata strategy

2- Understand requirements

3- Define architecture

4- Query, report, and analyze
1- Metadata strategy

2- Standards

3- Control process

4- Dependency analysis
Data Quality1- Develop techniques to measuring and improving data quality in accordance with stated business rules

2- Define the standards and needs for integrating data quality control
1- Establish a data quality culture and specify requirements

2- Create and implement data quality operations

3- Conduct an initial data quality evaluation.
1- Data quality strategy and framework

2- Reports of top quality

3- Reports on governance

Why Data Management is Important?

So many of us interact with data as part of day to day work. Whether you realize it or not, you handle data every day.

While one may argue that data is important for achieving a competitive edge, the problems that businesses confront cannot be overlooked.

The main purpose of data collection and its proper management is to provide management with information about resource responses so that they can make the most accurate business decisions based on the data and the information collected from the data.

Therefore, it is very important to ensure that the data collected is accurate and reliable so that you do not make false decisions that will affect your company’s future.

Some of the advantages of data management include:

Visibility

A solid data management strategy will raise the visibility of your organization’s digital data assets. Employees may be certain that data will be at their fingertips when they need it.

Data Management Improves Decision Making

When the data quality knowledge area is properly applied, you can ensure that all data gathered and created are of high quality, and that the decisions made by management based on this data are incredibly useful and contribute to the overall business performance.

Eliminate Redundancy

How many times have you had to clear up data duplication in your organization? It is quite simple in large organizations and without a proper management plan to develop redundant data, which takes a long time to manage and costs money to store.

Minimize Data Loss

A effective data management approach is ensuring that all data is available when needed and that backups are performed on a regular basis in order to restore it in the event of an emergency.

It is critical to reduce, if not eradicate, data loss since it may have a wide-ranging impact on your organization.

Reliability

Employees may be certain that data is always available and of good quality thanks to regulations and practices. Companies can adapt to market changes and client requirements more effectively if they have trustworthy, up-to-date data.

Data Management Challenges

It is no secret that implementing a good data management program in medium to large organizations is a challenging task.

Let’s look into the top challenges that organizations face when dealing with their data.

Data Quantity

As the amount of data continues to rise and, eventually, all companies recognize the value of data as “the new oil,” it has become critical to have a strategy in place to make the most of this most valuable commodity.

Having a large amount of data streaming from many sources is something that businesses must carefully examine and implement the best strategies to handle.

Keep Up With Required Compliance

Compliance laws are always changing, necessitating effort to cope with their complexities. Organizations must constantly monitor changes in rules and regulations and adapt their internal policies accordingly. Failure to do so puts your company at danger of fines, legal action, and penalties.

Data Silos

The most significant difficulty we face in businesses is the large number of available data repositories or silos. This might result in duplication, higher management and security costs, and an expansion of the integration strategy.

Bridging the gap between data silos is something that must be done in order to maximize the value of the most available data, and I expect this to remain a difficult effort.

Data Management Best Practices

Implementing best practices can assist your company in addressing and reaping the advantages of various data management difficulties. Make the most of your data by implementing an efficient data management plan.

Focus on Data Quality

Organizations should prioritize data quality over quantity. The allocated team should ensure that the data collected is of good quality so that it can be processed and evaluated for better business choices.

It all starts with how we collect data. Capturing correct data and storing it for subsequent processing should be automated, and the latest technologies for data categorization and indexing should be used.

Focus on Data Security and Privacy

Data breaches occur most frequently from internal resources; thus, you should ensure that data kept is safe and that only those with appropriate access may view it.

It is critical to establish a plan for document access and control, as well as to monitor audit trails and how data is accessed and utilized throughout the lifespan.

It is a difficult process since data will be spread over numerous systems and repositories, each of which must be handled in the most efficient manner.

Build Effective Communication

Do not overburden data governance with bureaucracy, and never consider communication to be an afterthought. Communication is an essential while dealing with DG.

Managing data governance necessitates good communication skills as well as dealing with a variety of personnel difficulties. As a result, excellent communication is one of the most important success factors for data governance managers.

Outsource If Needed

If your business is having difficulty implementing a strong program, you may outsource this job to third-party well-known corporations to put the plan together for you and make recommendations on what works and what doesn’t based on your organization’s needs.

Conclusion

Understanding how data management works and how businesses can put up the most effective policies and practices to get the most out of it is the key to unlocking the potential of data as a driver for organizational success.

source: Microsoft PowerPoint – DAMA DMBOK2 (dama-dk.org)

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