What is data management?

Data management is creating, organizing, storing and managing the data collected and created by any organization.
Effective data management is a crucial part of today's world. Data management provides analytical information on the vast amount of data collected from business applications daily, which helps make critical business decisions and plan and bring out the best strategies.

Data management cycle is shown by a circle. The various process involved in data management is the parts of the ring.
CC-SA | Image Credits : Steps of Data Management

Principles of Data Management

There are six fundamental principles of data management which are listed below.

  • Plan a data management strategy- One of the essential parts of data management strategy is creating the best plan. The organization needs to have an excellent strategic approach for managing data. Some of the best strategies for data management are listed below.
    • Describing a vision and roadmap for data.
    • One should plan which data to use, when to use and how to use it.
    • Plan for security, storage of data and perform documentation.
    • Use only good quality data.
  • Defining roles - In a database management system, one should assign roles to individuals. Management of data is not done by a single person and a whole bunch of people or teams are involved. Every individual's role is different but interdependent. Organizations' three most common roles for data management are data owner, data stewards, and data custodians.
  • Control the data throughout the whole life cycle- Another vital part of data management is managing data throughout the entire life cycle process. It can be done by using proper procedures and policies. An organization should keep in mind that the data should be correctly managed, stored, and validated throughout the process.
  • Good data quality- Assuring good data quality is the most crucial aspect of the data management principle. The data is of high quality only if it is timely, accurate, non-repetitive, consistent and complete. High-quality data gives positive outcomes, higher profit and more benefits. Similarly, low-quality data is not suitable for organizations. It is less profitable.
  • Gathering and analyzing meta data-Metadata is a collection of data that describes the data. Metadata should be of good quality as it enhances the quality of data. Overlooking the metadata can diminish the quality of data. Organizations must use an excellent strategic approach for collecting the metadata.
  • Maximize data usage- The last principle is to maximize the usage of data. Maximization of data usage is essential for the organization in order to get high ROI.

Importance of Data Management

Data management is a critical aspect to deploy valuable data. Adequate data provides valuable insights and adds value. People want to access trusted data, and effective data management provides this for their end-users. So, data management is a necessary process. Some key benefits that are provided by effective data management are given below-

  • Reliability- Data management provides reliability by decreasing potential errors by delivering policies and best processes. With reliable, up-to-date data, it is easy for the business to attract potential users and maximizes data usage.
  • Security-Proper management of data protects the organization from attacks, data thefts, data loss and breaches. Robust data security provides trusted data. Safety is vital if your data is more confidential or may consist of personal information that one wants to hide from others.
  • Visibility- Data management can increase the visibility of data. As the data is more visible, more users can access it, and organizations can profit.
  • Scalability- The organization must effectively scale data and its usage.

Best Practices of Data Management

Data management is a business driver used to ensure that the data is stored, managed, protected, and validated standardized. It is crucial to develop and deploy the correct data and information to the end-users in order to use them confidently. Best practices the organizations use to deploy the best data are listed below.

Create strong file names and catalogue conventions- It is imperative to create strong file names. It must be descriptive in nature and it must obey the naming conventions so that users can locate the files easily.

Catalogue conventions consist of many things such as the name of the data author, what information this set holds or contains, description of different fields, where the data was created, how and why the data was created.

Consider metadata for data sets carefully- Metadata is a piece of extra and descriptive information about data. Metadata contains lots of information such as data permissions, content and structure of data.

Data storage- Data storage is an essential aspect of the business. It is necessary to plan for data back-ups and data preservation methods.

Processes in Data Management

The data management process helps to improve the quality of your data. A good strategy results in high quality data.

Let's look at ten steps to use in their data management process.

Define the data architecture- Defining a data architecture is one of the essential parts of the data management system. Data architecture is a blueprint that an organization needs to define. It consists of all the data tools and databases the organization is using. Without creating an architecture, the organization will not know where to store your data without knowing which data is related.

Defining a data architecture is necessary when the size of an organization is more significant and handles a considerable amount of data.

Assign Responsibilities- During data management, the organization must assign responsibilities to the individual. The lack of individual roles and clarity causes low quality data and creates uncertainty. Assigning roles makes it easier to handle the data.

Define how to name things- Nomenclature is like setting standards. The organization must define standards for naming files and decide how the modification affects the file name. So, naming should be done in a standardized way. It makes things for searching, inserting and updating.

Collect data- Organization has to collect good quality data. Gathering data is the important part, and it also tells about data and what you need. All the important decisions are based on collecting data and afterwards. Data collection is the most critical step of the data management process.

Prepare data- After collecting all the data and information, preparation can be done. Data preparation comes under data manipulation. It's impossible to process raw data, so one must validate the data and check its accuracy. For accuracy, one can do exploratory data analysis.

Process data- The next step that comes in is data processing. Data processing includes tasks like the formatting of data. For example, you want to check whether all the data is in the same format or indifferent. Or, you may have different field names for additional data.

Analyze data- Data analysis is the most critical step. You can say that magic begins here. In this step, an Examination of data occurs, and then we collect meaningful results. For this, we have software that analyzes data for us. The software is designed that Process and analyze data and find patterns in those data. After analyzing data, the organization get the results, and it helps an organization to improve services and processes.

Interpret data- In this step, the Organization document all the previous actions and their outcomes. Organizations can gather the data in the presentation, report, video, or audio.

Share documentation- Sharing of documentation is essential among the organization. Documentation consists of all the data management processes and results. The data management process only succeeds if all members understand the standards and agree upon them.

Collaborate- For a large organization, communication and collaboration is an essential part. Different departments can have different management processes. So, collaboration is a crucial aspect.

Strategy used for Data Management

Data management strategy is the roadmap followed by the organization to achieve its goals or success. These strategies work together efficiently and effectively to get effective results.

There are five strategies that organizations follow for effective data management.

Identify business objectives- Identifying business objectives is crucial as businesses create lots of data per day. According to the purpose, the organization build strategies. What is the aim of your organization? What kind of data is required to fulfil that objective. So, the goal is essential to know to form good strategies.

Create strong data processes-It involves preparing, storing, collecting and distributing the data.

Find the right technology- To build a robust data management strategy, one needs the right tools, technology and platforms. The organization requires the proper hardware, software and technologies to build a robust data infrastructure.

Establish data governance- Data governance ensures that data should be used consistently and correctly in the organization. The increased use of data and the growth of your data infrastructure brings many responsibilities to the organization.

Train and execute- Sometimes, for good data management and to use data effectively, organization needs data experts. The essential part of the data management strategy will be to provide the skills and knowledge to the team members. To train people, organizations can use data tools or data experts to teach them.

Platforms of Data Management

Data management platforms are used by an individual or an organization for efficient management of huge amount of data.
The top 10 Data management platforms are:-
Salesforce DMP
SAS Data Management

CC - SA | Image Credits: https://cms-media.bartleby.com/

Common Mistakes

-Not paying attention to data architecture.
-Ignoring the data quality.

Context and Applications

This topic is critical within the professional exams for both undergraduate and graduate courses, especially for:

  • Bachelors in Computer Science
  • Bachelors in Information Technology
  • Masters in Computer Science
  • Masters in Computer Application
  • Data Warehousing
  • Data Modeling
  • Data Retention
  • Metadata Management

Practice Problems

Q1 What are the platforms used for data management?

  1. IOT
  2. ML
  3. Salesforce
  4. None of the above

Correct Option: 3. Salesforce

Explanation: IOT(Internet Of Things) and ML(Machine Learning) are two technologies that relates to artificial intelligence. Salesforce is a platform used for data management.

Q2 What is the first step of creating the strong data processes?

  1. prepare
  2. store
  3. analyze
  4. collect

Correct Option: 4. collect

Explanation: Data collection is the first step of data management. This step can be followed by other steps in the order prepare->analyze->store.

Q3 Which is the most important step of data management process?

  1. Defining data architecture
  2. Assign roles
  3. prepare data
  4. process data

Correct Option: 1.Defining data architecture

Explanation: Data architecture is a blueprint that organization needs to define in. It consist of all the data tools and database the organization is using. So defining data architecture is the most important step.

Q4 Data management process involves _________ steps.

  1. 6
  2. 7
  3. 9
  4. 10

Correct Option: 4. 10

Explanation: There are a total of 10 steps in Data management process:-Define the data architecture, Assign Responsibilities, Define how to name things, Collect data, Prepare data, Process data, Analyze data, Interpret data, Share documentation, Collaborate.

Q5 which of the following is not a data management platform?

  1. snowflake
  2. salesforce
  3. Mapp
  4. Facebook

Correct Option: 4

Explanation: The Mapp, snowflake, and salesforce are data management platforms. Facebook is a social networking and social media platform.

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