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Data Management: Types, Benefits and Challenges

  • Sangita Kalita
  • Jun 14, 2022
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“Data is a precious thing and will last longer than the systems themselves.”

-Tim Berners-Lee

 

Every little thing in today’s business economy owes its existence to data. Data is required to feed all the algorithms on which the whole world functions. The future of computing as well as the competitive strategies of many industries can be accomplished only by data. Looking at the scope of data, a robust data management system is critical to ensure that data is utilized to the fullest without any mishaps.

 

In this blog, you will learn more about Data Management.

 

What is Data Management?

 

Data Management refers to the practice of collecting, storing, using and archiving data effectively. All stages of the information lifecycle are strictly governed by data management. Data Management ensures that an organization is using the most updated form of data available.

 

An effective data management strategy will help to identify the activities of employees by utilizing data management strategy to its best capability to meet the demands of the organization in order to secure a profitable future for the organization.

 

Types of Data Management


Types of Data Management :1. Master Data Management 2. Data Stewardship 3. Data Quality Management 4. Data Security 5. Data Governance 6. Big Data Management 7. Data Warehousing

Types of Data Management


The experts in data management focus on specific specialities. A blend of all of these types of data management is required to build a stable organization. 

 

Adequate research and utilization of data management would help the data magers to build suitable solutions for any kind of environment. The types of data management which falls under this are given below.

 

  1. Master Data Management

 

In Master Data Management (MDM), all the data is encapsulated into one single version and it ensures that the organization is working on that specific version. It also ensures that the data is up to date and reliable and divides the data into different systems for usability.

 

  1. Data Stewardship

 

The work of a data steward is to look over data policy implementation and rules enforcement. They watch over data collection and ensure that the movement of data is according to policies.

 

  1. Data Quality Management

 

Data quality management, as the name suggests, watches over the quality of data. They look for any duplications or errors in data. They look over all the collected data in order to do this. They handle the data management system.

 

  1. Data security

 

Security is the most important aspect in all kinds of work. This holds true in Data Management too. Data security is enforced at every level of Data Management and they are tasked with preventing unauthorized access, encryption management, guarding against accidental movement or deletion and other security related activities.

 

  1. Data governance

 

Data governance sets all the laws for management of data. They are the policy makers for data intake, flow and security. They oversee all the other types of data management work like quality management professionals, data stewards, security teams and other people required for data management.

 

  1. Big data management

 

Big data management is the collection and analysis of all digital data to improve operations in an organization. They collect and analyze the raw data so that other aspects of data management can use the processed data to improve the operations of the organization.

 

  1. Data warehousing

 

The huge amount of data which is collected needs its own space for storage. Data warehousing provides physical or cloud based infrastructure required for storage and sorting of data.

 

Also Read | Guide to Data Profiling

 

 

Practices adopted for Data Management

 

In order to utilize the approach of data management to the fullest one needs a well defined framework. Below we've listed some of the practices adopted for data management.

 

  1. Planning

 

A data management plan (DMP) is the most important tool to get started. This document states all the guidelines including accessibility, estimated data usage, ownership, archiving approaches and more. 

 

DMP can be used both as a reference and as a record and can be updated according to the requirements of the organization. A DMP can also be used to bridge the gap between the company and the other people who are involved with the company like auditors and investors. This gives those people insight into the company's tactics for data management. 

 

DMPs can be created by many online services under specific templates. Some of the most common details which can be included in a DMP are as follows:

 

  • Required file format
  • Naming parameters
  • Access functions for stakeholders
  • Archiving process of data
  • Partners of the organization and their functions
  • Excellent documentation

 

  1. Storing

 

There are many options for data storage. An organization must know which kind of storage option will best suit their needs and proceed from there. A person may choose between a data warehouse or a data lake. 

 

They can also choose between offline storage of data or cloud storage. Storage of data is very important since improper storage may result in errors or inconsistencies in data. Two things that one should keep in consideration while data storage are given below.

 

  • Security and data encryption must be considered while in storage. Regulations like periodic audits need extra security requirements. If the security fails, a backup plan must be in motion to prevent total collapse of the organization. 

 

There should be three copies of data in all organizations - original copy, locally stored copy and remote copy. Cloud storage also provides duplication of data at cost effective prices.

 

  • While storage of data, documentation is very necessary. New employees of the organization will be able to analyze and utilize the previous data with proper documentation. This will be helpful if the original holders of data are no longer available.

 

  1. Sharing

 

After the necessary collection, handling and storage of data, the next important step is sharing. Corporate secrets, copyrights, proprietary intellectual property, and other limitations should be kept in mind while choosing the appropriate people for sharing data. 

 

The means of sharing data was previously called repository, but now many softwares have come into being to make this task easier.

 

Also Read | Hybrid Cloud Storage


 

Benefits and Challenges of Data Management

 

Data Management provides great benefits to any organization that realizes its true potential. When the data is under management, data mining can take place for information. The many benefits of data management are given below.

 

  1. Advertising potential that targets a particular demographic who might be interested in the products.

 

  1. Customer satisfaction as a result of customized shopping experience.

 

  1. Adequate security to protect corporate secrets.

 

  1. Following compliance standards, saving money and time.

 

  1. Utilization of machine learning, which has become more robust over time, making huge improvements.

 

  1. Reduction of operating expenses by utilizing only the required space.

 

Even though it has many benefits, data management is sometimes challenging to organizations and they require more tools for adequate management. Below are some of the challenges of Data Management faced by organizations : 

 

  1. Lack of data insight

 

Mere collection of data is useless without proper implementation. Data can be mined from a large number of sources like smart devices, sensors, video cameras, social media, etc. All of this can be utilized by an organization only if they have proper scale and are able to give adequate performance from their side.

 

  1. Difficulty in maintaining performance level

 

More collection of data means a more intensive database. An organization should try to maintain a peak response time by changing index continuously while looking at the queries answered by the database. This might cause difficulty in maintaining performance level across the organization.

 

  1. Challenges regarding changing data requirements

 

Data compliance requirements are very complex and ever changing. Continuous data review is necessary to keep up the data at par with new regulations. Personally identifiable information (PII) is the main aspect which should be monitored regularly to comply with strict global privacy requirements.

 

  1. Difficulty processing and converting data

 

Data processing is required to properly utilize the collected data. If data processing and conversion takes a lot of time then the data becomes obsolete and, and proper analysis of the data will not take place. This means that the collected data will become useless for the organization.

 

  1. Difficulty in effective storage of data

 

Data needs to be stored in data lakes or data warehouses in any format necessary. The data scientists should be able to work under time constraints to transform the data into required format for storage. There are many shapes and models for the data to be structured into. Only proper formatting would render the data useful for analysis.

 

  1. Difficulty in optimizing IT agility and costs

 

With the advent of modernization in the business sector, many online options have arisen for previously offline systems. One can now store data on the cloud, in premises or in a hybrid mode. 

 

The IT sector should know how to optimize the data and where to store the data in such a way so as to maximize agility and minimize costs.

 

Also Read | Introduction to Database Management System

 

 

How to overcome the Challenges of Data Management

 

In order to overcome the aforementioned challenge, comprehensive practices can be carried out by an organization. Depending on the type of data and organization, different practices may be adopted. Some of the major practices that one can adopt to face the challenges are given below.

 

  1. Creation of discovery layer

 

Creation of a discovery layer on top of the data collected by an organization will help the data scientists and analysts to access the data more easily and browsing of usable data becomes easier.

 

  1. Development of data science environment

 

A good data science environment helps in the transformation of data and the need for manual transformation is eradicated. This helps in increasing the pace of data testing for creation and evaluation of data models.

 

  1. Use of autonomous technology

 

Machine learning and AI is used by autonomous technology to monitor database queries and create indexes keeping those queries in mind. 

 

This helps to generate faster response time for the database and the data scientists are free from such time consuming manual tasks and can properly utilize their time in other necessary tasks.

 

  1. Use of data discovery for compliance

 

Compliance requirements were a major challenge in data management and the use of data discovery to monitor data and to identify the different links which needs to be followed for proper compliance helps to overcome those challenges. 

 

Compliance demands have been increasing globally at a faster rate than ever and this will ensure that the company always stays up to date with the updated compliance regulations.

 

  1. Use of converged database

 

Native support for all new data types and the newest data models operable buly one product is known as converged database. Converged database systems help to carry out different kinds of data management works like machine learning, IoT, graphs and blockchain.

 

  1. Proper performance and scale of database

 

Timely decision and execution is the main motive of analyzing data. Use of a high scale and high performance database helps the organization to analyze data from different sources using machine learning and advanced analytics in a timely manner. This helps the organization to make better decisions for the future.

 

  1. Use of common query layer

 

Different data management repositories are used for storage of different types of data. Using a common query layer helps to bridge the gap between different repositories and helps data analysts and scientists by making access of data easier without making them jump through hoops like knowledge of storage location or transformation of data into usable format.

 

Also Read | What is Data Labeling and How Does it Work?

 

Data has made a new role for itself as business capital and now organizations are discovering the many advantages of Data Management. Identification of trends, making decisions and staying ahead of competitions can be accomplished by data and therefore data management is a useful tool to increase capital. 

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