Data Life Cycle in the Enterprise

Data In Enterprise, Its Importance, and Stages In The Data Life Cycle

In business, data life cycle is always circular, is renewed and updated regularly. Most managers in the digital age understand that data is an enterprise's most valuable and vital asset. Data is the core of almost every business decision. The following article by Viindoo will provide you with the concept of enterprise data, the importance of data, as well as the formation and stages of the data life cycle.

Data life cycle in Enterprise 

Enterprise data are structured information units formed from the operation of a business. 

Data includes information about what and who the enterprise interacts with, such as employees, customers, suppliers, sales orders, purchase orders, production processes, etc. 

Each of these will have attributes, such as name, address, date, etc. They also have certain relationships that the enterprise can rely on to make analytics. 

the definition of data life cycle in enterprise
The definition of data life cycle in enterprise

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The Importance of Data in Business 

Data helps business leaders make decisions based on changes, trends, and statistics. For example: 

HR managers use data such as recruitment success rate and cost of each channel to evaluate the effectiveness of recruitment by channel. 

Marketing managers use customer data, quotes, and sales orders to classify customers, analyze customer behavior, and analyze the effectiveness of campaigns. 

Higher management levels such as the CEO or Board of Directors use data to see macro issues: fluctuations in input prices of goods and raw materials, shipping cost; production efficiency; revenue changes, etc. 

A data sample table enterprise

Enterprises can optimize operational and manufacturing processes by efficiently using power of data and dramatically enhancing the customer experience. From there, businesses can grow revenue strongly while still ensuring cost optimization. 

How is data formed and used? 

Most data formed in the enterprise goes through a multi-stage life cycle. According to The Wiley CMAexcel Learning System (WCMALS) - 2020 Edition, there are eight stages in a Data Life Cycle, as shown in the diagram below: 

Enterprise data loop diagram

Enterprise data loop diagram
  1. Data Capture: This is the first stage in the Data Life Cycle. Typically, data in the enterprise is collected through three forms:

    1. Data Entry: generate data by manual input. We can think of using ERP software in the business, the salesperson creates the quotations, or the accountant records the Invoices on ERP software. A data item is created.

    2. Data Acquisition: import existing data generated from organizations outside the business. A good example of Data Acquisition is a feature that integrates data automatically through the APIs of some modern ERP software with the internet banking system to serve the reconciliation and accounting of payment transactions.

    3. Signal Reception: collect data from devices used in business operations. For example, in the entry and exit control process of company personnel, data is generated from employees performing fingerprint scanning to log in and log out. Then the data is integrated into the administrative software or extract in Excel form.

  2. Data Maintenance: Data needs to be converted into a user-friendly form to be usable. This transition can include: migration, integration, cleaning, enrichment, changed data collection, etc.

  3. Data Synthesis: This stage of data life cycle includes statistical methods that combine data from multiple sources or experiments. This is to obtain better overall estimates and answers to data requests. A good example of Data Synthesis is using modeling methods to support investment decisions, such as risk modeling, financial modeling, and actuarial modeling.

  4. Data Usage: Data usage is how data is used to support a business mission, such as strategic planning, customer relationship management (CRM), processing invoices, sending purchase orders to suppliers, and more.

  5. Data Analytics  In this stage of data life cycle, people use data analysis methods to turn raw data into useful information and business insights. A widely used method in business data analysis is Data Visualization.

  6. Data Publication: Data is sent or disclosed outside the organization, such as sending quotes to customers, sending a debit comparison, or publishing financial statements to the company website.

  7. Data Archival: The process includes removing data from a usable environment, putting it into storage, and being reused in the future.

  8. Data Purging: This is the last step of a data life cycle to remove unuseful or unnecessary data from the storage system. Deletion of data should be planned and considered with legal requirements or the company's information protection policy. 

For example, according to Decision 376/2003/QD-NHNN on preservation and storage of electronic documents used for accounting and capital payment of payment service providers, electronic certificates directly related to accounting records at payment service providers: must be kept 20 (twenty) years from the end of the accounting year or when the payment service providers complete the capital payment settlement.  

many businesses have their own data analysis teams or hire consulting - analysis companiMany businesses have their own data analysis teams or hire consulting - analysis companies

Many businesses have their own data analysis teams or hire consulting - analysis companiMany businesses have their own data analysis teams or hire consulting - analysis companies

In conclusion, understanding data life cycle, controlling and effectively using it is a vital task for a business. Viindoo hopes after this article, you have acquired the fundamental knowledge of data that can apply to your business.

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Data Life Cycle in the Enterprise
Phạm Thị Xinh 30 December, 2020

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