In general, DLM is a policy-driven approach that can be automated to take data through its life, from inception to destruction. For instance, imagine an employee data is captured and stored in a database. The stored data can then be accessed for either analytics, reporting or other use. You can apply logic and validation the data throughout it useful life. But at some point the data might be of no use, say an employee retire or leaves the enterprise, and can be deleted, archived or purged.
Defining and organizing this process into different stages and steps for organizations is known as Data Life Cycle Management.
Data Life Cycle Management Stages and Best Practices
Organizations depend on various type of data to generate and grow revenue, create new opportunities and compete favorable in the market.
Data offers limitless potential that can be harnessed by focusing on data security, data resiliency and data compliance. Today, data is considered a physical asset in an organization since it plays a vital role in business decision making process.
DLM Vs ILM
It is common to confuse Data life cycle management with Information lifecycle management (ILM). The two are completely different.
DLM deals with raw data that is stored in relational-database or NoSQL database. The data can be both structured and unstructured. DLM is not concerned with the individual pieces of data within a record, but with the record itself. On the other hand, ILM refers to the tangible piece of information that is constructed using one or more pieces of data. It ensures the stored data is accurate and up-to-date.
In a way, data lifecycle management and information lifecycle management are two sides of a coin. But DLM cannot exist without ILM, since ILM drives various stages of DLM. The cycle starts at inception or generation of data.
DLM stages and best practices
In today’s market, everything we do will result in generation of data. While there is no industry standard for data lifecycle, experts from nearshore software development services agree that the cycle looks like this:
Stage 1: Data capture or generation
This is the first step in the cycle. Organization acquires new, vetted information. The data can be in the form of image, word or excel sheet document, or PDF format. The data is stored in a data infrastructure and is accessible to certain devices or roles within a hierarchy.
At this stage it is important to capture accurate information as much as possible.
Best practice: Well defined data type
Carefully categorize your data into a schema that takes into account the sensitivity of the information and well as its value to the organization. Common classification schema involves categorizing data as public, sensitive, or public.
Stage 2: Data management and maintenance
Once data is captured, it is stored in relational databases or NoSQL databases. Data management and maintenance is the process that ensure accurate data is available in real time for use and publication. At this stage, data is classified as internal, sensitive, restricted, or public. Data protection policies such as access control, data masking and data encryption are applied at this stage.
Best practice: Have a comprehensive data policy that is shared throughout the organization so that it can be implemented.
Stage 3: Analyze and visualize
Here data is cleansed and shared within the organization, customers and other third parties. Some IT systems used to provide access to data include enterprise resource planning, customer relationship management and data warehouse.
Stage 4: Data retention or Destruction
In the last stage, the data is either destroyed or retained. For long time availability the data is archived in cloud storage in encrypted format or disks. Depending in the nature of data, most archived data can be deleted, mostly derived data.
Best practice: Research and visit guidelines for destroying data to ensure compliance.
For organizations looking to increase efficiency and agility, View Zircon.tech help you to come up with the best DLM strategy. DLM is a vital investment in coming up with a risk management approach that ensures your company remains compliant at all time.