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Data quality - Data life cycle

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Data Quality Management – Data life cycle

All over the world, the future of every company is linked to data. All business sectors must now integrate data as a whole into their strategy. The new technologies of Big Data and connected objects (IOT) do not consider data in isolation, but as a whole.

Nevertheless, as technology and user behavior change ever more rapidly, data collected a year ago is already no longer valid. To maintain high data integrity, we need to take into account the data life cycle of each piece of data, and bear in mind its influence on inherent quality.

Data life cycle, definition and management

We can compare the data life cycle to that of any living thing.

Indeed, data should be considered as a living being. It will go through various stages, from the moment it is created, collected and recorded in a system, to the definition of its use, its growth by agglomerating with other data, its cleansing and final deletion. All the stages in this process constitute the data life cycle.

What is Data Life Management?

Once the notion of the data lifecycle has been integrated, it’s time to think about how it will be organized within the company, and how it will ensure data longevity.
Depending on their activity and purpose, each company will propose different ways of managing its data.

Nevertheless, there are 5 major stages that we find in every DLM:

Step 1: Creation
Data is created or collected in raw, unstructured form. The multiplication of sources and collection points multiplies the number of formats that must be integrated into systems (images, text, video, etc.).
Step 2: Storage
This is a vital step in guaranteeing data security. Storage must be accompanied by detailed data backup and recovery procedures.
Stage 3: Preparation and analysis
In the day-to-day life of every department, data enables decisions to be taken on a greater or lesser scale, with a potentially major impact on society. Data processing and analysis is an essential step in managing the data cycle. Without analysis with a clear, identified objective, the data collected is useless.
Stage 4: Archiving
If the data is to be used only once, or if its relevance is no longer proven, then the company has the choice of archiving or deleting it. The difference lies in the fact that archiving enables the data to be retrieved or consulted at a later date, if necessary (for comparison, verification, etc.) Archived data is stored as it is, and no further processing is carried out on it.
Step 5: Removal
Definitive deletion is generally carried out for 2 main reasons: storage costs that make simple archiving impossible, or regulatory constraints that impose a maximum retention period. Deletion is generally carried out from previously stored data.

This data lifecycle management of your information will optimize the following processes:

  • Limiting the use of data: knowing the different stages in the life of your data, and guaranteeing that it is stored securely, curbs any possible drift or inappropriate use of your data.
  • Increased data availability: knowledge of the management stages, available formats and storage of your data considerably facilitates access to it.
  • Increased data integrity: the implementation of distinct management processes optimizes data quality by avoiding the creation of duplicate or unreliable data.

Implementing tools to manage and validate data relevance has become vital to guaranteeing data quality and integrity.

A company’s activity, success and profitability are based on data management and processing at every stage of the data lifecycle. Optimizing this management is a demanding task, requiring day-to-day organization, for which Data Enso provides support.

We propose to become your partner in your data reliability processes by implementing one of our Data Quality Management solutions.

Our validation, verification and enrichment solutions can be integrated quickly and easily into your collection tools, with little development.

As a complement to our online solutions, we can intervene on your stored data via batch processing.