Why You Need a Data Lifecycle Management Program

Today’s world is driven by data. Business decisions are influenced by it and the foundations of organizational infrastructures are supported by it. Data-driven approaches to business have been and will continue to trend upwards. In fact, according to McKinsey Global Institute, data-driven organizations are not only 23 times more likely to acquire customers, but they’re also six times as likely to retain customers and 19 times more likely to be profitable.

The data that is used by a company can be simple or complex, and used to process an order or attract a customer. It’s clear that data is vital and valuable for any company. However, to be fully useful, data must be managed throughout its entire lifecycle — from creation to deletion and everywhere in between. This can be a challenge without a data lifecycle management (DLM) program.

What is Data Lifecycle Management?

At a high level, data lifecycle management refers to the defined process, policies, and procedures for managing data throughout its entire lifecycle across different applications, systems, databases, and storage.

In order to understand DLM, you need to understand the concept of the data lifecycle. I’ll explain it in a simple scenario that includes a single piece of data.

The Lifecycle of a Piece of Data

Here’s the scenario: A potential customer completes an online form on your site, and that data needs to be saved to review and store. Once that data is captured, it is saved to a secure database. That new data will be accessed and shared for analytics, order processing, and/or stored for future use. The data may be joined with other data that is connected to it (for example: other data about the same company or the same person), and shared internally for applicable business processes. Once the data is considered no longer useful, it is archived or destroyed.

DLM and the Data Lifecycle
So where does data lifecycle management come into play in the data lifecycle? It is the established process that moves the data from one stage to the next in the cycle. One of the main purposes of DLM is to establish visibility throughout the data lifecycle. Better visibility presents the opportunity to improve data processing efficiency, security, and costs, and get the most out of data.

In addition to process improvements, a DLM program is essential when considering data protection. Defining how data is processed, stored, shared, and controlled is important when designing security measures. Defining these within a DLM program is one of the first things an organization can do to mitigate risk of data losses and breaches. Depending on the industry, these items may be required to be compliant with laws and regulations.

Six Stages of Data Lifecycle Management

1. Data Creation. Data creation is the beginning of the life cycle, occurring when an organization obtains new information (created internally, collected from apps or webpages, shared from a third party, etc.)
2. Data Storage. Data storage refers to the processes relating to redundancy and security implementations for active and inactive data. Data storage procedures must be compliant with laws and contract obligations.
3. Data Usage. This stage in DLM defines who can use the data and for what purposes.
4. Data Access and Sharing. Data is constantly being shared. Access and sharing procedures define secure methods of sharing data, accessing data and for what purposes.
5. Data Archiving. Data undergoes an archival process that ensures redundancy. DLM strategies define when, where, and for how long data can be archived.
6. Data Destruction. The final stage of the data lifecycle, when data is purged from all records and destroyed.

The Importance of Understanding Data Lifecycle Management

Everything we connect to creates data of varying importance. There is no one concrete way to manage your data, as data lifecycle will complement an organization's unique operational processes. Because data often spreads across an organization, understanding the data lifecycle isn't just for those working in compliance or on data projects. A comprehensive understanding of the importance of data and the data lifecycle is key to maximizing cybersecurity and data security efforts and reducing risk.

For more information on how Locus Robotics handles our internal and customer data, visit our TRUST Center.