The value of data and its life cycle in organizations

The value of data and its life cycle in organizations

Data has become central to modern life. On the one hand, people interact with each other and with organizations more frequently with devices that generate data. And on the other hand, increasingly and in any organization, decisions must be justified with data at all levels. It is for this reason that monitoring its usefulness is essential to get more out of it and minimize errors. Therefore, data lifecycle management is an unavoidable task. 

What is the data lifecycle 

The data life cycle or information life cycle is the entire period in which data remains in the system, from its generation to its disposal. In general, several stages can be detected in this cycle, which respond to different needs. 

DocuSign and IBM, among others, propose five stages that help organizations to improve the management of information flow along its path: 

Data creation or capture from many different sources with information that can come in many formats and forms, such as text files, photos, videos, spreadsheets, among others and from different places, such as applications, websites, mobile, IoT, forms, etc. 

Storage: to store and protect them. Data may differ in the way it is structured, which has implications for the type of storage. 

Once the storage type for the data set is identified, the infrastructure can be assessed for security vulnerabilities and the data can undergo different types of processing, such as data encryption and transformation, to protect it from malicious actors, or to store it correctly. 

Another aspect is the focus on data redundancy. A copy of the stored data can act as a backup in situations, such as data deletion or data corruption.   

Data sharing and use: making data available to users. It is defined who can use it and for what purpose. Once data is available, it can be leveraged for a variety of analyses. From basic exploratory and data visualizations to more advanced data mining and machine learning techniques. All of these methods play a role in business decision making. 

The use of data is not necessarily restricted to internal use only, but can be used, for example, by external service providers for purposes such as marketing analytics and advertising among others. 

Archiving: copies of data that are kept in case of need, or for possible litigation and investigations. Archived data can be restored in a live production environment.  

Data deletion: a cleansing process so that non-relevant information or information to be deleted does not take up space. Storage costs and the need for efficiency are pushing towards the elimination of what is no longer needed. The main challenge is that what is destroyed is done correctly. 

What is data lifecycle management? 

An IBM report says that in data lifecycle management (DLM), data is separated into phases based on different criteria and moves through these stages as it completes different tasks or meets certain requirements. These processes are necessary to avoid data loss due to security breaches or disasters.  

However, do not confuse data lifecycle management with information lifecycle management. The former protects the files, the latter the information they contain. 

Benefits of data lifecycle management 

Data lifecycle management has several important benefits that include improving processes, maintaining data quality throughout its lifecycle, ensuring that data is available to users accurately and reliably, controlling costs by archiving or deleting it when necessary. 

Alberto Pan, CTO of Denodo’s company, pointed out in the Chief Data Officer Club Spain & Latam publication, that “today, in large organizations, decisions have to be justified with data, not only at the top management but at all levels. And they are able to provide decision-makers with the information they need when they need it. Classic data management techniques do not scale well in the face of this increased demand for data, and often BI/IT teams are not able to respond in time to business needs. That’s why we need more agile methodologies and tools: so that decision makers have the information they need when they need it.” 

Berna Marcos, head of the AI Engineering practice in Spain and Portugal at Accenture, told the same publication that “giving value to the data lifecycle is directly proportional to the value generated in the organization. We must not forget that data is the element that, together with a good digitization strategy, will allow organizations to reach a higher level in terms of performance and business impact”. 

In this regard, he pointed out that “the development and expansion of new architectures is a key area to focus on, due to the potential benefits in terms of agility, information integration and ease of information availability. From a business perspective, there is a greater adoption of data as a business asset. Data is perceived as a key element in all aspects and there is an appetite to provide organizations with intelligence”. 

Having the right tools 

At each stage of the data lifecycle, processes and needs arise that must be addressed at an early stage. Due to the dimension acquired by the volumes of data generated, exchanged and stored in any organization today, it is no longer possible to manage data management processes manually or with the tools that companies have been using for the last 20 years. Integrating, validating the veracity of data, unifying their formats, extracting information from different types of files, consolidating and reconciling information, are indispensable processes for the correct management of real, validated, and organized data for the use that the business requires. With a Data Management platform such as Conciliac EDM it is possible to centralize this management and make it automatic, performing, secure and error-free. 

If you want to know more leave us your contact or write us to