Data is your company's life blood. Every
activity, process, and procedure performed to support and deliver service and
products to your customers is built around data. As such, it needs to be
protected from vulnerabilities using Data Lifecycle Management, or DLM.
Many of these vulnerabilities are part of the
data lifecycle. This dictates where data is stored: on-premise, cloud storage,
with third-party providers, and so on. Furthermore, knowing where the data
resides within the system allows you to take consistent security and data privacy measures.
Data is most vulnerable at five key points:
collection, storage, sharing, analysis, and deletion. These points present the
possibility of a data leak, and no one needs to be told how devastating such a
situation could be. Managing these vulnerabilities should be a cornerstone of
any organization's business strategy, not simply as something that separate
departments carry out based on basic governance principles.
That's why an organization's executives should
be in charge of the data lifecycle management plan, and it should be handled
seriously.
What is Data Lifecycle
Management?
Data lifecycle management (DLM) should not be
confused with Data Loss Prevention (DLP). While DLP involves identifying and
preventing exfiltration, unwanted destruction, and loss of sensitive data, DLM
is far more comprehensive.
Data lifecycle management is a policy-based
strategy concerned with data management for information systems. Management
covers all stages, from data creation to storage and eventual deletion after
use.
It is more than simply a procedure for
controlling data from start to finish within an organization; it should be
viewed as a comprehensive practice that your enterprise should apply to all the
data you manage. DLM goals are targeted at improving data storage,
availability, and resilience.
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The following elements should be included in
the Data Lifecycle Management process:
People
This covers governance, risk management, and
ensuring that everyone involved understands how to handle data safely. For
example, the organization and its clients must adhere to regulatory
requirements, data integrity, security, and confidentiality standards,
especially with a hybrid workforce.
Process
The process should be officially defined in
terms of data stages by the organization. Determine the data's RACI (the basic
tenets of the Responsibilities Assignment Matrix, the acronym stands for
Responsible, Accountable, Consulted, Informed), and set up data pipelines, and
translations. Process controls (management, standards, and policies),
activities, process enablers, and organizational assets should all be included.
Technology
This should be available to support effective
and efficient data management at all data stages. The technology should make it
possible to take a holistic approach to data management from all sources,
including the cloud.
Analytics Metrics
Data should include key performance
indicators, crucial success criteria, and goals connected to the desired
outcome. This allows firms to refine their data and gain a better understanding
of their limitations.
Research
The process of analyzing data to aid decision
support is known as the Research Data Lifecycle (RDL). Several firms have
already turned to automation to complete certain data analysis tasks using
artificial intelligence or machine learning solutions such as AWS
sagemaker.
Legal Scale
Data must be governed, risks should be
appropriately managed, and compliance procedures should be implemented.
Personal Identifiable Information (PII) and sensitive data should be subject to
stricter controls.
Suppliers
Data Lifecycle Management can be supported by
third-party vendors, such as cloud storage providers. Almost anything,
particularly capabilities that an organization lacks, can be provided as a
service by vendors to help your organization save the time and cost of
acquiring capabilities like cloud security.
This usually leads to greater productivity and
a shorter time-to-market for your products. You should be vigilant about the
cost of services and products over time, and consider the return on investment.
What are the Stages of Data
Lifecycle Management?
There are several variations of DLM because
each business has its unique model, tools, and data sets. Most firms, on the
other hand, normally follow these stages:
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Data Gathering or Data Creation
This is the first stage of the data lifecycle,
in which new data value arrives in your organization's information systems.
Either by leveraging existing data created by the company, obtaining data
created by external sources, or by receiving signals from various devices like
Internet of Things (IoT) devices. The data gathered or created could be
structured or unstructured.
You can categorize data based on file type,
such as private, internal, sensitive, and public, at this step to determine how
the data will be managed in subsequent phases.
Data Storage/Maintenance
It's important to securely store and maintain
data once it's been created or collected. To ensure that data is kept safe
throughout its lifecycle, a complete data backup and recovery mechanism with
solutions like cloud computing should be in place.
To prevent data tampering, you must employ
appropriate security measures. Data should be stored in a way that complies
with all applicable laws and contracts. This stage isn't about extracting any
meaningful information from the data.
Data Usage
This is among the most crucial stages in the
data lifecycle. You can see, handle, alter, and store data at this point. This
stage entails utilizing data for a variety of organizational purposes such as
sending recurring emails.
Some data governance issues come with data
usage. Understanding whether or not your organization can use certain data in
the way it wants can have legal ramifications.
Data Sharing
Employees, consumers, stakeholders, and other
authorized users are all given access to data at this point. Since data is
shared both internally and externally for purposes such as marketing and
advertising, this stage is one of the most vulnerable in the data lifecycle.
Data Archiving
Data archiving is the process of keeping a
copy of data that isn't regularly accessed or utilized but needs to be kept for
legal and investigation purposes. Archived data can be restored to a live
production environment if necessary. The DLM approach for your company should
specify when, where, and how long data can be archived.
Data Reuse and Data Deletion
It is difficult to store all of the data
generated every day, which can reach staggering levels. Storage costs and
compliance regulations add to the challenge. As a result, you must remove data
that is no longer needed to free up storage for active data.
When data surpasses the specified retention
time or no longer serves a relevant function for the organization, it is
deleted from archives during this phase.
What are the Benefits of Data
Lifecycle Management?
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Besides simplifying information flow and
optimizing data during the lifecycle, DLM provides several other advantages,
including:
Data Governance
Data is used by businesses to enhance their
operations and make informed judgments. A good data lifecycle management
approach ensures that your data is always accessible, reliable, and protected,
as well as being compliant with data protection laws.
Value and Efficiency
Today's businesses are data-driven. Data is
critical for your organization's strategic goals to succeed. As a result, you
must verify that your data is secure, current, and authentic.
An effective DLM approach guarantees that data
provided to users is valid and accurate, allowing businesses to get the most
out of their data. DLM aids in the preservation of data quality during its
lifecycle, allowing for process optimization and increased efficiency.
How Does Data Lifecycle
Management Contribute to Data Security and Optimization?
In general, a system for protecting
unstructured data should help your organization determine the value of
available data, prevent data breaches while in use, and store that
data in a long-term repository that protects it not only today but also in the
future.
On the other hand, DLP (Data Loss Prevention)
software requires a clear policy to identify sensitive data and lock it down by
quarantining it.
Data lifecycle management takes a different
approach to the problem. It aids an organization's proactive awareness of its
data and the application of policies so that it can be secured while in use and
throughout its lifecycle.
It includes features like data discovery, data
classification, encryption, obfuscation, and defensible disposition, which
allow the data to be removed once its commercial purpose has been achieved.
The value of information and the people who
require access to it changes with time, and a data lifecycle management system
understands this. A document's business relevancy window is roughly two years;
for emails, it's closer to 90 days. While the value of a particular document
may decrease, the need to keep it might not.
DLP responds to documents in transit in
accordance with policies. It will react to activities in the vicinity of the
document. When people are working on a project together, this might be an
issue.
These kinds of hitches are avoided because
data lifecycle management can be used to determine who can access a document.
DLP can be useful, but you need a more comprehensive toolkit to make sense of
the data you have and are producing to assess its value and safeguard it.
Furthermore, data lifecycle management enables
modifications to access over time to accommodate the changing nature of your
documents.
If it's the type of document that needs to be
kept for compliance or legal purposes, it'll go into the long-term repository,
where it'll be accessible to those who need past data, such as lawyers.
This repository has the potential to be your
organization's single source of truth.
DLM can complement proactive solutions too,
ensuring data security and optimization by helping you cross off items on your
data integrity checklist.
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It will inspect, study, and analyze the data
to discover value by identifying sensitive and low-value data. For instance,
this could be anything from customers' details to a sample bookkeeping proposal. DLP alone cannot
give such a complete approach to data security and optimization.
This viewpoint is incredibly valuable to a
business. Especially as businesses must now analyze large amounts of data to
comply with new rules such as the California Consumer Privacy Act (CCPA) and
the General Data Protection Regulation (GDPR). You now have to cope with data
in the petabytes.
Many businesses aren't sure where their
sensitive data lies in this deluge of information. You need to identify it
quickly and make a decision about whether or not to keep it. So, while you
focus on outgoing marketing efforts: asking questions like "what is a brand ambassador program?", you
should also consider your data security.
Master Data Lifecycle Management
to Improve Data Security
Data Lifecycle Management is a crucial
practice for any company. Holistic data management must not be left to chance
but instead tackled head-on. Data trustees, data custodians, data stewards,
data owners, and data managers are all vital roles that your company should
take into account.
Failure to manage data effectively poses a
huge danger to your organization and its clients. Data is growing at a
breakneck pace, and its management cannot be ignored. Open structures and the
desire to connect to information systems from anywhere provide a better
experience since data is more available.
However, they also present additional cases of
data security concerns, notably concerning illegal data exploitation. To nip
these concerns in the bud, make data lifecycle management a company-wide strategic
priority for your organization today.
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ABOUT
THE AUTHOR
Severine Hierso
Severine Hierso is EMEA
Senior Product Marketing Manager for RingCentral Office, the leader in cloud communications solutions, and is passionate about
creating value, differentiation and messaging, ensuring a better experience for
customers and partners. She has gained extensive international Product
Marketing, Market Research, Sales Enablement and Business development
experience across SaaS, Telecommunications, Video Conferencing and Technology
sectors within companies such as Sony, Cisco, Cogeco Peer 1 and Dimension
Data/NTT. Severine Hierso also published articles for
domains such as Recruiterflow and CEO Blog Nation.