Investments
Big Data and the Risk of Digital Obsolescence
Businesses are
increasingly relying on big data to inform their decision-making and drive
growth as technology continues to evolve at a rapid pace.
Big data is a
critical tool for organizations of all sizes, from customer insights and market
trends to operational efficiency and risk management.
However, as we
generate and store more data, there is an increasing risk of digital
obsolescence, which can have serious consequences for businesses and their
bottom lines.
The inability
to access, read, or use electronic data because the technology required to do
so is no longer available or has become obsolete is referred to as digital
obsolescence.
This can occur
when data is stored on obsolete hardware or software that is no longer
supported, or when it is saved in a proprietary format that modern systems
cannot read.
As a result,
there is an increasing mountain of digital information that is essentially
useless, and the risks of digital obsolescence are only growing as technology
advances.
The loss of
valuable data is one of the most serious risks of digital obsolescence.
Companies may
have spent years collecting and analyzing data to inform decision-making and
improve operations, but if that data cannot be accessed or used, it is
effectively worthless.
This can lead
to the loss of critical business intelligence and make meeting regulatory
requirements for data retention and retrieval difficult.
The
Costs Incurred by Businesses
In addition to
data loss, digital obsolescence can be costly for businesses. Data migration
from old systems to new ones can be a time-consuming and expensive process,
requiring significant investment in new hardware, software, and expertise.
Furthermore,
businesses may be required to pay to gain access to proprietary data formats or
to convert data into a more accessible format, which can increase the overall
cost of managing big data.
Businesses can
take several steps to reduce the risks of digital obsolescence, including:
Keeping up with
the latest technologies and trends in big data is essential, as is ensuring
that data is stored in a format that will be accessible and usable in the
future.
Data
migrations on a regular basis
Data migrations
on a regular basis can help ensure that data is stored in a format that is
accessible and usable over time. This could include transferring data from
older systems to newer ones or converting data into a more accessible format.
Purchasing
data management software
Data management
tools, such as data warehouses, data lakes, and cloud storage, can assist
organizations in managing and preserving big data over time. These tools can
also help businesses avoid vendor lock-in, which occurs when data is stored in
a proprietary format that only a single vendor can access.
Documenting data formats
It is critical
to document the format and structure of data so that future generations can
easily understand and use it.
This
documentation should include information about the data’s origin, collection,
processing, and storage.
Creating an
archival strategy: Archiving is an essential component of data management, and
businesses must devise a strategy for preserving and accessing their data over
time.
This could
include storing data in the cloud or using data archiving software to manage
and preserve the data.
Wrapping
Up
To summarize,
while big data has the potential to generate significant business value, it
also carries significant risks, including the risk of digital obsolescence.
Businesses must
take proactive steps to mitigate these risks and preserve their data over time,
such as staying current with technology, performing regular data migrations,
investing in data management tools, and documenting data formats.
Big Data FAQ
What
is big data?
The massive
volume of structured and unstructured data generated and collected by
organizations is referred to as big data. Customer transactions, social media,
machine logs, and other sources can all provide this data. Big data is
distinguished by its sheer volume, velocity, and variety, and it can be
challenging to store, process, and analyze using traditional data management
techniques.
What
is the significance of big data?
Big data is
important because it allows businesses to gain valuable insights into customer
behavior, market trends, and other key drivers of business success. Companies
that use big data can make better decisions, improve operational efficiency,
and gain a competitive advantage.
How
does big data get analyzed?
Advanced data
analytics tools and techniques, such as machine learning, predictive analytics,
and data mining, are typically used to analyze big data. These tools enable
organizations to identify patterns, trends, and relationships in large datasets
quickly and easily, which can then be used to inform decision-making.
What
are the difficulties associated with working with big data?
Working with
big data presents challenges such as managing and storing large amounts of
data, processing and analyzing data in real time, and ensuring data privacy and
security. There may also be issues with data quality and accuracy, as well as
the cost and complexity of implementing and maintaining a big data
infrastructure.
How
can businesses use big data to increase business value?
Organizations
can use big data to improve customer insights and experiences, optimize
operations and supply chains, reduce risk and fraud, and develop new products
and services. Companies can gain a better understanding of their customers,
markets, and operations by leveraging big data, and then use that knowledge to
drive growth and profitability.
Is
big data safe to use?
Big data comes
with the promise of massive opportunities so one can easily overlook its
inherent risks.
In fact, big
data if use maliciously gathered, unsafely stored, or downright wrongly used
can lead to serious risks.
Luckily, overcoming
the dangers comes down to the matter of understanding them.
There are at
least 2 categories which are interlinked and comprise some of the main risks surrounding
big data:
Big data
security & abuse
Collecting data
is both expensive and difficult to store safely. And the more a company
collects it, the harder it gets.
With data
breaches becoming more and more prevalent, it becomes extremely important for
organizations to invest in data security.
But while some
companies are required to operate under data protection laws, others simply don’t.
With today’s
unprecedented level of data accesses and with personal information being used
for KYC, and other sensitive data being submitted, it becomes increasingly important
to know to trust your data.
In the case of
a security breach, if a malicious player finds its way onto sensitive information,
phishing, fraud, and other scams will surely ensue.
Big data and
ethical dilemmas: consent, privacy, and ownership.
Just because companies
have the technology to store personal, sensitive data, doesn’t mean they
should.
The presumption
that organizations are keeping our data safe widely differs from those very
same companies misusing said data themselves.
This is in fact
a grey area which isn’t covered by data protection laws and leaves the door
open to things like invasive profiling.
Consequently,
one can immediately understand that the question arises on how personal
information can be used by companies after having it obtained legally.
Once you add
machine learning into the mix, the plot thickens as while the algorithms they
use are their own, they need to be programmed on how to learn, meaning human
bias can leak into them as well.
Businesses are
increasingly relying on big data to inform their decision-making and drive
growth as technology continues to evolve at a rapid pace.
Big data is a
critical tool for organizations of all sizes, from customer insights and market
trends to operational efficiency and risk management.
However, as we
generate and store more data, there is an increasing risk of digital
obsolescence, which can have serious consequences for businesses and their
bottom lines.
The inability
to access, read, or use electronic data because the technology required to do
so is no longer available or has become obsolete is referred to as digital
obsolescence.
This can occur
when data is stored on obsolete hardware or software that is no longer
supported, or when it is saved in a proprietary format that modern systems
cannot read.
As a result,
there is an increasing mountain of digital information that is essentially
useless, and the risks of digital obsolescence are only growing as technology
advances.
The loss of
valuable data is one of the most serious risks of digital obsolescence.
Companies may
have spent years collecting and analyzing data to inform decision-making and
improve operations, but if that data cannot be accessed or used, it is
effectively worthless.
This can lead
to the loss of critical business intelligence and make meeting regulatory
requirements for data retention and retrieval difficult.
The
Costs Incurred by Businesses
In addition to
data loss, digital obsolescence can be costly for businesses. Data migration
from old systems to new ones can be a time-consuming and expensive process,
requiring significant investment in new hardware, software, and expertise.
Furthermore,
businesses may be required to pay to gain access to proprietary data formats or
to convert data into a more accessible format, which can increase the overall
cost of managing big data.
Businesses can
take several steps to reduce the risks of digital obsolescence, including:
Keeping up with
the latest technologies and trends in big data is essential, as is ensuring
that data is stored in a format that will be accessible and usable in the
future.
Data
migrations on a regular basis
Data migrations
on a regular basis can help ensure that data is stored in a format that is
accessible and usable over time. This could include transferring data from
older systems to newer ones or converting data into a more accessible format.
Purchasing
data management software
Data management
tools, such as data warehouses, data lakes, and cloud storage, can assist
organizations in managing and preserving big data over time. These tools can
also help businesses avoid vendor lock-in, which occurs when data is stored in
a proprietary format that only a single vendor can access.
Documenting data formats
It is critical
to document the format and structure of data so that future generations can
easily understand and use it.
This
documentation should include information about the data’s origin, collection,
processing, and storage.
Creating an
archival strategy: Archiving is an essential component of data management, and
businesses must devise a strategy for preserving and accessing their data over
time.
This could
include storing data in the cloud or using data archiving software to manage
and preserve the data.
Wrapping
Up
To summarize,
while big data has the potential to generate significant business value, it
also carries significant risks, including the risk of digital obsolescence.
Businesses must
take proactive steps to mitigate these risks and preserve their data over time,
such as staying current with technology, performing regular data migrations,
investing in data management tools, and documenting data formats.
Big Data FAQ
What
is big data?
The massive
volume of structured and unstructured data generated and collected by
organizations is referred to as big data. Customer transactions, social media,
machine logs, and other sources can all provide this data. Big data is
distinguished by its sheer volume, velocity, and variety, and it can be
challenging to store, process, and analyze using traditional data management
techniques.
What
is the significance of big data?
Big data is
important because it allows businesses to gain valuable insights into customer
behavior, market trends, and other key drivers of business success. Companies
that use big data can make better decisions, improve operational efficiency,
and gain a competitive advantage.
How
does big data get analyzed?
Advanced data
analytics tools and techniques, such as machine learning, predictive analytics,
and data mining, are typically used to analyze big data. These tools enable
organizations to identify patterns, trends, and relationships in large datasets
quickly and easily, which can then be used to inform decision-making.
What
are the difficulties associated with working with big data?
Working with
big data presents challenges such as managing and storing large amounts of
data, processing and analyzing data in real time, and ensuring data privacy and
security. There may also be issues with data quality and accuracy, as well as
the cost and complexity of implementing and maintaining a big data
infrastructure.
How
can businesses use big data to increase business value?
Organizations
can use big data to improve customer insights and experiences, optimize
operations and supply chains, reduce risk and fraud, and develop new products
and services. Companies can gain a better understanding of their customers,
markets, and operations by leveraging big data, and then use that knowledge to
drive growth and profitability.
Is
big data safe to use?
Big data comes
with the promise of massive opportunities so one can easily overlook its
inherent risks.
In fact, big
data if use maliciously gathered, unsafely stored, or downright wrongly used
can lead to serious risks.
Luckily, overcoming
the dangers comes down to the matter of understanding them.
There are at
least 2 categories which are interlinked and comprise some of the main risks surrounding
big data:
Big data
security & abuse
Collecting data
is both expensive and difficult to store safely. And the more a company
collects it, the harder it gets.
With data
breaches becoming more and more prevalent, it becomes extremely important for
organizations to invest in data security.
But while some
companies are required to operate under data protection laws, others simply don’t.
With today’s
unprecedented level of data accesses and with personal information being used
for KYC, and other sensitive data being submitted, it becomes increasingly important
to know to trust your data.
In the case of
a security breach, if a malicious player finds its way onto sensitive information,
phishing, fraud, and other scams will surely ensue.
Big data and
ethical dilemmas: consent, privacy, and ownership.
Just because companies
have the technology to store personal, sensitive data, doesn’t mean they
should.
The presumption
that organizations are keeping our data safe widely differs from those very
same companies misusing said data themselves.
This is in fact
a grey area which isn’t covered by data protection laws and leaves the door
open to things like invasive profiling.
Consequently,
one can immediately understand that the question arises on how personal
information can be used by companies after having it obtained legally.
Once you add
machine learning into the mix, the plot thickens as while the algorithms they
use are their own, they need to be programmed on how to learn, meaning human
bias can leak into them as well.
Subscribe to our weekly newsletter below and never miss the latest NEWS or an exclusive offer.
