Businesses areincreasingly relying on big data to inform their decision-making and drivegrowth as technology continues to evolve at a rapid pace.
Big data is acritical tool for organizations of all sizes, from customer insights and markettrends to operational efficiency and risk management.
However, as wegenerate and store more data, there is an increasing risk of digitalobsolescence, which can have serious consequences for businesses and theirbottom lines.
Digital Obsolescence Explained
The inabilityto access, read, or use electronic data because the technology required to doso is no longer available or has become obsolete is referred to as digitalobsolescence.
This can occurwhen data is stored on obsolete hardware or software that is no longersupported, or when it is saved in a proprietary format that modern systemscannot read.
As a result,there is an increasing mountain of digital information that is essentiallyuseless, and the risks of digital obsolescence are only growing as technologyadvances.
The loss ofvaluable data is one of the most serious risks of digital obsolescence.
Companies mayhave spent years collecting and analyzing data to inform decision-making andimprove operations, but if that data cannot be accessed or used, it iseffectively worthless.
This can leadto the loss of critical business intelligence and make meeting regulatoryrequirements for data retention and retrieval difficult.
TheCosts Incurred by Businesses
In addition todata loss, digital obsolescence can be costly for businesses. Data migrationfrom 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 orto convert data into a more accessible format, which can increase the overallcost of managing big data.
Businesses cantake several steps to reduce the risks of digital obsolescence, including:
Keeping up withthe latest technologies and trends in big data is essential, as is ensuringthat data is stored in a format that will be accessible and usable in thefuture.
Datamigrations on a regular basis
Data migrationson a regular basis can help ensure that data is stored in a format that isaccessible and usable over time. This could include transferring data fromolder systems to newer ones or converting data into a more accessible format.
Purchasingdata management software
Data managementtools, such as data warehouses, data lakes, and cloud storage, can assistorganizations in managing and preserving big data over time. These tools canalso help businesses avoid vendor lock-in, which occurs when data is stored ina proprietary format that only a single vendor can access.
Documenting data formats
It is criticalto document the format and structure of data so that future generations caneasily understand and use it.
Thisdocumentation should include information about the data’s origin, collection,processing, and storage.
Creating anarchival strategy: Archiving is an essential component of data management, andbusinesses must devise a strategy for preserving and accessing their data overtime.
This couldinclude storing data in the cloud or using data archiving software to manageand preserve the data.
WrappingUp
To summarize,while big data has the potential to generate significant business value, italso carries significant risks, including the risk of digital obsolescence.
Businesses musttake 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
Whatis big data?
The massivevolume of structured and unstructured data generated and collected byorganizations is referred to as big data. Customer transactions, social media,machine logs, and other sources can all provide this data. Big data isdistinguished by its sheer volume, velocity, and variety, and it can bechallenging to store, process, and analyze using traditional data managementtechniques.
Whatis the significance of big data?
Big data isimportant because it allows businesses to gain valuable insights into customerbehavior, market trends, and other key drivers of business success. Companiesthat use big data can make better decisions, improve operational efficiency,and gain a competitive advantage.
Howdoes big data get analyzed?
Advanced dataanalytics tools and techniques, such as machine learning, predictive analytics,and data mining, are typically used to analyze big data. These tools enableorganizations to identify patterns, trends, and relationships in large datasetsquickly and easily, which can then be used to inform decision-making.
Whatare the difficulties associated with working with big data?
Working withbig data presents challenges such as managing and storing large amounts ofdata, processing and analyzing data in real time, and ensuring data privacy andsecurity. There may also be issues with data quality and accuracy, as well asthe cost and complexity of implementing and maintaining a big datainfrastructure.
Howcan businesses use big data to increase business value?
Organizationscan use big data to improve customer insights and experiences, optimizeoperations and supply chains, reduce risk and fraud, and develop new productsand services. Companies can gain a better understanding of their customers,markets, and operations by leveraging big data, and then use that knowledge todrive growth and profitability.
Isbig data safe to use?
Big data comeswith the promise of massive opportunities so one can easily overlook itsinherent risks.
In fact, bigdata if use maliciously gathered, unsafely stored, or downright wrongly usedcan lead to serious risks.
Luckily, overcomingthe dangers comes down to the matter of understanding them.
There are atleast 2 categories which are interlinked and comprise some of the main risks surroundingbig data:
Big datasecurity & abuse
Collecting datais both expensive and difficult to store safely. And the more a companycollects it, the harder it gets.
With databreaches becoming more and more prevalent, it becomes extremely important fororganizations to invest in data security.
But while somecompanies are required to operate under data protection laws, others simply don’t.
With today’sunprecedented level of data accesses and with personal information being usedfor KYC, and other sensitive data being submitted, it becomes increasingly importantto know to trust your data.
In the case ofa security breach, if a malicious player finds its way onto sensitive information,phishing, fraud, and other scams will surely ensue.
Big data andethical dilemmas: consent, privacy, and ownership.
Just because companieshave the technology to store personal, sensitive data, doesn’t mean theyshould.
The presumptionthat organizations are keeping our data safe widely differs from those verysame companies misusing said data themselves.
This is in facta grey area which isn’t covered by data protection laws and leaves the dooropen to things like invasive profiling.
Consequently,one can immediately understand that the question arises on how personalinformation can be used by companies after having it obtained legally.
Once you addmachine learning into the mix, the plot thickens as while the algorithms theyuse are their own, they need to be programmed on how to learn, meaning humanbias can leak into them as well.
Businesses areincreasingly relying on big data to inform their decision-making and drivegrowth as technology continues to evolve at a rapid pace.
Big data is acritical tool for organizations of all sizes, from customer insights and markettrends to operational efficiency and risk management.
However, as wegenerate and store more data, there is an increasing risk of digitalobsolescence, which can have serious consequences for businesses and theirbottom lines.
Digital Obsolescence Explained
The inabilityto access, read, or use electronic data because the technology required to doso is no longer available or has become obsolete is referred to as digitalobsolescence.
This can occurwhen data is stored on obsolete hardware or software that is no longersupported, or when it is saved in a proprietary format that modern systemscannot read.
As a result,there is an increasing mountain of digital information that is essentiallyuseless, and the risks of digital obsolescence are only growing as technologyadvances.
The loss ofvaluable data is one of the most serious risks of digital obsolescence.
Companies mayhave spent years collecting and analyzing data to inform decision-making andimprove operations, but if that data cannot be accessed or used, it iseffectively worthless.
This can leadto the loss of critical business intelligence and make meeting regulatoryrequirements for data retention and retrieval difficult.
TheCosts Incurred by Businesses
In addition todata loss, digital obsolescence can be costly for businesses. Data migrationfrom 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 orto convert data into a more accessible format, which can increase the overallcost of managing big data.
Businesses cantake several steps to reduce the risks of digital obsolescence, including:
Keeping up withthe latest technologies and trends in big data is essential, as is ensuringthat data is stored in a format that will be accessible and usable in thefuture.
Datamigrations on a regular basis
Data migrationson a regular basis can help ensure that data is stored in a format that isaccessible and usable over time. This could include transferring data fromolder systems to newer ones or converting data into a more accessible format.
Purchasingdata management software
Data managementtools, such as data warehouses, data lakes, and cloud storage, can assistorganizations in managing and preserving big data over time. These tools canalso help businesses avoid vendor lock-in, which occurs when data is stored ina proprietary format that only a single vendor can access.
Documenting data formats
It is criticalto document the format and structure of data so that future generations caneasily understand and use it.
Thisdocumentation should include information about the data’s origin, collection,processing, and storage.
Creating anarchival strategy: Archiving is an essential component of data management, andbusinesses must devise a strategy for preserving and accessing their data overtime.
This couldinclude storing data in the cloud or using data archiving software to manageand preserve the data.
WrappingUp
To summarize,while big data has the potential to generate significant business value, italso carries significant risks, including the risk of digital obsolescence.
Businesses musttake 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
Whatis big data?
The massivevolume of structured and unstructured data generated and collected byorganizations is referred to as big data. Customer transactions, social media,machine logs, and other sources can all provide this data. Big data isdistinguished by its sheer volume, velocity, and variety, and it can bechallenging to store, process, and analyze using traditional data managementtechniques .
Whatis the significance of big data?
Big data isimportant because it allows businesses to gain valuable insights into customerbehavior, market trends, and other key drivers of business success. Companiesthat use big data can make better decisions, improve operational efficiency,and gain a competitive advantage.
Howdoes big data get analyzed?
Advanced dataanalytics tools and techniques, such as machine learning, predictive analytics,and data mining, are typically used to analyze big data. These tools enableorganizations to identify patterns, trends, and relationships in large datasetsquickly and easily, which can then be used to inform decision-making.
Whatare the difficulties associated with working with big data?
Working withbig data presents challenges such as managing and storing large amounts ofdata, processing and analyzing data in real time, and ensuring data privacy andsecurity. There may also be issues with data quality and accuracy, as well asthe cost and complexity of implementing and maintaining a big datainfrastructure.
Howcan businesses use big data to increase business value?
Organizationscan use big data to improve customer insights and experiences, optimizeoperations and supply chains, reduce risk and fraud, and develop new productsand services. Companies can gain a better understanding of their customers,markets, and operations by leveraging big data, and then use that knowledge todrive growth and profitability.
Isbig data safe to use?
Big data comeswith the promise of massive opportunities so one can easily overlook itsinherent risks.
In fact, bigdata if use maliciously gathered, unsafely stored, or downright wrongly usedcan lead to serious risks.
Luckily, overcomingthe dangers comes down to the matter of understanding them.
There are atleast 2 categories which are interlinked and comprise some of the main risks surroundingbig data:
Big datasecurity & abuse
Collecting datais both expensive and difficult to store safely. And the more a companycollects it, the harder it gets.
With databreaches becoming more and more prevalent, it becomes extremely important fororganizations to invest in data security.
But while somecompanies are required to operate under data protection laws, others simply don’t.
With today’sunprecedented level of data accesses and with personal information being usedfor KYC, and other sensitive data being submitted, it becomes increasingly importantto know to trust your data.
In the case ofa security breach, if a malicious player finds its way onto sensitive information,phishing, fraud, and other scams will surely ensue.
Big data andethical dilemmas: consent, privacy, and ownership.
Just because companieshave the technology to store personal, sensitive data, doesn’t mean theyshould.
The presumptionthat organizations are keeping our data safe widely differs from those verysame companies misusing said data themselves.
This is in facta grey area which isn’t covered by data protection laws and leaves the dooropen to things like invasive profiling.
Consequently,one can immediately understand that the question arises on how personalinformation can be used by companies after having it obtained legally.
Once you addmachine learning into the mix, the plot thickens as while the algorithms theyuse are their own, they need to be programmed on how to learn, meaning humanbias can leak into them as well.
