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.
Digital Obsolescence Explained
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 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 access 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 their way to 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 of 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.
