The last decade witnessed how computing and machine learning rose to become utility services, and now entered the age of data as a utility service.
In this age of data, everything around us is linked to data or a source of data, and everything in our physical lives is captured digitally. We’ve turned the physical world into raw information, such that everything around us is considered data. These include: Companies obtain and store a lot of data in massive quantities. In fact, many companies today have data petabytes. If you put petabytes into perspective: 1 petabyte (PB) = 1 million gigabytes (GB), which is nearly equal to 13.3 years of HDTV video. They store all this data on massive server farms – sometimes in the cloud.
Some companies leave data to stay in the stores forever, never never to be seen again. While they add (dump) more data on top of the already existing ones, a big virtual data graveyard is formed.
What are data graveyards, and what are their related risks and challenges?
What Are Data Graveyards?
Most companies spend millions of money to collect, use, and store data. However, some of them store all the data they collect without using it. As a result, they end up having giant repositories of unused data stored in their servers,- also called data graveyards.
Having large amounts of data is not wrong (if collected in a compliant way). In fact, it’s important for companies to collect and store as much data to help them make informed future business decisions. Without data, they can’t move forward.
Are you getting more work done with your data? If not, your lakes of data will become data swamps and eventually turn into data graveyards.
If lost, damaged, or corrupted, know that the recovery of large data is not something you can do using PC tips and tricks like you do for your computer. It can require constant effort and can be time-consuming. Worse, it may lead to a number of losses.
Challenges and Risks of Data Graveyards
Having the opportunity to store so much data is a good thing. After all, collecting large amounts of data has become a normal part of the business world. But is it bringing you more problems than advantages?
Here are the risks and challenges associated with data graveyards:
1. Storage Infrastructure
Data needs a safe and secure location. If you’re planning on storing large volumes of data, you need to invest in the needed storage infrastructure, such as high-tech servers. Sometimes, physical servers can occupy a significant amount of space, so companies opt for cloud hosting and storage of data. Data storage, whether physical or in the cloud, is often expensive.
2. Maintenance Costs
Running a data center is expensive. You need to spend on the initial expensive setup and data maintenance and management costs, including human resources. Other costs include the security and safety of the data, which is often a target of people who would like to access it. The cost of maintaining a large amount of data can be a burden to a company. Besides, large data infrastructures can have unnoticed security gaps.
3. Compliance with the GDPR
Stockpiling data and having no idea when you have collected data, from whom or why will leave you extremely vulnerable to GDPR fines, (German DPA issued 14.5 million euro fine for non-compliant data removal), not to mention all your efforts to comply will be sent down the drain.
GDPR introduces the storage limitation principle that dictates you shouldn’t keep personal data for longer than it is necessary for the purposes for which you collected and processed personal data.
Therefore, the best approach is to define data retention periods and orchestrate data removal accordingly.
4. Security and Security Gaps
Data security is one of the most expensive aspects you should consider when storing large amounts of data, and it is something you cannot ignore. Data security can have so many layers, beginning with storage and including encryption. It is put in place to block third-parties from accessing the data.
To guarantee complete data security, you’ll need a tight data security plan and a team working 24/7. You have to keep the team adhering to best security practices. If you’re outsourcing data storage, you have to choose the best partners you can trust.
Additionally, you have to be vigilant and protect your data from emerging threats with particular attention to cyberattacks. Your data storage facility needs to use a robust system and have physical security measures 24/7/365. Otherwise, risk suffering from serious data breaches.
It isn’t easy to achieve absolute security, but you have to put in all the measures. Security software such as anti-malware and other security systems may not be a guarantee for data safety because data breaches can also come from an inside job. Sometimes, an attacker may pose as a client and upload a zero-day payload to your server.
When it comes to data security, you should not leave anything to chance.
5. Data Sources
The volume and velocity of data are a major challenge in data security. Each source of data usually has particular access points, restrictions, and security policies.
Furthermore, each data source speaks a different data language. This makes it more challenging to manage servers and data security, while aggregating data from different sources. This can be a data breach opportunity for roaming cybercriminals.
6. Risk of Data Corruption
Every form of data storage can be corrupted, and stray data management particles can often interfere with stored data. In addition, electromagnetic interference can corrupt anything relying on electric storage or magnetic strips. In data management, forgetting to delete junk files can cause data corruption. Nevertheless, even without tamper or damage, data will typically degrade over time.
7. Too Many Choices
Large data volumes come with the “paradox of choice.” A large volume of data requires actions, which can be overwhelming when deciding the right solution for your business. This especially true when it will likely affect several departments.
8. Scale or Growth of Data
The need for data can suddenly change. For example, with every increase in the amount of data, you will need an improved security system and approach. Storing large amounts of data may prevent you from scaling your business operations because of the increased data management needs.
9. Pace of Technology
Technology is advancing so fast, and each subsequent technological advancement is built on the previous one. This concept allows the newer technologies to become more efficient. Examples of technologies that are advancing at high rates include cloud computing, machine learning, and artificial intelligence.
You don’t want these fast-paced technologies to make your data tools outdated, or your data incompatible with future technologies. What if the massive amount of data lying in your store becomes obsolete tomorrow? That will be wasted money, time, and resources.
10. Constantly Changing Data
Implementing data infrastructure and management is not a setup-and-forget task. Data is a constantly changing phenomenon. For instance, your customers’ demographics base, details, and sometimes orders will keep changing. When you store large amounts of data, they may become obsolete before you even get to use them. The secret is to store just an appropriate amount of data.
11. Lack of Skilled Human Resource
While the AI and data analysis tools are advancing swiftly, and technological demand is getting high, the lack of skilled data human resource personnel is increasingly hampering data storage. Large amounts of data can be difficult to manage, and it can be worse if you do not have the personnel with the right skills. You must take caution and ensure you have the right skills to help you manage your data.
Data volume and management go hand-in-hand, and both cannot be ignored. Do not wait until you ask yourself what to do with a large amount of data. Learn how to uncover the value of data before you store it.
Instead of collecting large volumes of data without a plan, you may consider delivering data or information for decision-making in small bits. At the same time, cautiously spread out maintenance costs. Defining data retention periods for every data set will help you follow the deletion plan and stay compliant.
We believe the information in this article has been helpful. We understand that the better you understand the risks and challenges involved in storing large amounts of data, the better you are positioned to find a solution to manage data.