The New Letter? The Weightiness of “S” in Big Data
Finding and exploring the two propelling “S’s” in the realm of Big Data.
SAFETY
Nowadays, Big Data is embraced more widely than ever, with firms, enterprises, and individuals all attempting to grasp the dynamic and brisk data. However, in the hands of millions of people and machines, is the data secure, and is it intricate enough for our deep and systematic analysis? Ultimately, the concept of big data comes down to a critical concern–how do we maximize the data’s potential during its proliferation with security?
Hence, the first aspect worth earnest discussion is “safety”. Safety in the sector of Big Data refers to the monitoring and protecting of data in the mining, sourcing, analysis, and preservation of the mountainous mass data. It not only ensures safe and compliant operations, further, it also safeguards data from public use, access, or theft of outcasts and unauthorized users (Figure 1). However, in more thorough terms, safety is merely an umbrella term for the 3 major components that make up this characteristic: confidentiality, integrity, and availability.
- Confidentiality ensures that only users who have been granted legal access and possess the necessary credentials can access the data.
- Integrity assures that all data is correct, reliable, and prone to unjustified modifications.
- Lastly, availability secures the acquired data promptly for ongoing and future business needs.
For any business, data is a priceless resource. Putting it in the right hands is key to avoiding malfeasance, which might result in severe consequences, such as a company’s financial loss, reputational damage, and a decline in consumer confidence. The most prominent danger is that the consequences of data transgression are almost not immediate at all; rather, it alludes to long-term effects. For instance, a data breach can lead to penalties from the Payment Card Industry Security Standards Council. An additional fine may be imposed depending on the circumstances. Hence, the importance of data security is clear.
With such vast quantities of data, individuals who get a hold of the information can easily make massive steps in all fields, including medicine, finances, agriculture, biological analysis, and so on. Therefore, data breaching is not new to the eyes of scientists. A notable example is the Yahoo data leak that stretched from 2013 to 2016, which stands at the top of all large data breaches. This egregious incident ruptured 3 billion of Yahoo’s accounts’ privacy walls, as shown in Figure 2, with digital thieves peculating the names, birth dates, phone numbers, and passwords of users that they thought were secure in the database. Even more, the stolen data was used “to spy on Russian government officials and business executives,” as the federal prosecutors claimed.
As a result, safety and security need to be carefully scrutinized, and methods have been developed to regulate this issue.
To begin, firms are looking to hire and manage properly trained employees to reduce unnecessary internal data leakage within the enterprise due to incompetence and blunders. This also implies that the data is properly regulated with limitations and is distributed to each department accordingly.
In addition, large companies also practice security monitoring regularly as the data in the software grows on a daily basis. Regular security audit allows for the chance to evaluate larger-scale concerns before they develop into actual security risks.
Even more, the industry has adopted countless other methods of safeguarding their data, such as data erasure, backups and disaster recovery, encryption, data masking, etc., all of which appear to be effective in one way or another in creating the outlook for a more propelling and reliant data web.
SOPHISTICATION
The second aspect we need to consider is data sophistication. The term refers to the state of data that displays noticeable levels of richness, depth, and complexity. So really, in understanding the sophistication of the abundance of data, we are making an effort to raise the quality of data.
The second aspect we need to consider is data sophistication. The term refers to the state of data that displays noticeable levels of richness, depth, and complexity. So really, in understanding the sophistication of the abundance of data, we are making an effort to raise the quality of data.
- The “Aspirational” category is equivalent to the “No Data” stage in the Data Maturity Model. This is when the company has no clue as to how to harness the data they require but is in the process of exploring foraging for useful information.
- Then companies move to the “Experienced” or “Big Data” phase. In this phase, users have made significant progress in data gathering. The quantity is expanding, however, that’s all they have–extensive amounts of data points.
- Finally, they reach the “Transformed” stage of data analytics, or the so-called “Right Data” stage. Now, users need to polish their data by looking for the most desired trend in the substantial dataset to escalate the quality (Figure 3).
Sophisticated data is diverse and comprehensive; it provides a deeper understanding of the subject and matter and reveals the endless complex relationships and interconnections between data points. Sophistication allows for the identification of patterns, correlations, and even dependencies among patterns. This enables us to obtain a more nuanced and comprehensive analysis of the data in forming meaningful predictions and dig into a more structured view of how the data connect to each other. From there, the granularity becomes visible.
With all being said, it is arguable that a new letter has emerged in the realm of big data. The first “S” — Safety — depicts a current hidden crisis within the grand datasets. The Yahoo data breach mentioned previously goes to show the problems the world has encountered with “safety” in big data. Beyond Yahoo, there have been countless similar cases that affected various groups of people. However, conspicuous responses are being made regarding this issue to alleviate the pressure and concerns in society. The second “S” — “Sophistication” — emphasizes the 21st-century society’s demand for purchasing a highly efficient and more intricate workload. Using the MIT and IBM Institute’s big data model, we see the stages of data maturing in the hands of analysts, and we can get a much more transparent and implicated view of the data. Therefore, I believe it will only be a matter of time before “S” surrounds big data as “V” is today with its own uniqueness.
Citations:
Crockett, Emma. “What Is Big Data Security? Challenges & Solutions.” Datamation, 13 June 2023, www.datamation.com/big-data/big-data-security/.
“The Importance of Data Security in 2023.” Intellipaat Blog, 10 July 2023, intellipaat.com/blog/importance-of-data-security/. Accessed 06 Sept. 2023.
Maycotte, H.O. “Data Sophistication: Why It Matters & How to Achieve It.” Forbes, Forbes Magazine, 22 Sept. 2016, www.forbes.com/sites/homaycotte/2016/09/22/data-sophistication-why-it-matters-how-to-achieve-it/?sh=29a6b5dd18cc.
Peek, Sean. “What Data Complexity Rise Means for Business.” Business.Com, 2023, www.business.com/articles/what-the-rise-in-data-complexity-means-for-business-departments/.
Perlroth, Nicole. “All 3 Billion Yahoo Accounts Were Affected by 2013 Attack.” The New York Times, 3 Oct. 2017, www.nytimes.com/2017/10/03/technology/yahoo-hack-3-billion-users.html.
