Five Reasons for Big Data Breaches – Melanie Wieland

Data breaches are all too common this day and age. It’s not even mentioned as big news, just put on the side notes or back burner. Most breaches happen because of people’s carelessness, followed by corrupt individuals. In turn one has to ask, how do these breaches occur? Here are five reasons.

1. Stolen credentials: Passwords obtained from stolen or lost phones/computers, the careless disposal of old devices, malware or data stolen in another data breech are the leading cause of network intrusion.
2. Data stealing malware: Used in the breaches at Home Depot and Target recently, this is software that steals data, whether it is passwords, credit cards, keystrokes or any of a number of other types of private data.
3. Phishing: The act of pretending to be a trusted entity for the sole purpose of eliciting usernames, passwords, birth date, Social Security Numbers, etc. from an unsuspecting target.
4. RAM Scraping: This is the act of capturing data being temporarily stored in RAM, as happened in the infamous Target breach. There is a millisecond of time between a debit card swipe and bank approval of the transaction. It is during this holding period that the data is unencrypted, and is thus vulnerable to capture.
5. Backdoor Malware: Malware delivered as a Trojan that attacks unpatched security vulnerabilities.

 

Work Cited

Bazen, Barry. “Big Data, Big Breaches, BIG PROBLEMS AHEAD?” Pulse. LinkedIn. 24 September 2014.

<https://www.linkedin.com/pulse/20140924173304-17950695-big-data-big-breaches-big-problems-ahead&gt;

PR & Big Data – Melanie Wieland

Today, as time, knowledge, and technology advance, so does big data. Each year technology and big data largely improvement and expand. This makes big data technologies continuously evolve into a more in-depth, sufficient, and multifaceted technological world. It’s the main key factor for shaping the future and will never stop. Along with cutting-edge analytics, big data lets organizations unlock insights from data with precision and speed.

On the other hand, public relations is the strategic management in the best interests of one’s own association, as well as for the public in general. With the correct use of information collected from Big Data, it can help a firm stay ahead of potential public relations crises, decipher shifts in consumer behavior and cultures, uncover new market segments in a global marketplace, and help with financial forecasting. This is anywhere  from staying on top of public trends to corporate storytelling.

Work Cited

“Big Data: It’s Powers and Perils.” Accountancy Futures Academy. The Association of Accountants and Financial Professionals in Business. Lincoln’s Inn Fields. London,United Kingdom. 2013. <http://www.accaglobal.com/bigdata&gt;

“A New Approach to Public Relations in the Era of Big Data.” 86 Pillars. 2011-2012. 26 November 2015. <http://www.86pillars.com/class-program/a-new-approach-to-public-relations-in-the-era-of-big-data&gt;

 

PR Advance with Big Data? – Melanie Wieland

Because of Big Data, PR pro’s easily have access and ability to a vast amount of information they would have never been able to gather without. This allows them to correct and adapt to the publics mindset and need, through Big Data’s feedback. Thus giving PR teams the ability to put together a much more effective and all-inclusive crisis management strategy, as well as circumvent future crises. This allows PR pro’s to find ways to address these issues with company management before they get out of hand.

 

Work Cited

 

“Stay on Top of a PR Crisis with Real-Time Analytics.” DATAFLOQ. 01 May 2015.

<https://datafloq.com/read/stay-top-pr-crisis-real-time-analytics/78&gt;

How to Avoid Mistakes in Big Data by Melanie Wieland

The past few years have seen an explosion of new technologies for storing, analyzing and displaying the enormous amount of data available to businesses today.

Therefore, modern day, successful businesses must not only have the latest and the greatest to demonstrate to their stockholders, to inspire them to invest, but also realize that in reality that success is neither automatic nor assured.

The three biggest mistakes companies make in Big Data are:

 

1. Don’t rely on technology alone.

 

2. Don’t use old models.

 

3. Making your big data analytics program a success.

Five Problems with Big Data by Melanie Wieland

Are there problems pertaining with Big Data? Of course. Nothing in the world is perfect. Just like many issues, small amounts of Big Data are able to be managed, maintained, and verified with analytical means. However, Big Data is NOT small. Therefore, problems naturally have arisen. According to Nate Silver, the founder of data-driven journalism site FiveThirtyEight (owned by ESPN), at the HP Big Data Conference in Boston in August of this year, there are five problems accompanying Big Data. They are: where to put it, big bias, false positives, big complexity, and that’s not what I was looking for.

 

Where to put it?

Not only do you have to store and analyze big data, but you also have to factor in other aspects. Such as: buying hardware, cloud storage, latency issues, and how often you have to access it.

 

Big bias?

The more data that is available, the bigger the chance of a sway in statistics and/or information.

 

False positives?

Big Data “thinking fast” and not analyzing the data fully, can lead to false positives and faulty information/responses.

 

Big complexity?

The more data available, sometimes makes it harder to find true value from the data.

 

That’s not what I was looking for…

Big Data systems can attempt at creating alternative “faster” shortcuts. However, many times it’s not the answer or relevant/adequate information you are searching for.

 

All in all, the more information in Big Data, the bigger the problems are created from its complexity.

Work Cited

Butler, Brandon. “5 Problems with big data”. Cloud Chronicles. Network World, Inc. 1994-2015. 20 August 2015. <http://www.networkworld.com/article/2973963/big-data-business-intelligence/5-problems-with-big-data.html&gt;