Voice recordings are useful at any point in time. If you want to save a lecture (or presentation) that you are attending, or record a voiceover for a project, the iPhone can be your solution. You don’t have to use an old tape recorder, or a standalone recorder. The iPhone’s fantastic technology records high quality voice recordings on demand, whenever you need it.

To get started, look for an application named “Voice Memos.” Most likely, this particular application is found in your Utilities category by default. Once you open it up, you are greeted by a large microphone, indicating you are in the correct application. When you want to start recording, simply tap on the large red button in the bottom left, and you are on your way to begin recording. What is important about recording voices on this application to the highest degree of quality, ensure that your meter in the bottom is within the middle range; with the pointer in the red zone, your recording will be awfully loud, while otherwise, it will be too quiet. Finished recording? Simply tap on the square button, and you have your recording.

What can you do with these recordings? Once you have finished recording, you are able to now share this recording through email or even in a text message; email it to yourself so you can save it on your computer, and listen to it later, or start editing it to put into a movie production, or text it to a friend for their needs.

Big data is now in an incredibly important part of how many major businesses function. Data analysis, or the finding of facts from large volumes of data, helps businesses make many of their important decisions. Companies that conduct business on a national or international scale rely on big data in order to plot the general direction of their business. The concept of big data can be very confusing due to the sheer scale of information involved.  By following a few simple guidelines, even the layman can understand big data and its impacts on everyday life.

What Exactly is Big Data?

Just about everyone can understand the concept of data. Data is information, and information is everywhere in the modern world. Anytime you use any piece of technology you are making use of data. Anytime you read a book, skim the newspaper or listen to music you are also making use of data. Your brain interprets and organizes data constantly from your senses and your thoughts.

Big data, much like its name infers, simply describes this same data on a large sale. The internet allowed the streaming, sharing and collecting of data on a scale never before imaginable and storage technology has allowed ever increasing hoards of data to be accumulated. In order for something to be considered “big data” it must be at least 10 terabytes or more of information. To put that in perspective, consider that 10 terabytes represents the entire printed collection of material in the Library of Congress. What’s even more remarkable is that many businesses work with far more than the minimum 10 terabytes of data. UPS stores over 16 petabytes of data about its packages and customers. That’s 16,000 terabytes or the equivalent to 1,600 printed libraries of congress. The sheer amount of that data is nearly impossible for a human to comprehend, and analysis of this data is only possible with computers.

How do Big Data Companies Emerge?

All of this information comes from everywhere on the internet. The majority of the useful data includes customer information, search engine logs, and entries on social media networks to name a few. This data is constantly generated by the internet at insane rates. Specified computers and software programs are created and operated by big data companies that collect and sort this information. These programs and hardware are so sophisticated and so specialized that entire companies can be dedicated to analyzing this data and then selling it to other companies. The raw data is distilled down into manageable reports that company executives can make use of when handling business decisions.

The Top Five:

These are the five biggest companies, according to Forbes, in the business of selling either raw data reports or analytics programs that help companies to compile their own reports.

1. Splunk
Splunk is currently valued at $186 million.  It is essentially a program service that allows companies to turn their own raw data collections into usable information.

2. Opera Solutions
Opera Solutions is valued at $118 million. It serves as a data science service that helps other companies to manage the raw data that pertains to them. They can offer either direct consultation or cloud-based service.

3. Mu Sigma
Mu Sigma is valued at $114 million.  It is a slightly smaller version of Opera Solutions, offering essentially the same types of services.

4. Palantir
Palantir is valued at $78 million.  It offers data analysis software to companies so they can manage their own raw data analysis.

5. Cloudera
Cloudera is valued at $61 million.  It offers services, software and training specifically related to the Apahce Hadoop-based programs.

The software and services provided by these companies impact nearly all major businesses, industries and products. They impact what business offer, where they offer them and how they advertise them to consumers. Every advertisement, new store opening or creation of a new product is at least somewhat related to big data analysis. It is the directional force of modern business.

Sources:
http://www.sas.com/en_us/insights/big-data/what-is-big-data.html

http://www.forbes.com/sites/gilpress/2013/02/22/top-ten-big-data-pure-plays/

http://www.whatsabyte.com/

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Data has always been important to business. While it wasn't long ago that businesses kept minimal information on people who bought their products, nowadays companies keep vast amounts of data. In the late 20th century, marketers began to take demographics seriously. It was hard to keep track of so much information without the help of computers.

Only large companies in the '60s and '70s could afford the research necessary to deliver real marketing insight. The marketers of yesteryear relied upon focus groups and expensive experiments to gauge consumer behavior in a controlled environment. Today even the smallest of companies can have access to a rich array of real-world data about their consumers' behavior and their consumers. The amount of data that is stored today dwarfs the data of only a few years ago by several orders of magnitude.

So what kind of information are businesses storing for marketing purposes? Some examples include:

- Demographic information — age, gender, ethnicity, education, occupation and various other individual characteristics.

Millions of people experienced the frustration and failures of the Obamacare website when it first launched. Because the code for the back end is not open source, the exact technicalities of the initial failings are tricky to determine. Many curious programmers and web designers have had time to examine the open source coding on the front end, however, leading to reasonable conclusions about the nature of the overall difficulties.

Lack of End to End Collaboration
The website was developed with multiple contractors for the front-end and back-end functions. The site also needed to be integrated with insurance companies, IRS servers, Homeland Security servers, and the Department of Veterans Affairs, all of whom had their own legacy systems. The large number of parties involved and the complex nature of the various components naturally complicated the testing and integration of each portion of the project.

The errors displayed, and occasionally the lack thereof, indicated an absence of coordination between the parties developing the separate components. A failed sign up attempt, for instance, often resulted in a page that displayed the header but had no content or failure message. A look at end user requests revealed that the database was unavailable. Clearly, the coding for the front end did not include errors for failures on the back end.

Bloat and the Abundance of Minor Issues
Obviously, numerous bugs were also an issue. The system required users to create passwords that included numbers, for example, but failed to disclose that on the form and in subsequent failure messages, leaving users baffled. In another issue, one of the pages intended to ask users to please wait or call instead, but the message and the phone information were accidentally commented out in the code.

While the front-end design has been cleared of blame for the most serious failures, bloat in the code did contribute to the early difficulties users experienced. The site design was heavy with Javascript and CSS files, and it was peppered with small coding errors that became particularly troublesome when users faced bottlenecks in traffic. Frequent typos throughout the code proved to be an additional embarrassment and were another indication of a troubled development process.

NoSQL Database
The NoSQL database is intended to allow for scalability and flexibility in the architecture of projects that will use it. This made NoSQL a logical choice for the health insurance exchange website. The newness of the technology, however, means personnel with expertise can be elusive. Database-related missteps were more likely the result of a lack of experienced administrators than with the technology itself. The choice of the NoSQL database was thus another complication in the development, but did not itself cause the failures.

Another factor of consequence is that the website was built with both agile and waterfall methodology elements. With agile methods for the front end and the waterfall methodology for the back end, streamlining was naturally going to suffer further difficulties. The disparate contractors, varied methods of software development, and an unrealistically short project time line all contributed to the coding failures of the website.