Creation:

We have always created data. Think of data as just information that is generated. Throughout history, we have written books, sent letters, recorded information, taken photos and more; therefore we have created data for year. Today, we create big data. We are able to create so much more data, primarily for two reasons: technology can record data and our new methods of communication. We can turn just about anything into data. In the 1800’s, a person walking, was just that- a person walking. Today, that person walking becomes a data set. Sensors and customized software record loads of data. A person’s geographic location, the number of steps a person takes, and even credit card purchases are recorded as data. Changes in the way we communicate have also increased the amount of data we create. Letters became emails, telephone calls became text messages, and then we increased the number of people we could communicate with via social media. Social media has played a major role in data creation, as it greatly increases the speed with which data is created. Not only is it created in real-time but it can also be analyzed and used in real time. Pretty much all devices in modern times create data. Most are digital devices, but not all. Many devices have sensors in them that collect data, but you may not even know it.

Processing:

Due to high costs, large volumes of data have not been captured and widely analyzed by businesses. But new technologies and discoveries have made this easier and cheaper. Software frameworks, like Hadoop have been introduced to help process large, unstructured data sets. Businesses don’t have to organically go on social media sites to know what people are saying about them. This would take hours! Now they can know, in real-time, all the time. However, these processing tools are certainly far from perfected. They often have difficulty processing data from a combination of structured and unstructured data, and do not operate consistently enough to be considered reliable. You can read more about data tool hype here. Although we may not have the tools, we have the skill. Many data analytics and consulting firms, offer this as a service. The introduction of the cloud as a storage solution has also played a big role in the processing of data. It allows companies to use pre-built data solutions, or to quickly build and deploy servers and solutions without the huge costs of the physical hardware. Processing data is the 2nd stage in the big data life cycle.
Output:
While it has gotten easier and easier to capture and process data, turning that data into valuable insight remains difficult. In order for the data to be valuable, it must be readily available to the right people, and it must be accurate and timely. Data visualization tools have made this easier. They enable decision makers, and even the average business user to review large and complex data sets. However, these tools are only helpful if the data is not coming from many disconnected, disparate systems (the vast majority of businesses have over 6 systems). They create a visualization of data, but some insights can be missed without a team of proper data analysts. At this stage in the life cycle, it is simply data in the form of numbers, charts and graphs. But at this point, we need to ask, is this data complete? And is it telling me the whole story? the output of data can be in the form of data visualization, but this does not mean you gain all the insights you would from data analysis. There is another step of processing involved
Resources and Analyzing:
Although we may not have the tools to conduct a thorough analysis, we do have the human capital to. There is a new breed of employee, called the data scientist. These employees have a special skill set and knowledge to handle the complexities of big data and the ability to simplify the big data output for daily use by the company. However, analysts are expensive…and you can’t really just have one. The alternative is working with a data analytics and/or consulting expert. This method is often preferred by companies that do not have a team of data experts, because it is both easier and more economical. Data analyst firms prepare and analyze your data for you, simply providing you with the insights generated from the data. They also often perform a better, more complete analysis than any tool can. Finally, they can also assist you with any hiccups along the way, and some will even help you seamlessly implement the solutions that are based off of the actionable insight. Those that have never worked with data analysis firms before may be nervous about someone coming in with access to all their data. If this is a fear of yours, listen to the Podcast from Data Talk Show below. Business Data Analytics Fears

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