Raw data Assignment help
What is raw data?
Raw data is data that has not been processed for use. An important distinction between raw data and information is that information can be seen as the product of processing raw data. Raw data that has undergone processing is sometimes referred to as “cooked data.” Raw data is a type of data that has not been processed for use. Data that has been processed for use is called information. An important distinction between raw data and information is that information can be seen as the product of processing raw data. Raw data that has not been processed for use is a form of evidence, which means it consists of facts and information about a particular subject without interpretation or analysis. Assignmentsguru is a place where you can hire writers who will do your work at no extra charge with any specific deadline set by you. We are your dedicated Raw data assignment writing site that provide top quality assignments services at affordable price with our team of experts working on your behalf all day long!
Although raw data has the potential to become “information,” it requires selective extraction, organization and sometimes analysis and formatting for presentation. Raw data sometimes ends up in a database, which enables the data to be accessed for analysis in different ways.
How raw data works
Tremendous amounts of raw data surround us and are produced every day. The human brain is incredibly good at taking in raw data, processing it and using it to make decisions.
For example, imagine you are trying to cross a busy road. The eyes capture raw data as flashes of light and dark. Then the brain takes these flashes and resolves them into objects such as street signs and cars. The working memory can tell you if that car is sitting still, getting bigger as it comes toward you, or getting smaller as it drives away. Meanwhile, the ears take in raw information in the form of vibrations in the air, which the brain translates into sounds that can be interpreted as the wind, voices or a car engine. Finally, all this processed data that came in through the eyes, ears and memory helps you make the informed decision to cross the street or not.
Computers are generally not able to understand raw data, which is why it’s usually put through a process called ‘data cleaning’. This generally includes removing personal information & unnecessary material Extra processing is required to turn it into useful information. Additionally, the final data from one system may be used as raw data in another.
For example, imagine a simple home thermostat. Its raw data source is a temperature probe — usually read as an analog voltage level. The system takes this voltage level as raw data and turns it into a temperature reading. It can then use this processed data to meet a predetermined desired temperature for turning on and off a heater or air conditioner.
Furthermore, the system may feed this temperature reading and the current time into another climate control system as that system’s raw data. The data is then stored and analyzed over time to yield a predictive modeling algorithm. The end result helps with making better heating and cooling decisions
How to process raw data
Many sources can produce raw data. How it is processed and stored depend on its source and intended use, though. Data that should be analyzed and extracted can come from a point of sale terminal, computer logs or even participant eye tracking data. If you want to find the best and most relevant insights, it should go through the process of being analyzed before extraction. Applications and devices can save raw data in various formats, but the most common format for interchanging raw data between systems is comma-separated values (CSV) files.
In many instances, users must clean raw data before it can be used. Cleaning raw data may require parsing the data for easier ingestion into a computer, removing outliers or spurious results and, occasionally, reformatting or translating the data — a process sometimes called massaging or crunching the data.
There are many ways to process raw data, ranging from simple to complex. One spreadsheet such as Microsoft Excel or Google sheets allows users to format, organize, and graph data to reveal simple trends and help summarize data. Business intelligence (BI) programs may use raw data for financial trending or forecasting purposes. In the future, it’s possible that alerting systems will use raw data to predict changes in behavior patterns so machines can predict poor customer experiences and security risks.
Value of raw data
The primary value in data is after it has been processed and interpreted. There is generally not much value in holding onto raw data without a way to use it, but as the cost of storage decreases, organizations are finding more and more value in collecting raw data for additional processing — if not right away, then later.
As raw data may contain identifiable information, organizations need to be careful about their storage. They also want to understand the implications of the information they are storing. To ensure they aren’t giving unauthorized individuals access to PII, organizations can use data anonymization techniques or implement controls & measures that prevent unauthorized access.
Organizations can feed raw data into a database or a data warehouse, which can collect raw data from many sources for automatic or manual correlating and processing. An analysist can then query the data using BI tools to produce useful information from the data.
Many large businesses today recognize the value of raw data. Consumer data is a hot commodity that they can buy and sell to build profiles of users or target a specific audience, for example. Businesses can also store operational and logging data for use in performance metrics and to streamline business practices, while they can use access logs and the like to identify computer breaches and track what data may have been accessed by hackers.
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