The world revolves around data. Just about any important decision organizations make depends on the data they have. Data helps organizations mitigate risks, plan marketing campaigns, devise research-backed strategies, and tons more. But a resource this valuable has rather humble, or jumbled, beginnings.
What is Raw Data?
Raw data is the primary form of data. The data that organizations use is derived from raw data. This is because raw data is disorganized and hard to read or understand.
Raw data needs to be put through a series of processes before it can be used. At first glance, raw data appears to be a heap of garbage. But with time and resources, raw data will be converted into a readable format, and before you know it, it will be used in making important organizational decisions.
The Source
The source of raw data depends on the type of information organizations are after. The scope is important, as there is no point in collecting irrelevant raw data. Raw data is usually collected from sources like
- Interviews
- Data Archives
- Data sets
- Online surveys
- The Internet
- Printed media
For example, if a business is planning to launch a premium line of toddler shoes, data must be collected from online surveys or third-party sources based on the target crowd. The target crowd, in this case, is usually moms from the upper strata of society.
Another thing to consider when collecting raw data is the method used. Electronic collection methods tend to have lower error rates. The same cannot be said for other methods. There is a high probability of errors when converting raw data that is in the form of audio, video, or handwritten text. An interpreter may find it difficult to understand audio data containing accents that belong to remote parts of a country.
Raw Data Processing Techniques
As said earlier, raw data needs to be processed before it can be used. There are 6 techniques used in data processing. They are:
- Real-time data processing
- Distributed data processing
- Time-sharing data processing
- Batch data processing
- Multi-processing
- Transaction data processing
Real-Time Data Processing
This type of data processing is common in financial transactions, cybersecurity, e-commerce, and GPS tracking systems. As the name suggests, data processing starts as soon as data is received from the source. The receiving and processing of data happen in under a second.
Distributed Data Processing
Distributed data processing is also known as DDP. In DDP, large sets of data are broken down into smaller, manageable chunks. These small chunks are then shared among different servers to be processed. DDP uses fewer resources and takes less time, making it a great option for data-reliant businesses. DDP also has higher fault tolerance.
Time-Sharing Data Processing
In time-sharing data processing, only a single CPU unit is used. All the users and applications that need data will have access to this large CPU unit, which handles all their requests. This method is predominant in organizations that use centralized systems to manage user queries.
Batch Data Processing
As the name suggests, in batch data processing, raw data is processed in chunks. The raw data used comes from multiple sources collected over a period of time. Data processing via batch data is comparatively accurate. But the process takes longer and is more complex. Employee payroll systems and analyzing short-term figures are a few places where batch data processing is used.
Multi-Processing
Multi-processing is a type of data processing that is often used in weather forecasting and in training machine learning and AI models. In multi-processing, a single chunk of data is broken into multiple parts, making it easier to process. The broken chunks are accessed by multiple CPU units, making multi-processing one of the fastest and most efficient methods of data processing.
Transaction Data Processing
This type of data processing is used on larger server computers. The point of transaction data processing is to read the incoming sets of data without interruptions. Transactions data processing is among the most resource-hogging types of data processing.
Why is Raw Data Valuable?
Because it is a goldmine of information. It may be useless in its current stage, but when processed, raw data can produce valuable insights that will be of immense help to an organization. The data derived will be crucial in making decisions and backing them. For business owners, raw data can shed light on areas that need development and identify patterns in customer behavior. This information will go a long way toward meeting the ever-changing needs of the modern consumer.
