How do data scientists handle raw data?
What is raw data?
The title “raw data” refers to information that has not yet undergone any automated or manual processing when it was transmitted by one data entity to another. Raw data is collected from a source and used to describe the data exactly as it was collected before it gets cleaned or if any additional data gets added to it through derivation or calculation. It doesn’t seem or signify anything by itself, but processing can make it useful for analysis. It’s also referred to as primary data.
Source: 365DataScience
Examples of raw data
- Let’s say the raw data collected included a zip code field but not city and state since that information can be derived from the zip code field.
- In another example, there is a field for the total sales price. The raw data doesn’t include the amount of sales tax paid. But that data can be calculated based on the percent of sales tax charged in that location derived from the zip code.
Challenges in the raw data
Raw data is rarely directly applicable. Users can face several problems including missing numbers, corrupt records, data collection failures, etc.
- Redundancy occurs when the same piece of data appears various times. If duplicate data samples are contained in the machine learning system, the system’s performance may not be as good as it may be.
- Raw data may contain noise, such as missing or faulty values, which might occasionally affect the remaining data.
- Raw data is frequently denormalized, out-dated, or poorly organized.
- Raw data can come in various forms as it is obtained from multiple sources. Data differs from source to source and application to application. Since most of this data is unstructured, a relational database can accommodate it.
Cleaning data is essential to creating successful machine learning models because it improves the model’s performance and accuracy, which is the primary factor behind the data science process. To determine if the dataset may be enhanced to produce the desired results, data scientists analyse the dataset’s applicability and quality.
How data scientists handle raw data challenges
Data always comes first before anything else. Any organization requires data because it enables decision-making based on facts, statistics, and trends. The basis of data science is data. A data scientist works to transform raw data into processed data by organizing and cleaning the data.
There are many questions that data science answers, such as…
- What should we do with this much information?
- How can it be used to everyone’s advantage?
- What practical uses may the data be put to?
Analytics and machine learning (ML) are increasingly utilized to extract crucial information from the vast volumes of raw data available, which may be used for a variety of purposes.
Here, we explain different approaches that a data scientist uses to handle raw data challenges
- Observation: Data scientists mainly focus on two things, analysis and then creating products based on that. He/she first makes an observation which is a quick and effective way to collect data with minimal intrusion.
- Data de-duplication: Data scientists employ a data de-duplication approach to identify duplicate data blocks and removes redundant data. As a result, it improves processing speed and obtains precise information with high accuracy.
- Maintain consistency: When there is a discrepancy in the data, the data scientist examines the data’s completeness, dependencies, and significance to the desired outcome.
- Indexing/ Data profiling: Data scientists use different techniques for resolving and managing data variety from different sources, such as data profiling, indexing, and universal format conversion. Data profiling identifies irregularities and connections between various data sources. Indexing helps to link together disparate and incompatible data.
- Data storage: The amount of data must be processed while working with big data is enormous as it included both structured and unstructured data. A data scientist uses different approaches such as Distributed nodes, and object storage to store this large amount of data.
Final thoughts
In fields where data analysis is a frequent occurrence, such as healthcare, retail, and manufacturing where there is an ample amount of data to collect for a broad range of use cases, working with raw data is essential.
However, it is important to acknowledge that unprocessed raw data isn’t particularly useful. Only relevant information that is useful to us is required. Data by itself has no value at all in the end. However, information is produced when data is structured meaningfully, and we require that information. Normally, it’s just a bunch of code, like a user cookie, which doesn’t provide much information, but when a data scientist combines this user cookie data with relevant user profiles then it can be a game changer for business analysts and marketers.
Neal data scientists use modern techniques and tools to work with enormous amounts of raw data to identify and gather meaningful information. We can then use different machine learning algorithms to build predictive models and help our customers improve decision-making with data.
Interested in getting more out of your organization’s data? Contact us to chat with one of our data science experts!
