What is Data Handling?
Data handling is an overarching term that refers to a range of processes for the collection, storage, and processing of data in a way that ensures its authenticity and accuracy. It’s no secret that data has become valuable for organizations, making it increasingly useful in decision-making, predictive analysis, and business strategies.
Now, Big Data handling is crucial for many organizations and is regulated, which makes enacting the right processes necessary to stay compliant and maximize the value of collected data.
Data handling involves several steps focusing on collecting, processing, and utilizing data to maintain quality data at every step. Keep reading to learn more about data handling and following the right processes to ensure accuracy and compliance.
Different Types of Data To Understand
Being aware of the overall data classification types is crucial to data collection and analysis. Let’s quickly discuss the two primary data types before exploring data handling activities.
Qualitative data is about understanding a given subject’s inherent properties or qualities rather than quantifiable information. Most qualitative data falls into one of two categories:
- Nominal: This data can be categorized but not ordered or ranked. Think of the different breeds of dogs or a list of countries. Based on this data alone, they can be categorized based on their names, but you can’t say one is ‘greater’ or ‘lesser’ than the other.
- Ordinal: When your data can be categorized and ranked, it’s ordinal. An example would be socio-economic classes like low, middle, and high income. While they can be categorized like nominal data, they can also be ordered, such as high income being above middle income.
Quantitative data includes numerical attributes, like measurements, counts, or KPIs. We can further divide this data type into two categories:
- Discrete: This data is specific and can only take particular values. Think of the number of children one family has. You can’t have half a child, so the data is discrete.
- Continuous: When data can take infinite values within a given range, it’s continuous. Consider a person’s weight. It could be 70 kg, 70.5 kg, or 70.555 kg. The possible values between any two weights are essentially infinite.
Typical Steps and Best Practices for Data Handling
Data handling best practices form the basis for effectively collecting and handling data. We can break down data handling into specific steps or phases, but it’s crucial to understand the process isn’t always linear. Workflows are often iterative, have feedback loops, and require continual refinement before the task is completed.
So, let’s take a look at the common overall steps involved in data handling and how to enact them effectively.
The first phase of dating handling is collecting the necessary data. During this phase, having a comprehensive data handling policy is crucial to ensure collection remains compliant and the correct data is collected.
Data collection is the process of gathering raw, unfiltered information from a range of sources. These sources include online platforms, intricate sensors, manual surveys, or expansive databases.
At this stage, it’s crucial to maintain accuracy, relevancy, and timeliness. Additionally, several regulations also require confidential and sensitive data handling.
Depending on the intended use of the collected data, it may be worth taking the time to preprocess data before usage. This step is usually necessary for training a machine-learning model and can be helpful for data visualization.
Data preprocessing is a series of steps that aim to transform raw data into a more effective format for training machine-learning models. Steps can include several steps, such as:
- Data cleaning: This step involves removing or correcting erroneous data points, dealing with missing values, and smoothing noisy data.
- Normalization and scaling: Adjusting the scales of features so they have, for example, a mean of 0 and a standard deviation of 1. This step is essential for algorithms that are sensitive to the scales of input features.
- Data transformation: Converting data into a format suitable for the chosen model, like turning categorical data into a numerical format using one-hot encoding.
- Handling Imbalanced Data: In scenarios where one class of data is underrepresented, techniques such as oversampling, undersampling, or generating synthetic data can be used.
Data analysis is at the heart of data handling, transforming raw data into meaningful insights. This step varies depending on the specific use case. Generally speaking, it’s when a range of tools and expertise are used to dive into the data to gain the desired goal.
The exact techniques range from basic statistical tests to cutting-edge machine-learning models. The goals vary, but proper data collection and preprocessing will significantly affect any results. It’s crucial not to focus entirely on analysis and remember the entire process matters.
The next phase revolves around structuring and organizing the data and any insights gained from it. Data is often sorted, categorized, or sometimes aggregated into visualization methods to offer a clear snapshot, typically stored in spreadsheets or databases.
The main challenges include avoiding redundancy, ensuring a consistent data structure, and creating a format that integrates with subsequent analytical tools. The analytical tool can then generate popular visualization formats like tables and graphs.