Common practices for Data Processing

Common practices for Data Processing

Data is everywhere. In a business setting of any kind, the ability to utilise data for deep insights relies on data processing. When you’re able to transform data from large files and spreadsheets of numbers into visual graphs and reports, you can make informed decisions and improve your organisation.

Although the steps may seem complicated, we will share some powerful software solutions so that you can readily access the usable data you need, no matter where you may be. Let’s take a look at everything you should know about data processing.

What is Data Processing?

Data processing is the process of collecting raw data and transforming it into a usable form. This is done through a sequence of operations. To achieve this manually, data scientists or data engineers are typically required. However, automation software has made it easy to carry out these steps with little to no human intervention needed.

The entire process involves the collection, cleansing (filtering and sorting), processing, analysis, and storage of data so that internal and external parties can utilise the valuable information. Data processing helps organisations achieve smoother operations, increased productivity, enhanced security, and, ultimately, can positively impact the bottom line.

What Types of Data Need Processing?

Mostly every type of data needs some sort of processing. When you think of data, it’s likely for numbers to come to mind. But, data can be in the form of images, graphs, survey answers, transactions, and more. Data can be categorised into things like: personal information, financial transactions, banking details, etc. Depending on what type of information you want to glean and the type of data available, the processing time or steps can vary.

What are the Applications of Data Processing?

Data processing is used across industries and businesses of all sizes. Here’s a look at some of the most common applications of data processing:

Commercial Data Processing:

Commercial data processing is when a large volume of input data is used to produce a large volume of output. For enterprises and big businesses, you can quickly understand the value of being able to process massive amounts of data. For example, banks and insurance companies withhold countless records that are both sensitive and necessary for doing business.

Real-World Applications:

Outside of business, data processing is needed in realms where the information can literally change lives. To exemplify, the healthcare industry may be able to process massive amounts of data to better understand public health crises so that solutions can be found and executed. ?Additionally, data is processed in academic settings so researchers can use scholarly material that is accurate.

Data Analysis:

Data analysis is a main function that involves data processing where algorithms and forecasts can be used to make important choices today that will affect what’s yet to happen.

What are the Stages of Data Processing?

There are six main stages of data processing, namely:

Data Collection

The first step of data processing is, of course, data collection. At this step, data is pulled from all available sources, be it a data lake, data warehouse, or disparate systems. Since this is the raw information that will be translated into insights, it’s important that the data is of the highest possible quality. Some examples of raw data may include: user behaviour, monetary figures, and website cookies.

Data Preparation

Once the data is collected and ready to be transformed, the data preparation stage is initiated. This is where data is organised and cleaned before it moves into the next step. At this stage, data is checked to remove redundancies, incorrect data, or incomplete data (records with gaps). While this could be an immense time waste when performed manually, automation solutions like ours can do this work for you in seconds.

Data Input

Now, the pre-processed data gets entered into the system in which it will be utilised. This could be a CRM or data warehouse, for example. The data gets translated into a language that the machine can understand and starts to take its shape of usable information.

Data Processing

With the same name as the overall procedure, the data processing step is the core component of these steps. Here, the computer system in which the data was input begins to process the data by way of algorithms and machine learning. The way in which this happens will depend on the data source and intended output (or use case).

Data Output

After being processed, the data output stage equates to the data interpretation stage. It’s at this point that individuals (who are not data scientists) can understand the data. The information takes the format of graphs, plain text, images, videos, etc. which makes it easy for anyone to be able to utilise. The solution from Spanscom allows users to create customisable dashboards for this purpose.

Data Storage

Even though the main goal of data processing is to reap the benefits of the insights, data storage is a ever important piece of the process. After data has been used, it should be stored for future reference. Not only is it valuable to have it accessible to review should the need arise, but there is also compliance involved when collecting and storing information. To ensure that your business is covering all its bases and adhering to regulations, the bank-grade security inherent in our product can protect your data as an overall component of your data management.

Future of Technology and Data Processing

Given the fact that businesses collect data every second from different sources, the overall value appears when data is centralised so that all data records are processed for use. The future of data processing exists in cloud technology. Cloud technology makes it possible to access data from various systems without timing delays to maximise effectiveness. When software updates (which is inevitable), the cloud technology used to process data is unaffected. Additionally, cloud platforms are a more cost effective option than having to store data on servers on-premise. Since most work is being done remotely, cloud software also ensures that users can access data securely, wherever they may be.

What are Data Cleaning Best Practices?

It’s time to reiterate: your data insights will only be as good as your data. With data processing and data management, the primary focus should be on the accuracy and cleanliness of your data.

Importance of Data Cleaning This brings into light the importance of data cleaning. Data cleaning is the process of clearing out your database of any inaccurate records. It matters because your entire organisation will make decisions based on the data at hand.

From marketing to sales, compliance to finance, and overall operations, every choice will involve data. So, you better make sure that your data is right in the first place.

Data Cleaning Process

To execute the data cleaning process, you and/or your automation software will cover these bases:

  • Audit data - find the bad data and allocate resources to fix or remove it.
  • Clean data - specify the workflow to remove duplicates, irrelevant data, syntax errors, or type conversion differences (formatting).
  • Verify data - after cleaning the data, review it once more to check that values are still matching the type of information required.
  • Report data - utilise the software system to generate a report that summarises the quality of your data.

Benefits & Challenges of Data Cleaning

Data cleaning will provide your business with:
  • Enhanced compliance
  • Streamlined processes
  • Expedited sales cycles
  • Increased revenue
  • Informed decision-making
  • Improved integrity of financial data
  • The opportunity to increase revenues

However, data cleaning may pose some challenges that you’ll want to overcome, including:
  • Maintaining clean records over time
  • Losing information while cleaning
  • Cleaning only a portion of data (if it’s not integrated)

Automation & Data Cleaning

To reap all the benefits of data cleaning while overcoming the challenges, an automation solution can reduce the risk of losing data, continuously update records so that they are maintained, and integrate all data together while automatically cleansing records so you never have to worry about inaccurate data again. For 95% of businesses, unstructured data poses a problem. Having raw data without the ability to process, clean, and use it becomes costly and counter productive to being able to make smart business decisions.


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