Every day, we use and generate massive amounts of data: browsing the Internet, shopping online, reading news, and using messaging platforms or navigation. The data you collect can carry enormous value, but only if you process them correctly. So what does it really mean to process data, and what are the stages and benefits of this process? Read on to find out!
What is Data Processing?
Data Processing includes methods related to collecting raw, original data and transforming them into useful insights.
The Data Processing cycle
Input data are the original, unchanged data, and their processing includes a few steps. The key is the order of these steps, as it helps you ensure they will become valuable sources of information.
The data processing cycle can be repeated, as input data from one cycle can be stored and used as input data in another process. This is why we’re calling it a cycle.
Let’s quickly discuss the cycle’s steps:
1. Collecting raw data
In this stage, remember that your data sources should be verified and reliable. If you follow the „trash in, trash out” rule and your input data are poor quality, you won’t achieve satisfying processing results.
2. Preparing and cleaning data
This includes sorting and filtering original data. You do that to delete redundant or incorrect data. In this stage, you verify whether your raw data has any errors, duplicates, or incorrect values and check their completeness. You should also ensure their format allows you to analyze and process them further. You can learn more about this issue in our article “Data errors. What are the consequences of using poor-quality data?”.
3. Entering data
In this step, your raw but prepared and cleaned data are transformed so that the application or machine responsible for further processing can read them. Some examples of data entry include manual data entry, a file upload, or document scans.
4. Processing data
In this stage, your data are subjected to different processing methods and techniques that ensure your desired results. This step depends on your data sources and the reasons for processing them.
5. Data processing results
You can present processed data in a clear, user-friendly form, such as charts, tables, vector files, raster files, or reports. Such data can be further interpreted, stored, and used in the next data processing cycles.
6. Storing and archiving data
The last stage is storing and archiving raw input data and processing results, including all metadata. Store your data correctly to quickly access and use them in the future.
Useful tools and technologies for Data Processing
Considering the amounts of data generated these days, processing them manually is basically impossible. This is why different tools are used to automate, accelerate, and simplify the entire process. The most popular include different programming languages, SQL language, Business Intelligence or ETL tools, and integration platforms. Each of these methods has its strengths and weaknesses. Let’s take a quick look at them.
Business Intelligence tools for business analysis
A friendly interface, predefined analytical models, and a wide range of options for presenting results are the factors that make BI tools massively popular. Users value the clear and uncomplicated visualizations of complicated analyses and large datasets. These visualizations can be presented to management boards and customers. They, in turn, can use the insights to inform their decisions.
However, in their output version, BI tools have a relatively small number of available data source connections. These connections are required for conducting analyses. BI tools also have limited options for preparing data for further analyses. Therefore, it’s common to use both BI tools and ETL/ESB solutions.
Tools and software for statistical analyses
These tools allow you to create very precise analyses, e.g., correspondence, reliability, or cluster analyses. Often, these are the only tools that can provide analyses with the accuracy and complexity required by companies or institutions in medical or lab research sectors. There is no alternative to them. The target user group includes specialists in specific fields. On the contrary, BI systems are dedicated to business representatives and management boards.
The downside of statistical analysis solutions is the high purchase and maintenance costs. These costs are related to the fact that this kind of tool is often divided into different modules that each generate additional expenses.
Different programming languages
Using different programming languages is still a common approach. One perk is the option for creating advanced machine-learning models. But programming techniques aren’t too flexible compared to other methods. This is the case especially when there’s a need to introduce changes related to, e.g., dynamically transforming business conditions.
This method also has downsides unrelated to the data analysis itself. It requires qualified data processing specialists to be skilled in programming languages and possess a vast knowledge of business processes. This is needed to correctly interpret analysis results and create new scenarios. Maintaining such a skilled team might be a big challenge.
SQL consoles that handle queries in the SQL programming language are useful for many analytical scenarios and for achieving precise feedback.
However, queries will only bring satisfying results if data are structured in the right way, maintaining relations between them.
Growing databases and the need for managing data source accesses may make it challenging for administrators.
ETL tools and data integration platforms
Integration tools weren’t created to present results or perform very complicated calculations and analyses. However, an increasing number of companies choose to include them in their data processing.
The main task of these solutions is creating connections between systems or databases, sending notifications, verifying data accuracy and completeness, and transforming them while maintaining crucial attributes and schemes. This maximizes the usefulness of data in future analyses.
Data integration platforms…
can be used by business owners that aren’t qualified data processing specialists.
Thanks to the technical openness and support of many data formats, they can help people in different industries and positions.
save time and money.
A significant advantage of data integration platforms is their no-code/low-code model. They can be used by business owners that aren’t qualified data processing specialists. The features of these tools can be expanded with additional scripts in Python or R programming languages. After acquiring the necessary competencies, the users can successfully expand their solution environment, limiting the so-called vendor lock – dependency on the software provider.
Thanks to the technical openness and support of many data formats (including rare ones), they can help people working in different industries and positions. With integration platforms, they can process tabular, vector, and raster data, as well as databases and data warehouses. Moreover, they can process data from network services such as WMS or WFS, different APIs, and information from IoT sensors.
With integration platforms, you can also automate your designed processes. This saves you time and money. Moreover, the skills of employees who work with data can be used in other areas.
Deciding on ETL tools or an integration platform, you should analyze your data processing goals to avoid unnecessary costs. These are complex solutions that offer nearly infinite possibilities. They might be left wasted if it turns out your organization could only use much simpler tools.
What are the benefits of Data Processing?
As mentioned before, collecting data without processing and analyzing them makes them useless. Prepared in the right way, data can give you measurable business benefits.
Processing data brings:
Properly chosen Artificial Intelligence mechanisms can help you with data analysis and make this process even faster.