With the advancement of information technology, it has become possible to analyze a wide range of data that could not be fully evaluated in the past. However, the data required for analysis is often collected in an incomplete form, and to be usable, the data must be processed in a way that makes it suitable. If highly reliable information can be obtained through data analysis, it can be used in marketing activities and new product development, which will directly lead to an increase in corporate competitiveness. Therefore, this time we will introduce the benefits, normal flow, and efficient progress method as a prerequisite for correct data processing.
What is data processing?
Data processing is the process of preparing complex and diverse data into a form that can be analyzed. By performing operations that will eliminate data gaps and ensure consistency, we form the basis for obtaining highly reliable analysis results. By correctly analyzing the processed data, it becomes possible to make decisions based on objective data, which helps companies gain a competitive advantage.
Data processing mainly falls within the scope of “transformation” (extraction, transformation, export) of ETL, known as the data processing process. Although this corresponds to the initial preparation in the data analysis process, it is so important that “80% of the data analysis process is devoted to data collection and processing, and the required costs are high. The types of data processing are extensive, including “format conversion”, “data cleaning” and “name matching”.
Three Benefits of Data Processing
Next, we will introduce three advantages of data processing. Data processing is particularly effective in terms of productivity, accuracy of data analysis, and data-based accuracy, and is necessary to maximize the results obtained from data use.
Increase productivity
In enterprises that frequently process data, productivity can be expected to increase depending on the quality of the processed data. For example, there will be less time for low-quality data that needs to be constantly corrected, such as “the expression model is not integrated” or “there are too many unnecessary duplications”. By using properly processed data, this unnecessary work and stress can be reduced and productivity can be increased.
Improve the accuracy of data analysis.
The use of BI (Business Intelligence) tools and artificial intelligence is effective in analyzing data to discover customer needs and find positive factors that increase product quality. However, if the quality of the analyzed data is low, you cannot get the results you want, no matter how good the tools are. Analyzing data with many missing values or improperly transformed data is time-consuming and costly.
If the data is preprocessed appropriately, it will not only reduce the cost but also make it easier to obtain the most reliable results by leveraging the power of the tools.
Improve data-based accuracy
“Data-driven” refers to making decisions based on objective data rather than relying solely on experience and intuition, and the application of this way of thinking to corporate management is called “data-driven management”. Big data, which has attracted attention with the use of big data in recent years, is indispensable for improving business performance and strengthening corporate competitiveness by encouraging the creation of new jobs.
In particular, it can be said that unstructured data such as images, audio, and real-time data obtained from IoT sensors is a rich new perspective that traditional analysis techniques miss. Improving the accuracy of analysis based on correct data processing is directly linked to the quality of data-based management.
4 stages of data processing
Since data that can be used in management activities, such as customer information and sales data, are diverse and complex, they must be processed in a way that is easy to analyze. Here we will introduce the general flow of data processing.
Check the processing data.
Systems and electronic devices used in companies, such as business systems and company smartphones, contain large amounts of data. Since it is not possible to analyze all the data with limited resources, it is important to carefully evaluate the data to be preprocessed.
For example, use the ERP (core system) as a target to gain insight into management resources such as financial accounting and production management. Similarly, we target CRMs (customer management systems) that store data such as personal information and purchase history to gain insight into the relationship between a company and its customers.
Remember that these large systems may have multiple pieces of data representing the same content. In this case, it is important to choose which data is considered “correct”.
Align the data format.
It is important to standardize the data being examined in a format that the tools used in data processing can read.
For example, in the case of CSV or TXT format files, multiple character codes such as Shift_JIS and UTF-8 may coexist; therefore, use one of these to avoid mixed characters. Even when expressing “date”, you need to take into account the difference in notation, such as using “YYYY/MM/DD” or “YYYY.MM.DD”.
Thus, by determining the combination of file formats, character codes, data types, etc., we can align the data format and organize the data into easy-to-process data.
Identify and complete/correct data gaps and anomalies.
The data to be processed is filled with missing values and corrected with outliers.
“Missing values”, occur in data that cannot be retrieved correctly for some reason, and “abnormal values”, are data that should not actually be included in the processing target, thus significantly reducing the accuracy of the count analysis. It is important to eliminate data loss as soon as possible to avoid unnecessary rework at a later stage of data analysis.
In addition to the consolidation of the data format mentioned earlier, a series of operations aimed at eliminating defects in the data being processed is called “data cleaning”.
Remove duplicate data and delete or match names.
If the data being processed contains duplicate content, it is determined whether the data will be deleted or whether name matching will be performed.
Name linking is the process of linking related data to multiple databases by organizing common objects as “keys”. In addition to linking studies, we also adjust data based on keys such as grouping items with the same meaning, and grouping information.
By combining names, data based on information such as names and phone numbers from multiple locations belonging to the same person can be brought together. This is useful in cases where the data of a certain person is accidentally recorded more than once or its location changes due to transfer.
Processing such duplicate data is necessary to improve data usage and the quality of analysis results, and can be called an important process in data processing.
Tools that help increase data processing efficiency.
Therefore, data processing involves several steps and requires a large number of man-hours depending on the size and type of data being processed. Therefore, it is common to use tools that will increase performance when processing data. We will introduce some representative people.
ETL (Transformation Load Off)
ETL is the process of extracting data from multiple data sources spread across multiple locations, transforming it to suit a purpose, and then writing (loading) a series of data processing processes that organize the data into a data warehouse. ETL tools that can process large amounts of data quickly and automatically can be extremely useful.
However, while it is easier than performing all the processes manually, a certain amount of specialized knowledge is required if you want to implement complex processes. A basic understanding of databases and SQL is especially important.
Data preparation tools
Data preparation tools are tools designed to allow people without IT skills to process data as they wish. Most tools use a GUI (Graphical User Interface) that can be operated intuitively using the mouse; this makes it possible to process data without any code, without requiring advanced programming skills.
Using data preparation tools, those responsible for the business departments that ultimately need data can prepare the required data themselves; this is expected to greatly promote data usage across the company.
Personal Information Anonymization Tools
Personal information anonymization tools are tools that promote the use of personal data, which is essential for modern marketing and product development while protecting individual privacy. Since the Personal Information Protection Act was significantly revised in May 2017, it is now possible for companies to use personal information for purposes other than the intended purpose without obtaining the individual’s consent, and to anonymize the information necessary so that the individual cannot be identified there.
However, the stagnation in the use of personal data has become a problem as processing this information takes time and effort. This is where anonymous processing tools come in handy. The unique processing techniques and algorithms of the tool enable more efficient anonymous processing.
System development for Sky Corporation
Now that IT technologies such as artificial intelligence and IoT devices are evolving, efficient use of all types of data has become an urgent issue for corporate development. In order to continue to increase corporate competitiveness in the medium and long term, it is important to create a system that will collect and analyze data, and proactively manage and control data.
Sky Corporation offers various solutions depending on the implementation stage of data usage. We provide consulting for the strategic introduction of data analytics platforms, support data engineering for fast and secure data collection, processing, and analysis and provide in-house production of data usage platforms.
Since we support a wide range of services from stable cloud services provided by major vendors to the latest trending technologies, we can offer the best recommendations considering compatibility with the customer’s IT assets and peripherals.
Summary
So far, we have presented an overview of data processing, including the benefits, overall flo,w, and tools that help increase performance. In order to obtain highly reliable information from diverse and complex data, it is important to create an environment where data processing can be performed correctly.
Data-based decision-making, which includes the results of highly accurate data analysis, makes it possible to increase corporate competitiveness without relying on individual experience or insight, such as streamlining operations and strengthening marketing capabilities.