Data processing is what? Processes and optimal solutions for modern enterprise

In the digital age, data is becoming a type of “resource” essential, not what other inputs in production. From customer information, transaction history, and shopping behavior to financial data, operation – all the operations in the business are generating data. According to research by IDC, the volume of data globally is expected to increase 3-fold in the next 5 years, in which more than 80% coming from operations of the organizations and enterprises.

However, the problem lies in that: the majority of businesses collecting data but not able to exploit efficiently, since the data is distributed in many systems, the format is not uniform, teen, standardized or not handled properly. This makes the process of analysis, reporting, decision-making becomes the delay, inaccurate – even lead to deviations strategy.

The data processing properly help information becomes valuable, ready for analysis, visualization and decision support fast more accurate.

In this article Lac Viet Computing will go deep into:

• Data processing is what?
• Why this is the life-force energy, also in the operation of modern business?
• The standard steps in the process of data processing
• These common mistakes and solutions tailored according to each stage of development business

1. Data processing is what? Why business need care?

1.1 data Processing, what is? Explains simple and easy to visualize

Data processing is the process of turning raw data into valuable information through steps such as sorting, cleaning, normalization, transformation format. Learn a simple way, the following data when it is collected not able to use immediately to analyze, as it may be omissions, lack of information, duplicate or non-uniform.

Illustrative example: A business are storing customer information in multiple sources: sales software, CRM systems, Excel file. A customer can record different names in every system (“Nguyen Van A” – “A. Nguyen” – “Nguyen V. A.”), cause the aggregate purchase behavior is false. When processing data, businesses will need to merge, clean, standardized this information to determine the exact customer profile.

Compare image: data processing like filtering, sharpens, packing raw materials before putting into the production lines. If not done carefully, finished products output will be bugs, non-standard or even cause harm to the entire operating system.

1.2 Role practical benefits of data processing in business

Data processing is not a step in management, which is an integral part if the business wants to effective decisions, smooth operation. Below is the actual value that the business will get when serious investment in the processed data:

• Reduce the deviations in the report: When the data is processed thoroughly, financial statements, sales, marketing becomes more accurate, help businesses avoid decisions based on false information.
• Increase the speed of decision making: once the data is ready, the leader does not need to wait synthesis, editing, crafts. This is especially important in situations need to react quickly to market fluctuations.
• Optimal operational efficiency and cost: The detection of anomalies in data to help businesses identify early error in the process, cut the unnecessary expenses, and allocate resources more efficiently.
• Support risk management and legal compliance: accurate Data are stored properly to help businesses easily check the traceability, transparency of information, especially in the industry under the supervision of the financial or security of customer data.
• Create platforms for advanced analytics, BI, AI: Data after being processed can put into the system the automatic analysis, dashboard or tool artificial intelligence to dig deeper about trends, behavior, forecast.

According to reports of IBM, the business is damage to 20-30% revenue every year just because the data is of poor quality or are not handled properly. This is an alarming figures, shows the data processor is the factor directly affects business performance.

2. The steps in the process of handling business data

Data processing is not a single, which is a multiple-step process, closely linked with each other. The implementation from the stage collect to host, integration will help the data become more reliable, easy to tap and ready to serve for every activity analysis, decision-making in business.

Step 1 gather data from different sources

In fact, data in the enterprise are often scattered in many systems and parts:

• CRM (customer management)
• ERP (management enterprise resource)
• Accounting software
• System sales, website, contact form, survey Google
• The Excel spreadsheet internal

Common problem is that each data source has the structure, format, different standards. Therefore, the first step need to do is clearly define the data source, the system, ensure full and reliable.

Illustrative example: A retail business, have the customer data from the website (registration, purchase) from CRM (care information), from Excel (report promotion internal). If not properly determine the source “priority”, very easy to happen duplicate or misleading when analyzing shopping behavior.

Enterprise value received:

• Avoid data conflict between departments
• Get comprehensive picture of customers, products, finance
• Laying a solid foundation for the processing steps next

Step 2 clean the data (Data Cleaning)

Data cleaning is the step to remove or edit the information is misleading, incomplete, or in excess of the data. This stage is mandatory if the business would like to have accurate analysis.

The common errors include:

• Data duplication: a customer has many different ID
• Information missing: the email address or phone number blank
• Format is not consistent: the date entered in different ways (dd/mm/yyyy vs mm/dd/yyyy)

Illustrative example: A column “product Price” has the value as “1.000.000” format (Vietnam) and “1000000” (digital format-standard). If not cleaned, the system will not be able to calculate the total value of the order is accurate.

Business benefits received:

• Increase the accuracy of reporting and analysis
• Reduce the processing time of handmade and limit errors operated
• Create the most when sharing data between the parts

Step 3 Standardized data (Data Standardization)

After the data is cleaned, the next step is to standardize – brought about unified format. The goal is to ensure that data from multiple sources can understand, handle homogeneous in the system.

Activities standardized downloads:

• Unified unit (VND, USD, products, hours)
• Gross and sort the items using general: product category, region, customer group
• Standardized name (for example: “ho chi minh CITY.CITY” and “Ho Chi Minh” should be provided on a standard)

Actual value:

• Increase the ability to connect data between the system software
• Help multidimensional analysis according to the standard list (for example: revenue comparison by region, customer segment...)
• Ensure data can be re-used for the report model, advanced analysis

Step 4 Transform and organize data (Data Transformation)

This is the “processing” data – processing the original data into a form ready for use for goal analysis or integrated into the reporting system.

The manipulation frequently:

• Separate or pooled data field: For example, split full name into first and last name separately to convenient analysis
• Calculate the new school: Create column “revenue = Unit price × quantity”
• Classification group data: label the customers by age, region, industry

Typical situation: The marketing department want to analyze the proportion of potential customers by region, but the original data only detailed address. Thanks to the step change, the data will be assigned the label “Northern”, “Central”, “South” to analyze more convenient.

Business benefits achieved:

• Flexibility in the analysis according to your business goals
• Speed up the process handle when putting data into dashboard report
• To help businesses understand more about the structure, the potential in the data

Step 5 storage and data integration

After the data has been standardized, the organization, the last step is stored in a systematic manner, ensuring the ability to integrate with other platforms (analytics, AI, dashboard...).

Two ways of storing downloads:

• Data warehouse (data warehouse focus): store processed data from many different sources, serves the purpose of analysis, reporting.
• Data lake (lake data): save all raw data was processed, aligned with big business needs analysis, diversity.

Integrate data into the system:

• Accounting software, sales management, marketing automation
• Analysis tools such as Power BI, Google Looker Studio
• Specialized solutions such as LV Financial AI Agent: connect automatically with accounting software AccNet, data processing, financial and suggested action strategies help business leaders decisions quickly and accurately

Benefits:

• Data are always ready to access, analyze or share
• Create a system information, contact information consistency
• A solid foundation for business step by step application WHO, conversion of

Process business data, not merely as error handling technique, which is a sequence of steps that strategies aimed at transforming raw data into information assets really worth. When business is done right from collect to host, the result is a capacity to operate based on the data faster, more efficient, more competitive in the digital age.

3. Common mistakes when processing data in the enterprise

Although awareness of the role of data increasing, but in fact, many businesses still have the basic mistakes in the process of data processing. These mistakes not only reduce the quality of analysis that also directly affect business performance, decision-making abilities.

• Distributed data, there is no common standard

One of the greatest difficulties is that data is scattered in multiple systems, which are collected in different ways in each of the departments. Each parts “feed” a collection of data private, non-uniformity of format, input, or standard name.

Practical example: sales Office call the product is “Package A”, while the marketing department says, “Gói_A”, also accounting income is “GOI A”. Connecting these data to general reports will meet many obstacles, even lead to misleading when analyzing revenue.

Impact: analysis of slow-consuming processing craft, can not connect data related departments.

• Only clean data once and then left open

Many businesses only perform the cleaning the original data when deploying new systems, then left for data arising freedom. This leads to the status of “clean at first, dirty later”, that data becomes cluttered, not to be trusted when it should be used.

Impact: Data deviations accumulate over time, affect the quality of forecasts, reports, and other strategic decisions.

• Depends on handling, easy to errors

Many small and medium enterprises still use Excel to aggregate data analysis manually. This is not wrong, but if there is no process control, obviously, just a bug recipe or false, the line input can also make the entire report is wrong.

Impact: Loss of time check – fix bugs, increase the risk of a wrong number, difficult to expand when the scale of big data.

• Lack of connection between the parts leads to duplicate and conflicting data

When no system data management focus, the department will create a separate file to cater to individual needs. The result is the same customer, a product or a transaction can be re-entered multiple times with different information.

Impact: Cause difficulties for the comprehensive analysis of loss of confidence in the data, spanning the time of the decision.

4. The solution supports business data processing efficiency

To overcome the mistakes mentioned above, businesses do not need to invest too big right from the start. Instead, it can incrementally deploy the solution processing of data in accordance with current, capacity-oriented long-term development. Here are three practical steps:

4.1 Using the software supports processing data analysis

With small and medium enterprises, the common tools such as Excel advanced Google Sheets, Power BI or Google Looker Studio can solve the needs handling, data visualizations original.

• Excel advanced: Flexible, familiar, suitable for periodic analysis or reports by month/quarter.
• Power BI: connect data from multiple sources, create dashboard synthesis helps leaders easily track financial situation, the sale in real time.
• Google Looker Studio: optimized for business operating ecosystem, Google, especially suitable for tracking, marketing, website, advertising channels.

4.2 automated data processing tool integrated AI

With the business need to process financial data regularly or have the volume of big data, the application of technology automation is the necessary step.

LV Financial AI Agent is a solution typical applications artificial intelligence to automatically:

• Aggregate financial data from accounting software (for example: AccNet)
• Clean, standardize and organize data, financial statements
• Analyze performance indicators (ROA, ROE, debt, gross profit,...)
• Suggestions the action adjust the budget, detect risks early

4.3 Training skills data processing for the hr team

The tool only works when people use competently. Business should invest in skills training, processing data for the relevant parts – not just team IT that all accounting, marketing, sales, operation.

• Training content should be mounted with the real work: how to filter data, detect errors, standardization, and reporting with Excel or Power BI.
• Organize training sessions short, thematic or invited expert support direct deployment in the enterprise.

Values bring:

• Employees more proactive in managing data
• Increase productivity, reduce errors in manipulation craft
• Contribute to building culture out decisions based on data (data-driven)

Data processing no longer is the support that is core foundation to help business decisions accurately and quickly, more efficiently. When the data is processed properly, businesses save time, reduce errors and opens the opportunity to harness the value deeper from available information.

If you are want to optimize process of data processing in the enterprise, let's start from the small step and choose the right tools as LV Financial AI Agent to companion on the journey to number conversion efficiency.