Analysis of business data: Methods & tools effectively

Data analysis business is becoming an essential element in business management modern. In the context of competition is increasingly fierce, with the rapid development of technology, businesses can't just rely on intuition to make decisions that need to leverage data to get the accurate analysis, in a timely manner. The application analyzes data to help businesses optimize performance and predict market trends contribute to improving competitiveness.

This article Lac Viet Computing will help the business understand data analysis, the method of common, useful tools and how the application into practice to optimize operational efficiency, sustainable growth.

1. Data analysis what is business?

1.1 definition, data analysis, business

Data analysis business is the process of collecting, processing, analyzing, and visualizing data to make strategic decisions in business. Through the use of tools, methods of analysis, businesses can transform raw data into valuable information, from which to make decisions accurately, optimize operations, predict the future.

Data analysis includes many different levels, from the description of data, detect trends, predict risks to proposed solutions business intelligence.

1.2 the Role of data analytics in business

Data analysis business not only help businesses understand the financial picture of commissioning, but also bring many important benefits such as:

• Decision support based on the actual data: Instead of relying on sentiment, businesses can rely on pattern analysis data to make decisions quickly and accurately.
• Optimized financial strategy business: Data to help businesses determine what products/services effectively, optimize operating costs, adjust marketing strategies accordingly.
• Advanced performance activities: to Help businesses identify strengths and weaknesses in business processes, thereby improving operational efficiency.
• Forecast market trends: data analysis help businesses identify new opportunities, predict customer trends, adjust strategy to catch the market.

For example, A retail company can use data analysis to track purchasing trends of customers, optimize inventory management, improve customer experience, thereby increasing revenue.

2. Why business need, data analysis, business?

In the era of digitization, businesses can reach a huge amount of data from various sources such as sales data, financial data, customer data and data operation. However, if there is no strategy, data analysis, effective business will be difficult to leverage maximum value from this data.

Here are the main reasons why data analytics business become necessary for every business:

2.1 decision Support based on data

• Help businesses make decisions faster, more accurate, reduce risk.
• Provides real-time data to help leaders angle correct about the business situation.

For example: A company e-commerce use data analysis to determine the selling products, optimize product portfolio.

2.2 optimization strategy finance business

• To help businesses understand spending patterns, adjustable reasonable budget.
• Define channel marketing most effective way to optimize the cost of advertising.

For example, A manufacturing business use data to optimize supply chain, reduce costs of storage and operation.

2.3 Improve performance and trend forecasting

• Data analysis help businesses enhance performance, personnel, process optimization work.
• Forecast consumer trends to help businesses proactively market to meet customer needs.

For example, A bank uses AI to analyze customer data, detect fraud and proposed financial products is suitable.

3. The method of data analysis business popular

Businesses can apply various methods to harness the value from business data. Here are four methods of data analysis, the most important:

3.1 descriptive analysis (Descriptive Analytics)

Is the most common method to help businesses understand the past by synthetic data display.

Main tool: Dashboard (dashboard), financial reports, charts visually.

For example, A retail business using descriptive analysis to track sales by each month, from which adjust business strategy, seasonal.

3.2 analysis of diagnostic (Diagnostic Analytics)

• Help your business find out the cause behind the fluctuations in financial or business.
• Use the statistical model to determine the factors that affect business performance.
• For example, If sales fell sharply in a specific area, businesses can use the diagnostic analysis to identify causes such as price, competition, or consumer trends change.

3.3 forecast analysis (Predictive Analytics)

• Based on historical data to predict trends, future scenarios.
• Apps, AI and Machine Learning to forecast sales demand market or financial risk.

For example: An insurance company uses predictive analytics to estimate the proportion of a claim in the future, help to adjust the appropriate premium.

3.4 analysis recommendations (Prescriptive Analytics)

• The highest level of data analysis help businesses make recommendations based on actual data.
• Application in optimization, operational, financial, marketing.
• For example: A business logistic use the analysis to propose to the optimal delivery route, minimizing the shipping costs.

4. The type of important data in business analysis

In the process of analyzing business data, identifying the right kind of data have a large influence on effective decision-making, optimization, operation and improve financial performance. Business data can be divided into four main groups: financial data, customer data, data commissioning, production, marketing data, sales. Each data group, this brings the different values, plays an important role in the analysis, forecast and adjust business strategy.

4.1. Financial data

Financial data is the foundation of every decision in business. This is the data group to help business reviews, financial situation and performance measurement activities, plan your budget effectively.

Revenue, cost and profit

• Revenue: Total income from business operations can be analyzed according to each product, service, region or time.
• Cost: Includes fixed costs (rent, staff salaries), variable costs (raw materials, transport).
• Profit: important indicators to assess business performance, including gross profit, net profit and profit margin.

Application analysis of financial data:

• Determine which products/services are most profitable to cut these expenses are not necessary.
• Trend analysis, profit to financial planning long-term.

Cash flow analysis and financial performance

• Operating cash flows: Cash real business gain from business activity.
• Cash flow and investment: The investment in fixed assets, stock or expansion project.
• Cash flow financing: cash Flow from loans or raised from shareholders.

Application data analysis cash flow:

• Tracking cash flow in/out to ensure solvency.
• Detect problems in financial management, to avoid loss cash flow balance.

For example, If cash flow analysis shows businesses often suffer from a shortage of liquidity at the end of the month, can businesses need to adjust the policy on debt collection or optimize operating costs.

4.2. Customer data

Customer data to help businesses understand consumer behavior, needs, buying habits, from which building strategic approach effective.

Shopping behavior and consumer trends

• Frequency of purchase: how often customers return to buy products/services.
• Order value average (AOV – Average Order Value): the Total value of transactions on average each purchase.
• Product/service preferred: The categories are customers most interested in.

Application analysis of customer data:

• Adjust the product portfolio according to the actual demand.
• Optimized pricing strategy, promotions.

For example: If the business finds customers tend to buy more at the end of the week, can deploy the program discount or promotional more powerful at this point to increase revenue.

Customer segment – needs analysis

• Classify customers by age, gender, income, shopping behavior.
• Needs analysis preferences to personalize the customer experience.

Application analysis of customer data:

• Building marketing strategy according to each customer segment.
• Product development/new services in accordance with market demand.

For example: If your business noticed younger customers prefer to buy goods via e-commerce, can focus on promoting ads on the platform instead of traditional store.

4.3. Data operator production

Data operation and production help business performance management, labor optimization of the production process to minimize waste.

Process performance, productivity labour

• Production time on average: a Measure of time to complete each order or product.
• The error rate products: analysis of causes errors in the production process.
• Labor efficiency: a Measure of work productivity of employees.

Application data analysis operators:

• Optimized the production process to reduce the time and operating costs.
• Improve product quality by identifying the cause of the error.

For example, If data indicate that a stage production had the error rate is high, businesses can re-check process, improved technology to minimize risk.

Optimized supply chain inventory management

• Inventory turnover: Determine the speed of consumption goods.
• Inventory safety: Guarantee top quality sufficient to avoid deficiency or excess.

Application data analysis, supply chain:

• Improve demand forecasting to optimize the amount of inventory.
• Reduce the costs of storage, avoid waste of raw materials.

For example, If analysis data showed inventories rose, but sales fell, businesses need to adjust policy import or promote sales strategy to liberate inventory.

4.4. Marketing data sales

Marketing data sales to help businesses evaluate effective marketing strategy, performance measurement, sales, and optimize ROI (Return on Investment).

Analyze campaign performance marketing

• CTR (Click-through Rate): Measure the click through rate on ads.
• Index conversion (Conversion Rate): analysis of the ratio of potential customers who perform the desired action (purchase, registration).

Application data analysis, marketing:

• Determine the marketing channels is most effective to optimize the budget.
• Adjust the advertising content based on customer feedback.

For example, If data shows that email marketing campaigns are open rate, email low, businesses can test title more attractive or adjust the timing of the email.

Measure ROI and optimize sales strategies

• Revenue per sales channel: performance Evaluation each channel (online store, agent).
• Cost per potential customers (Customer Acquisition Cost – CAC): cost analysis to get a new customer.

Application analysis of sales data:

• Adjust pricing strategy based on market demand.
• Optimize sales model to increase revenue at the lowest cost.

For example, If analysis data showed that the conversion rate from ads, Facebook, higher than Google Ads, businesses can increase advertising budget on Facebook to optimal efficiency.

5. The tool analyzes business data to top

In the era of digital data, the selection tools data analysis fit will help businesses optimize operations, financial management, efficiency and decision-making more accurate. Currently, there are many tools to support the business of collecting, processing, analysis, visualization of data, helps convert raw data into valuable information.

Here are four tools to analyze business data, leading, widely used in businesses, from small to large.

5.1. Vietnam Financial AI Agent – solutions AI, financial analysis, smart

Vietnam Financial AI Agent is tools of financial analysis integrated artificial intelligence first in Vietnam to help businesses automate financial reporting, forecasting, business trends, detect financial risks.

Feature highlights of Vietnam Financial AI Agent

• Automation of financial statements: the System automatically updates the aggregate data from accounting software, ERP systems, the data bank.
• Forecast financial intelligence: AI analyzes financial data in the past to predict profits, revenues, financial risk.
• Integrated BI Dashboard: Provides an intuitive interface to help businesses track the financial health in real time.
• Adhere to standard accounting IFRS/VAS: business Support, financial reporting according to international standards.

App Vietnam Financial AI Agent in the business

• Monitoring the financial situation in real time.
• Cash flow forecasting, early warning of financial risks.
• Performance analysis each branch, department, business department.

5.2. Power BI – visualization tool powerful data

Power BI is a cross-platform data analysis developed by Microsoft to help businesses connect, visualize analyze data from many different sources. This tool allows to create the dashboard (dashboard) act, helps users to monitor the financial indicators, business performance in real time.

Salient features of Power BI

• Connect to multiple data sources: Power BI can connect to SQL Server, Excel, Google Analytics, accounting software, ERP system to aggregate data.
• Automation of financial statements: Business can set the report automatically updates in real time, reducing processing time data.
• Support AI & Machine Learning: integrating AI helps in trend analysis, revenue forecasting, anomaly detection in financial data.
• The ability to visualize: interactive Chart helps businesses easily track KPIS, profitability, cost and sales performance.

App Power BI in business analysis

• Financial analysis: monitoring reported profits, cash flow, cost analysis, revenue by region, product.
• Optimized sales process: Businesses can track employee performance, business, conversion rate, customer.
• Revenue forecast profit: Based on historical data, AI in Power BI can help businesses predict business trends.

5.3. Tableau – depth tools for data analysis

Tableau is one of the tools, data analysis, most popular, especially in accordance with the business needs big data analysis (Big Data) and model building financial forecast and business. With drag & drop interface intuitive, Tableau helps businesses build reports, charts the complex without the need for programming knowledge.

Salient features of Tableau

• Support big data analysis: Processing, visualizing large amounts of data from many different sources.
• Build forecast models: application AI, statistical models to predict financial trends, revenue growth.
• Dynamic reporting: users can customize the report according to each perspective, filtering data in real time.
• Connected to many systems: Integration with SQL, Google Sheets, Excel, CRM systems, ERP to collect and analyze data.

Application Tableau in data analysis, business

• Analysis of financial performance: track only the number of profits, cash flow, budget allocation.
• Forecast consumer trends: construction model forecast customer demand, help optimize your marketing strategies.
• Analysis effective marketing campaign: tracking ROI, measuring ad performance across multiple channels.

5.4. Google Data Studio – tools free for small business

Google Data Studio is a visualization tool data free, suitable for small businesses want data analysis, sales, marketing, finance, without the big cost.

Salient features of Google Data Studio

• Easy connection with Google Analytics, Google Ads, Excel.
• Create dynamic reporting, easily share with the management team.
• Integrate with Google Sheets to collect data quickly.
• Free, suitable for small business startup.

App Google Data Studio in the business

• Analyze sales performance in each channel, each campaign.
• Track advertising costs and ROI on Google Ads.
• Generate reports automatically without manual intervention.

In the context of competition increasingly fierce, any business data mining better will have greater advantage in the expansion of the market, enhance customer experience and sustainable development. Not to be left behind, businesses need to start investing in data analysis business right from today to build a solid foundation for success in the future.