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In-depth data analysis services

The Institute for Research on Organizations and Digital Economy (RIDE) provides in-depth data analysis services to help businesses and organizations turn data into decisions, optimize operations, and support strategic planning. Based on a foundation of research, scientific analysis methods, and an understanding of the Vietnamese context, RIDE partners with clients throughout the entire data lifecycle – from cleaning, mining, and modeling to visualization and management reporting.

Why is data analysis necessary?

In the context of digital transformation, data is not just an "asset," but a foundation for designing business models, optimizing operations, and driving innovation. However, many organizations struggle with connecting disparate data, lack analytical capabilities, or do not have a deep expertise team.

The Data Analytics service of RIDE is designed to:

  • Support businesses in understanding existing data, detecting anomalies, and identifying potential opportunities.
  • Build models, reports, and dashboards to support decision-making at both the strategic and operational levels.
  • Gradually building internal analytical capacity for the organization through consulting, method transfer, and training.

Target customers:

  • Medium and large enterprises are implementing or preparing to implement digital transformation.
  • Agencies and organizations need to analyze survey data, project programs, and periodic reports.
  • Startups and AI labs need to leverage the value from existing data to develop products/services.

Characteristics of RIDE's data analysis services

Solid research foundation & methodology:

RIDE is a research unit focused on digital economy, organization, and AI, applying modern analytical methods (statistics, machine learning, behavioral analysis, time series analysis…) combined with an economic-management perspective.

Deep understanding of the context of Vietnam and the region:

The models, indicators, and recommendations are designed to fit the operational realities in Vietnam, rather than mechanically applying international "templates."

Close connection with labeling & data cleaning services:

RIDE not only analyzes data but also provides preprocessing, cleaning, and labeling services – creating a closed data value chain: Clean & Label → Analyze → Insight & Action.

Leveraging expertise in LEAN and data-driven productivity improvement:

RIDE has the capability to consult on process improvement according to LEAN, Kaizen, and Six Sigma, helping to transform data analysis results into specific actions to increase productivity and quality.

Multidisciplinary team:

Combine data experts (data scientists, data analysts) with field specialists (finance, manufacturing, education, agriculture…) and a network of faculty and students from partner universities.

Compliance and data security:

All data analysis projects are carried out according to the principles:

  • Protection of personal data and sensitive information according to Decree 13/2023/ND-CP, Cybersecurity Law.
  • It is possible to design a structure that complies with GDPR (EU), CCPA (US), PIPA (KR), APPI (JP) according to customer requirements.
  • Implement a mechanism for authorization, anonymization/pseudonymization, and sign NDAs with all participating personnel.

RIDE specializes in several types of data distribution as follows

Operational & Business Data Analysis

  • Analyze revenue, costs, and profit margins by product/channel/region.
  • Customer behavior analysis, conversion rate, churn, CLV.
  • Analyze the effectiveness of marketing, sales, and customer service campaigns.

Analysis of production and quality data

  • Data analysis from the production line: output, errors, downtime.
  • Root cause analysis for defects and complaints.
  • Developing an early warning model to support predictive maintenance.

Financial data analysis – risk

  • Analysis of cash flow, cost structure, asset structure.
  • Analysis of credit risk and operational risk based on historical data.
  • Building a scoring model, customer segmentation, financial scenarios.

Analysis of educational & social data

  • Analysis of learning data (LMS, tests, behavior in using digital learning materials).
  • Develop indicators to monitor the training program and track learner progress.
  • Analysis of social surveys, community development projects, public support programs.

Data visualization & dashboard building

  • Designing management dashboards for leaders (CEO, CFO, COO…).
  • Building interactive reports for departments (sales, production, human resources...).
  • Consulting on the selection of BI tools (Power BI, Tableau, open-source solutions...).

The process of implementing a data analysis project

1

Survey of the problem & existing data

Clarify business/management objectives; Inventory data sources: current systems, Excel files, ERP, CRM, IoT, etc.; Finalize scope and budget.

2

Data model design & cleaning

Standardize the structure, handle duplicates, missing data, and discrepancies; Combine with labeling services (if needed) to enhance the quality of training data.

3

Analysis & modeling

Descriptive and inferential statistical analysis; Data mining, building forecasting models or clustering.

4

Visualization & report building

Design dashboards, periodic reports, or scenario-based reports; Describe insights in management language, easy to understand, linked to specific decisions.

5

Transfer & Accompaniment

Hand over the model, reports, dashboard, and user manuals; Train the client's internal team, support monitoring and adjustments during the initial phase.