1. How Do Companies Make Decisions?
The traditional approach: When facing a decision, leaders typically rely on experience, industry knowledge, or "business sense" — making intuition-based judgment calls to decide on a course of action.
Making data-driven decisions with a Business Intelligence (BI) platform
The data-driven approach: Decision-making shifts from "intuition" to "data." Once information is synchronized, cleaned, and modeled, leaders no longer need to rely solely on experience — they can draw on data-driven insight to strengthen their reasoning, assess risk, and forecast trends.
Once a company has built a data-driven decision-making culture and adopted a Business Intelligence platform (Power BI, Analytics Cloud, Tableau, etc.), it becomes capable of building data stories and visual dashboards developed from a defined set of KPIs and standardized data sources.
Data is no longer just a "storage repository" — it becomes a strategic foundation that helps leaders gain deeper understanding, respond faster, and make more accurate decisions.
2. What Is Business Intelligence (BI)?
Business Intelligence (BI) is a platform that helps companies extract maximum value from their internal data. Through BI, leadership and departments can gain deeper insight into operations — from revenue and costs to customer behavior and performance — enabling faster, more accurate, and better-grounded decisions.
Learn what Business Intelligence (BI) is
When BI comes up, most leaders immediately think of visualization tools such as Power BI, SAP Analytics Cloud (SAC), or Tableau. Behind the charts and dashboards, however, lies an entire strategic system that requires careful preparation across people, process, and data foundation. In practice, a successful BI project cannot stop at simply "choosing the right tool."
According to Mr. Vu Hoang – BI & Analytics Expert at Citek, tools such as Power BI and SAC sit at the final stage of a company's data strategy — technology only delivers real value once the people and data processes behind it are ready.
2.1. Application (Operational Systems)
This is the input data layer – where information is generated from day-to-day operational activities.
Nature: Includes the operational systems a company already uses, such as ERP, HRM/HRON, DMS, TMS, CRM, and others.
Role: Supplies the raw data source for the subsequent layers of data exploitation.
Current state: Most Vietnamese companies today are still stuck at this stage – meaning they collect data but have not yet exploited it deeply enough to support decision-making.
2.2. Data Centralization
This is the core, mandatory layer – the foundation on which BI and AI applications are built.
Nature: Building a centralized analytical database system, such as a Data Warehouse or Data Lakehouse.
Objectives:
- Consolidate data from all operational systems into a single data repository.
- Standardize, clean, and model the data so it is ready for analysis.
Skipping this stage leaves the data landscape fragmented and siloed, undermining data consistency and weakening its ability to support decision-making.
2.3. Data Visualization Tools (Business Intelligence – BI)
Only once data has been centralized, standardized, and cleaned can a company apply BI tools to visualize, analyze, and extract practical value from it. At this layer, tools such as Power BI, SAP Analytics Cloud (SAC), and Tableau serve to present and tell the data story (data storytelling), helping leaders not just see the numbers but understand the company's full operating picture.
An effective BI system typically helps leaders answer four core questions in data governance:
What – How is current performance?
Example: Are revenue and profit on plan? What are the profit margins by product or region?
Why – What is driving the change?
Example: Why did revenue miss the target? Is the promotion program actually effective? How are exchange-rate swings or input-cost changes affecting results?
KPI levels within a reporting system
How – What action should the company take?
Example: What measures are needed to hit targets – increasing production capacity, adjusting pricing policy, restructuring the product portfolio, or expanding into new markets?
Forecast – What trends lie ahead?
Example: Forecasting revenue for the coming month or quarter, customer growth rate, or the likelihood of hitting the annual profit target.
3. The Benefits of BI for Companies
3.1. BI Helps Companies Make Faster, More Accurate Decisions
One of the biggest pressures managers face today is speed. The faster and more accurate a decision, the greater the competitive edge it creates. With manual reporting, leadership can only review reports once every department has finished submitting and consolidating data – typically a lag of days to weeks – resulting in slow, outdated decision-making.
With Business Intelligence, instead of spending days consolidating data across departments, information is updated continuously and presented visually in real time. This lets leaders quickly identify operational "hot spots" and make timely, accurate decisions.
3.2. Forecasting Trends and Controlling Costs Effectively with BI
BI goes beyond simply describing the current state – it also supports predictive analytics, helping companies anticipate upcoming trends such as market demand, customer behavior, cost fluctuations, or the performance of each sales channel.
These insights give companies a solid basis for proactively planning their business, adjusting costs in a timely manner, and optimizing resources in real time – rather than simply reacting after a problem has already occurred.
3.3. Strengthening Management Capability Through Effective KPI Reporting
Building dashboards: BI helps companies visualize data through dashboards and data stories built on a defined KPI set and standardized data system, ensuring every piece of information accurately reflects operational reality.
Management reports are tiered by management level, ensuring each leadership position receives the information relevant to their scope and responsibilities, for example:
- CEO: Focuses on the overall picture – revenue, profit, and profitability ratios.
- Sales Director: Tracks revenue by flagship product, receivables, selling costs, employee KPIs, and the performance of each sales channel.
- CFO: Monitors cash flow, receivables/payables, capital turnover, inventory, and indicators reflecting the company's financial health.
Every metric can be compared against actuals, the prior period, and the same period last year, alongside detailed breakdowns by product, customer, region, or sales channel. This makes it easy for leaders to track KPIs, promptly spot operational issues, or identify performance gaps between departments and branches so they can act accordingly.
3.4. BI Visualizes Data and Strengthens Internal Sharing and Collaboration
Data only creates real value when it is understood and used correctly. BI turns complex data into dynamic charts and KPIs that are easy to grasp, even for people without deep data-analysis expertise.
More importantly, BI creates a "common data language" that lets every department work from the same unified set of figures. This keeps communication, collaboration, and decision-making aligned and consistent, from the enterprise level down to each individual department.
3.5. Supporting In-Depth Analysis and Trend Forecasting (Operational Optimization)
Modern BI systems such as SAP Analytics Cloud (SAC) and Power BI now come with built-in intelligent features like Smart Insight or Copilot, helping users quickly spot unusual data patterns and gain a deeper understanding of the operating picture.
For example, with a single simple query, the system can automatically identify the customer contributing the highest revenue that month, the product with the best profit margin, or the key driver behind a change in business results. This lets companies optimize operations and make data-driven decisions proactively.
Introducing the Business Intelligence (BI) solution and AI applications in data analysis
4. Which Companies Can Invest in Business Intelligence?
One common misconception is that Business Intelligence is only suitable for large companies or those that already have an ERP system. In reality, any company — regardless of size, and whether or not it already runs an ERP — can adopt Business Intelligence if it needs to make data-driven decisions.
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Current Challenges
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The Role of Business Intelligence
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Companies without an ERP system
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- Data is scattered across legacy software. - Lack of standardized processes and information
- Manual reporting, difficult to consolidate comprehensively.
- Lack of analytical tools, decisions made on intuition.
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- Connects and consolidates data from multiple sources into a Data Warehouse and visualizes it on a system, creating a consistent foundation for decision-making.
- Visualizes reports, saving time otherwise spent on manual processing.
- Helps the company build the habit of managing by data.
- Serves as a stepping stone for assessing needs and designing a future digital transformation roadmap.
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Companies that already have an ERP system
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- ERP is strong on operations but still needs BI to drive deeper analytical capability.
- Difficult to customize reports to actual needs.
- A desire to accelerate data-driven decision-making.
- A need for deep, detailed analysis from a specific region or module.
- Companies with multiple branches/factories/plants need detailed, in-depth analysis of performance in each area.
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- Adds advanced analytics and data visualization capability on top of the ERP system.
- Consolidates data from multiple modules (finance, sales, warehouse, etc.) for multi-dimensional analysis.
- Supports real-time decision-making, reducing information lag.
- Increases the return on the ERP investment through a powerful layer of deep analytics and visualization.
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Regardless of company size, Business Intelligence can deliver real value when implemented correctly — strengthening competitiveness and building a solid foundation for future digital transformation.
5. Factors Companies Should Consider Before Investing in BI
According to Mr. Vu Hoang, an expert at Citek: "Process, together with People and Technology, are the three factors that must all come together for a company to exploit its data effectively and sustainably."
— BI & Analytics Expert
The key principle: A company cannot expect technology alone to deliver results without alignment across people and process. Over-relying on technology before the organization has the data capability and standardized processes in place makes any BI rollout prone to failure. The core lies in building a Data-Driven Culture — where data becomes the common language, people are the ones who put it to use, process is the foundation, and technology is simply the tool that creates compounding value.
A company's data strategy built on three pillars
5.1 The People Factor
The people factor is one of the prerequisites for building and executing an effective data strategy.
Commitment from leadership: Building a data-driven decision-making culture (data culture) needs to be championed and driven from the top. This is not simply an investment in technology, but a shift in management mindset – one that treats data as a strategic asset used consistently across every major decision.
Data capability: Staff need to be able to read, analyze, and apply data in their daily work. Companies must continuously build data literacy through training, standardized processes, and by encouraging staff to base decisions on evidence and figures rather than intuition.
Defining the right objective: Before investing in a BI tool or designing dashboards, a company needs to clearly define what it is aiming for, such as:
- Which specific management or operational problem needs solving?
- Which KPIs do managers need to track in order to make decisions?
- Which data actually creates value, rather than just being tracked "for the sake of completeness"?
Setting the right objective from the outset keeps the BI rollout focused, avoids wasted resources, and ensures data is exploited the right way, for the right value.
5.2 The Process Factor
A company runs many operational processes, and running these processes generates a huge volume of data. This is exactly why managing and standardizing processes is essential to ensuring that data is exploited effectively.
Consolidating data from operational processes: Day-to-day operational systems (the Application Layer) — ERP, HRM, DMS, TMS, CRM, and others — are the original data source and the first layer in a company's data architecture. Standardizing the operating processes on these systems is a prerequisite for building a unified, centralized analytical database (Data Centralization) — the foundation for all subsequent BI and advanced analytics work.
Standardizing the analytics workflow: Companies need to establish a clear process covering collection, cleaning, storage, and processing through to visualization and decision-making (Extract – Load – Transform). This ensures data flows are stored and used correctly, while remaining controllable and traceable whenever needed.
The ETL data extraction and cleaning process in Business Intelligence (BI)
Avoiding data chaos: Without a standard process, data is easily accessed and manipulated inconsistently, leading to data chaos – duplicated information, inconsistencies between departments, or reports that produce conflicting results. At that point, the company no longer has a reliable data foundation to make decisions from.
For this reason, before deciding to invest in Business Intelligence, a company needs to:
- Review all existing data sources.
- Standardize data to a consistent format and set of standards.
- Consider building a Data Warehouse if the data is large in scale and complex.
- Ensure data is clean, complete, free of duplicates, and always up to date.
5.3 The Technology Factor
Technology only delivers real results once a company is ready on the people and process fronts. When the team has the data capability and processes are standardized, choosing a technology — whether BI or a broader data platform — can be implemented successfully, because the underlying mindset and operating foundation are already in place.
Gartner's ranking of today's leading BI solutions
According to the latest Gartner – Magic Quadrant for Analytics & Business Intelligence Platforms report, platforms such as Microsoft (Power BI), Tableau, and Google are ranked in the Leader quadrant of today's BI market.
Power BI (Microsoft): Offers a user-friendly interface, tight integration with Excel and the Microsoft ecosystem, and reasonable cost — a good fit for small and mid-sized companies or those just starting out with BI.
Tableau: Stands out for its powerful data visualization and interactivity. It's an ideal choice for companies with a dedicated analytics team and a need to present complex data in a vivid, engaging way.
Looker Studio: Well suited to companies that need centralized control over analytical logic through LookML, and offers strong integration with BigQuery and the Google Cloud ecosystem.
Learn more – SAP Analytics Cloud (SAC): Designed for seamless integration with SAP ERP and SAP S/4HANA. SAC supports real-time data analysis, financial planning, and enterprise strategic modeling all on the same platform, helping companies connect operational data and planning data in one unified environment.
6. Conclusion
In a competitive landscape increasingly driven by speed and data capability, a company that is slow to adopt — or fails to invest in — data analytics technology while competitors are already using these platforms effectively risks falling seriously behind. It loses not only its edge in decision-making, but also its ability to react quickly to the market and seize business opportunities.
To implement a Business Intelligence (BI) system effectively, a company needs a clear, phased roadmap that keeps the three core factors — people, process, and technology — aligned. This calls for an experienced implementation partner, one capable of understanding the company's operating model and industry specifics, and able to turn data into strategic value for management and growth.
With a strong track record of implementing BI for leading Vietnamese companies such as Gelex Electric, Hoa Phat Group, An Cuong, and Dien May Cho Lon, Citek has demonstrated its ability to partner with companies throughout their data transformation journey.
Citek doesn't just provide a Business Intelligence (BI) solution — it also conducts a direct survey and consults with leadership to identify the critical KPI set. From there, Citek proposes a BI implementation roadmap tailored to each industry's specifics, helping companies shorten implementation time and optimize return on investment (ROI).
Read more: GELEX Electric runs its management reporting system on SAP Analytics Cloud
