The Power Of Data: How Can Businesses Use Data To Their Advantage

The Power Of Data: How Can Businesses Use Data To Their Advantage

As the world progresses technologically, almost everything we do can become valuable data for businesses to thrive on. From our online searches to our purchase habits and even our daily commutes, these data can help businesses understand our preferences and predict our behavior with the right data analysis tools.

We all know that the ultimate goal of businesses is to maximize shareholder value. In other words, we want to maximize profits by increasing the amount of purchases, as well as the number of customers. But how can we use data to help us achieve that? This blog post will dive deeper into data-driven decision-making, and how businesses can reap the benefits of transforming such raw data into actionable insights.


1)    The importance of direction

To start, we must first ask: What do we want to achieve from a data project? We must understand what direction we are heading in. In some cases, businesses want to reduce costs by identifying inefficiencies, while some businesses want to create a better customer experience through personalization. With different focuses, data projects will vary according to the different project objectives. As such, before businesses embark on the data project journey, one must always remember that a successful data project starts with good business questions and objectives.


2)    Types of data

The world of data is vast and can be complicated. One can easily understand data in 2 main categories – structured and unstructured. Structured data is data that has a defined length and format. For example, numbers in a sales report, or dates a customer makes a purchase.  Unstructured data, on the other hand, are data with no identifiable structure. This includes images, videos, and social media posts. Due to the nature of such data, we can use technology like machine learning to make them machine-readable and utilize them to their full potential.

Data can be found in various systems within a business. Some common ones include:

  • Customer Relationship Management (CRM) Software: Stores customer records such as contact information, demographics, purchase history, and estimated lifetime value.
  • Loyalty Programs: Provide insights on customer preferences (favorite product/brands etc.) and engagement rate (how often they interact with the program).
  • Point-of-Sale (POS) System: Stores sales data and shows trends and demand patterns.
  • Inventory Management System: Gives an overview of stock levels and demand patterns.


3)    Data-driven decision making

Gone are the days when we make business decisions solely based on intuition and guesses. One should always aim to make these important decisions based on data, evidence, and proper analysis. Data-driven decision-making can support businesses in effectively reducing inefficiencies through bottleneck identification, optimizing resource allocation by understanding where will result in the greatest impact, and predicting future trends or customer behavior with linear and logistics regression.

To put things into perspective, these are some common processes in various industries that can leverage data to make good business decisions:

  • Marketing industry: Use customer data to identify target segments, personalize marketing campaigns and promotions, and measure campaign effectiveness.
  • Healthcare industry: Analyze patients’ data to improve diagnosis, predict potential health issues, and develop personalized treatment plans.
  • Trading industry: Analyze historical market data and trends to make informed investment decisions and predict stock prices.

These examples are not exhaustive, but they clearly demonstrate how data analysis can help businesses make decisions that can enable them to be more efficient, more competitive, and grow.

ไฟล์:Grab Logo.svg - วิกิพีเดีย


4)    Case Study – Grab

Let’s now look at Grab as a case study. Grab is a mobile technology company which started off as a ride-hailing platform. Today, Grab has grown into a super app that offers ride-hailing, food delivery, and digital payment solutions, making it a prime example of how data analytics can be used in businesses. For this case study, we will focus on Grab’s ride-hailing service to narrow the scope of this example.

Grab uses data in several different ways such as dynamic pricing, personalized recommendations, and route optimization.

  • Dynamic Pricing: Grab adjusts its fares based on demand and supply in real-time. This is done by analyzing factors such as traffic congestion, weather conditions, and events happening around the area. In doing so, Grab incentivizes drivers to meet increased demand in peak hours and areas, ensuring short waiting times for passengers while allowing drivers to earn a fair price.
  • Personalized Recommendations: Grab analyzes customers’ data to determine which products and features to recommend to users. This information can include a customer’s current location, past trips, and even the current time. Based on such factors, Grab can recommend a drop-off location, a nearby pick-up location, and even promotions he or she can use like discounts or vouchers.
  • Route Optimization: Grab uses data such as traffic conditions, weather conditions, and demand to determine the best route a driver can take to go from point A to point B. This minimizes travel time and distance, which can allow drivers to pick up more passengers in the same amount of time.

Grab has revolutionized the ride-hailing industry in Southeast Asia with its data-driven strategies. The strategic use of data has allowed Grab to ensure fair pricing for both passengers and drivers, while also ensuring short wait times. It is evident through this case study, that data analytics is a strong tool that companies can use to achieve their goals effectively.

NGDATA Powers New Loyalty Program at Central Group - NGDATA

5)    Case Study – Central Group

The next case study is on Central Group. Central Group, a Thai retail conglomerate with diverse corporations spanning retail, property development, brand management, hospitality, food and beverage, and digital lifestyle sectors, operates both domestically and internationally. It exemplifies how businesses can thrive by utilizing data to enhance customer experiences, gain a competitive edge, and boost sales.

Central Group’s loyalty program, ‘The 1,’ is central to the conglomerate’s data analysis efforts, providing valuable insights into customer preferences, buying habits, and demographics. Their analytics capabilities were further enhanced with the launch of the Central App in December 2020. Together, The 1 and the Central App leverage data to significantly improve the customer experience.

  • Personalization and Customized Campaigns: Central Group uses past purchasing and browsing history to strategically target their consumers with promotions and offers for relevant products and services.
  • Optimize store layout and product placement: Data analysis can reveal which products are frequently purchased together and what is the most common path that Central Group customers take. Therefore, it allows for the strategic placement of products, maximizing sales opportunities.
  • Inventory management: Central Group monitors real-time data on stock levels and past sales trends to optimize their inventory levels. This ensures that there is sufficient stock in any store while preventing stockouts.

This case study effectively points out how data is so important in today’s landscape, specifically in the retail industry. By embracing change towards a data-driven strategy, Central Group has grown significantly to what it is today. As technology evolves, we can expect new ways in which Central Group continue to innovate and grow.


6)    Challenges of handling data

All these to say, any data-driven strategy does have its own share of challenges that companies have to overcome. The most known and perhaps the most important, are Data Privacy and Data Security. Today, anything can become valuable data. With all these searches for data, how do we, as consumers, know that our data is being used ethically, not being exploited, and will be kept safe?

  • Data Privacy: Laws and regulations like the Personal Data Protection Act (PDPA) have been established to protect consumers from being exploited. Businesses must come up with ways to collect, process, share, and store such data in a way that is compliant with laws and regulations, and clearly communicate such information.
  • Data Security: When dealing with large amounts of data, businesses must find robust ways such as encryption and access controls to protect these valuable data from any unauthorized third party. Data breaches, surface attacks, etc, can be very damaging to a company, incurring many losses monetarily, reputationally, and even legally.

By acknowledging these challenges and prioritizing data privacy, security, and ethical considerations, businesses can build trust with their customers and leverage data responsibly to achieve their goals.

7)    Emerging Technologies

Let us now move on to emerging technologies, and how it is increasingly more common for businesses to take advantage of these technologies to move forward and stay relevant. More often than not, these technologies work hand-in-hand with data to achieve the desired outcomes.

Cloud Technology

Cloud Technology is a digital infrastructure where systems and applications will reside. Some advantages of such a system include:

  • Scalability, Flexibility, and Agility: According to individual companies’ needs, it is easy to scale up or down to accommodate the changing data and processing needs.
  • Accessibility: Storing data on a central cloud computing system will also allow all business divisions or regions to access all relevant data from anywhere in the world.
  • Data Quality: Cloud platforms also allow consistent data collection and data quality across the company. This facilitates data analysis and the generation of useful insights in the long run.

BigQuery (Google Cloud) and Azure SQL (Microsoft Azure) are popular cloud-based data warehouse solutions.

Internet of Things (IoT) & Industrial IoT (IIoT)

IoT describes a network of physical devices embedded with sensors and software that collect and exchange data, opening doors to real-time data collection and monitoring of various assets. IoT enables:

  • Real-Time Insights and Predictive Maintenance: Sensor data from connected devices provide valuable insights into their performance and operational health. This allows for preventative maintenance, reducing downtime, and optimizing resource utilization.
  • Data-Driven Insights: By analyzing data from connected devices, businesses can gain valuable insights into customer behavior, product performance, and market trends. This data can aid in strategic decision-making and drive innovation.

Industrial IoT (IIoT) takes the concept of IoT to a higher level. It involves networks of smart devices with built-in computing capabilities, collecting and analyzing data for industrial applications. While the advantages of IIoT are similar to that of IoT, the risks are a lot higher. This is due to the system’s complexity, the nature of the data it handles, and how these systems are connected to critical infrastructure such as manufacturing plants and power grids.

  • Enhanced Operational Efficiency: IIoT data can optimize production processes, improve energy consumption, and streamline logistics. This translates to significant cost savings and production efficiency gains.
  • Predictive Maintenance: By analyzing sensor data, companies can anticipate equipment failures and schedule maintenance before disruptions occur. This proactive approach minimizes downtime and ensures smooth operation.

Artificial Intelligence (AI) & Machine Learning (ML)

AI and ML play a crucial role in simplifying, analyzing, and learning from data.

  • Unstructured Data Analysis: AI and ML models can be trained to analyze unstructured data like images, videos, and text, extracting valuable insights that would be difficult or impossible with traditional methods.
  • Predictive Analytics and Forecasting: AI algorithms can analyze historical data and identify patterns to predict future trends, customer behavior, and potential risks. This allows businesses to make data-driven decisions and proactively address challenges.
  • Image Recognition and Natural Language Processing: AI can be used to identify objects in images, translate languages, and generate human-like text. These capabilities have vast applications in various industries.

Through the integration of these technologies, businesses can further leverage the potential of data analytics. Cloud technology provides a scalable foundation, while IoT and IIoT generate a wealth of valuable data. AI and ML tools empower businesses to analyze this data effectively, gaining actionable insights and transforming their operations for success.

For those who are looking to start having a team to help with data analysis or create a data storage system in your organization, Davoy is ready to provide comprehensive data services starting at 25,000 baht per month. If you are interested in learning more, you can add us on Line: @DAVOY.

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