Data-based decision making and big data analysis trends in 2024

Introduction: “I think...” vs “Looking at the data...”

What is the most dangerous thing to say in a business meeting? It is an argument that begins with "I think..." and relies solely on a person's 'intuition' or 'feeling'. In the past, the acumen of an experienced leader may have been the key to success. However, in the modern business environment where market conditions change every day and numerous variables are intertwined, making decisions based on intuition is like gambling.

What successful companies have in common is that they make thorough Data-Driven Decision Making (DDDM). Through objective data analysis, we identify customers’ hidden needs, reduce unnecessary costs, and create new profit models. In this article, we will take an in-depth look at the value of data-based decision-making, which has become a core survival strategy for businesses, and the 2024 big data analysis trends that support it.

3 ways data-driven decision making (DDDM) is changing business

1. Discovering the Blind Spot: We often fall into confirmation bias and tend to see only what we want to see. You may have thought, “The response to the recently launched product A has been enthusiastic,” but if you analyze the actual data, you will find that product A has an abnormally high return rate, and that product B, which no one expected, has an overwhelming repurchase rate. Data coldly points out our cognitive blind spots.
2. Providing hyper-personalized customer experience: Netflix and YouTube’s recommendation algorithms are representative examples. Using data, you can go beyond simply dividing customers by age/gender, and synthesize past purchase history, page stay time, click patterns, etc. to suggest “the product this customer needs right now” at the right time. This is key to maximizing your marketing ROI (return on investment).
3. Risk Minimization and Agile Response (A/B Testing): Arguing in the boardroom about whether the button color on your website should be red or blue is a waste of time. The answer becomes clear when you compare actual click-through rate (CTR) data through A/B testing that exposes both versions to your customers. Through data, you can build an agile organizational culture that repeats small failures and finds the optimal answer.

4 big data analysis trends to watch out for in 2024

1. Combination of generative AI and data analysis (Augmented Analytics)

The biggest recent innovation in the field of data analysis is the introduction of generative AI such as ChatGPT and Data Analyst (formerly Advanced Data Analysis). In the past, extracting and visualizing data required writing complex SQL queries or Python code. But now, if you command in natural language, “Draw the sales trend by region last month as a line graph and analyze the cause of the decline,” AI will immediately generate a report with insights. As the entry barrier to data analysis is dramatically lowered, 'data democratization' is accelerating, allowing not only professional data scientists but also working-level professionals such as marketers and planners to handle data.

2. Real-Time Data Streaming Analysis

As important as the ‘quantity’ of data is the ‘speed (freshness)’ of the data. Data from a day ago may already be dead. The introduction of real-time data streaming processing technologies such as Apache Kafka and Flink is becoming essential in areas where every second of delay is critical, such as abnormal transaction detection (FDS) in the financial sector, real-time customer response analysis in live commerce, and routing optimization in logistics systems.

3. Data Fabric and Data Mesh Architecture

As companies grow, data becomes fragmented and stored in various places, including in the cloud, on-premises, and various SaaS applications. Finding and collecting them one by one is extremely costly and time consuming.

• Data Fabric: An architecture that uses AI technology to connect scattered data sources into one large virtual network, allowing users to easily access the data wherever it resides.
• Data Mesh: A decentralized data management philosophy that breaks away from the exclusive management of data by a single central organization (such as the IT department) and allows each domain department, such as marketing, sales, and finance, to directly manage its own data and share it with other departments like a 'product'.

4. Edge computing-based analysis

Transmitting all the massive data pouring from IoT sensors in self-driving cars or smart factories to a central cloud for analysis causes network delay and bandwidth cost problems. Edge analytics technology, which analyzes and processes primary data in real time from the device itself at the edge where the data is generated, is expected to grow explosively in 2024.

Conclusion: Increase data literacy

Data itself is just a series of numbers. Ultimately, it is up to people to find meaningful patterns and derive insights that can be used in business decisions.

Therefore, the most powerful personal weapon in the coming era will be Data Literacy, the ability to read, understand, and utilize data to communicate. You don't immediately need a ton of math knowledge or advanced Python coding skills. Starting with Excel or Google Spreadsheets, the habit of constantly asking the question “Why?” to the figures related to my work is the first step to truly data-based decision-making.