Business

Data-driven strategy for business growth -Harnessing the power of big data

AnyFans 2025-09-25

By Juliet Etefe

In today’s digital age, the rapid growth of data has fundamentally changed how a company operates and creates value for its people. The rise of Big Data, characterised by its volume, velocity, variety and veracity, has placed it at the forefront of modern business strategy.

Big Data Sources

Regarded as a complex dataset, Big Data can be obtained from large, disparate and often external (data) sources that are usually beyond a traditional data-processing system’s capacity. Big Data can be structured (human-generated) or unstructured (machine-generated). There are broadly four sources of Big Data.

Transactional data is generated from all daily transactions that occur both online and offline. These include invoices, payment orders, storage records, and delivery receipts. All of these are characterised as transactional data. However, data alone is almost meaningless, and most organizations struggle to make sense of the data they generate and how it can be effectively used.
Business transactions: Data generated from business activities can be stored in structured or unstructured databases. Due to the large volume of information and its production frequency (sometimes occurring at a very rapid pace), thousands of records can be created in a second, especially for large companies like supermarket chains recording their sales.
Sensors/meters and activity records from electronic devices: the quality of this kind of source depends mostly on the sensor’s capacity to take accurate measurements in the way it is expected. Machine data is defined as information generated by industrial equipment, sensors installed in machinery, and even web logs that track user behaviour. This type of data is expected to grow exponentially as the Internet of Things (IoT) becomes increasingly pervasive and expands globally. Sensors, including medical devices, smart meters, road cameras, satellites and the rapidly growing Internet of Things, will soon deliver high velocity, value, volume and data variety.
Social interactions: This includes data generated by human interactions across networks. The most common source is data from social media platforms. This type of data depends on the accuracy of algorithms used to interpret content, which is often unstructured text written in natural language. Examples of analysis performed on this data include sentiment analysis, trend topic identification, and more. Social data comes from Likes, Tweets, Retweets, Comments, Video Uploads, and other media shared through popular social media platforms. This data offers valuable insights into consumer behavior and sentiment, making it highly influential in marketing analytics. The public web is another important source of social data, and tools like Google Trends can help enhance the volume of Big Data.
Citizen-generated data (CGD) refers to data created by non-state actors (World Bank, World Trade Organization, multinational corporations) with the active consent and participation of citizens, primarily to monitor, demand or drive change on issues that directly affect them.

Big Data Growth Statistics

The global Big Data market is projected to reach $103 billion by 2027, more than doubling its expected size in 2018. With a 45% share, the software segment is expected to become the largest segment of the Big Data market by 2027. (Statista)
The global market for Big Data was estimated at $185.0 billion in 2023. It will reach $383.4 billion by 2030, growing at a CAGR of 11.0%. (Research and Markets)
In a related development, the Big Data market is projected to increase by $193.2 billion from 2024 to 2029, growing at a CAGR of 13.3%.

Using Big Data to Outperform a Competitor

The application of Big Data in business strategy is increasing, so enterprises need to leverage it to gain a competitive advantage. This means that a company must integrate and automate its processes with a digital tool or a software application that can collect large volumes of data, aggregate it and process it for insightful analysis. Therefore, it is essential for business owners to have adequate network infrastructure, a Big Data management and governance system capable of helping them harness or explore the following:

Innovation: Big Data is a catalyst for innovation or a source of new product development. Enterprises can uncover new product opportunities, identify market trends and respond swiftly to changing consumer demands. Enterprises can gain a competitive advantage by leveraging data insights to outperform rivals. For example, in the financial industry, an algorithmic trading system can use real-time market data and predictive analytics to execute trading rapidly and gain an edge over competitors.

Cost Optimisation: Big Data implementation can transform a traditional organisation completely and turn it into an agile company that will stay ahead of the competition. By analysing operational data, businesses can streamline processes and allocate resources more effectively. Yu, X., & Zuo, H. (2022) conducted research on Construction Cost Control Technology of Construction Project Based on Big Data Analysis Theory. Two main effects of the theory were proposed: direct and indirect costs and various reasons for construction cost control. Based on this, a system of evaluation indicators was created for controlling the construction costs of construction projects. The accuracy of construction cost management indicators based on Big Data theory is higher than that of the traditional analytical hierarchy process. Zhang, Y. (2021) also conducted research on Cost Control of Real Estate Companies in the Era of Big Data. In order to compete in the competitive market, the findings revealed that real estate companies must strengthen awareness of cost control, improve cost control systems, apply information technology, improve cost accounting methods, improve cost control assessments and implement effective accountability systems.

Enterprise Risk Management: There is risk embedded in any opportunity to scale up business operations. As a result, an application of Big Data in enterprise financial risk assessment can help build a more accurate risk assessment model to analyse and predict potential risk factors. With the integration of Big Data systems, enterprises can build a more comprehensive, scientific and accurate multi-dimensional financial risk early warning system. Big Data will not only help enterprises to quickly capture the changing trend of risks but also provide more timely and accurate support for the management to make decisions that will improve the resilience of enterprises. It must also be noted that enterprises in different industries and in different scales and stages of development have significant differences in the objectives, methods and priorities of financial risk management due to their different operational characteristics and management needs.

Challenges of Big Data

While Big Data offers remarkable opportunities, it also poses several challenges:

Data Privacy and Ethical Dilemma: The risk to privacy and security increases with large data volumes, making protection against breaches and unauthorized access complex. Data leaks can lead to identity theft, legal actions and loss of consumer trust. Businesses value their data and are unwilling to share it freely for fear of losing a competitive advantage.

Data Quality and Veracity: Big Data often contains noise, inconsistencies and errors. Big Data sources are diverse and often incompatible, thereby creating difficulties in integrating data for comprehensive analysis. Ensuring data accuracy and trustworthiness requires rigorous cleansing, validation and quality control.

Technical Complexity: Managing Big Data requires specialised tools and expertise in distributed computing, storage and machine learning, thus, presents a steep learning curve.

Cost-Benefit Analysis: Handling massive data volumes demands significant investments in storage, processing power and infrastructure, which can be costly to maintain and scale. Organisations must weigh the benefits of Big Data initiatives against the significant costs involved to ensure investments provide adequate returns.

The integration of Big Data into business strategy represents a paradigm shift in how organisations operate, innovate and compete in the digital age. The transformative potential of Big Data is evident in its growing market size, adoption across sectors, where it fosters responsiveness to market trends, enables predictive analytics and supports more informed decision-making. However, this opportunity is not without significant challenges. Integrating diverse data types, ensuring data quality and managing the complexity of analytics systems demand a blend of skilled talent, advanced infrastructure and ongoing investment. In essence, Big Data is not a panacea but a powerful tool. Businesses that can strategically balance innovation with risk and technological ambition with ethical responsibility will be best positioned to harness Big Data as a catalyst for sustainable growth and competitive advantage.