Role of Big Data in Business Transformation: New Perspectives and Challenges Ahead
Volume: 1 Issue: 01 | Feb-2025
Article | Open Access | Published: 21 February 2025
Role of Big Data in Business Transformation: New Perspectives and Challenges Ahead
Mohammed Ismail
Abstract
Big data as a frontier technology is fast growing, impacting almost all business operations. Small and big businesses employ it for improvement in business operations. This paper provides a view of the role of big data in the modern business world and how enterprises use it to optimize their processes and reach their goals. It also discusses some of the core problems businesses encounter in effectively implementing and integrating this technology into their operations. It concludes with trends, possible future, and solutions that could come in handy to enhance operational efficiency and results.
Introduction
Big data as a technology is growing exponentially. It refers to large and complex, structured and unstructured, refined and unrefined datasets. Various types of computational techniques and tools are used for effective data collection, processing, cleaning, storage, and analysis. Barnes (2013) defines it as technology that provides businesses an opportunity to delve into the complex nature of their operations and also solve various kinds of business problems. Chen et al., (2014) explain that it has an impact on major business operations such as marketing, human resources, business planning, and other internal and external business functions. A study conducted by Apsilyam and Yakhshiboyev (2025) shows that its use is growing far and wide and companies benefit from it immensely too. Though there are risks associated with its usage, it is deemed more advantageous.
Leveraging Big Data And Digital Transformation
Digital transformation is well defined as the use of multiple technologies to propel businesses toward new realms of success. Its sole purpose will be to improve business operations. Its implementation leads to fundamental changes in business functioning. Today, businesses prefer to be on the cloud. The data is now effectively moved to the cloud where it becomes easy for enterprises to perform a variety of data-related tasks. Moving data to the cloud as per convenience such as hybrid, private, or public will produce good results when it is backed up with the right cloud and data strategy.
However, leveraging big data through key technologies involves a proper strategy. Data generated from different sources has a great potential. The four attributes of data such as velocity, volume, veracity, and variety are inseparably crucial. Statistical models, machine learning, AI techniques, and other special algorithms are used to turn this complex data into a more powerful solution that could resolve the problems (Vassakis et al., 2018). Customer data can be in various forms. Identifying the data sources and collecting the data are two big tasks. The data sources could be both right and wrong. Therefore, businesses need to be double sure what data sources or data they are using. The analysis will generate results based on the data fed into the system. Companies that heavily rely on data-driven decision-making are seen to be struggling to find the right datasets or clean the data, making it more useful for generating insights. The use of the Internet of Things, Point-of-Sale (POS) Systems, Social Media, Mobile Applications, Web Scrapting, Cloud Storage Systems, Chatbots, Wearable Technology, Fintech, and Smart Devices is common these days as firms know the real value of these data sources.
Of all these, web scraping is more convenient as it provides a route to extract information from web platforms. There are various techniques such as DOM (Document Object Model), HTMML parsing, Xpath Queries, and Browser Scraping that the techies use to extract information from internet sources (Ahluwalia and Wani, 2024). Companies also prefer machine learning models to extract the data which could be in any form such as structured or unstructured form. No doubt, extracting data from the sources doesn't end the job as it is just one component of effective data planning. It needs to be cleaned. It should be made worthwhile for extracting insights. Using mean, median, and imputation models, missing values are integrated into the data. Such a technique also helps with removing duplicates or inconsistencies or identifying and obliterating similar anomalies which is crucial as such inconsistencies or anomalies might skew the results.
Data Storage: A Viable Option
Data storage has always been a big challenge for the companies. Traditional methods of data storage included the use of hard disks, and solid-state disks and drives but more modern storage techniques include cloud storage, data lakes, edge storage, and data warehousing (Wang et al., 2024). The most preferred form of data storage for modern companies is the cloud technique where large sets of data are stored and such big data is made more accessible to regional and global teams. Platforms such as Azure, Google Cloud, Amazon Web Services, and other similar cloud storage and cloud technology companies have made it easy for companies to store their data more securely.
Cloud storage is fast turning as the most viable option for firms. Especially for startups, it is emerging as a boon as it provides them with the opportunity to integrate data technology at the lowest costs, and that too without having to worry over huge and advanced infrastructure setup and security. There are cloud storage options for businesses that allow them to scale up and down their data and storage centers and storage plans based on their growing data needs. This ensures efficiency and also saves them a large deal of investment. Moreover, the employers and stakeholders have access to the data from anywhere they want or the businesses have the option to restrict the data access to their employees as they want. This promotes collaboration and also makes it easier for businesses to tap into the correct use of their data. Apart from this, the companies have the freedom to integrate their data centers with their legacy systems too which will allow them to handle the data more effectively. This in turn also increases business agility and makes them more adaptable to market changes (Shirke et al., 2024).
Data Collection Made Simple
Most businesses rely on data to optimize their operations. Different departments and legacy systems can be connected to the cloud where companies can directly integrate their business data into the cloud system to operate more effectively. For instance, data across financial transactions, marketing budgets, sales data, customer data, HR-related data, and other types of business-sensitive information can easily be connected to cloud technology platforms and stored online (Lin, 2024). However, it is always incumbent upon the companies to analyze what type of data collection methods they are using, and whether those data collection resources are error-free or not.
Big Data and Business Intelligence
Digital transformation has made it simple for businesses to combine all applications and business systems effectively. All these applications, systems, and software programs can well be integrated into the central repository systems for report generation. Business intelligence is key to success in business. Companies that analyze data or business intelligence with insights into the data always have an advantage over the other firms in the industry that do not adopt this technology or strategy (Adewusi et al., 2024).
Data Insights to Improve Business Operations
Data is defined as a key to finding new ways to resolve business problems. In today's modern, digital-driven business landscape, it is regarded not only as essential but also a big necessity to compete and strengthen one's position in the market. It is an invaluable asset that helps innovate through decision-making. It allows businesses to gain a good understanding of customer behavior, marketing operations, and the functioning and problems of other business units (Kaggwa et al., 2024),
Today, companies operate in a dynamic environment where risks are more prevalent. Conventional methods of business decision-making relied mostly on intuition, or historical trends which often led to choices that could work or not work. Such suboptimal guesses, decisions, or choices were proved to be less beneficial for the businesses. But, with the advent of data technology in business, firms, now are well informed and they know what they are doing and what could be the possible results of their present actions based on the data and insights (Adesina et al., 2024). Companies use predictive analytics to identify potential market risks and trends in the market. Companies use analytics to detect and overcome anomalies, detect fraudulent activities, risks, and growth patterns.
Transforming Customer Experience
Dealing with the customers and providing them with the type of services and products they need will help optimize customer experience. Better customer experience is made possible with the introduction of digital technologies. Personalized services to customers and product-based recommendations work wonders and enable enterprises to execute ROI-friendly methodologies.
Data and AI technologies are synced to generate analytics. Firms gain a better understanding of what their customers need and how they want the products to be (Brown et all, 2024). Based on customer behavior, and interactions across all channels - email, social, and phone calls, the firms streamline their customer experience and thus improve their sales. Personalized recommendations no doubt play a key role in enhancing satisfaction and engagement (Ijomah et al, 2024).
Moreover, existing or new customers want the service providers to be available for interactions as needed. Today, the proliferation of chatbots which integrate machine learning, artificial intelligence, and data technology facilitates easy communication with customers. Customers interact with the bots. The bots provide all possible answers to the queries the customer might raise. Past experiences, data from previous interactions, and data with insights into expectations, assumptions, support, and queries, are trained to deliver a better chat experience (Joshi et all, 2022). Mobile apps and websites now come with the chatbot facility to make it easy for businesses to be available for customers 24/7. Especially e-commerce platforms are seen using such bots as buyers on the selling platforms show concerns about products and want to be double sure before making a purchase. The support system delivered in real-time makes a great impact and also enhances the customer base (Kumar et al., 2024).
Data Technology - Collaboration
Chen (2024) stresses that data technology enables collaboration, improves team productivity, and ultimately business growth. Cloud platforms allow teams to share data, access files from anywhere, get updates on the projects, manage the project flow, secure communications, provide feedback, and improve project deliverables at all stages. Scalable cloud solutions well synced with the personalized and tailored data needs of the companies make work seamlessly productive for the firms Owolabi et al., 2024). Similarly, AI along with data analytics power project management in an influential way. AI-powered assistants, bots, or virtual assistants save a lot of time by automating a large number of repetitive tasks (Wollweber et al., 2024)
Big Data and Digital Transformation: Challenges Ahead
Digital transformation with big data comes with some risks too. There are challenges that businesses need to address or overcome.
Data Accuracy and Analysis Issues: Serious Threat
Data is collected using various types of sources and the sources must be synced well. They should be connected in the right way so that the right amount of data or the right type of data can be collected. But, often, data silos appear, and this makes integration of data into cloud platforms a big task. Apart from data accuracy is one problem that businesses need to deal with. Inconsistencies in data occur in various forms and can be difficult to identify especially in the initial stages (Susanto et al., 2024). If data is not accurate then results will not be reliable. Therefore, it is quintessential that the data teams should focus on data cleaning, data preparation, accuracy improvement, and connectivity of the sources of data so that it can be processed in real-time and in the right way to deliver better results.
Security Challenges
Enterprise data in the cloud is often prone to cyber-attacks. Storing sensitive information in the cloud is a decision that should come with a strong data security plan too. Before making a move to the cloud, it is always advisable that the companies should frame their data security strategy and implement the plan accordingly to save huge losses in the future. Private cloud platforms can offer more benefits for enterprises to store data but dealing with regulatory compliance, data governance, and other related issues can be overwhelming. Similarly, shared cloud platforms and hybrid cloud platforms can also increase the risks of data breaches, and data leakage (Diyora and Khalil, 2024).
Infrastructure-Related Challenges
Old technology systems or legcy systems may not fully support modern data technology. Integration is often impossible and the need for improved infrastructure poses a great challenge. High costs associated with the implementation of the advanced infrastructure are one of the biggest reasons why startups prefer not to move to advanced data technology.
Apart from this Kuru (2024) explains that companies also face problems when it comes to selecting a possible solution for their data strategy, whether a high-cost in-house cloud team will work for them or whether they need to move to a third cloud service provider to host their data. Scalability of the data systems, storage functionality, and maintenance come into the picture when the companies move to the second phase of their growth in sales and revenue. Data migration is itself a big task and if it is not carried out in the right way, then it could lead to bigger problems. Moreover, deciding whether the companies need to focus on edge computing implementation or not is a serious decision. Managing data processing is simplified with edge computing technology but there can be risks associated with the security.
AI and Analytics
Dealing with inconsistencies, or errors in data analysis, and data reports is common when the data technology is paired with the AI. This is often a result of poor data training. AI predictions go wrong because the data is not sourced correctly, or their accuracy is not verified before building analytics (Khatoon, 2024). Apart from this, there is a shortage of skills in implementing AI-driven analytics systems that could be capable enough to interpret complex data patterns or provide insights into various components of business, and scale along with the business systems.
Resistance to Change: Adopting New, Data-Driven Technology Systems
Resistance to change, and adopting big data technology systems need to be dealt with in the right way. Reports show that companies implement data technology in their business systems but do not focus more on strategies to deal with the change brought up by the use of modern big data systems and thus they suffer a lot. Every business department shall have a bespoke approach to data adoption, and it should be implemented Jin-Kwon, 2024).
Conclusion
Big data for business transformation is rightly regarded as the backbone of effective business planning, enabling businesses to leverage innovation and improve business operations. It influences data-driven decision-making, operational efficiency, and customer experience. Combined with AI, big data and analytics can deliver much better results. Though there are challenges in real-time data processing, data security, and management, companies can tap into the potential of big data with strategic approaches and innovativeness. The future is certainly beset with unforeseen challenges, but companies will be in a better position to streamline workflow, automate collaboration, and overcome transparency issues by pairing big data with more advanced technologies.
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