Organizational Data Management Problem

Organizational Data Management Problem

Introduction

As organizations continue to focus on data-driven techniques towards solving management and financial challenges, the significance of proper data management and retrieval of quality data becomes more evident. One of the challenges facing organizational data management is the sheer volume of data collected and finding the most efficient methods to overcome this volume challenge. Since the invention of the World Wide Web, the amounts of data generated have been a significant challenge for organizations that rely on raw data (Dhavapriya & Yasodha, 2016). Humans create quintillions of bytes of new data each day in the modern world, and organizations focused on data-centric management styles face the challenge of finding the most relevant data and sorting it to make meaningful decisions. The data is also collected through numerous different channels leaving organizations with fragments of the same information that may or may not provide business value (Yafooz, Bakar, Fahad, & Mithun, 2020). Business intelligence methodologies applied by these organizations, therefore, have to account for all these variations in technologies and confirm the completeness of the data before creating new business decisions.

Organizations producing consumer products in the modern world utilize emerging technologies such as social media platforms to find trends and analyze consumer preferences using data-mining tools (Shaban, Bikkulova, & Klimin, 2019). The volume of data stored in these platforms continues to grow exponentially, necessitating new technologies for data analysis every couple of years. This paper presents the results of the research into the problems caused by the sheer volume of data in business intelligence models. The solutions suggested are based on recommendations and observations made by researchers and academic articles studied during the research.

The Problem: Its Existence and Impact

Business intelligence systems rely on data from both the public and private sectors  (Gaardboe & SVARRE, 2018), making these systems robust and dynamic since they should handle data that changes over time. Apart from the need to update these systems periodically to meet new requirements as organizations grow and learn from previous mistakes, legacy data is critical in making predictions. The need to store this legacy data in an ever-changing system poses significant problems for developers and organizations as the volume of data grows (Shaban, Bikkulova, & Klimin, 2019). Scientists innovate new data retrieval technologies to handle the large and increasing amounts of data, and databases with better data retrieval and querying techniques become a critical component of these modern systems. The challenge posed to the organizations is that of recognizing the dangers of using fragmented data to make predictions as the data collected exceeds the capabilities of most systems over time.

One of the most ambiguous terms used in organizations is ‘intelligence’ as it relates to technology and business intelligence (BI). The fascination with the term intelligence in technology has grown over the past few decades with the innovations made in big data analytics and artificial intelligence (Sun, Sun, & Strang, 2018). As companies embrace BI and data-centric decision-making models, the application of ‘intelligence’ to analyze ‘big data’ has grown exponentially in prominence. The apps, however, face numerous challenges due to reliability challenges in the data analysis processes. Since BI deals with the transformation data into useful information, knowledge, and wisdom in the organization, the analysis of big data collected from multitudes of sources needs to be accurate and precise (Sun, Sun, & Strang, 2018). The correct technologies and tools need to be applied in every stage of the process to avoid making incorrect assumptions, especially when training of AI systems is concerned.

The impact of large volumes of data available through modern information technologies has been both positive and negative, depending on the utilization of this data by individuals and organizations. Most modern organizations are leveraging big data to gain sustainable advantages over organizations that seem overwhelmed by this challenge (Yafooz, Bakar, Fahad, & Mithun, 2020). Business intelligence has, therefore, emerged as one of the defining factors for business success in the modern world. Organizations with the right tools can collect vast volumes of consumer information from social media platforms and relate this data to archived details stored in their systems. Differences in the performance of large organizations in the modern world, especially organizations in the same sector, are brought about by how effectively each organization uses the available data to gain a competitive edge. Organizations utilize data analysis tools to find patterns in this data and extrapolate the results using modern data manipulation tools to make predictions that propel the organization into an unprecedented success. This process, however, is dependent on the accuracy of the data collected, the level of intelligence applied to the analysis of the data, the assumptions made during the collection and analysis, among other factors that influence the outcomes.

Most small and medium-sized companies lack the proper technological tools to handle the massive amounts of data available to make meaningful business predictions (Yafooz, Bakar, Fahad, & Mithun, 2020). This shortcoming creates a ceiling for these organizations, where large organizations with adequate business intelligence tools continue to gain a competitive advantage while SMEs struggle to find the correct tools to make profitable predictions correctly. We classify the large volume of data available for analysis from various sources into structured and unstructured data that cannot be entirely stored in regular relational databases (Muniswamaiah, Agerwala, & Tappert, 2019). Several processes are, therefore, involved in the cleaning and normalization of this data to convert it into a form that can be stored in regular databases and be analyzed using the currently available data analysis tools. These processes are just prerequisites in the analysis of big data. Most small and medium enterprises lack the proper tools for performing these processes, making it very challenging for them to effectively use this data to make predictions that could help their organizations in any substantial way.

The impact of business intelligence and the challenge posed by substantial volumes of data on the consumers have also been significant since the advent of social media. Cost-reductions resulting from the developments in big data analysis have improved the quality of life for most consumers (Yafooz, Bakar, Fahad, & Mithun, 2020). Better analysis of user need and accurate predictions ensure that organizations innovate products that better suit the needs of the customers. The awareness of the need for privacy of data has also been one of the significant impacts of the increased demand for business intelligence in the modern world. Social media platforms such as Facebook have been noted to violate consumer privacy concerns in a bid to profit from the vast amounts of raw consumer data available in their systems. These breaches in customer trust and confidentiality have, therefore, exposed weaknesses in the policies and legislation concerning the rights of consumers to privacy in technological platforms such as social media.

Why/How do Large Volumes of Data Pose a Challenge to Business Intelligence?

As discussed above, raw data is available in the structured and unstructured form in most data-mining processes conducted by organizations. Current computing capabilities make it very difficult to store and analyze this data effectively, necessitating the application of business intelligence to optimize these processes. BI utilizes data arrays that store and process large chunks of data quickly and make optimum operational decisions while the system continues to process the raw data in the background (Shaban, Bikkulova, & Klimin, 2019). This process relies on assumptions made about the data under processing, which means that all the assumptions made should be based on facts. Extrapolating wrong assumptions during subsequent operations result in catastrophic decisions down the line. Predictions based on false assumptions or fragmented data may also lead to the production of incorrect products, resulting in enormous losses for the organization. The volume of data available is, therefore, a fundamental challenge in any organizational BI, requiring careful consideration before any managerial decisions are made based on the resolutions recommended by these systems.

Recommended Solutions

Organizations handle the challenge of voluminous data in business intelligence using a combination of technologies, processes, and integrations that make the process of storing and analyzing this data more manageable (Muniswamaiah, Agerwala, & Tappert, 2019). The most challenging segments of dealing with ‘big’ data include the storage of this data, analysis of the stored data, and data transfer (Shaban, Bikkulova, & Klimin, 2019). Other critical processes necessary for BI include searching and sorting, querying and updating, visualization, and maintaining information privacy. BI utilizes several technologies to optimize these processes, and the choice of specific tools is dependent on the organizations conducting the analysis. Data collection, which is the first step in the process, is critical since the collection of incomplete data may lead to inaccurate assumptions and false predictions. Artificial intelligence (AI) advances have made it easier to conduct data collection and analysis on a massive scale. Researchers note that AI and big data have enjoyed a symbiotic growth, and one would not advance without the other (Harrison et al., 2019). BI, therefore, relies heavily on advancements in AI and innovations in big data analysis techniques to optimize the data collection and analysis process. SMEs should invest in the acquisition of these tools to gain the same advantage as their larger counterparts successfully utilizing these technologies.

Parallel programming and computing is also a recommended solution to enhancing the speed at which systems handle big data. Companies dealing with the collection and analysis of vast volumes of data such as Google and Facebook have demonstrated the advantages of this technique using software such as Google’s Map Reduce  (Dhavapriya & Yasodha, 2016). Parallel processing describes the technology where processes are split into smaller tasks (threads), which can be processed independently, and concurrently. The final results are then aggregated to produce feedback at fractions of the time it would take to process the entire process as one chunk. This method is useful for big data analysis, searching, sorting, and querying of data from the servers. Combining these methods with innovations in the field of AI can lead to practical techniques for handling large volumes of data, whether structured or unstructured.

Researchers also recommend the formulation of correct assumptions and hypotheses before choosing the technologies and processes to use in the collection and analysis of large volumes of raw data (Arnott, Lizama, & Song, 2017). Realistic assumptions help in narrowing down the type of data to collect and prioritizing the analysis techniques to use to enhance the speed and efficiency of the system. Caution is, however, noted to be a critical component to consider during this stage since oversimplified or overly complicated assumptions may lead to wrong results or the collection of irrelevant data. The cost of storage is critical when dealing with vast volumes of data. The relevance of data collected, therefore, helps in conserving space and reducing storage costs for the organization. These recommendations are significant for optimizing current BI systems in modern organizations. Future innovations in quantum computing will be critical in the enhancement of big data analysis. Researchers and stakeholders in this field are encouraged to invest in quantum computing to reap the benefits of this technology.

Conclusion

One of the biggest challenges facing BI in organizations is the sheer volume of data available for analysis through various platforms and databases. This paper discusses the existence of this challenge and its impact on organizations and consumers. Advances in the collection and analysis of big data have led to the exponential growth of data-centric organizations. Modern organizations utilize business intelligence models to innovate techniques to leverage the advantages offered by the availability of this data. Organizations with the most useful data analysis tools and processes often gain a considerable competitive advantage over other organizations. The volume of data available, however, is too voluminous for any specific device to conduct practical analysis and give accurate predictions entirely. Most organizations, therefore, utilize different combinations of techniques and processes to optimize the operations, including AI and parallel computing. Researchers anticipate that the advent of quantum computing technologies will offer a significant boost in all aspects of big data analysis.

References