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The research paper acknowledges these issues and calls for continued research and innovation to overcome these limitations.
The authors of the paper followed a three-phase methodology while reviewing BDPA development in the past decade:
The thorough approach in developing a comprehensive taxonomy of BDPA applications ensured an in-depth review, helping SMEs and industry experts enhance their understanding of relevant technologies.
The systematic research paper has seven categories. Out of them all, the analytics applications and their scale of adoption in the e-commerce industry are remarkable.
For instance, the authors highlight the significance of leveraging big data analytics to predict stock market trends and credit risks. These financial tools are easily available in smartphone apps.
Similarly, product or service configuration recommendations, churn forecasting, and sentiment analysis are already widely adopted, reflecting the digital-first nature of online commerce and its agility. It is no surprise that e-commerce brands often edge out other companies from other industries in BDPA technology adoption.
The remaining six domains leverage BDPA technologies for a variety of purposes:
The implementation of BDPA faces both technical and ethical challenges.
From a technical perspective, large volumes of data from diverse sources can suffer from inconsistencies, inaccuracies, and incompleteness. Users interact with brands across platforms and media from different devices, complicating data collection further.
Consequently, teams may have to wrestle with data quality issues, such as missing values, noise, and errors. This makes analytics complex as professionals have to invest in data-cleaning techniques, which affects data democratization and drives up costs.
Ethical concerns also persist, particularly regarding user privacy and organizational data security. Using cloud-based BDPA solutions translates to organizations sending private data to proprietary LLMs, posing a security vulnerability.
Companies, particularly those in industries such as healthcare and finance, have to build tailored solutions to protect their clients' interests. These tailored solutions include on-premise solutions and dedicated IT hardware such as servers and computers.
While these solutions are effective in enhancing security and privacy, they do pose a significant challenge — scaling up in a cost-effective manner. Businesses must maintain an in-house technical team that maintains and continually upgrades the internal digital infrastructure.
Enhanced data quality and real-time collection of accurate data are essential areas of improvement for strengthening BDPA foundations for brands. It is critical to build efficient pipelines that record user data in real-time and transmit it safely to cloud or local servers.
Moreover, a majority of BDPA solutions help analyze past data and extract reports and insights from available datasets. While that is useful, fast-moving teams might need more.
Industry leaders, such as Pyramid Analytics, are already progressing toward diagnostic analytics. This goes a step beyond descriptive analytics, where LLMs recommend solutions to real-world problems.
Here, safety can be a concern as many enterprises in regulated industries, like pharmaceuticals, might be against sharing sensitive information with proprietary conversational AI models. Fortunately, there is a feasible workaround.
LLMs capture the user’s needs in natural language, which is then converted into machine-level SQL queries. These queries are then run on the databases in a secure environment, disconnected from the LLMs themselves.
After the queries retrieve the relevant information from the database, it is sent to the LLM. Finally, the AI chatbot converts the retrieved data into visualizations, insights, or recommendations, depending on the user's needs.
This approach enables companies to get the best of both worlds. Teams can leverage LLMs’ advanced reasoning capabilities while upholding their stakeholders’ privacy. Another benefit of this tactic is that it works well with on-premise systems as well. Instead of connecting the internal database with the cloud, IT professionals can create an API that just sends the retrieved data to the servers.
Apart from speeding up analytics, it also scales up AI adoption in descriptive, predictive, and diagnostic use cases.
BDPA is indispensable in modern business environments where making data-driven decisions is pivotal.
The systemic review examines over a hundred papers highlighting various developments in BDPA technologies and strategies and segmenting them into multiple sectors, such as industrial and ICT.
While business intelligence and predictive analytics have made significant progress, further advancements are necessary to secure data, enhance forecast accuracy, and remain compliant.
Disclaimer: The author is completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE's position nor that of the Computer Society nor its Leadership.