LCBB5014 Data Handling and Business Intelligence

LCBB5014 Data Handling and Business Intelligence

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Introduction

Business Intelligence is one of the critical assets for enhancing the efficiency of decision-making and increasing competitiveness in a world of big data. Business Intelligence is a phenomenon that encompasses technologies, application platforms, and approaches that organizations use to turn raw data into intelligence. These are important because these offer organizations a different approach to making small tactical decisions, where the correct motion is supported by data while the average guess can be an expensive mistake hence leading to better performance, cost optimization, and better advantage against competitors. As the Business intelligence market around the world is anticipated to grow to $56,200 million by 2028, more and more organizations are turning their attention to the current developments within the field. That is why as an operational manager of a modern retail business the necessity of integrating up-to-date Business intelligence trends could be considered crucial. The outline of this report consists of a critical analysis of BI trends, tools, and applications and the benefits accruing to organizations that embrace the trend in decision-making.

BI Trends, Tools and Applications that are the Future for the Business

The future of businesses will increase in future as BI tools and trends are used strategically for utilizing data capabilities. When the digital environment changes and organizations gather more data than ever before, BI is essential in transforming that data into useful information.

1. Data-Driven Decision-Making

In the age of big data, the actions taken in line with intuition, or based on limited information are highly risky. BI tools offer organizations the capability to analyze big data and draw conclusive facts and figures that can lead to better decisions being made (Maurer, 2021). It is most valuable where environments are volatile, and businesses have to react promptly to market swings, as is the case with the retail sector.

Real-time Example:

BI is the best example of the way Amazon uses it, to bring about massive changes in the way a business can be run. Today, the company is capable of employing BI tools in identifying customer shopping history, interests, and other related issues in the form of exclusive products to be recommended to customers. This approach sharpens customer interaction, and sales, with Amazon reported to be enjoying 30-35 % of their revenues from its recommendation engine.

Positive Consequences:

Improved accuracy: Decisions made are informed decisions, which result in enhanced organizational relevance and decreased probability of making expensive mistakes.

Negative Consequences:

Dependence on data quality: The inaccurate data means that businesses will make wrong decisions based on wrong information in the decision-making process.

2. Optimisation of business operations

This ability makes BI tools significant for the future which means a future that will mostly rely on efficiency and effectiveness. With the help of BI tools the issue of dealing with data becomes much less manual and time-consuming as the tools perform all the steps related to data collection, processing, and reporting and also, help find untapped opportunities and enhance specific aspects of business functioning (Adesina, Iyelolu and Paul, 2024).

Real-time Example:

Walmart is a company that applies BI tools to manage its inventory. With the usage of the research sales data and consumer need patterns, Walmart maintains optimum levels of inventories to avoid massive overstocking and stockouts. This use of BI has revolutionized the company’s operation on a very large scale.

Positive Consequences:

Cost reduction: The public believes in the efficient use of technology to minimize the time that employees spend on their everyday activities so that they can focus on projects that can help shape the company.

Negative Consequences:

High initial investment: The usage of various forms of Business Intelligence can be costly, especially for small firms and companies that are small.

3. Enhancing Customer Experience

The new trends in business intelligence center on increasing the share of customer-oriented services and product activations through more effective marketing campaigns. Customer analysis hence provides the organization with important information about the target customers enabling the production of goods and services that will fit the particular consumer (Nwosu, Babatunde, and Ijomah, 2024).

Real-time Example:

Starbucks relies on BI to monitor its customers through the coffee giant’s loyalty program. Hence, the company also uses the information gathered to perform individual targeted promotions including the sale of cheaper preferred products among the customers. This strategy helps Starbucks to retain its customers and therefore sell its products and in the process increase its brand loyalty.

Positive Consequences:

Customer retention: There is always a direct correlation between those organizations that increase their base of service to clients and those that enjoy the loyalty of customers and higher profitability in the long run.

Negative Consequences:

Data management complexity: The large amounts of customer data can be problematic across channels without good data management systems in place.

4. Cloud-based BI solutions & its scalability

One of the advantages of Cloud-based business intelligence solutions is specifically in scaling and flexibility and thus, these solutions will be suitable for business companies that plan to develop dynamically during a short time with minimal investments in infrastructure (Dziembek and Ziora, 2022). These solutions allow firms to store, manipulate, and analyze large sets of data in real-time as a way of getting access to information wherever they are.

Real-time Example:

An e-commerce platform Shopify provides its store owners with cloud-based analytics that provides various features. With the usage of Business Intelligence dashboards store owners can receive up-to-date information about sales, customers, and marketing campaigns, which enables them to make effective decisions on action.

Positive Consequences:

Cost-effective scalability: Business Intelligence systems can be built up progressively with organizational growth and development, with little investments in infrastructure.

Negative Consequences:

Security risks: The negative consequences consist of outsourcing delicate working information in the cloud is generally insecure, in case general appropriate measures for protection are not taken.

5. Data Governance and Compliance.

It consists of these scales of data being amassed by organizations, it becomes imperative for such information to be correct, safe, and rightly regulatory compliant. Contemporary Business Intelligence technologies contain a data monitoring solution that lets organizations handle data freshness and be compliant with laws like data protection laws and regulations.

Real-time Example:

There are strict data governance policies put in place in Business Intelligence systems at Best Buy to address data privacy laws as well as to protect its customer data. This also assists the company in gaining the trust of its customers and also shows that poor data management causes legal problems with clients.

Positive Consequences:

Data integrity: It consists of data stewardship that questions the principle of data integrity and makes sure that all information used in decision-making is correct and trustworthy.

Negative Consequences:

Implementation challenges: That is why it would require a great amount of time and a considerable amount of effort to work out a consistent and smoothly operating data governance plan.

Five Business Intelligence Trends will assist in making the right decisions

The adoption of the following five modern BI trends can significantly enhance decision-making in retail by providing timely and accurate insights:

1. Artificial Intelligence (AI) and Machine Learning (ML)

AI and ML are changing the Business Intelligence perspective by providing the feature of analyzing the data and predicting it. These technologies can therefore locate issues within very large data sets that humans may not easily spot. Artificial Intelligence that correlates to business intelligence can be of great utility for retail businesses that provide various features, particularly in demand forecasting, price and promotion planning, and other repetitive activities (Bharadiya, 2023). For example, Target employs AI algorithms to determine the likely time that a customer is likely to buy a product and therefore nudges its customer through promotions, only to realize that its sales are skyrocketing, yet its customers are much more loyal.

Positive Consequences:

Whenever there is a large number of customers, AI and ML can help to make quicker decisions, eliminate some human error, and give forecasts that can help increase sales and optimize inventory.

Negative Consequences:

Artificial Intelligence and Machine Learning integration can be expensive and using customer data can be problematic for organizations.

2. Data Visualization Tools

Business Intelligence at analysis tools that enable decision-makers to visualize large sets of data in easy-to-understand data dashboards, and reports. This is convenient for managers since they can assess the trends of their KPIs and make appropriate decisions based on actual data. In a more specific retail type of application, these tools may be applied to measure relative sales and compare them across geographically dispersed locations for better understanding and managing sales performance variance in a specific region (Shao, et al., 2022).

Positive Consequences: Information representation makes the raw information more manageable by extracting the most important features thus enabling the decision-makers to make informed decisions.

Negative Consequences: Many organizations make flawed decisions due to poor dashboard design, and faulty data interpretation.

3. Natural language processing (NLP)

Natural Language Processing (NLP) helps the Business Intelligence system to engage users in natural language and data interrogation, thus making it easier for ordinary users. There are others like IBM Watson analytics where a user can ask his questions in the data in an English way without straining himself writing queries. In retail, this can allow a cross-functional manager at many levels to work with data insights rather than a data analyst, thus making decisions faster (Bharatiya, 2023).

Positive Consequences:

Natural Language Processing means in the sense that it gives equal opportunity to gather and analyze data for all levels of an organization to make a decision.

Negative Consequences: The negative consequences consist of insights obtained from NLP tools that may be misleading in case the right information is not utilized appropriately.

4. Predictive Analytics

Predictive analytics is the utilization of historical information to cast the likelihood of the contexts in the future. In retail, of course, predictive analytics is used to forecast demand, manage inventory, and even the behavior of consumers (Brynjolfsson, Jin, and McElheran, 2021). For example, H&M will use big data to forecast which particular products consumers are likely to have a demand in the next season to manage its supply chain effectively.

Positive Consequences:

The positive consequences consist of analytical tools to predict sales helping businesses follow market trends, control inventory systems with greater efficiency, and increase customer satisfaction levels.

Negative Consequences:

The negative consequences consist of historical data that might not be entirely useful as market tendencies might rapidly shift over time.

5. Self-service Business Intelligence

Self-service business intelligence enables nontechnical users to produce their reports and analytics independently of IT sectors (Michalczyk, et al., 2022). This trend has been embraced by many retail firms and it’s evident at Zara where store managers can interact with business intelligence tools sales and inventory status, and make decisions at the store level without waiting for head office to feed them with reports.

Positive Consequences:

Self-service BI provides an ability to make faster decisions for the overall business employees leading to increased business flexibility (Passlick, et al., 2022).

Negative Consequences:

The negative consequences consist of a lack of training could lead to the wrong interpretation of the data or the production of the wrong report leading to wrong decisions being made.

Conclusion

The Business Intelligence has turned out to be a necessity for companies that would want to succeed in the competitive retail world. With the help of Accenture modern BI trends like analytics, data visualization and presentation, natural language processing, predictive analysis, and self-service BI helps companies make better and more accurate decisions toward growth and profitability. However, the realization of these trends poses some issues such as data governance, the cost of implementation, and wrong interpretation. This not withstanding, modern BI tools come with a lot of advantages outweighing the risks in the competitive business environment in the current world market.

References

Adesina, A.A., Iyelolu, T.V. and Paul, P.O., 2024. Optimizing business processes with advanced analytics: techniques for efficiency and productivity improvement. World Journal of Advanced Research and Reviews, 22(3), pp.1917-1926. https://www.researchgate.net/profile/Abayomi-Adesina/publication/381907305_Optimizing_Business_Processes_with_Advanced_Analytics_Techniques_for_Efficiency_and_Productivity_Improvement/links/668b5e700a25e27fbc2fcc66/Optimizing-Business-Processes-with-Advanced-Analytics-Techniques-for-Efficiency-and-Productivity-Improvement.pdf

Bharadiya, J.P., 2023. A comparative study of business intelligence and artificial intelligence with big data analytics. American Journal of Artificial Intelligence, 7(1), p.24. https://www.researchgate.net/profile/Jasmin-Bharadiya-4/publication/371988416_A_Comparative_Study_of_Business_Intelligence_and_Artificial_Intelligence_with_Big_Data_Analytics/links/64b58091b9ed6874a52688d7/A-Comparative-Study-of-Business-Intelligence-and-Artificial-Intelligence-with-Big-Data-Analytics.pdf

Bharadiya, J.P., 2023. Machine learning and AI in business intelligence: Trends and opportunities. International Journal of Computer (IJC), 48(1), pp.123-134. https://www.researchgate.net/profile/Jasmin-Bharadiya-4/publication/371902170_Machine_Learning_and_AI_in_Business_Intelligence_Trends_and_Opportunities/links/649afb478de7ed28ba5c99bb/Machine-Learning-and-AI-in-Business-Intelligence-Trends-and-Opportunities.pdf?origin=journalDetail&_tp=eyJwYWdlIjoiam91cm5hbERldGFpbCJ9

Brynjolfsson, E., Jin, W. and McElheran, K., 2021. The power of prediction: predictive analytics, workplace complements, and business performance. Business Economics, 56, pp.217-239. https://dspace.mit.edu/bitstream/handle/1721.1/137842/11369_2021_224_ReferencePDF.pdf?sequence=1&isAllowed=y

Dziembek, D. and Ziora, L., 2022, August. Cloud-Based Business Intelligence Solutions in the Management of Polish Companies. In International Conference on Information Systems Development (pp. 35-52). Cham: Springer International Publishing. https://www.researchgate.net/profile/Damian-Dziembek/publication/371888024_Cloud-Based_Business_Intelligence_Solutions_in_the_Management_of_Polish_Companies/links/65a3383fbc30165e6e379570/Cloud-Based-Business-Intelligence-Solutions-in-the-Management-of-Polish-Companies.pdf

Maurer, F., 2021. Business intelligence and innovation: a digital innovation hub as intermediate for service interaction and system innovation for small and medium-sized enterprises. In Smart and Sustainable Collaborative Networks 4.0: 22nd IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2021, Saint-Étienne, France, November 22–24, 2021, Proceedings 22 (pp. 449-459). Springer International Publishing. https://hal-emse.ccsd.cnrs.fr/emse-03346103/document

Michalczyk, S., Nadj, M., Azarfar, D., Maedche, A. and Gröger, C., 2020. A state-of-the-art overview and future research avenues of self-service business intelligence and analytics. https://www.researchgate.net/profile/Sven-Michalczyk/publication/341821870_A_State-Of-The-Art_Overview_and_Future_Research_Avenues_of_Self-Service_Business_Intelligence_and_Analytics/links/5eda115e92851c9c5e818a4f/A-State-Of-The-Art-Overview-and-Future-Research-Avenues-of-Self-Service-Business-Intelligence-and-Analytics.pdf

Nwosu, N.T., Babatunde, S.O. and Ijomah, T., 2024. Enhancing customer experience and market penetration through advanced data analytics in the health industry. World Journal of Advanced Research and Reviews, 22(3), pp.1157-1170. https://www.researchgate.net/profile/Tochukwu-Ijomah-2/publication/383847388_Enhancing_customer_experience_and_market_penetration_through_advanced_data_analytics_in_the_health_industry/links/66dc439efa5e11512ca4ed44/Enhancing-customer-experience-and-market-penetration-through-advanced-data-analytics-in-the-health-industry.pdf

Passlick, J., Grützner, L., Schulz, M. and Breitner, M.H., 2023. Self-service business intelligence and analytics application scenarios: A taxonomy for differentiation. Information Systems and e-Business Management, 21(1), pp.159-191. https://link.springer.com/content/pdf/10.1007/s10257-022-00574-3.pdf

Shao, C., Yang, Y., Juneja, S. and GSeetharam, T., 2022. IoT data visualization for business intelligence in corporate finance. Information Processing & Management, 59(1), p.102736. https://e-tarjome.com/storage/panel/fileuploads/2022-06-27/1656302037_e16734.pdf