Introduction
Business Analytics is one of the areas in big data analytics that is gaining traction among organizations. Business analytics applies big data technology to create business solutions, drive growth, and make data-driven insights. Many technologies are available for application in the world of business analytics. The choice of technology by an organization depends on the organization’s needs, size, objectives, and availability of funds to implement the technology. In addition, massive chunks of data that organizations accumulate require sophisticated technology distinct from traditional software. To understand business analytics, exploring how it impacts organizations is imperative.
Background
The current business world has many strategies designed to enhance performance and increase revenue, but one aspect that defines high-flying businesses is how they utilize related data. In the business world dominated by technology, firms no longer depend on instincts and gut feelings to make critical decisions; they use data analytics. According to the article “Big data and business analytics” by Ajah and Nweke (2019), published by Big Data and Cognitive Computing, data analysis involves collecting, transforming, cleaning, and modeling data to extract invaluable information. Business analytics is the discipline that uses data analysis and statistical models to solve business problems.
The three main business analytics classes are descriptive, predictive, and prescriptive analytics, and they use data analysis to aid decision-making. The business analytics process involves five stages (data collection, data processing, data cleaning, data analysis, and data visualization) and various tools in data analysis to gain data insight for decision-making. For example, analytics can be used in marketing to predict churn rates, supply chain to predict order fill rates or inventory turnover, or human resource management to forecast turnover rates. Chiang et al. (2018), in the article “Strategic value of big data and business analytics” Published by Journal of Management Information Systems, notes that the technology gives easy time management to make decisions since they are presented with reports and dashboards showcasing business performance and areas that need improvement. Therefore, business analytics deploys data analysis processes to benefit the business in the prediction of customer behavior, gain in competitive and real-time intelligence and increase revenue.
Types of Business Analytics
Data-oriented businesses use three types of analytics to extract invaluable information from big data to assist in business decisions. The first type of business analytics is descriptive analytics. The descriptive analysis uses data mining and aggregation methods to unfold trends and patterns in data. Descriptive focus represents behaviors in past data and not a future prediction. Predictive analytics is where future prediction happens. It uses data mining, statistical modeling, and machine learning to create probabilities to assess future trends and patterns of data based on past data. The third analytics is prescriptive analytics. After data is analyzed descriptively and predictions are made, prescriptive analysis guides on the best cause of action. Prescriptive analytics advise the organization on the best decision based on information from descriptive and predictive sources. A decision made out of the three analytics is an informed decision with the potential to drive organizations forward.
Business Analytics Process
Pappas et al. (2018), in their article “Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies published on Information Systems and e-Business Management, illustrates how data analysis in a firm follows a predetermined process. Sincora et al. (2018) illustrate how analytics processes are essential in business analytics, and it is worth mentioning the stakeholders involved in every analytics process. The other points worth highlighting in the presentation are tools used in analytics, the benefits of embracing analytics, and the skills needed for good analysis.
Chiang et al. (2018) argue that practical data analysis should follow seven critical pipelines to help in informed decision-making. These steps include data collection, cleaning, exploratory data analysis (EDA), data analysis, data visualization, predictive analytics, and taking business action. However, in most organizations, these steps vary as much as they intercept. This presentation analyzes the stages compulsory in data analysis in all data-oriented organizations. Before exploring the steps, the first thing to note is that the data analytics process begins with data collection and ends with data-driven decisions.
The first step in the business analytics process is data collection. Data collection follows the stage where the business decides what to do with data, what problem to solve, and the required type of decision. First, the company starts collecting data with a clear objective. The article “How to pivot a first-party data marketing strategy” by Kaykas-Wolff (2022), published by the Journal of Digital & Social Media Marketing, states that data collection involves three types of data depending on the need of the company; first-party data, second-party data, and third-party data. First-party data is from the customer, second-party data is the first-party data from other companies and trusted partners, and third-party data comes from the organization without a direct relationship with one collecting data. Depending on the problem and decision to be made, a company decides on which data to analyze without the restriction of analyzing from all sources.
The second stage is often holistically described as data preparation or manipulation. Nam and Lee (2019), in the article “Business analytics adoption process: An innovation diffusion perspective” by the International Journal of Information Management, says that some refer to this phase as data wrangling, where many quality checks and assurances are going on. The stage involves preparing and manipulating data to ensure it is excellent and ready for analysis. The cleaning process is the backbone of quality analysis—garbage data results in garbage analysis and outcome (garbage in, garbage out). Data cleaning is thus an essential process in analytics.
The third stage is the analysis of data. This is the stage where trends are analyzed, and insights are given to the stakeholders. The analysis depends on the business questions that should be answered (Sincora et al., 2018). Additionally, Sincora et al. (2018) argue that business questions can range from last month’s sales, weekly churns, yearly inventory turnover, etc. The fourth stage is the sharing of insights through visuals and reports. The final step is making data-driven decisions based on the given insights. The figure below shows the business analytics process, from data collection to data-driven decisions.
Tools used in Business Analytics
The business analytics process uses different tools in analysis depending on the size of the business, its personnel, and its objective. There all many tools a business can choose from. For small companies, the advanced functionalities of Excel are enough. Big organizations that generate high volumes of data can choose from SQL, Python, R, Google Data Studio, Tableau, Power BI, Qlik Sense, etc. Each tool has its specific functioning, each providing a unique analysis. For instance, Qlik is a data analytic tool with data visualization and self-service business intelligence. It applies data integration, literacy, and analytics to accelerate business values. Additionally, Sincora et al. (2018) posit that businesses that deal with unstructured data use NoSQL databases like Apache Spark. These tools provide packages, libraries, functions, and capabilities that make analysis seamless.
Benefits of Embracing Analytics
The research by Mikalef et al. (2020) on Big data and Business analytics shows how
business analytics provides businesses with unparalleled market intelligence. This is through analysis and prediction of consumer behavior. Customer behavior from purchase history tells about future buying intentions. Analytics gives organizations competitive intelligence through insight into competitors’ operations, markets, and customer trends. It also provides the business with real-time intelligence. Real-time intelligence helps companies to make quick decisions to draw more customers depending on their real-time moods and feelings.
The other advantage of business analytics to an organization is the proactivity and anticipation of needs. Through forecasting, an organization gets to understand its needs. Also, business analytics helps mitigate risks and fraud through predictive modeling. For example, Sincora et al. (2018) illustrate how banks use machine learning to detect possible loan defaulters and frauds. Lastly, data analytics is vital for optimizing and improving the customer experience. Besides, embracing analytics helps deliver the right products and personalization as businesses can provide the required goods to customers based on their tastes and preferences.
However, it is worth exploring essential skills businesses should look for while embracing analytics. For example, Google company confessed that they hire enthusiastic people eager to learn due to the dynamics of analytics. Amazon also opined that communication skills and business acumen must accompany the technical proficiency of their data analysts. Therefore, analysts’ most required skills are communication skills, business acumen, and technical proficiency.
Potential Recommendations
One of the recommendations for businesses venturing into analytics is the proper need identification. The companies must accurately define their actual goals and objectives for easy management and data analysis. Therefore, adequate identification of company needs is essential in determining the best tool for analysis.
The second recommendation is the mapping of the context. Nam et al. (2019) argue that mapping the context is beneficial for identifying the factors that can affect the outcome of the implementation efforts. Additionally, context mapping helps devise plans to convince those cynical about the change process. Mapping is crucial in identifying people, institutions, and organizations that may impact the implementation process. Mapping the context also links networks to people and peer businesses that can help provide other vital services and information.
The third recommendation while implementing the analytics is the identification of the critical stakeholders. Nam et al. (2019) say that for an effective analytics process, there is a need to know the key stakeholders. The stakeholders help the analysts to identify business needs and questions that need solutions. The stakeholders also guide the analysts on the right metrics and the key performance indicators (KPIs) to be used during the presentation. Therefore, stakeholders’ needs are crucial to the choice of data to be used during the analysis.
Conclusion
Business analytics is a trending process proving beneficial in the current data-driven business world. The benefits are the ease of making decisions, avoiding risks, preventing fraud, improving customer experience, delivering required products, etc. The analytics processes are distinct among institutions, albeit with a few overlaps—however, process range from data collection to data-driven decision-making. For example, software like Excel, Power BI, Tableau, Google Data Studio, R, Python, AWS, Azure, MYSQL, etc., are some tools used in analytics. The future is data, and the organizations that take the initiative to embrace analytics will have an edge in the competition. However, data comes with the need for confidentiality and privacy. Therefore, more research is needed on data protection laws on what constitutes a breach of data laws.
References
Mikalef, P., Pappas, I., Krogstie, J., & Pavlou, P. A. (Eds.). (2020). Big data and business analytics: A research agenda for realizing business value.
Ajah, I. A., & Nweke, H. F. (2019). Big data and business analytics: Trends, platforms, success factors and applications. Big Data and Cognitive Computing, 3(2), 32.
Kaykas-Wolff, J. (2022). How to pivot to a first-party data marketing strategy. Journal of Digital & Social Media Marketing, 10(1), 52-60.
Chiang, R. H., Grover, V., Liang, T. P., & Zhang, D. (2018). Strategic value of big data and business analytics. Journal of Management Information Systems, 35(2), 383-387.
Nam, D., Lee, J., & Lee, H. (2019). Business analytics adoption process: An innovation diffusion perspective. International Journal of Information Management, 49, 411-423.
Pappas, I. O., Mikalef, P., Giannakos, M. N., Krogstie, J., & Lekakos, G. (2018). Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies. Information Systems and e-Business Management, 16(3), 479-491.
Sincorá, L. A., Oliveira, M. P. V. D., Zanquetto-Filho, H., & Ladeira, M. B. (2018). Business analytics leveraging resilience in organizational processes. RAUSP Management Journal, 53, 385-403.