Other View of Business Analytics

Using quantitative techniques to extract meaning from data to make business decisions confidently is business analytics (Matt Gavin, 2019, Harvard Business School). Business analytics describes the expertise, procedures, strategies, and equipment used to convert data into information for more informed business decisions. It is among the most crucial things modern organizations require to respond to the plentiful availability and significant data variability. Business analytics will be especially crucial for online businesses to segment customers, price their goods and services, and customize their offerings to meet customer needs and maximize shareholder value. 

Business analytics focuses on deriving valuable insights from data and visualizing them to aid decision-making. Business analytics has benefits besides being a valuable resource when approaching essential decisions. Analytics and data-driven initiatives can have a significant financial impact on businesses. Analytics also can be used to improve business processes and operations. There are four types of business analytics such as descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. 

Descriptive analytics is the process of identifying trends and relationships in current and historical data. It is sometimes called the simplest form of data analysis because it describes trends and relationships but does not delve deeper. Descriptive analytics is likely used daily in an organization. Descriptive analytics is beneficial. It uses trends as a jumping-off point for additional analysis to inform decision-making. A streaming provider, Netflix’s trend identification is an example of an excellent use case for descriptive analytics. Netflix gathers data on users’ in-platform behaviour. It identifies customer preferences and behaviour trends and makes assumptions about the demand for specific movies and television shows. The data collected is not only to see what is famous and what people might enjoy watching but also to give Netflix information about types, themes, actors, and other things users like at a particular time. It can influence future decisions regarding original content creation, contracts with existing production companies, marketing, and retargeting campaigns. However, descriptive analytics has drawbacks. Descriptive analytics cannot be used to test a hypothesis or understand why the data looks the way it does; it cannot be used to predict what will happen in the future; and it does not tell someone anything about the data collection methodology, which means the data set may contain errors. 

Diagnostic analytics analyses data to determine the causes of trends and correlations between variables. It is a logical next step after identifying trends with descriptive analytics. Manual diagnostic analysis, algorithmic diagnostic analysis, and statistical software are all options. Diagnostic analytics can be used to figure out why something happened and the connections between different factors. Running tests to determine the cause of a technology issue is one example of diagnostic analytics that necessitates using a software program or proprietary algorithm. Some of these algorithms run in the background of the machine all the time, while a human must initiate others. Solution-based diagnostic is a diagnostic test that detects and flags symptoms of known issues before performing a scan to determine the root cause. It allows us to fix the problem and escalate it if necessary. Analytical diagnostics help identify the underlying causes of organizational problems. However, the main disadvantage of diagnostic analytics is that it is based solely on historical data. It must be constrained in its ability to conclude potential future events, and it also takes more time and better skills than descriptive analytics. 

According to the Harvard Business School, predictive analytics uses data to forecast future trends and events. It forecasts potential scenarios using historical data to help drive strategic decisions. The forecasts could be for the immediate future—for example, forecasting a malfunction of a piece of equipment later that day—or the longer-term future, like the company’s cash flows for the following year. Manual or machine-learning algorithms can be used to perform predictive analysis. In either case, historical data is used to make future predictions. 

Another example is Harvard University’s Wyss Institute collaborated with the KeepSmilin4Abbie Foundation to create a wearable device that predicts anaphylactic allergic reactions and automatically administers life-saving epinephrine. The AbbieSense sensor recognizes early physiological symptoms of anaphylaxis as predictors of a subsequent reaction, and it does so much more quickly than a human. An algorithmic response is triggered when a reaction is predicted to occur. The algorithm can gauge the severe reaction, notify the patient and any nearby caregivers, and automatically inject epinephrine when necessary—the ability of technology to predict reaction times faster than manual detection could save lives. However, just like the others, predictive analytics also has its drawbacks. Predictive analytics has some drawbacks. The completeness and accuracy of the data used limit the accuracy of predictive analytics models. Because analytical algorithms attempt to build models based on available data, flaws in the data may result in flaws in the model. 

The last one is prescriptive analytics. Prescriptive analytics, according to stitchdata.com, is based on artificial intelligence, specifically the subfield of machine learning, which consists of algorithms and models that allow computers to make decisions based on statistical data relationships and patterns. Prescriptive analytics is the most advanced level of human comprehension. This type of analytics provides the business decision-maker with options and assists in making the best choice. Prescriptive analytics is intrinsically value-driven because it assists businesses in discovering previously unknown sources of value. River Logic, a SaaS solution provider, has built its reputation on prescriptive analytics and offers business value chain optimization. TikTok, a popular social media application, also employs prescriptive analytics. TikTok’s “For You” feed is one example of prescriptive analytics in action. According to the company’s website, a user’s interactions on the app are weighted based on the indication of interest, similar to lead scoring in sales. “For example, if you finish a video, that is a strong indicator that you are interested; videos are then ranked in order of likelihood of interest and delivered to each unique ‘For You’ feed.” TikTok’s website says. These prescriptive analytics use cases can result in higher customer engagement rates, increased customer satisfaction, and the ability to retarget customers with ads based on their behavioural history. Prescriptive analytics, as powerful as it can be, is not always the holy grail many believe it to be. When decision-making is automated, there is a risk that an algorithm will make an incorrect decision. Prescriptive analytics necessitates close monitoring by highly qualified analysts with machine learning experience, which is time-consuming and costly. 

The benefits of business analytics are stated in the preceding paragraph. It Improves operational efficiency through daily activities, assists businesses in better understanding their customers, businesses use data visualization to provide projections for future outcomes, and many more. These insights support decision-making and future planning. There are also common problems that a business analyst can solve. A business analyst can also help with joint problems. For example, stakeholders’ expectations of a project’s delivery may be unclear or conflicting, impeding progress and leading to disappointment. Making sure that all parties involved share a common understanding of what is feasible and what the project will deliver is one way that business analysts can help to alleviate this issue. A Business Analyst can assist in resolving this issue by facilitating workshops with stakeholders to reach a consensus on project outcomes and by creating clear documentation of requirements that can be referred to throughout the project. Another common issue that Business Analysts face is a lack of stakeholder engagement. It can be due to various factors, including stakeholders being overburdened, not feeling invested in the project, or even mistrust of the business analyst. The Business Analyst can address this by ensuring a clear stakeholder engagement plan is a crucial activity throughout the project. The Business Analyst can also work to establish relationships with stakeholders and keep them informed of project status and progress. 

References 

• Business Analytics – Departemen Manajemen Teknologi ITS. (n.d.). Institut Teknologi Sepuluh Nopember (ITS). Retrieved January 20, 2023, from https://www.its.ac.id/mt/academic/field-of-study/business-analytics/ 
• Cote, C. (2021, October 19). 4 Types of Data Analytics to Improve Decision-Making. HBS Online. Retrieved January 20, 2023, from https://online.hbs.edu/blog/post/types-of-data-analysis 
• Cote, C. (2021, November 2). What Is Prescriptive Analytics? 6 Examples | HBS Online. HBS Online. Retrieved January 23, 2023, from https://online.hbs.edu/blog/post/prescriptive-analytics 
• Descriptive, Predictive, and Prescriptive Analytics. (2020, January 29). UNSW Online. Retrieved January 21, 2023, from https://studyonline.unsw.edu.au/blog/descriptive-predictive-prescriptive-analytics 
• Gavin, M. (2019, July 16). Business Analytics: What It Is & Why It’s Important | HBS Online. HBS Online. Retrieved January 20, 2023, from https://online.hbs.edu/blog/post/importance-of-business-analytics 
• Lessing, E. (2022, July 27). 10 Common Problems Business Analysts Help Solve – Business Analyst Articles, Webinars, Templates, Jobs. BA Times. Retrieved January 25, 2023, from https://www.batimes.com/articles/10-common-problems-business-analysts-help-solve/ 
• Prescriptive Analytics Guide: Use Cases & Examples. (n.d.). Stitch Data. Retrieved January 21, 2023, from https://www.stitchdata.com/resources/prescriptive-analytics/