Business Intelligence and Analytics
Section 1: Chapter One and Two summary
AI is a technology collection that excels in drawing insights and trends from large data sets and then predicting the future based on the data. That includes the data from places like content management systems, CRMs, frameworks for optimization, Google Analytics, and more. Machine learning “powers the most fantastic functionality of AI (Kaput, 2020). The computer to make predictions uses these trends. Then, more and more data is used to refine those forecasts over time.
Machine learning is a form of AI that detects patterns based on large datasets.
Machine learning-powered technology gets better over time, mostly without human intervention. As with your custom accounting software, a traditional non-AI program relies on human inputs to function. The program is hard-coded by people with the rules. Then, specifically, to help you do your taxes, it follows specific rules. The framework only works when human programmers develop it. Yet machine-learning software can be self-improved. This improvement stems from a computer, which evaluates its performance and relevant information (Ilgu & Nilsen-Hamilton, 2016).
Artificial intelligence excels in seeking facts and trends in massive datasets that simply cannot be seen by humans. It does this at both size and speed too. There are AI programs that use data from the analytics to help you predict results and efficient course of action. AI-powered systems can analyze data from hundreds of sources and make forecasts about what is working and what is not. It can also dig deep into customer data to make forecasts about consumer preferences, product growth to marketing channels.
AI is used for unifying data across networks. That involves using AI’s speed and scale to bring all the customer data together in a single, integrated perspective. AI is also capable of uniting data from multiple sources, including those that are difficult to track, such as call data.
In this new age, which is focused on Artificial Intelligence and Big Data, it is beneficial to need to keep data sorted into a category or particular form so that it can be easier to locate. The term “big data” refers to data that is so large, fast, or complex that it is hard or impossible to understand computation using traditional methods. For data analysis, the practice of getting and processing vast amounts of data has long been around. However, the idea of big data gained momentum in the early 2000s when market analyst Doug Laney proposed the now popular definition of big data as the three Vs. (SAS, 2020).
In many aspects of our lives, data is used, but typically, it is only used in digital technology. Information needs are essential for computers since most computers are comprised of a bunch of data, and without data, computers wound not work correctly. Data can be derived from various processes, such as tests, surveys, or even from competence. From the data most people receive, they prefer to use it for research, or also to make an invention for the company for which scientist works. Scientists working for a corporation have vast data, but there is not just one scientist who is employed in a single organization.
Section 2: Applied Learning Exercises
The blending of a growing unpredictable environment, comprehensive information, and the increasing need to remain at the frontline of rivalry have provoked institutions to concentrate that use analytics to guide the critical decision of business (Singh, 2018). Business analytics lets managers comprehend their business requirements, foresee market swings, and mitigate threats. Instead of “going with the gut” when hiring talent or pricing solutions, reliable measurement, or firms embrace analytics and accountability act reasoning to make decisions that will increase productivity, risk mitigation, and increased profit margins. The most commonly available consulting institution include Analytics8, Spins, Boston Consulting Group, Caserta, Saama Cervello, etc.
Sensing the immense size, velocity, and varieties of information available to companies are often equivalent to trying to drown in the ocean or drinking from a hosepipe. Irrespective of one’s favored aquatic metaphor, one thing is for sure: the obstacle is not whether companies can gather vast amounts of information, but how they can derive valuable droplets of intellect from those downpours of data. This is where big data consulting companies come in.
These consultancies are where MBAs meet IT — it is their job to integrate the appropriate data innovations for a specific industry as well as deliver tangible consultative services. This entails many subs – components: cloud computing, statistical analysis (statistical and visual data analysis), data management (maintaining data security and integrity), and data science (providing real valuable intelligence, sometimes with the help of machine learning).
- Data Analytics
Data analysis discusses the processes of transforming, analyzing, and organizing a given collection of data primarily to analyze its numerous components and gain useful data. Analytics is a discipline of science or an umbrella that supports the comprehensive management of information. It not only comprises of research, organization, data collection, storage, and all the methods and techniques used in data analysis (Getsmarter, 2020).
Data Analytics encompasses applying a mechanical or algorithmic procedure to derive perceptions from a dataset. For example, to search for meaningful correlations between different data sets (Monnappa, 2016). It is used in numerous businesses to enable companies and institutions to make enhanced decisions and to validate and invalidate existing models or concepts. Data Analytics’ emphasis is on implication, which is the process of drawing deductions that are based exclusively on what the researcher knows already.
Data and analytics disrupt current ecosystems and concepts. The prevalence of big sets of data and the emergence of capability for massive data movement undermine existing data and technological storage facilities (Singh, 2018). From using robust data to customize products and services, to expanding media frameworks to match sellers and buyers, business metrics are used to encourage speedier, fact-based decision-making.
Data has the potential to give businesses a lot of value, but you need the analytics component to unlock this value. Techniques of analysis provide a company with access to relevant information that could help them enhance their efficiency. It may help you to improve your customer awareness, advertising campaigns, expenditure, and more. According to (Lotame, 2019), Data analytics can be used for Provision better customer services, Effective marketing, Improve decision making, and making more efficient operation.
Data analytics is often mistaken for data science. Data analytics, while comparable, is more interested in solving concerns through specified data sets, while data science involves programming and coding to create new techniques and methods. Business Intelligence (BI) relates to data analytics. However, BI is looking at the analysis of historical data, while analytics can also predict the future (Davidson, 2019). When data analysis extends further than BI extends and covers areas such as data/text mining, machine learning, predicting, semantic assessment, sentiment analysis, machine learning, and pattern recognition, this can be called “advanced analytics.” Advanced analytics is autonomous or semi-autonomous and usually involves less human interpretation or interaction.
- RISKTURN
“The Ultimate Software for Risk-Based Investment Valuation and Capital Budgeting” (Daniele, 2020) I have tried the Riskturn software to incorporate in a management course project. The software permits simulation of the risk effect on project cash flows and the related financial metrics with just a few clicks. I enjoyed the software because it is user-friendly, helping project managers to examine risks in their projects that usually require comprehensive technological expertise. Furthermore, in comparison with other applications, Riskturn introduces time-related threats: for instance, an operation may last longer or less than anticipated, resulting in increased expenses or disruptions in revenues. There are also unique properties for the more advanced users to understand the different cost/revenue curves, amortization, and risk analysis quantitatively.
- Simons Philosophy
Decision-making is indeed synonymous with the management process. Decision-making is an essential component of modern policymaking. Fundamentally, management takes rational or sound decision-making as its primary function. Each manager takes hundreds of decisions consciously, unconsciously or deliberately, making it the critical component of a managerial position. Decisions play a significant role in determining both the institutional and administrative operations. Decision-making is an indispensable and continuous part of corporate or company management. Decisions are taken to support the commercial enterprises and operating activities of the organization. For example, planning involves deciding, what, when, where, why, and how an operation will be done and who to perform the task.
Section 3: Conclusion
In this unpredictable world of data-driven disruption, project leaders need to look through two lenses at the same time. First, they need to recognize and reward opportunities, such as modifying traditional business concepts or breaking into new markets. Second, they will continue to concentrate on integrating analytics into their vital decision-making processes. Business managers can simplify internal management mechanisms by incorporating data analytics into their overall strategy, identifying consumer inclinations, interpreting and monitoring evolving threats, and building systems for continuous enhancement and feedback. This will empower businesses to achieve competitive advantage and stay at the frontline of the volatile world of data-driven disruption.
Businesses must remain focused on analytics because data is a valuable part of making core business decisions in the market today. It enables companies to stay ahead of this digital disruption, ensuring continued success. Companies such as the Research Optimus help companies make better decisions through the process of data analysis.
References
Daniele. (2020). Sign up for risk-based investment valuation | Riskturn free trial. Retrieved from https://www.riskturn.com/free-trial-risk-based-investment-valuation?campaign=GetApp
Davidson, L. (2019, October 16). What is data analytics? Retrieved from https://www.springboard.com/blog/what-is-data-analytics/
Getsmarter. (2020, February 3). What’s the difference between data analytics and data analysis? Retrieved from https://www.getsmarter.com/blog/career-advice/difference-data-analytics-data-analysis/
Ilgu, M., & Nilsen-Hamilton, M. (2016). Aptamers in analytics. Analyst, 141(5), 1551-1568.
Kaput, M. (2020). How is artificial intelligence used in analytics? Retrieved from https://www.marketingaiinstitute.com/blog/how-to-use-artificial-intelligence-for-analytics
Lotame. (2019, September 23). What is data analytics? Retrieved from https://www.lotame.com/what-is-data-analytics/
Monnappa, A. (2016, April 5). Data science vs. big data vs. data analytics. Retrieved from https://www.simplilearn.com/data-science-vs-big-data-vs-data-analytics-article
SAS. (2020). Big data: What it is and why it matters. Retrieved from https://www.sas.com/en_us/insights/big-data/what-is-big-data.html
Singh, H. (2018, December 1). Using analytics for better decision-making. Retrieved from https://towardsdatascience.com/using-analytics-for-better-decision-making-ce4f92c4a025