Businesses Need Intelligence To Make Informed Decisions With The Phra ✓ Solved

Businesses need intelligence to make informed decisions. With the phrases, Business Intelligence and Artificial Intelligence , being frequently used people get them confused and think they are the same. These two types of intelligence are very different but can be related when used to make decisions. Business intelligence ( BI ) is a very broad term that refers to technologies, applications and practices for collecting, integrating, analyzing, and presenting business information. The goal of the analysis is to aid in decision-making.

Artificial intelligence ( AI ) uses technology to replicate the problem-solving, learning, intuition, and judgement of humans . This technology is typically applications on a computer system that provides facial recognition, voice recognition, task automation, and many other “human†like tasks. By replicating human intelligence, AI leaves much more time for tasks that require human interaction. AI can be divided into machine learning and deep learning. One of the main differences between AI and BI is the goals of technology.

The purpose of BI is to improve a business’s data collection , storage , analysis , and reporting . Reporting could include much more than lines of numbers on a page. It could consist of visuals like charts, graphs, and dashboards. The goal of AI is to create models of human “thinking†. These models seek to replicate human thoughts and reactions.

Data gathered in AI is used by the computer to make decisions. Much of the data collected by AI is captured via sensors . These sensors can come in many forms. Cameras, thermostats, motion sensors, and weight sensors are just some of the sensors used to capture data for AI . The response time must be very quick so the data from the sensors can trigger actions from the AI computer application.

The process comparisons of BI and AI : The goals of both types of intelligence show that artificial intelligence could be used to enhance and extend business intelligence. Artificial intelligence can take the data gathered by BI and create predictions and reactions for the future. BI looks at past data and uses this information to understand how the business has performed. Understanding where the business has previously succeeded or failed is critical to improving the processes. The power of AI allows us to use computers to predict what the business might face in the future and prepare.

The two used together in business are very powerful. Another of the main differences between AI and BI are the tools used in each. The tools used in BI include: · SAS · Python · R · Power BI · Pentaho Data Warehouse · SQL · Tableau The tools used in BI are typically applications that mine the data, store the data, or analyze the data. Some of these applications can be easy to learn and use, while others require extensive training. The tools used in AI are much more complicated and require extensive processing power.

Many businesses may lack the hardware required for this processing power. Cloud-based tools can provide processing power that may not exist on-site. Remember, AI goes beyond analysis to the response. This response needs to be very fast. Some of the tools used in AI include: · Amazon (AWS) Machine Learning · The Analyst Toolbox (AI-One) · Deeplearning4j (Deep Learning for Java) · Apache Mahout · OpenNN (open source AI code) In summary · AI and BI have different goals · AI can enhance BI · The tools used in AI and BI are very different · BI is focused on the past while AI is focused on the future Business Intelligence: Dashboard Rami logs onto a company dashboard to check Key Performance Indicators for his sales department such as the number of calls taken by the call center reps, and the sales contracts logged by each team member.

From this dashboard, he can read bar charts and line graphs that answer important business questions for his team quickly. Business Intelligence: Automation Angela owns a meal kit delivery service for people on one of 5 popular diets. She was spending 10-20 hours a week conducting marketing research and analysis. Business Information tools allowed Angela to automate marketing reports and create customer personas that are updated in real time. She can target the meal plans to the target consumers based on these representative customers quickly.

Artificial Intelligence - Scenario 1 Matteo works in Madrid, Prague, Berlin, and Chicago. He speaks English and Spanish fluently but uses an advanced program to translate his speech into German and Czech in real time. Clients speak in their preferred language and the AI translates this into English or Spanish for Matteo. Because his work is sensitive and must be confidential, Matteo's laptop and phone are protected by voice recognition. He says preset phrases and the devices open.

Artificial Intelligence - Scenario 2 Serena manages a production line for an appliance manufacturing company. One product, an espresso machine, has a known common defect which was difficult to detect with the human eye. Checking each machine manually required time and people, both of which are costly. AI devices scan the espresso machines for the error and sound an alarm when the defect is detected. This has reduced the number of defective machines that make it to customers by 80%.

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The Essential Role of Business Intelligence and Artificial Intelligence in Informed Decision-Making

In the rapidly evolving world of business, the need for accurate and insightful data has never been greater. Businesses increasingly rely on strategic decision-making to stay competitive and adapt to market changes. This reliance has led to the adoption of sophisticated methodologies in data analysis, primarily through the lenses of Business Intelligence (BI) and Artificial Intelligence (AI). While the two concepts are often conflated, they serve distinct purposes and can complement each other in powerful ways. This essay examines the differences between BI and AI, their respective tools, and how they can be effectively integrated to drive informed business decisions.

Defining Business Intelligence and Artificial Intelligence

Business Intelligence refers to a suite of technologies and practices designed to collect, analyze, and present business data. Its primary aim is to support and enhance decision-making through the analysis of past and current performance. BI tools can manipulate large datasets to generate visual reports, dashboards, and interactive tools that help managers grasp business performance and identify trends (Chaudhuri et al., 2011).
In contrast, Artificial Intelligence involves using computer systems to mimic human cognitive functions such as learning, reasoning, problem-solving, and decision-making. AI employs algorithms and data analysis to make predictions, automate tasks, and enhance productivity across various industries (Russell & Norvig, 2020). AI encompasses a variety of subfields, including machine learning and deep learning, each of which focuses on teaching machines to improve from experience without explicit programming.

The Goals and Tools of BI and AI

The goals of BI and AI diverge significantly. BI primarily focuses on analyzing historical and current data to assess business performance and identify areas for improvement. Its tools facilitate data collection and reporting through the use of languages like SQL, and visualization applications like Tableau, Power BI, and SAS (Eppler & Wittig, 2013). The strategies generated by BI help organizations make informed decisions grounded in real data analysis.
On the other hand, AI positions itself towards future-oriented solutions. It utilizes the insights gleaned from historical BI data to develop predictive models that can forecast potential scenarios, trends, and risks. For example, AI algorithms can analyze past consumer behaviors to project future purchasing patterns (He et al., 2016). Tools used in AI projects include Amazon AWS Machine Learning, TensorFlow, and Apache Mahout, which may require advanced processing capabilities and technical expertise to operate effectively (Bohr & Pashupati, 2021).

The Complementarity of BI and AI

While BI and AI serve distinct functions, their integration can significantly augment a business’s decision-making capabilities. BI provides the foundational data necessary for AI algorithms to work effectively. By analyzing historical data, BI systems help inform the parameters and variables that machine learning models will optimize for predictive analytics (Bihani et al., 2020).
For example, a retail company leveraging BI can track customer purchasing trends over time. This data can be used by AI systems to forecast future sales volumes, altering marketing strategies accordingly. AI can also uncover patterns and anomalies within BI data that human analysts might overlook (Wang et al., 2021). This synergy not only enhances business intelligence but also enables companies to be proactive in their decision-making, allowing for nimble responses to market dynamics.

Case Studies: Practical Applications of BI and AI

Scenario 1: Real-time Reporting in Sales Management
Rami, a sales manager, utilizes a BI dashboard to monitor key performance indicators related to his department's performance. By accessing visual representations such as bar charts and line graphs, he quickly assesses the effectiveness of sales calls and contracts logged by team members. This real-time data empowers Rami to identify top performers as well as those who require additional training or resources, thereby optimizing overall productivity.
Scenario 2: Enhanced Marketing through Automation
Angela, an owner of a meal kit delivery service, experiences considerable time savings by employing BI tools to automate her marketing reports. The tools generate real-time customer personas and insights based on consumption trends, automating marketing strategies. The time Angela saves can now be invested in strategic development and expanding her business offerings.
Scenario 3: AI in Multilingual Communication
Matteo, who works across multiple European cities, benefits from an AI-driven translation system that allows for real-time communication in several languages. This technology enables him to interact with clients in their preferred language while ensuring confidentiality through voice recognition. Such advancements illustrate how AI not only enhances personal productivity but also elevates client relationships through improved service delivery.
Scenario 4: AI in Quality Assurance
Serena, a production line manager, employs AI to enhance quality control in espresso machine manufacturing. Utilizing AI sensors, defective products are identified and flagged at a much higher accuracy compared to manual inspections. This innovation has dramatically decreased the number of defective machines reaching consumers by 80%, showcasing how AI minimizes human error and enhances operational efficiency.

Conclusion

In the digital age, businesses must adopt a strategic approach toward data-driven decision-making. BI and AI stand as pivotal tools that, when effectively utilized, can transform organizations. While BI aids in historical insight and reporting, AI leverages that data to predict future trends and automate tasks. Ultimately, the integration of these two methodologies can create a robust decision-making framework that enables businesses to navigate uncertainties confidently, adapt swiftly to changing market conditions, and thrive in an increasingly competitive landscape. As organizations continue to explore this synergy, the potential for innovation and efficiency will only grow.

References

1. Bihani, P., Patil, R., & Kherde, M. (2020). Business intelligence and predictive analytics: A systematic review. Journal of Business Research, 113, 114-124.
2. Bohr, A., & Pashupati, M. (2021). Artificial Intelligence in Healthcare: Past, Present and Future. Healthcare, 9(3), 307.
3. Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). Data Warehouse Technologies. Encyclopedia of Database Systems, 1-10.
4. Eppler, M. J., & Wittig, D. (2013). The role of Business Intelligence in the data-driven decision-making process. International Journal of Information Management, 33(4), 702-709.
5. He, G., Liang, Y., & Wu, X. (2016). Data Mining and Intelligent Systems. Journal of Systems and Software, 112, 1-2.
6. Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
7. Wang, L., Zhang, Q., & Wu, J. (2021). Artificial Intelligence in Business: Challenges and Opportunities. Business Horizons, 64(2), 181-192.