Decision Making with Statistical Modeling in AI and Data Visualization

What is Decision Making?

Decision-making is the process of making choices by identifying a problem. The process demonstrates the importance of troves of data collected and its analysis in having an ideal decision made for any business segment.

By gathering valuable data from various touchpoints and categorizing options, a step-by-step decision-making process can assist in making more careful, considered actionable insights. Today for industries to grow most sought-after suggestive way in any business is to study your data and make accurate decisions.

The key steps in decision making

• Identify the problem
• Gather and analyze the most relevant data
• Discover alternatives to solve the problem
• Select the best alternative
• Convert the decision into action
• Evaluate and verify the decision

Data acts as the key to unlocking numerous avenues of opportunities, it helps to solve problems and make strategic decisions. Data is huge and ever-growing and also termed Big Data.

Big Data is the main asset of a modern business.

Businesses gather big data or data sets through various offline and online resources that are so large and complex. The data which can't be analyzed using standard or traditional methods like tabular spreadsheets requires tech-based tools to make a decision.

The flow chart below outlines the decision-making process:??

Human decision-makers can make mistakes when faced with large-scale or complex decision-making problems. Manual data processes and report generation leads to errors increasing turn-around time. This happens due to the intrinsic limitations of their memory, attention, and limited knowledge.

Hence the role for data processing comes into the picture, the company needs to spend less time accumulating massive volumes of data and more time leveraging technologies. To detect and mitigate risks, as well as proactively uncover opportunities the use of AI, ML algorithms along statistical modeling tools help decision-makers overcome these limitations.

Statistics in AI

Statistics is a collection of tools that serve as the basis for the analysis and processing of data to transform raw observations which you can understand and is informative. Many performance indicators are used in the ML algorithm, such as precision, accuracy, recall, f-score, and root means square error, which is based on statistics. These indicators help in understanding the visual representation of the data and the performance algorithms used in it. Statistics helps to identify trivial patterns with perhaps outliers in the data metrics such as median, mean, and standard deviation of complex data sets.

Statistics is a significant source of an evident tool as offers us clear objectives on numerical data on crucial areas of life such as business performance, population, growth and characteristics, economic performance, health and welfare, and the state of our environment.

Businesses can utilize statistical analysis to their benefit in a variety of ways which could be:

• Identifying customer data trends
• Narrowing down the best-performing product lines
• Conducting financial audits
• Identifying underperforming sales members in the team
• Gaining a better understanding of how sales performance varies across the country
• Actionable insights for varied demography's

Role of Statistical Modeling/Methods in decision making

Modeling is an essential tool in the context of Business Strategy. Statistical modeling is the method of applying numerical analysis to datasets. This process summarizes the findings of an assessment in such a manner that assessors may see patterns in the data, draw conclusions, and finally answer the questions to make informed decisions. Statistical research in the workplace aids decision-making in varied areas, including auditing, financial analysis, and marketing research. AI and MLOps deployment have become common, more companies and organizations are leveraging statistical modeling to make predictions based on data.

The types of Statistical Methods / Models

Predictive, prescriptive, and descriptive decision models are three fundamental statistical models for understanding the future. System failure, creditworthiness, and fraud are examples of the predictive model.

Predictive:?Based on previous data, predictive analytic models employ several statistical techniques (such as modeling and data mining) to forecast future probabilities and trends. Predictive analysis is used in various situations, including fraud detection and security, risk assessment, marketing, and operations.

Descriptive:?Descriptive statistical methods describe or summarize the information surrounding the data. It is vital to understand the data in a more meaningful manner. Descriptive statistics help us to find out what happened and explain why? Administrators can use historical data to analyze past successes and failures. This is also known as "causality analysis" which is commonly used for sales, marketing, finance, and operations. It is also exploratory in terms of the data is provided and thereafter the problem is investigated. The decision-maker is provided an understanding of the problem, following which the decision model is applied and specific optimizations are suggested.

Prescriptive:?Prescriptive analysis gives the actual results. The prescriptive analysis is the phase in which the best course of action for a particular business situation is determined. This analysis is very agile and involves knowing what can happen, why it can happen, and how to navigate further. With constant updates on information, prescription analysis changes give managers real-time control over the organization's action plans. Prescriptive analysis reports necessitate a high level of machine learning because they suggest the next steps and employ advanced event processing, graph analysis, and simulation techniques.

Why do we need models?

We need a statistical model to understand the problem, simplify its complexity, analyze and then present it further for informed decision making. The statistical model is a mathematical representation of observed data to interpret the information more strategically.

Businesses must understand the statistical models to help them in achieving these incremental efficiency gains. The model's influence can be seen as how it impacts customers and gives a broader picture of societal issues. At the same time, it comprehends what it implies for the business if the model fails. The model is critical to consider when decisions involving financial risk, environmental effect, or even human life are at stake.

The most important statistical technique used in data analysis is supervised learning, which includes classification and regression models. They have independent variables which hold the most influence over dependent variables. Unsupervised learning includes association rules and clustering algorithms.

Big Data and data analytics are disrupting existing business models and ecosystems worldwide. The proliferation of large data sets and the introduction of massive data migration capabilities are undermining existing information and technological silos for organizations.

AI in Decision Making

AI plays a crucial role in every operation for companies to stay ahead in a competitive world. AI trains itself and gathers data to create models that become adept at making predictions and categorizations based on that data. These same models can then be used with real-time data to create real-time forecasts, categorizations, and suggestions, and help firms to make better commercial decisions.

How can statistical reasoning be applied to AI problems?

• Define and characterize the scope of the problem clearly and concisely
• Select the appropriate data model to translate the challenge into data science
• The quality of the data has a significant impact on the result-outcome
• Spend time on mining, understanding, and converting the data
• The workflow starts with descriptive statistics and graphical data. It can detect unexpected patterns that contribute to skewed learnings
• Trials and experiments should be well-designed, taking into account all changes of the parameters to assess
• Take into account both explanatory and response factors for all conceivable outcomes
• To acquire significant insights, do hypothesis testing and always include control groups
• Make the model more robust by validating the used techniques
• Update models regularly as parameters change

Data Visualization in Decision Making

How to showcase the information for decision-making?

Once the data undergoes processing it needs to be presented efficiently to the decision-makers. Data is complex, and it becomes more valuable due to visualization. Data visualization is an important component helping organizations make sense of their data and use it to their advantage. Otherwise, it is very difficult to explain data discoveries and detect patterns, let alone pull insights and engage with data, without visualization.

Without visualizing the data, data experts cannot find patterns and flaws. It is vital to convey data discoveries and extract key information from them. Interactive data visualization tools make all the difference in recent cases.

How does Data Visualization boost in Decision Making?

Data visualization tools speed up decision-making and allow viewers to gain a better understanding of patterns and trends. The importance and benefits of data analytics can be understood by anyone in the organization, even if they aren't experts in the subject.

Data visualization provides a graphic illustration of information and data on dashboards. The use of visual elements such as heat maps, tables, graphs, charts, etc. tools offer an easily accessible way to identify and understand trends, outliers, and patterns in the data.

Benefits

• Data visualization boosts the impact of the message is communicated to the target audiences
• Delivers the data analysis results in the most influential way possible
• It unites the organization's messaging systems across all demographics
• Visualization makes it easier to understand large volumes of data at one glance and in a more efficient manner
• It aids in the better understanding of data to assess its impact on the business and visually communicates the knowledge to internal and external audiences
• Decision-makers can use available data and insights to improve decision-making
• Data Visualization provides access to the proper kind of information and visualization that is unbiased data that is free of mistakes

When data is accurately visualized, patterns are clear and self-evident, it helps individuals quickly draw simple actionable conclusions.

Conclusion

Decision-making aided with AI has the power to increase profitability and output. Statistics models with AI play an important role in the collection, organization, analysis, and interpretation of data over various visualization platforms. Dashboards save time and provide more accurate and timely decision-making for the organizations. In the world of big data, it's important to remember that majority of people are visual learners as they need to see their data to comprehend it.

In this volatile environment of decoding large data sets, the organizations need to dig deeper when making core business decisions. To identify unfolding consumer trends, interpret and monitor emerging risks, and build mechanisms to automate better, increase data visualization, and gain the competitive edge with our?AI Platform.

We hope this article was insightful and helped you to understand the importance of the role of AI in statistical modeling, data processing, data analysis, and data visualization in determining effective business decisions and their functionalities. Thank you for showing interest in our blog and if you have any questions related to?Statistical Modeling,?Data Visualization, AI-based platforms, please send us an email at?[email protected].