Improving data quality and avoiding errors
Quality of data has a vital role in predictive analytics that shapes the accuracy, reliability and utility of its outcomes. Data quality dimensions encompass accuracy, completeness, consistency, timeliness, reliability, and relevance. Common challenges in ensuring high data quality include human errors in manual data handling, interpretation, and processing, as well as issues related to inadequate and inconsistent data accuracy. The establishment of a robust data governance framework, the formulation of clear data standards, and the regular monitoring of data quality metrics are essential for effective data quality management. Standards such as ISO 8000 and ISO/IEC 25012 address key data quality issues, including accuracy, completeness, credibility, accessibility, compliance, confidentiality, efficiency, precision, and traceability. Furthermore, the General Data Protection Regulation (GDPR) underscores the rights of individuals to data accuracy, while the Basel Accords mandate that banks maintain precise and comprehensive records of their lending activities, risk exposures, and financial reserves.
Efficiency of the data is affected by inconsistencies and incompleteness of the data, often resulting low accuracy. Outcomes of the modelling could be affected by fluctuations in data, leading to overfitting. Overfitting of the data can be corrected by using cross-validation, regularisation, pruning and ensemble methods. Whether it’s a linear regression or classification model, accuracy determines the model performance. Error is a measure of actual values and predicted values. Errors can be standard or relative; standard error calculates the scale of the errors whereas relative errors such as Mean Percentage Error (MPE) and Mean Absolute Percentage Error (MAPE) compute average errors as a percentage. Mean Absolute Deviation(MAD) is a standard measure to calculate the scale of the errors. Mean Squared Error (MSE) calculates the error by squaring the errors that increases its sensitivity to outliers, more applicable for the computations where large errors should be avoided.
Errors can be systematic or random errors. Systematic errors are predictable that occurs as a result of bias in the data collection process. For example; inaccuracies in the rainfall data by 0.5 mm less than normal value due to faulty measurement device. Random errors can occur as a result of unexpected error during data collection such as errors of pollution level measurement. Random errors don’t have predictable pattern and the variations are not consistent. Data cleansing techniques such as standardisation, missing data handling, validation and outlier detection can be applied to improve reliability and accuracy of the data.
Understanding Customer Insights
Businesses can take advantage of Artificial Intelligence and Machine Learning to study consumer behaviour which is useful to businesses to tailor their products, services and marketing strategies. Individuals can select, purchase, use and dispose the goods and services in different ways, individuals or group of consumers have different ideas or experiences.
Clustering, classification, trend analysis and regression analysis of the consumer data can generate in-depth insight of the consumer behaviours as well as relationships between the variables such as sales based on geography and age of an individual or seasons such as summer, easter or Christmas periods. Raw data can be transformed into actionable and meaningful insights with the help of AI and best available ML models.
AI and predictive analysis aids enhanced decision-making, customer segmentation analysis or the analysis of customer based on age, sex, geography and purchasing behaviour. AI can be effective in predicting consumer behaviour by recommending products, services or contents tailored to individual preferences. Further, AI enables businesses to study consumer feeling about a brand; detect common issues or complaints and assess the effectiveness of marketing strategies.
Real time analysis of the consumer data using AI and ML allow businesses to formulate adaptive marketing strategies and higher chances of conversion and increased engagement. Dynamic pricing strategy, adjustment of product prices based on real-time demand, competitor pricing and consumer behaviour, behaviour targeting such as personalised-ad recommendation based on real-time consumer and consumer journey mapping or the strategy to track and predict consumer interactions, can be effectively implemented with the help of AI and ML.
Businesses can leverage AI and ML technologies by enabling accurate forecasting, personalised marketing and efficient resource allocation, leading to increased customer engagement, better decision making and higher operational efficiency. Despite of concerns such as data privacy and security, transparency and biases , businesses can enhance marketing, improve customer experience and streamline sales process with the help of AI by predicting and understanding consumer behaviour.
AI and Forecasting Analytics Explored
The transformative force of AI can be leveraged into business strategies to enhance decision-making, personalise customer interactions, improve operational efficiencies and foster innovation. Real-time information and analysis of the data, predictive analytics and accurate forecasting enable businesses to enhance decision-making.
AI algorithm generates better insights into businesses by enabling them to understand behaviour patterns and predict market shifts. AI driven machine learning models such as risk assessment and credit scoring provides a better financial scenario to the decision makers. AI enables decision making by generating better insights from the available data. Predictive analytics in the health sector could enhance the quality of healthcare and reduce the costs related to decision-making.
Tools such as natural language processing (NLP) and machine learning(ML) offer personalised shopping experiences, recommend products based on customer behaviour and provide customer support via intelligent chatbots. Customer data can be used to produce content, advertisements and strategies.
Competitive advantage can be achieved by enhancing customer satisfaction and customer experience. AI tools enable businesses to handle multiple queries at the same time and increase customer retention and loyalty. Further, strategic decision-making such as development of new products or services based on market conditions and customer demand enables competitive advantage to the businesses.
Application of Predictive AI Modelling
Real-world applications of predictive AI modelling pose some challenges including data quality, availability, and scalability. Incomplete, noisy and biased data can significantly affect the model’s accuracy and reliability. Scalability of the data or handling of huge volumes of data might require significant computational resources and optimised algorithms.
Ethical issues, biases or model interpretability are major concerns across various domains including public health, climate change and the financial sector. Although AI and ML applications facilitate the prediction of extreme weather events and forecasting climate patterns, standardization of the data formats, enhancement of model interpretability, and ethical considerations should be prioritized. Focusing on robust validation methods, generalisation strategies and interdisciplinary collaboration can address the existing limitations of AI applications.
Artificial Intelligence(AI) service in the health industry can bring positive results in finance, health improvement and care outcome. Healthcare services such as robot-assisted surgery, clinical-trial participation, image diagnosis, dosage error reduction, medication management and health monitoring can potentially utilize AI based methods such as machine learning, natural language processing (NLP), neural network and deep learning(DL).
Predictive models can be generated to apply in real-world scenarios however accuracy has to be improved and in some cases, high accuracy on training data may not perform well on the test data. Overfitting is one of the issues in predictive modelling such as fluctuations in data that can adversely affect the outcome when applied to slightly different datasets. However, mitigating techniques such as cross-validation, regularisation, pruning and ensemble methods can improve predictive models and generate more robust, reliable and generalisable models.
Exploring ARIMA models for better time series prediction in studies
Real-world data representing the time dimension can be used to forecast future scenarios using time series forecasting methods. Autoregression, moving average, autoregressive moving average and automated machine learning (automated ML) methods are time series forecasting methods which focus on establishing a relationship between historical data and future results. Autoregression assumes the future observations at the next timestamp have a linear relationship with prior time stamps. The auto Regression(AR) model makes predictions using previous values in the time series while the Moving Average(MA) makes predictions using the series mean and previous errors.
Accuracy always remains a question while modelling and forecasting data. ARIMA models can be combined with artificial neural network (ANN) models to enhance accuracy. Better accuracy can be obtained from ARIMA models for complex time series data when applied in combination with long-term memory (LSTM) networks. The performance of the model can be evaluated using metrics such as mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE).
ARIMA model is a robust method for time series forecasting and is applicable in various scenarios such as hydrological forecasting, predictions of atmospheric carbon dioxide levels and predicting stock price.
Exploring Predictive Analytics
Predictive analytics can help business sustainability by enabling organisations to make data-driven decisions and proactively address potential risks and challenges. By utilizing machine learning algorithms and predictive analytics, businesses can analyse large volumes of data to identify patterns, trends, and potential future outcomes. This allows them to anticipate and mitigate risks, optimize resource allocation, and make informed decisions that contribute to long-term sustainability.
Higher levels of accuracy help investors to make decisions based on data and take appropriate measures. Historical and current data can be used to predict future trends enabling organisations to make better decisions. Insights can be generated based on patterns and statistics of the data. Analysing the data to generate patterns, trends and potential future outcomes enables anticipating and mitigating risks, optimising resource allocation and making informed decisions.
For example, the e-commerce sector can take advantage of predictive analytics to forecast demand and manage inventory, customer segmentation, fraud detection and prevention, supply chain optimisation and website and user experience optimisation. Predictive analytics can analyse market dynamics, competitor pricing, demand elasticity, and customer behaviour to optimise pricing strategies.
The healthcare industry can utilise available predictive analytics tools to prevent disease progression by providing early predictions and risk scores based on various datasets. Patient outcomes can be improved by accurate disease predictions by using assembling techniques and comparing different models. Businesses can generate a better understanding of future demand enabling better inventory management. Efficient resource allocation and minimisation of overproduction are possible with the help of predictive analytics. Appropriate pricing for products and services based on demand forecasting enables competitive pricing to align with market demand and mitigate the overpricing or under-pricing risks.
Business sustainability can be positively affected by the use of predictive analytics to make a data-driven decision, risk anticipation, operation optimisation and customer relationship enhancements.
Business forecasting methods
Business forecasting including sales forecasting, demand planning and revenue prediction can be qualitative or quantitative. Quantitative methods are based on mathematical processes such as algebra, calculus and statistics whereas qualitative methods includes judgements, expert opinion, intuition and emotions. Quantitative forecasting methods can be divided into time-series models such as straight line method, moving average, exponential smoothing and trend projection and associate models such as simple linear regression and multiple regression.
Straight line method and moving average methods require minimum level of mathematics and are based on historical data. In contrast, regression methods require statistical knowledge and sample of relevant observations. Historical pattern of sales can be forecasted using straight line and moving average methods. For instance, a company's revenue is growing annually by 8 percent in the past few years can be expected to grow by the similar pattern. Moving average smoothens the trend by averaging three or five years' averages or monthly or daily averages. Regression methods explores the relationship between the dependent and independent variables.