Data Analysis and Abjad: Prophecy or Science?

In the contemporary world, data analysis has become an indispensable element of decision-making processes across a wide spectrum — from business to healthcare, from education to public policy. Statistical inferences drawn from large datasets, algorithmic predictions, and machine learning applications profoundly shape the functioning of modern society through their capacity to make projections about the future. At this point, however, a philosophically significant question arises: To what extent does this scientific approach of predicting the future based on historical data resemble ancient methods of prophecy? What Is Abjad? A Historical and Methodological Examination Abjad is a system that assigns specific numerical values to letters of the Arabic alphabet. Beyond its use as a numeral system in Arabic, it has also been employed — particularly in Sufi traditions — for symbolic and mystical interpretation. The origins of the Abjad system trace back to pre-Islamic Arab culture and even further, to civilizations that used Semitic languages. In the Abjad system, each letter carries a numeric equivalent:

• Alif = 1
• Ba = 2
• Jim = 3
• Dal = 4
• …and so on. The historical uses of this system have occurred in various contexts. In the classical period, it served as a practical tool for recording dates (tarikh düşürme), keeping chronological records, and performing mathematical calculations. Over time, however — especially in certain Sufi circles — attempts were made to extract hidden meanings and make predictions about future events by calculating the Abjad values of Quranic verses and words. Some commentators claimed that the Abjad values of certain words corresponded to historical events and made prophecies about events they believed would occur in the future. This approach sparked serious debate among Islamic scholars and was generally criticized for implying claims to knowledge of the unseen (ghayb). The methodological problem with Abjad calculation is its openness to the interpreter's subjectivity. There are no objective criteria for which words to select, which values to sum, or how to interpret the results. This removes the system from scientific methods and shifts it into a mystical realm. Data Analysis: Epistemological Foundations and Methodology Data analysis is a scientific discipline that systematically examines observable phenomena, uncovers the relationships between them, and makes probabilistic projections about future states based on those relationships. Modern data analysis rests on solid foundations such as mathematical statistics, probability theory, and computer science. The core components of data analysis are:

1. Data Collection and Quality Control: The first step in the analytical process is collecting reliable and representative data. Sampling methods, measurement errors, and data quality are of critical importance here.
2. Statistical Modeling: Mathematical models are constructed on the collected data. These models seek to define and explain the relationships between variables.
3. Hypothesis Testing: The models and assumptions formed are verified or rejected through statistical tests. This process is grounded in the testability principle of the scientific method.
4. Prediction and Forecasting: Probabilistic predictions about future states are made based on patterns found in historical data. These predictions are expressed with a certain confidence interval and margin of error. The epistemological strength of data analysis derives from its properties of reproducibility and verifiability. When an analysis is repeated by different researchers using the same data and methods, it should yield similar results; predictions made can be tested in the future and their success rates can be measured. However, data analysis also has its limits. Due to the assumptions underlying the models used, noise in the data, unmeasurable variables, and the nature of complex systems, predictions always contain uncertainty. For this reason, data analysis results express probability, not certainty. Similarities: The Common Ground of the Drive to Predict At a surface level, Abjad and data analysis share some fundamental similarities:
5. Starting from Existing Knowledge: Both methods attempt to draw inferences about unknown or future situations by departing from existing knowledge or data. Abjad uses the numerical values of words in texts; data analysis uses past observations.
6. A Systematic Approach: Both are systematic approaches based on specific rules and procedures. Abjad has letter-to-number conversion rules; data analysis has statistical methodologies.
7. Uncertainty and Interpretation: Both methods produce outputs open to interpretation rather than definitive conclusions. While the meaning of Abjad calculations depends on the interpreter, data analysis results also require context and interpretation.
8. Use in Decision-Making Processes: Historically, both methods have been used to influence human decision-making. Whereas Abjad was seen in certain eras as a guide to future events, data analysis is used today for strategy formulation and planning. Yet these surface-level similarities do not obscure the deep methodological and epistemological differences between the two approaches. Critical Differences: The Distinction Between Science and Mysticism The fundamental differences between Abjad and data analysis are precisely those elements that distinguish scientific method from mystical approaches:
9. Methodological Transparency and Objectivity: Data analysis relies on transparent and reproducible procedures. The methods used can be explained in detail, reviewed by others, and the same results can be obtained from the same data. This is a foundational principle of the scientific method. Abjad, on the other hand, depends on the interpreter's subjective preferences. There are no objective criteria for which words to include in a calculation or how to interpret the results. Different people working on the same text can arrive at entirely different conclusions.
10. Verifiability and Testability: The results of data analysis are testable. Once a predictive model is constructed, its success can be measured against future observations. If the model is wrong, it is corrected or rejected. This is the fundamental mechanism of scientific progress. There is no systematic method for testing the accuracy of Abjad calculations. The interpretations made are generally applied retrospectively (retrofitting), and when predicted future events fail to materialize, no systematic revision is made.
11. Margin of Error and Expression of Uncertainty: Modern data analysis explicitly states the level of uncertainty in every prediction. Confidence intervals, error margins, and levels of statistical significance are reported as a standard practice. This provides objective information about how reliable the predictions are. In Abjad calculations, no such measurement of uncertainty is made. Results are generally presented as definitive interpretations, without any systematic assessment of how reliable the underlying assumptions are.
12. Causality and Mechanism: Data analysis strives, wherever possible, to understand and explain the causal relationships between variables. A prediction should be based not merely on statistical correlation but, where possible, on an intelligible mechanism. In Abjad, there is no explanation of what causal link could exist between the numerical values of letters and future events. The system operates on a symbolic and mystical basis and offers no mechanical explanation.
13. Universality and Cultural Context: Statistical methods and data analysis techniques are universal. Scientists from different cultures speak the same language and use the same methodologies. Mathematical truths transcend cultural boundaries. Abjad, however, is specific to a particular cultural and linguistic context. It cannot be directly applied to languages that do not use the Arabic alphabet and requires different cultural interpretive traditions. The Islamic Perspective: Knowledge of the Unseen (Ghayb) and Tawakkul Islam explicitly emphasizes that knowledge of the unseen belongs to Allah alone. This is stated in numerous Quranic verses:

"Indeed, Allah is the Knower of the unseen of the heavens and the earth." (Fatir, 35:38) "Say: None in the heavens and the earth knows the unseen except Allah." (An-Naml, 27:65) This theological principle renders problematic any approach that claims to know the future with certainty. Divination arrows (azlam) are explicitly prohibited in Islam, and this prohibition rejects the attempt to intrude upon Allah's authority through claims to knowledge of the future: "O you who have believed, indeed, clouding the mind, gambling, stone altars, and divining arrows are but defilement from the work of Satan, so avoid it that you may be successful." (Al-Ma'idah, 5:90) The "azlam" (divining arrows) mentioned in this verse were instruments used in the Age of Ignorance (Jahiliyyah) to predict the future or reach a decision. By prohibiting such practices, Islam teaches that human beings should entrust their destiny to Allah's decree. So how should this principle be applied to prediction tools such as data analysis and Abjad? Abjad and Islamic Ruling: Using Abjad calculation for the purposes of recording dates, chronological calculation, or mathematics is not problematic. However, claiming knowledge of the unseen on the basis of Abjad calculations — asserting that one definitively knows future events — is unacceptable from an Islamic standpoint. Classical Islamic scholars approached such practices with caution. Thinkers such as Imam al-Ghazali, in defining the limits of knowledge, drew attention to the dangers of venturing into the realm of the unseen. There is broad scholarly consensus (ijma) among Islamic jurists that prophecy claims based on Abjad calculations are contrary to the fundamental principles of Islam. Data Analysis and Islamic Evaluation: Data analysis must be assessed in a different position from Abjad. Data analysis examines observable phenomena and makes probabilistic predictions; it does not claim definitive knowledge of the unseen. In this respect, it can be considered legitimate from an Islamic perspective. Islam encourages the use of reason, learning from experience, and planning ahead. What matters here is that predictions be understood to express probability rather than certainty, and that the final outcome be left to Allah's decree. However, placing excessive trust in data analysis, presenting results as absolute truth, or viewing these tools as determinative of Allah's will is problematic. From an Islamic perspective, data analysis:

1. Should be used as a decision-support tool, not as an absolute determinant.
2. Should have its uncertainty and margin of error clearly expressed.
3. Should preserve the principle of tawakkul: a person plans to the best of their ability, but leaves the outcome to Allah.
4. Should remain within ethical boundaries: it must not be used to harm people or reinforce injustice. The Pragmatic Dimension: The Correct Use of Predictive Tools Beyond the theoretical discussion, a practical question arises: How should we use predictive tools such as data analysis?
5. A Culture of Probabilistic Thinking: The predictions offered by data analysis express probability. Saying "There is an 85% probability that this customer will repay their loan" is very different from saying "This customer will definitely repay." Developing a culture of probabilistic thinking among society and decision-makers is important.
6. Knowing the Limits of Models: Every statistical model rests on certain assumptions. The saying "All models are wrong, but some are useful" reminds us that models are approximate representations of reality. Knowing the limits of models is the key to using them correctly.
7. Ethical Use: Data analysis can either reinforce or reduce social inequalities. For example, biases embedded in historical data can be reflected in algorithmic decision-making systems. For this reason, the ethical use of data analysis is critical.
8. Transparency and Accountability: Particularly in public policy or decisions that affect people's lives, the results of data analysis must be transparent and accountable. The defense of "the algorithm said so" is not sufficient. Knowledge, Wisdom, and Humility The comparison between Abjad and data analysis raises profound questions about the nature and limits of human knowledge. Both methods represent the endeavor to know the future, yet their epistemological foundations and methodologies differ fundamentally. Abjad is a symbolic and mystical system. Although it may have acquired meaning in certain historical and cultural contexts, it does not meet the criteria of the scientific method. When it carries claims to definitive knowledge of the future, it is problematic both epistemologically and — from an Islamic perspective — as a claim to knowledge of the unseen. Data analysis, by contrast, is a product of the scientific method. It is grounded in a methodology that is measurable, testable, and correctable. Yet data analysis too offers probabilistic, not certain, knowledge. Used within these limits, it can make valuable contributions to decision-making processes. From an Islamic perspective, using scientific tools such as data analysis is not only permissible but encouraged. However, one must not place absolute trust in the results of these tools, nor base one's fate entirely on their predictions. The principle of tawakkul must be preserved: a person plans by using their intellect and scientific methods, but leaves the ultimate outcome to Allah's decree. Acknowledging that human knowledge is limited is an expression of epistemological humility — a humility that forms the foundation of both scientific integrity and religious submission. In the modern world, preserving this humility while benefiting from the power of data analysis is both an intellectual and a spiritual necessity. The future is known only when it is lived. Our duty is to prepare with the best tools available to us — while never forgetting that the ultimate truth rests in the hands of Allah.