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Data Structuring and
Labeling

04
Analytical
Methods

Unless structured with a methodological discipline, raw data constitutes a corporate cost and burden. At Datametri, we transform your scattered corporate data (unstructured texts, undigitized records, complex tables with heterogeneous structures) into qualified strategic assets that advanced analytical models can process with high precision. Our "Data Structuring and Labeling" process is not merely a technical organization operation; it is a refinement process based entirely on scientific and methodological principles that unlocks the semantic value inherent in the data. This stage, preceding data mining and modeling, is the most critical phase that directly determines the accuracy of the analysis.

01
Text Data Structuring (Entity Extraction & NLP Categorization)
Natural Language Processing (NLP) NER Algorithms
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"Transform Unstructured Texts into Strategic Decision Tables"

CRM notes, complaint records, open-ended survey responses, or field operation reports, which form the memory of companies, are generally in free-text format. Such data cannot be processed with standard analysis tools and remains as "silent data." Datametri extracts key concepts, brand names, locations, sentiments, and root causes from these texts via Natural Language Processing techniques and Named Entity Recognition (NER) algorithms, converting the data into a column-based structured matrix.

Which Questions Does This Analysis Answer?
  • What are the systematic points of complaint or satisfaction hidden within hundreds of thousands of customer notes?
  • Can numerical metrics and KPIs be generated from the free-text reports of field teams to feed into performance evaluations?
  • Which product features or operational steps are most frequently mentioned in customer feedback?
What Could Be the Added Value to Your Business?
  • Power of Data Mining: Enables you to discover hidden trends and operational bottlenecks by converting massive text data, which would take months to read and sort manually, into manageable tables subject to statistical analysis in seconds.
Text Data Structuring via Natural Language Processing
This visualization demonstrates how semantic entities and sentiment scores are extracted from unprocessed free-text data and transformed into a relational table (matrix) format.
02
Missing Data and Outlier Optimization (Imputation & Outlier Labeling)
KNN / Multiple Imputation Outlier Analysis
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"Enhance Data Quality with Statistical Confidence Intervals"

Missing values or erroneously entered outliers in a dataset increase variance and manipulate the outcomes of statistical models. Instead of deleting these values from the dataset and causing information loss, Datametri scientifically imputes, characterizes, and labels them using K-Nearest Neighbors (KNN), Multiple Imputation, or Expectation-Maximization (EM) methods.

Which Questions Does This Analysis Answer?
  • Do the "abnormal" values in our dataset stem from a data entry error, a system malfunction, or a genuine operational crisis?
  • To what extent does the bias caused by missing data (N/A) distort our analysis results, and how can we scientifically restore it?
What Could Be the Added Value to Your Business?
  • Minimization of Faulty Decision Risk: Prevents erroneous investment and strategy decisions built upon "dirty and incomplete data"; ensuring your decisions are founded on a verified dataset with the highest degree of cleanliness.
Outlier and Missing Data Labeling Chart
The visual expresses how missing values in the dataset are repaired via statistical imputation methods and how outliers are safely labeled and isolated before they can skew the model.
03
Dynamic Segment Labeling (RFM & Behavioral Labeling)
Behavioral Classification Algorithmic Labeling
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"Characterize Your Customer Portfolio by Behavioral Characteristics"

Categorizing customers broadly into just "active" or "passive" is insufficient in today's marketing dynamics. At Datametri, we assign a dynamic segment label to each data row by algorithmically combining Recency, Frequency, and Monetary metrics. This process enriches your database by transforming your raw transaction records into "meaningful customer profiles."

Which Questions Does This Analysis Answer?
  • Which of the thousands of rows of transaction data in my database belongs to a "loyal" customer, and which to one "about to be lost"?
  • Do I possess an already labeled, characterized, and filterable dataset for precision-targeted campaigns?
What Could Be the Added Value to Your Business?
  • Operational Agility and Efficiency: Increases conversion rates by enabling marketing or sales teams to take instant and accurate actions based directly on the labels in the database, without waiting for complex analysis processes.
Behavioral Segmentation and Labeling Matrix
This matrix demonstrates how purely numerical transaction records (invoice amount, transaction date) are instantly transformed by business intelligence teams into queryable strategic labels (Champion, At Risk, etc.).
04
Time-Series Structuring and Seasonality Labeling
Time-Series Decomposition Seasonality Analysis
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"Transform Scattered and Irregular Data into Scientific Prediction Models"

Time-based data recorded with irregular time intervals, missing days, or in heterogeneous formats cannot be directly used for forecasting models and econometric analyses. At Datametri, we project this data into regular time periods (daily, weekly, monthly); and label the structural trend, periodic seasonality, and cyclical components inherent in the data via mathematical decomposition methods, rendering the data ready for advanced predictive analyses.

Which Questions Does This Analysis Answer?
  • Is the increase or decrease in our current data a genuine "trend," or a periodic "seasonal effect"?
  • How can we provide our scattered operational records, saved with irregular dates, as a healthy input into a demand forecasting model?
  • How can we filter out the "noise" in our data and focus on true operational performance?
What Could Be the Added Value to Your Business?
  • Strategic Predictive Capacity: Allows you to forecast future sales volume, inventory requirements, or demand fluctuations with high accuracy by transforming raw and noisy data into a clean, decomposed time series.
  • Erroneous Signal Filtering: Prevents ill-timed investment or inventory decisions by distinguishing temporary fluctuations from permanent trends.
Time Series Decomposition and Seasonality Analysis
The dashed gray line (Raw Data) in the graph represents the noisy and raw records stemming from operational processes. The dark blue line (Structured Trend) is the methodological output, stripped of this noise, showing the true growth or contraction tendency of the business. The red dots symbolize the "cleaned" data labels assigned to each time period for analysis.

Datametri Analytical Perspective

Semantic Labeling

We do not merely name the variables in datasets; we construct a hierarchical structure that establishes the semantic relationships and ontological links between the variables.

Automated Structuring

By utilizing Natural Language Processing (NLP) and machine learning algorithms in a hybrid architecture, we transform massive unstructured data piles into an analyzable, relational database format.

Analysis-Ready Datasets

We don't just clean your data; we bring it up to typological and dimensional standards that can serve as direct input for regression, clustering, time series, or deep learning models.

General Benefits of the Data Structuring and Labeling Process

Let's Get Your Data Ready for Analysis

Let us establish a reliable foundation for your strategic models by structuring your complex and scattered data.

Complimentary Preliminary Evaluation →
Related Analyses
Corporate Data
Integration of Multiple Data Sources
→ Research Data
Data Cleaning and Quality Control
→ Academic Data
Data Preprocessing (Missing Data, Outlier)
→