The Calejo Model
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
In Calejo Data Analysis, we are working with the following approaches to data analysis:
- Data mining, which is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive, rather than purely descriptive, purposes.
- Business intelligence, which covers data analysis that relies heavily on aggregation, focusing on business information.
- Exploratory Data Analysis (EDA), which focuses on discovering new features in the data.
- Confirmatory data analysis (CDA), which focuses on confirming or disproving existing hypothesis about the data.
- Predictive analytics, which focuses on the application of statistical models for predictive forecasting or classification.
How we work
Data analysis is a process for obtaining raw data and converting it into valuable information. We are able to deliver each analysis type separately or manage the whole refinement chain for our customers.
Our work Process
1. Data Mining
- Identfication of existing data.
- Identification of prospective data.
- Identification of suitable aggregates.
- Data storage.
- Quality control and data cleaning.
- Identify internal and external stakeholders.
- Plan strategic purchases of external data.
2. Data Exploitation: Value identification
- On-site interviews and discussions with experts.
- Find target functions per business unit.
- Identify relevant internal/external data for optimizing target.
- Identify adaptability and allowed action space of business unit.
- Comparative study of data potential through global trends, market leaders, competitors and science.
3. Data Exploitation: Prospects
- Identify comparable projects/prestudies globally (if any).
- Mathematical formulation.
- Match with relevant methodology and algorithms.
- Preliminary risk, value and cost estimate.
- Estimate and strategy for positioning of data usage within industry.
- IP strategy.
- Valuation of existing data resources.
- Plan for data exploitation.
- Recommendation of organizational management of data.
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