Logikview’s data mining services ensure timely, reliable results by supporting the Cross-Industry Standard Process for Data Mining (CRISP-DM).

World-Class Productivity

Acquire new customers; develop a more intimate picture of those you already have. Optimize your current process by backing your decisions with data. Harness the potential of big data or predictive modelling. And complement all of these with external data sources that will give you more meaningful insights.

Whatever your challenge, we’ll propose the right data strategy for your business.

Logikview’s data mining services ensure timely, reliable results by supporting the Cross-Industry Standard Process for Data Mining (CRISP-DM). Created by industry experts, CRISP-DM provides step-by-step guidelines, tasks, and objectives for every stage of the data mining process. CRISP-DM is the industry-standard process for data mining projects.

CRISP–DM demands that data mining be seen as an entire process, from communication of the business problem through data collection and management, data pre-processing, model building, model evaluation, and finally, model deployment. Mastering the methodology therefore requires the combination of abilities ranging from data affinity through quantitative reasoning and a sound business acumen to well-developed communication skills.

The methodology consists of six steps, each of them equally important in the generation of meaningful analytical insights and the production of actionable results.

Business Understanding

Data Understanding

Data preparation




Data Preparation

1. Select your data

Decide what data to use for analysis and list the reasons for your decisions. This involves:

  • Performing significance and correlation tests to determine which fields to include
  • Selecting data subsets
  • Using sampling techniques to review small chunks of data for appropriateness

The data preparation phase covers all activities to construct the final dataset (data that will be fed into the modelling tool(s) from the initial raw data. Data preparation tasks are likely to be performed multiple times and not in any prescribed order. Tasks include table, record and attribute selection as well as transformation and cleaning of data for modelling tools.

2. Assess the Situation

In this step, the data analyst outlines the resources, from personnel to software that are available to accomplish the data mining project. Particularly important is discovering what data is available to meet the primary business goal. At this point, the data analyst also should list the assumptions made in the project— assumptions such as, “To address the business question, a minimum number of customers over age 50 is necessary.” The data analyst also should list the project risks, list potential solutions to those risks, create a glossary of business and data mining terms, and construct a cost-benefit analysis for the project.

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