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 is, 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




I. Business Understanding

The Business Understanding phase focuses on understanding the objectives and requirements of the project. While many teams hurry through this phase, for us, establishing a strong business understanding is like building the foundation of a house – absolutely essential. This step has 4 tasks – 1. Determine business objectives. 2. Assess Situation. 3. Determine Data Mining Goals. 4. Produce Project Plans.

III. Data Preparation

A common rule of thumb is that 80% of the project is data preparation. This phase, which is often referred to as “data munging”, prepares the final data set(s) for modeling. It has five tasks:

  1. Select data
  2. Clean data
  3. Construct data
  4. Integrate data
  5. Format data

V. Evaluation

Whereas the Assess Model task of the Modeling phase focuses on technical model assessment, the Evaluation phase looks more broadly at which model best meets the business and what to do next. This phase has three tasks:

  1. Evaluate results
  2. Review process
  3. Determine next steps

II. Data Understanding

Next is the Data Understanding phase. Adding to the foundation of Business Understanding, it drives the focus to identify, collect, and analyze the data sets that can help you accomplish the project goals. This phase also has four tasks:

  1. Collect initial data
  2. Describe data
  3. Explore data
  4. Verify data qualit

IV. Modeling

What is widely regarded as data science’s most exciting work is also often the shortest phase of the project. Here we build and assess various models based on several different modeling techniques. This phase has four tasks:

  1. Select modeling techniques
  2. Generate test design
  3. Build model
  4. Assess model

VI. Deployment

A model is not particularly useful unless the customer can access its results. The complexity of this phase varies widely. This final phase has four tasks:

  1. Plan deployment
  2. Plan monitoring and maintenance
  3. Produce final report
  4. Review project
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