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Best Ways to Leverage the Cross-Industry Standard Process for Data Mining (CRISP-DM)

Nov 11, 2020 9:00:00 AM

Data mining helps analyze and find patterns in data. Data reliability can assure better accuracy while building models across different industries. Businesses can learn more about their customers and develop effective strategies related to various business functions. These strategies can help leverage resources in an optimally and insightful manner. Data mining can provide a profound advantage over competitors by enabling businesses to learn more about customers, develop effective marketing strategies, increase revenue, and decrease costs.

What Cross-Industry-Standard Processes are Required for Data Mining?

The cross-industry standard process for data mining, known as CRISP-DM, is an open standard process model that describes common approaches used by data mining experts. It is the most widely-used analytics model, and many issues, such as data cleaning and data transformation, can be caught early or even entirely avoided by following a data analysis process called CRISP-DM.

Best-Way-to-Leverage-the-Cross-Industry-Standard-Process-for-Data-Mining-(CRISP-DM)---NisumPicture credit: iStock

Data analytics deals with solving a problem to generate insights from data. To obtain an analytics-based solution using data, the following steps are necessary:

1. Understanding the Business Problem

  1. Determine the business objective:

    State the objective in technical terms.
  2. Identify the goal of the data analysis:

    State the goal in business terms.

In this first step, understanding the pain point and its impact on the business is pivotal to determining the business objective, which is of utmost importance. Then, specify the purpose and work towards achieving them in the CRISP-DM framework. 

Focus on understanding the project objectives and requirements from a business perspective. Next, convert this knowledge using data mining and create a preliminary plan designed to achieve the following objectives:

  • What exactly should it accomplish?
  • What are the key factors e.g. constraints, competing objectives, etc.

2. Recognizing and Understanding Data

Recognize and understand the various datasets or sources of data that can be leveraged to solve the problem at hand. To unravel a business problem, the best process is to understand the available data and identify relevant data points for proper analysis.

  1. Gather Relevant Data:

    Identifying and collecting the appropriate datasets helps in data analysis. They should be available within the firm. Otherwise, you may need to collect the information from other sources such as open-source repositories or government data sets. Some examples of these sources are the UCI Machine Learning Repository or Kaggle.
  2. Describe Data:

    Once the information set is identified, explain its contents and explore insights to increase understanding of the information and its business implications. Then, create a knowledge dictionary while listing down the various types of variables e.g., attributes, full forms, number of records, and etc.
  3. Explore Data:

    To explore data, plot simple graphs on Excel/R/Python. For example, to understand the sales of a specific product, plot daily, monthly, and yearly representations of data.
  4. Check Data Quality:

    Once the structure of data is understood, examine the standard of knowledge and address various factors such as:
  • Is the data complete? Does it cover all the cases and records?
  • Is the data correct? Does it contain errors, and if there are errors, how common are they? 
  • Are there missing values within the data? If so, how are they represented?
  • Where do the missing values occur? For example, if sales are reported inaccurately as $-50K or $5B when the traditional reported sales range is between 0 - $50M, the data input will likely have errors.

3. Preparing Data

This is a critical and time-consuming step in the complete analysis. Every Data Analyst/Data Scientist spends 70-80% of the time in data preparation as it plays a significant role before applying any modeling on top of the data. Data sets must be well understood and prepared for before the investigation.

  1. Stored information usually comes from multiple sources and is available in different files and formats. The first objective is to combine them to solve a specific business problem. 
  2. After uniting the data, move it to the data preparation stage, which should have data cleansing steps, such as treating missing values, outliers, and irrelevant data.
  3. If more information is needed to enrich the existing data, use feature extraction or feature engineering.

4. Modeling Data

Data modeling is the most exciting step of the entire CRISP-DM process. The insights can be generated from the information after the preparation of data and by building models to unravel business problems.

Data modeling plays an essential role in the CRISP-DM framework. It is important to:

  1. Choose the right ML algorithms/models based on the problem statements.
  2. Select a relevant model from the list of algorithms based on the data type.

Example: How to teach a machine to choose a winning cricket team in the India Premier League (IPL).

The algorithms identify patterns in data and learn which parameters are of the utmost importance in reliably predicting a team's performance, such as batting average, captaincy score, strike rate, and wickets. Some data models use expert opinions from coaches and past players to incorporate subjective details, such as leadership and solidarity alongside hard statistics. The chosen parameters are inputs to the model, which gives the output we are interested in – whether the assigned team will win or lose. The results can then be iterated to find the most likely winner.

5. Evaluating the Model

A data model evaluation is necessary to check its accuracy, usefulness, understand how well it is performing, and review its continuous process. 

Once a specific algorithm is set, testers can increase the accuracy by tuning/tweaking the parameters of models until it achieves satisfactory evaluation results.

6. Deploying the Model

The final step in the framework is model deployment. Once the model passes the evaluation criteria, it is ready for deployment.

Translation of a model into a business strategy is the last stage, and it is called model deployment. CRISP-DM is an iterative process. For instance, your data understanding can enhance your business understanding. Similarly, after model evaluation, if the model does not perform well, you will need to return to the data preparation stage then, develop the model again.

Example: Consider the IPL as a business where the objective might be either to win or to maximize profits. It is essential to have a well-defined business objective before you can identify the goals of the data analysis problem. If the business objective is to win, the purpose of the analysis might be to spot the highest scoring players or the bowlers with the top wicket. On the other hand, if the business objective is to maximize profits, the goal of the analysis might be to spot the top players that attract funding. It is vital to define the business objectives clearly then, the purpose of the data analysis problem becomes easier. 

The data mining process must be reliable and repeatable without a dependency on the type of resources. CRISP-DM is flexible and easily applicable to different businesses with different types of data.

How Nisum Can Help

Nisum can help businesses with data understanding and provide insights by leveraging proper data mining methods. Leveraging past successes, we customize technology solutions that can help improve sales, marketing, and customer services. We build within the following areas: Customer, Marketing and Sales, and Supply Chain. Contact us for further inquiries.

Murali Kommanaboina

Written by Murali Kommanaboina

Murali Kommanaboina has been working with Nisum as a Senior Data Scientist. He has experience working in the retail, BFSI, and healthcare sectors.


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