When I was actively working in the Marketing Research domain, I designed and programmed a lot of surveys on employee satisfaction/morale/happiness for US companies. That was around 2004, I guess a lot has changed then.
I came across this article on Workforce Analytics by Becca Goren on the SAS website. It sounds very promising and it seems to be THE RIGHT THING TO DO. I have summarized the article and edited it a bit for my blog.
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Most organizations today do not track who is critical, who will likely leave, or why they will leave, so there’s no opportunity to develop effective strategies to retain critical employees.
Workforce analytics is the missing link in today’s business strategy. It is imperative for organizations to know how to attract, grow and retain these employees, as well as sustain the already seasoned professionals that bring depth and value to the organization.
Everyone across an organization can play a role:
• Business managers need to identify pending skill gaps and a pipeline for tomorrow’s leaders.
• Finance managers need to determine costs related to vacancies, overtime, outsourcing, recruitment and loss of critical skills, and then model strategies to address these issues.
• HR needs to spot trends and develop strategies to support changing workforce demands while partnering with business and finance managers to determine the best organizational structure/restructuring to address change.
Five ways to optimize the organization through its work force
1. Align work force with business goals:
• Forecast the amount and types of talent required to execute business strategy.
• Gain full information needed to make decisions for tomorrow.
• Manage the work force to drive the organization to meet its goals.
• Identify specific talent gaps.
2. Address workforce demands at every stage of the talent life cycle:
• Acquisition: Match the right employee with the right skills at the right time at the right cost.
• Growth: Develop skills for today’s star performers and tomorrow’s leaders.
• Retention: Proactively respond to changing workforce demographics and trends.
3. Identify and mitigate risks:
• Analyze the past and look forward to spot trends in key factors related to voluntary termination, absences and other sources of risk.
• Determine the impacts of organizational change on employee performance.
• Predict where vacancies and leadership needs are likely to occur.
• Understand workforce supply-and-demand patterns, and create strategies with additional labor sources to meet that demand.
4. Plan for business change, such as mergers, acquisitions and downsizing:
• Model what-if scenarios of potential effects across divisions and geographies.
• Make strategic decisions to reduce the risk of losing good employees and keeping redundant or underperforming ones.
5. Synchronize financial and operational workforce strategies:
• Expand background for each employee to look beyond salaries and general workforce costs for a more granular understanding: absences, overtimes, training costs, headcount, salaries and other compensation.
• Develop a defensible position on how costs drive value for the organization.
But my biggest question is how many organizations actually put these into practice?
Tuesday, April 28, 2009
Sunday, April 5, 2009
Thursday, March 19, 2009
Software Dependence & Model Accuracy
I work a lot with the Data Mining/Analytics business development team at my current company. My primary role is to be there during client presentations/conferences and answer the client’s queries on modeling techniques, and the USP of our approach related to model performance and/or business benefits.
During one of these interactions, we found out that a particular client is using THREE Data Mining softwares. Not statistical softwares or the base versions, but the complete, very expensive Data Mining softwares – SAS EM, SPSS Clementine and KXEN.
I was like, “Wow!!! But do you really need 3 Data Mining softwares???” Our initial questions and the client’s answers confirmed that inconsistent data formats was not the reason as the client already has a BI/DW system. Their reason? Well, they have the opinion that some algorithms/techniques in a particular DM software is much better and accurate than the same algorithms/techniques in another DM software.
I was, and I am, not convinced. Unless a particular DM software has a totally different and new algorithm for which you can’t obviously make a comparison, I haven’t come across or heard of any stark differences among model performances and results for the same algorithms offered by the reputed DM softwares. Data Mining solutions and the subsequent business benefits are not solely driven by model accuracy, a lot depends on how you interpret and apply the model’s results too.
What’s your opinion on this?
On a slightly different but related note, I learned of an interesting case from Rob Mattison’s webcast on Telco Churn Management available on the SAS website. He mentioned an incident where a client’s existing churn model was giving an impressive “above 90%” accuracy. Feeling something amiss, he went and talked with the Marketing people and found out that they were sending the same communication (sent at the time of acquisition) to the list of customers identified by the model as the most likely churners.
The result? The already unsatisfied customers who were thinking of switching got an inappropriate message/treatment, got further irritated and eventually left. In other words, all customers identified as likely churners by the model were encouraged to leave thereby shooting up the model accuracy!!!
If you have come across such cases, please share them with me in your comments:-)
Thursday, February 19, 2009
Two Step Cluster - Customer Segmentation in Telecom
I love Cluster Analysis because unlike a lot of other techniques, I don’t have to make any assumptions about the underlying distribution of the data. Though there are a few assumptions for best performance, it’s perfectly okay to cluster data that may not meet these assumptions. Only the business requirements/goals can determine whether the clusters/segments are useful or the solution is satisfactory.
Customer Segmentation is the process of splitting a customer database into distinct, meaningful, and homogenous groups based on specific parameters or attributes. At a macro level, the main objective for customer segmentation is to understand the customer base, monitor and understand changes over time, and to support critical strategies and functions such as CRM, Loyalty programs, and product development.
At a micro level, the goal is to support specific campaigns, commercial policies, cross-selling & up-selling activities, and analyze/manage churn & loyalty
SPSS has three different procedures that can be used to cluster data: hierarchical cluster analysis, k-means cluster, and two-step cluster. The two-step cluster is appropriate for large datasets or datasets that have a mixture of continuous and categorical variables. It requires only one pass of data (which is important for very large data files).
The first step - Formation of Preclusters
Preclusters are just clusters of the original cases that are used in place of the raw data to reduce the size of the matrix that contains distances between all possible pairs of cases. When preclustering is complete, all cases in the same precluster are treated as a single entity. The size of the distance matrix is no longer dependent on the number of cases but on the number of preclusters. These preclusters are then used in hierarchical clustering.
The second step - Hierarchical Clustering of Preclusters
In the second step, the standard hierarchical clustering algorithm is used on the preclusters.
The dataset I am going to use has information on 75 attributes for more than 70,000 customers. Product/service usage variables for all customers in the dataset are averages calculated over a period of four months.
In SPSS Clementine, the Data Audit available under the Output nodes palette gives the basic/descriptive statistics (mean, min, max...) and the quality (outliers, missing values...) of the variables.
Out of the 75 variables in the dataset, I used about 15 original variables and 3 new derived variables after considering their quality and business relevance. These selected variables were a combination of demographic, billing, and usage information.
The two-step cluster analysis produced 3 clusters. A very interesting difference was observed between Clusters 1 and 2.
Customers in Cluster 2 display the following characteristics:
- few of them are married
- few of them have children
- few of them have a credit card
- owns the most expensive mobile set
- maximum # of incoming & outgoing calls
- maximum # of roaming calls
- maximum MOU (minutes of usage)
- maximum # of active subscriptions
- maximum recurring charge (or, subscribes to the most expensive calling plan)
- maximum revenue
- maximum # of calls to customer care
- has the largest proportion of customers with low credit rating
Customers in Cluster 1 display characteristics that were exactly the opposite in ALMOST all of the areas mentioned above. So we have these customers who are married with children, posses a credit card, own a cheap mobile set, subscribe to the least expensive calling plan, make the minimum # of calls (incoming, outgoing, roaming & customer care), and has the highest credit rating.
Customers in Cluster 3 follow the middle path (in almost all the attributes) and offered no interesting or meaningful insights.
So what can be the business application of this exercise?
To put it simply, cluster analysis has thrown up two very distinct groups of customers – highly profitable but high risk customers in Cluster 2, and low profitable and low risk customers in Cluster 1.
For the highly profitable but high risk customers, one or more of the following actions can be implemented:
- Enhance credit risk monitoring
- Establish stringent usage thresholds
- Educate customers about alternative payment options, or make CC a mandatory payment method
- Migrate to pre-paid plans
For the low profitable and low risk customers, usage stimulation campaigns can be attempted with or without further segmentation.
This is one of the most basic examples of customer segmentation. If we consider traffic analysis information by taking ratios of certain call/service usage parameters, we can identify customer groups who have increased or decreased their usage. If we consider customer tenure, we can have an understanding of customer loyalty. Accordingly, specific actions can be taken for these groups.
Customer Segmentation is the process of splitting a customer database into distinct, meaningful, and homogenous groups based on specific parameters or attributes. At a macro level, the main objective for customer segmentation is to understand the customer base, monitor and understand changes over time, and to support critical strategies and functions such as CRM, Loyalty programs, and product development.
At a micro level, the goal is to support specific campaigns, commercial policies, cross-selling & up-selling activities, and analyze/manage churn & loyalty
SPSS has three different procedures that can be used to cluster data: hierarchical cluster analysis, k-means cluster, and two-step cluster. The two-step cluster is appropriate for large datasets or datasets that have a mixture of continuous and categorical variables. It requires only one pass of data (which is important for very large data files).
The first step - Formation of Preclusters
Preclusters are just clusters of the original cases that are used in place of the raw data to reduce the size of the matrix that contains distances between all possible pairs of cases. When preclustering is complete, all cases in the same precluster are treated as a single entity. The size of the distance matrix is no longer dependent on the number of cases but on the number of preclusters. These preclusters are then used in hierarchical clustering.
The second step - Hierarchical Clustering of Preclusters
In the second step, the standard hierarchical clustering algorithm is used on the preclusters.
The dataset I am going to use has information on 75 attributes for more than 70,000 customers. Product/service usage variables for all customers in the dataset are averages calculated over a period of four months.
In SPSS Clementine, the Data Audit available under the Output nodes palette gives the basic/descriptive statistics (mean, min, max...) and the quality (outliers, missing values...) of the variables.
Out of the 75 variables in the dataset, I used about 15 original variables and 3 new derived variables after considering their quality and business relevance. These selected variables were a combination of demographic, billing, and usage information.
The two-step cluster analysis produced 3 clusters. A very interesting difference was observed between Clusters 1 and 2.
Customers in Cluster 2 display the following characteristics:
- few of them are married
- few of them have children
- few of them have a credit card
- owns the most expensive mobile set
- maximum # of incoming & outgoing calls
- maximum # of roaming calls
- maximum MOU (minutes of usage)
- maximum # of active subscriptions
- maximum recurring charge (or, subscribes to the most expensive calling plan)
- maximum revenue
- maximum # of calls to customer care
- has the largest proportion of customers with low credit rating
Customers in Cluster 1 display characteristics that were exactly the opposite in ALMOST all of the areas mentioned above. So we have these customers who are married with children, posses a credit card, own a cheap mobile set, subscribe to the least expensive calling plan, make the minimum # of calls (incoming, outgoing, roaming & customer care), and has the highest credit rating.
Customers in Cluster 3 follow the middle path (in almost all the attributes) and offered no interesting or meaningful insights.
So what can be the business application of this exercise?
To put it simply, cluster analysis has thrown up two very distinct groups of customers – highly profitable but high risk customers in Cluster 2, and low profitable and low risk customers in Cluster 1.
For the highly profitable but high risk customers, one or more of the following actions can be implemented:
- Enhance credit risk monitoring
- Establish stringent usage thresholds
- Educate customers about alternative payment options, or make CC a mandatory payment method
- Migrate to pre-paid plans
For the low profitable and low risk customers, usage stimulation campaigns can be attempted with or without further segmentation.
This is one of the most basic examples of customer segmentation. If we consider traffic analysis information by taking ratios of certain call/service usage parameters, we can identify customer groups who have increased or decreased their usage. If we consider customer tenure, we can have an understanding of customer loyalty. Accordingly, specific actions can be taken for these groups.
Tuesday, February 3, 2009
The Stakeholders
According to the Encarta dictionary a stakeholder is a person or group with a direct interest, involvement, or investment in something.
The most important task faced by a Data Miner is to understand the client’s business background and arrive at the business and data mining objectives by asking the relevant, right questions to the right people. And the right people here are the so-called stakeholders; and identifying them makes the job half done!
According to Dorian Pyle, these stakeholders can be divided into five groups:
1. Need Stakeholders – People who actually experience the business problem regularly, in their work. In most situations, they have developed intuitive ideas about what is causing the problem, what is the solution, and how it should be applied. They often expressed their needs as an expected/desired solution, and not as a description of the problem.
2. Money Stakeholders – People who will commit the resources that allow the project to move forward. The business case document written to support modeling/the data mining project is mainly addressed to these people. It is usually not possible for this stakeholder to say “yes” to a project – that is the prerogative of the decision stakeholder - but they can easily say “no” if the numbers aren’t convincing.
3. Decision Stakeholders – People who make the decision of whether to execute the project. Someone very important but difficult to identify as this person is not directly involved with the data miner but relies instead on input from people who have interacted with the data miner.
4. Beneficiary Stakeholders – People who will get the benefit of the results of the data mining project/model; people who will be directly affected. They usually have the ability to promote the success or bring about the failure of many data mining projects.
5. Kudos Stakeholders – People who have sold the project internally. Credit for the project’s success will accrue to them, so will the negative impact of a less than successful project. Very important to understand from these people what it is that determines success, and how the project result will be evaluated.
The most important task faced by a Data Miner is to understand the client’s business background and arrive at the business and data mining objectives by asking the relevant, right questions to the right people. And the right people here are the so-called stakeholders; and identifying them makes the job half done!
According to Dorian Pyle, these stakeholders can be divided into five groups:
1. Need Stakeholders – People who actually experience the business problem regularly, in their work. In most situations, they have developed intuitive ideas about what is causing the problem, what is the solution, and how it should be applied. They often expressed their needs as an expected/desired solution, and not as a description of the problem.
2. Money Stakeholders – People who will commit the resources that allow the project to move forward. The business case document written to support modeling/the data mining project is mainly addressed to these people. It is usually not possible for this stakeholder to say “yes” to a project – that is the prerogative of the decision stakeholder - but they can easily say “no” if the numbers aren’t convincing.
3. Decision Stakeholders – People who make the decision of whether to execute the project. Someone very important but difficult to identify as this person is not directly involved with the data miner but relies instead on input from people who have interacted with the data miner.
4. Beneficiary Stakeholders – People who will get the benefit of the results of the data mining project/model; people who will be directly affected. They usually have the ability to promote the success or bring about the failure of many data mining projects.
5. Kudos Stakeholders – People who have sold the project internally. Credit for the project’s success will accrue to them, so will the negative impact of a less than successful project. Very important to understand from these people what it is that determines success, and how the project result will be evaluated.
Friday, January 9, 2009
Q & A with Eric Siegel, President of Prediction Impact
It's my pleasure to welcome Eric Siegel, President of Prediction Impact on datalligence. He has kindly answered some of my questions related to Data Mining.
Q1. A brief intro about yourself and your DM experience
Eric: I've been in data mining for 16 years and commercially applying predictive analytics with Prediction Impact since 2003. As a professor at Columbia University, I taught the graduate course in predictive modeling (referred to as "machine learning" at universities), and have continued to lead training seminars in predictive analytics as part of my consulting career.
I'm also the program chair for Predictive Analytics World, coming to San Francisco Feb 18-19. This is the business-focused event for predictive analytics professionals, managers and commercial practitioners. This conference delivers case studies, expertise and resources in order to strengthen the business impact delivered by predictive analytics.
Q2. What are the most common mistakes you've encountered while working on DM projects?
Eric: The main mistake is not following best practice organizational processes, as set forth by standards such as by CRISP-DM (mentioned in your Dec 18th blog on "Methodologies").
Predictive analytics' success hinges on deciding as an organization which specific customer behavior to predict. The decision must be guided not only by what is analytically feasible with the data available, but by which predictions will provide a positive business impact. This can be an elusive thing to pin down, requiring truly informed buy-in by various parties, including those who's operational activities will be changed by integrating predictive scores output by a model. The interactive process model defined by CRISP-DM and other standards ensures that you "plan backwards," starting from the end deployment goal, including the right personnel at key decision points throughout the project, and establishing realistic timelines and performance expectations
Dr. John Elder has a somewhat famous list of the top 10 common-but-deadly mistakes, which is an integral part of the workshop he's conducting at Predictive Analytics World, "The Best and the Worst of Predictive Analytics: Predictive Modeling Methods and Common Data Mining Mistakes". As he likes to say, "Best Practices by seeing their flip side: Worst Practices". For more information about the workshop, see The Best and the Worst of Predictive Analytics
Q3. Translating the Business Goal to a Data Mining Goal, and then defining the acceptable model performance/accuracy level for the success of the DM project appears to be one of the biggest challenges in a DM project. One approach is to use the typical accuracy level used in that particular domain. Another method is to model on a sample dataset (sort of a POC) to come up with an acceptable model performance/accuracy level for the entire dataset/project. Which approaches do you recommend/use to define the acceptable accuracy/cut-off level for a DM project?
Eric: Acceptable performance should be defined as the level where your company attains true business value. Establishing typical performance for a domain can be very tricky, since, even within one domain, each company is so unique - the context in which predictive models will be deployed is unique in the available data (which reflects unique customer lists and their responses or lack thereof to unique products) and in the operational systems and processes. Instead, forecast the ROI that will be attained in model deployment, based on both optimistic and conservative model performance levels. Then, if the conservative ROI looks healthy enough to move forward (or the optimistic ROI is exciting enough to take a risk), determine a minimal acceptable ROI and the corresponding model performance that would attain it as the target model performance level. This is then followed as the goal that must be attained in order to deploy the model, putting its predictive scores into play "in the field".
Q4. One thing I hear a lot from freshers entering the DM field is that they want to learn SAS. Considering the fact that SAS programming skills are highly respected and earn more than any other DM software skills, it's actually a futile exercise to convince these freshers that a tool-neutral DM knowledge is what they should actually strive for. What's your opinion on this?
Eric: Well, I think most people understand there are advantages to taking general driving lessons, rather than lessons that teach you only how to drive a Porsche. On the other hand, you can only sit in one car at a time, and when you learn how to drive your first car, most of what you learn applies in general, for other cars as well. All cars have steering wheels and accelerators; many predictive modeling tools share the same standard, non-proprietary core analytical methods developed at universities (decision trees, neural networks, etc.), and all of them help you prepare the data, evaluate model performance by viewing lift curves and such, and deploy the models.
Q5. According to you, what are the new areas/domains where DM is being applied?
Eric: I see human resource applications, including human capital retention, as an up-and-coming, and an interesting contrast to marketing applications: predict which employees will quit rather than the more standard prediction of which customer will defect.
I consider these the hottest areas (all represented by named case studies at PAW-09, by the way):
* Marketing and CRM (offline and online)
- Response modeling
- Customer retention with churn modeling
- Acquisition of high-value customers
- Direct marketing
- Database marketing
- Profiling and cloning
* Online marketing optimization
- Behavior-based advertising
- Email targeting
- Website content optimization
* Product recommendation systems (e.g., the Netflix Prize)
* Insurance pricing
* Credit scoring
Q6. In spite of the fact that a lot of companies in India provide Analytics or Data Mining as a service/solution to many companies around the world, there are no institutions/companies providing quality and industry focused Data Mining education. There are no colleges/universities offering Masters in Analytics/Data Mining in India. I have a lot of friends/colleagues who will gladly take up such courses/programs if they are made available in India. Can we expect this kind of courses/trainings from Prediction Impact, The Modeling Agency, TDWI, etc. in the near future?
Eric: I'm in on discussions several times a year about bringing a training seminar to other regions beyond North America and Europe, but it isn't clear when this will happen. For now, Prediction Impact does offer an online training program, "Predictive Analytics Applied" available on-demand at any time.
Q1. A brief intro about yourself and your DM experience
Eric: I've been in data mining for 16 years and commercially applying predictive analytics with Prediction Impact since 2003. As a professor at Columbia University, I taught the graduate course in predictive modeling (referred to as "machine learning" at universities), and have continued to lead training seminars in predictive analytics as part of my consulting career.
I'm also the program chair for Predictive Analytics World, coming to San Francisco Feb 18-19. This is the business-focused event for predictive analytics professionals, managers and commercial practitioners. This conference delivers case studies, expertise and resources in order to strengthen the business impact delivered by predictive analytics.
Q2. What are the most common mistakes you've encountered while working on DM projects?
Eric: The main mistake is not following best practice organizational processes, as set forth by standards such as by CRISP-DM (mentioned in your Dec 18th blog on "Methodologies").
Predictive analytics' success hinges on deciding as an organization which specific customer behavior to predict. The decision must be guided not only by what is analytically feasible with the data available, but by which predictions will provide a positive business impact. This can be an elusive thing to pin down, requiring truly informed buy-in by various parties, including those who's operational activities will be changed by integrating predictive scores output by a model. The interactive process model defined by CRISP-DM and other standards ensures that you "plan backwards," starting from the end deployment goal, including the right personnel at key decision points throughout the project, and establishing realistic timelines and performance expectations
Dr. John Elder has a somewhat famous list of the top 10 common-but-deadly mistakes, which is an integral part of the workshop he's conducting at Predictive Analytics World, "The Best and the Worst of Predictive Analytics: Predictive Modeling Methods and Common Data Mining Mistakes". As he likes to say, "Best Practices by seeing their flip side: Worst Practices". For more information about the workshop, see The Best and the Worst of Predictive Analytics
Q3. Translating the Business Goal to a Data Mining Goal, and then defining the acceptable model performance/accuracy level for the success of the DM project appears to be one of the biggest challenges in a DM project. One approach is to use the typical accuracy level used in that particular domain. Another method is to model on a sample dataset (sort of a POC) to come up with an acceptable model performance/accuracy level for the entire dataset/project. Which approaches do you recommend/use to define the acceptable accuracy/cut-off level for a DM project?
Eric: Acceptable performance should be defined as the level where your company attains true business value. Establishing typical performance for a domain can be very tricky, since, even within one domain, each company is so unique - the context in which predictive models will be deployed is unique in the available data (which reflects unique customer lists and their responses or lack thereof to unique products) and in the operational systems and processes. Instead, forecast the ROI that will be attained in model deployment, based on both optimistic and conservative model performance levels. Then, if the conservative ROI looks healthy enough to move forward (or the optimistic ROI is exciting enough to take a risk), determine a minimal acceptable ROI and the corresponding model performance that would attain it as the target model performance level. This is then followed as the goal that must be attained in order to deploy the model, putting its predictive scores into play "in the field".
Q4. One thing I hear a lot from freshers entering the DM field is that they want to learn SAS. Considering the fact that SAS programming skills are highly respected and earn more than any other DM software skills, it's actually a futile exercise to convince these freshers that a tool-neutral DM knowledge is what they should actually strive for. What's your opinion on this?
Eric: Well, I think most people understand there are advantages to taking general driving lessons, rather than lessons that teach you only how to drive a Porsche. On the other hand, you can only sit in one car at a time, and when you learn how to drive your first car, most of what you learn applies in general, for other cars as well. All cars have steering wheels and accelerators; many predictive modeling tools share the same standard, non-proprietary core analytical methods developed at universities (decision trees, neural networks, etc.), and all of them help you prepare the data, evaluate model performance by viewing lift curves and such, and deploy the models.
Q5. According to you, what are the new areas/domains where DM is being applied?
Eric: I see human resource applications, including human capital retention, as an up-and-coming, and an interesting contrast to marketing applications: predict which employees will quit rather than the more standard prediction of which customer will defect.
I consider these the hottest areas (all represented by named case studies at PAW-09, by the way):
* Marketing and CRM (offline and online)
- Response modeling
- Customer retention with churn modeling
- Acquisition of high-value customers
- Direct marketing
- Database marketing
- Profiling and cloning
* Online marketing optimization
- Behavior-based advertising
- Email targeting
- Website content optimization
* Product recommendation systems (e.g., the Netflix Prize)
* Insurance pricing
* Credit scoring
Q6. In spite of the fact that a lot of companies in India provide Analytics or Data Mining as a service/solution to many companies around the world, there are no institutions/companies providing quality and industry focused Data Mining education. There are no colleges/universities offering Masters in Analytics/Data Mining in India. I have a lot of friends/colleagues who will gladly take up such courses/programs if they are made available in India. Can we expect this kind of courses/trainings from Prediction Impact, The Modeling Agency, TDWI, etc. in the near future?
Eric: I'm in on discussions several times a year about bringing a training seminar to other regions beyond North America and Europe, but it isn't clear when this will happen. For now, Prediction Impact does offer an online training program, "Predictive Analytics Applied" available on-demand at any time.
Thursday, December 18, 2008
Data Mining Methodologies
I use the CRISP-DM methodology for all Data Mining projects as it is industry and tool neutral, and also the most comprehensive of all the methodologies available. Some Data Mining software vendors have come up with their own methodologies though they are basically the same. Check them out.
MS SQL SERVER DATA MINING
1. Defining the Problem: Analyze business requirements, define the scope of the problem, define the metrics by which the model will be evaluated, and define specific objectives for the data mining project.
2. Preparing Data: Remove/handle bad data, find correlations in the data, identify sources of data that are the most accurate, and determining which columns are the most appropriate for use in analysis.
3. Exploring the Data: Calculate the minimum and maximum values, calculate mean and standard deviations, and look at the distribution of the data.
4. Building Models: Specify the input columns, the attribute that you are predicting, and parameters that tell the algorithm how to process the data.
5. Exploring & Validating Models: Use the models to create predictions, which you can then use to make business decisions, create content queries to retrieve statistics, rules, or formulas from the model, embed data mining functionality directly into an application, update the models after review and analysis or update the models dynamically, as more data comes into the organization.
ORACLE DATA MINING
1. Problem Definition: Specify the project objectives and requirements from a business perspective, formulate it as a data mining problem and develop a preliminary implementation plan.
2. Data Gathering and Preparation: Take a closer look at the data, remove some of the data or add additional data, identify data quality problems, and scan for patterns in the data. Typical tasks include table, case, and attribute selection as well as data cleansing and transformation.
3. Model Building and Evaluation: Select and apply various modeling techniques and calibrate the parameters to optimal values. If the algorithm requires data transformations, step back to the previous phase to implement them.
4. Knowledge Deployment: Can involve scoring (the application of models to new data), the extraction of model details (for example the rules of a decision tree), or the integration of data mining models within applications, data warehouse infrastructure, or query and reporting tools.
SEMMA from SAS
1. Sample the data by creating one or more data tables. The sample should be large enough to contain the significant information, yet small enough to process.
2. Explore the data by searching for anticipated relationships, unanticipated trends, and anomalies in order to gain understanding and ideas.
3. Modify the data by creating, selecting, and transforming the variables to focus the model selection process.
4. Model the data by using the analytical tools to search for a combination of the data that reliably predicts a desired outcome.
5. Assess the data by evaluating the usefulness and reliability of the findings from the data mining process.
CRISP-DM (CRoss Industry Standard Process for Data Mining)
1. Business Understanding: Understand the project objectives and requirements from a business perspective, convert this knowledge into a data mining problem definition, and a preliminary plan designed to achieve the objectives.
2. Data Understanding: Collect initial data and proceed with activities in order to get familiar with the data, to identify data quality problems, to discover first insights into the data, or to detect interesting subsets to form hypotheses for hidden information.
3. Data Preparation: Tasks include table, record, and attribute selection as well as transformation and cleaning of data for modeling tools.
4. Modeling: Select and apply various modeling techniques, calibrate their parameters to optimal values, step back to the data preparation phase if needed.
5. Evaluation: Evaluate the model, review the steps executed to construct the model, to be certain it properly achieves the business objectives. At the end of this phase, a decision on the use of the data mining results should be reached.
6. Deployment: Depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data mining process. In many cases it will be the customer, not the data analyst, who will carry out the deployment steps.
MS SQL SERVER DATA MINING
1. Defining the Problem: Analyze business requirements, define the scope of the problem, define the metrics by which the model will be evaluated, and define specific objectives for the data mining project.
2. Preparing Data: Remove/handle bad data, find correlations in the data, identify sources of data that are the most accurate, and determining which columns are the most appropriate for use in analysis.
3. Exploring the Data: Calculate the minimum and maximum values, calculate mean and standard deviations, and look at the distribution of the data.
4. Building Models: Specify the input columns, the attribute that you are predicting, and parameters that tell the algorithm how to process the data.
5. Exploring & Validating Models: Use the models to create predictions, which you can then use to make business decisions, create content queries to retrieve statistics, rules, or formulas from the model, embed data mining functionality directly into an application, update the models after review and analysis or update the models dynamically, as more data comes into the organization.
ORACLE DATA MINING
1. Problem Definition: Specify the project objectives and requirements from a business perspective, formulate it as a data mining problem and develop a preliminary implementation plan.
2. Data Gathering and Preparation: Take a closer look at the data, remove some of the data or add additional data, identify data quality problems, and scan for patterns in the data. Typical tasks include table, case, and attribute selection as well as data cleansing and transformation.
3. Model Building and Evaluation: Select and apply various modeling techniques and calibrate the parameters to optimal values. If the algorithm requires data transformations, step back to the previous phase to implement them.
4. Knowledge Deployment: Can involve scoring (the application of models to new data), the extraction of model details (for example the rules of a decision tree), or the integration of data mining models within applications, data warehouse infrastructure, or query and reporting tools.
SEMMA from SAS
1. Sample the data by creating one or more data tables. The sample should be large enough to contain the significant information, yet small enough to process.
2. Explore the data by searching for anticipated relationships, unanticipated trends, and anomalies in order to gain understanding and ideas.
3. Modify the data by creating, selecting, and transforming the variables to focus the model selection process.
4. Model the data by using the analytical tools to search for a combination of the data that reliably predicts a desired outcome.
5. Assess the data by evaluating the usefulness and reliability of the findings from the data mining process.
CRISP-DM (CRoss Industry Standard Process for Data Mining)
1. Business Understanding: Understand the project objectives and requirements from a business perspective, convert this knowledge into a data mining problem definition, and a preliminary plan designed to achieve the objectives.
2. Data Understanding: Collect initial data and proceed with activities in order to get familiar with the data, to identify data quality problems, to discover first insights into the data, or to detect interesting subsets to form hypotheses for hidden information.
3. Data Preparation: Tasks include table, record, and attribute selection as well as transformation and cleaning of data for modeling tools.
4. Modeling: Select and apply various modeling techniques, calibrate their parameters to optimal values, step back to the data preparation phase if needed.
5. Evaluation: Evaluate the model, review the steps executed to construct the model, to be certain it properly achieves the business objectives. At the end of this phase, a decision on the use of the data mining results should be reached.
6. Deployment: Depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data mining process. In many cases it will be the customer, not the data analyst, who will carry out the deployment steps.
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