Why Analytics Fail To Deliver?

Just came across this post from Vincent Granville on AnalyticBridge. Quite interesting and informative. Am sharing it here, along with a few additions of my own.

- The model is more accurate than the data: you try to kill a fly with a nuclear weapon.
- You spent one month designing a perfect solution when a 95% accurate solution can be designed in one day. You focus on 1% of the business revenue, lack vision / lack the big picture.
- Poor communication. Not listening to the client, not requesting the proper data. Providing too much data to decision makers rather than 4 bullets with actionable information. Failure to leverage external data sources. Not using the right metrics. Problems with gathering requirements. Poor graphics or graphics that is too complicated.
- Failure to remove or aggregate conclusions that do not have statistical significance. Or repeating many times the same statistical tests, thus negatively impacting the confidence levels.
- Sloppy modeling or design of experiments, or sampling issues. One large bucket of data has good statistical significance, but all the data in the bucket in question is from one old client with inaccurate statistics. Or you use two databases to join sales and revenue, but the join is messy, or sales and revenue data do not overlap because of a different latency.
- Lack of maintenance. The data flow is highly dynamic and patterns change over time, but the model was tested 1 year ago on a data set that has significantly evolved. Model is never revisited, or parameters / blacklists are not updated with the right frequency.
- Changes in definition (e.g. include international users in the definition of a user, or remove filtered users) resulting in metrics that lack consistency, making vertical comparisons (trending, for a same client) or horizontal comparisons (comparing multiple clients at a same time) impossible.
- Blending data from multiple sources without proper standardizations: using (non-normalized) conversion rates instead of (normalized) odds of conversion.
- Poor cross validation. Cross validation should not be about randomly splitting the training set into a number of subsets, but rather comparing before (training) with after (test). Or comparing 50 training clients with 50 different test clients, rather than 5000 training observations from100 clients with another 5000 test observations from the same 100 clients. Eliminate features with statistical significance but lack of robustness when comparing 2 time periods.
- Improper use of statistical packages. Don't feed a decision tree software with a raw metric such as IP address: it just does not make sense. Instead provide a smart binned metric such as type of IP address (corporate proxy, bot, anonymous proxy, edu proxy, static IP, IP from ISP, etc.)
- Wrong assumptions. Working with dependent independent variables and not handling the problem. Violations of the Gaussian model, multimodality ignored. External factor explains the variations in response, not your independent variables. When doing A/B testing, ignoring important changes made to the website during the A/B testing time period.
- Lack of good sense. Analytics is a science AND an art, and the best solutions require sophisticated craftsmanship (the stuff you will never learn at school), but might usually be implemented pretty fast: elegant/efficient simplicity vs. inefficient complicated solutions.

My additions:

- What is your problem?
Without a real business problem, modeling or data mining will just give you numbers. I have come across people, on both the delivery and client side, who comes up with this often repeated line “I’ve got this data, tell me what you can do?”
My answer to that – “I will give you the probability that your customer will attrite based on the last 2 digits of her transaction ID. Now, tell me what are you going to do to make her stay with your business?”

Start with a REAL business problem.

- What are you gonna do about it?
So if your customers are leaving in alarming numbers, don’t just say that you want an attrition/churn model. Think on how you would like to use the model results. Are you thinking of a retention campaign? Are you to going to reduce churn by focusing on ALL the customers most likely to leave? Or are you going to focus on a specific subset of these customers (based on their profitability, for example)? How soon can you launch a campaign? How frequently will you be targeting these customers?

Have a CLEAR idea on what you are going to do with the model results.

- What have you got?
Don’t expect a wonderful earth-shattering surprise from all modeling projects. Model results or performances are based on many factors, with data quality as the most important one, in almost all the cases. If your database is full of @#$%, remember one thing. Garbage in, Garbage out. Period.

- Modeling is not going to give you a nice easy-to-read chart on how to run businesses.

- Technique is not everything.
A complex technique like (Artificial) Neural Networks doesn’t guarantee a prize winning model. Selecting a technique depends on many factors, with the most important ones being data types, data quality and the business requirement.

- Educate yourself
It’s never too late to learn. For people on the delivery side, modeling is not about the T-test and Regression alone. For people on the client side, know what Analytics or Data Mining can do, and CANNOT do. Know when, where and how to relate the model results with your business.

Analytics: Reality and the Growing Interest

This is a guest post by Bhupendra Khanal, CEO of InRev Systems.

InRev Systems is a Bangalore based Decision Management Company, which works on Data Based Information Systems. Their interest areas are Marketing Services, Web Information, MIS Reporting, Social Media Services and Economic Research. Bhupendra also maintains a personal blog at Business Analytics.

Introduction
Huge amount of data is collected by any business houses today. There are also certain data collection agencies who have information like economic variables, demographic variables, police fraud list, loan default list, telephone and electricity bill payment history etc. All these data if analyzed, tend to separate people into various similar groups. These groups can be fraudulent group, defaulter group, risk averse and risk takers group, high income and low income groups, etc.

Based on this information, many business decisions can be made in a better and rational way. Analytics leverages almost the same concept but with assumptions:
· The behavior of people do not change with time
· People with similar profile behave similarly

Predictive Modeling and Segmentation are the major components of Analytics. The profile and the behavior of a set of people are taken, and a relation is found out. This same relation is used for building future profiles and predicting the behavior of people having the same profiles. This is commonly done using advanced statistical techniques like Regression Modeling (Linear, Logistic, and Poisson etc) and Neural Networks.

The Business Analytics Services Market comprises solutions for storing, analyzing, modeling, and delivering information in support of decision-making and reporting processes.

Analytics, regardless of its complexity, serve the same purpose – to assist in improving or standardizing decisions at all levels of an organization.

Size and Type of Market
The size of the Analytics market globally is estimated around $25 billion today. It is increasing very fast, doubling almost every five years for the last few decades, with $19 billion dollars in 2006. It is expected to grow to $31 billion by the end of 2011 (source - IDC 2007).

There are many areas for the implementation of Analytics. The most common Analytics practices are Risk Management, Marketing Analytics, Web Analytics, and Fraud Prediction etc. These functions are handled by different organizations in different ways, with most companies maintaining a fine balance with the in-house team, outsourcing partner and consulting project vendors. Such variation makes calculation of market size very difficult.

Risk Management is one of the largest component of the Analytics Industry today, and it is the pioneer component too. The market is huge in the US and Europe; Asia Pacific is coming up fast and it is yet to get full swing in China and South Asia, including Nepal.

Web Analytics (analytics of Web Data) has picked up very fast and it is increasing by more than 20% for the last few years, thanks to the revolution led by Amazon, Google and Yahoo. Web Analytics market is a late entrant but have already passed the one billion mark.

The biggest of all is the Marketing Analytics (MA) and Strategy Science component. This is really huge owing to the effort put by companies like Dunhumby, Axiom and others. MA is critical as it tends to compete directly with Marketing Research Firms and Strategy Consulting firms in the type of work it does. This makes it difficult to calculate the market size but it is worth billions of dollars.

Big Players in the Area
The size of the Analytics market is huge today but the industry is fragmented. None of the core Analytics and Decision Management Company has ever touch a billion dollar revenue mark.

Fair Isaac is the pioneer and the largest Decision Management Company with revenue of around 800 million dollars. The other core DM and Analytics companies are quite smaller. This has happened due to the aggressive moves by IT Services Companies and Information Bureaus to acquire Analytics companies.

Experian, Transunion and Equifax are three major bureaus in the US while there are others too - Innovis, Axiom, Teletrack, Lexis Nexis etc. Each of these bureaus has analytics services as their offerings. Apart from these the BI majors like SAS, SPSS, Salford Systems etc. offer Analytics Services.

The Indian Analytics Market is small but growing. This can be justified by the more than double salary hike rates in Analytics compared to the Software domain. The outsourcing shops and India-focused companies both have mushroomed in the last five years in India while the problem remains in getting proper talent and retaining them.

Major Banks like ICICI and HDFC have their strong in-house analytics unit while SBI has partnered with GE Money for Analytics support for their Cards portfolio. The smaller banks are yet to start.

Other majors in non banking industries are also using Analytics through Outsourcing or Consulting. Airtel and Reliance are leading the way for the use of Analytics in India in Telecom.

Scope on how it can grow: Indian Context
The Analytics Industry is growing fast. It has the scope of forming a separate process across industries like HR, Operations, and IT System. IT is now in the early stage of process metamorphosis, where each process start through consulting, grow through in-house establishments and finally settle down as outsourcing to third parties.

The future growth depends on the approach of major players and consolidation of the industry. All this will make it a high value and high growth industry, where players can provide high quality products and services while maintaining their profitability.

Another big challenge is the supply of quality man-power and training. Today India neither has good institutes training people on Analytics nor has it got an Infosys for Analytics (who can employ and train huge number of freshers). Even the number of good Statistics and Mathematics Institutes in India is less.

Amidst all these challenges, India is positioned fairly well in the world today, and it will be interesting to see if it can become a Knowledge Process and Analytics hub in the days ahead.

A Tale Of Two Banks and One Telecom Service Provider

I have bank accounts at ICICI & Citibank (India). I also use credit cards of these two companies. Let’s first talk about their Mobile Banking services. ICICI has the following options:

My account getting credited above
My account getting debited above
Salary credited to my account #
Cheque deposited in my account bounced
Account Balance above
Account Balance below
Debit Card Purchases above

Messages for the above can be received through SMS, Email or both.

Citibank calls it Alerts, and they offer the following options:

Withdrawal balance by account
Withdrawal balance by account
Time deposit maturity advice
Cheque Status
Cheque Bounce Alert
Time Deposit Redemption Notice
Cheque dishonor

These messages can be received through SMS, Email or Both depending on the alert type. Also, for some of the alerts, message frequencies can be chosen as Daily, Weekly, or Monthly.

At first glance, it seems that Citibank offers more options but a closer look will reveal ICICI has done more research and come up with a better offering. The Citibank alerts are based on how frequently you want them while ICICI’s alerts are based on particular (defined by the customer) credited and debited amounts. ICICI’s options make more sense as I don’t want alerts daily or weekly, but only when I have made a transaction.

And because of the lack of options, I continue to receive daily alerts on my Citibank account balance irrespective of the fact that the balance remains the same or I haven’t done any transaction for a month. Also, the system at Citibank looks like a typical CRM system while the one at ICICI looks more like a BI system.

Now, let’s discuss their Credit Cards and their Customer Analytics.

I have been using an ICICI Gold credit card for almost 3 years now. I used it to pay all my bills – electricity bill, mobile bill, internet bill, shopping bills….and I paid all dues in time. I applied for and got the Citibank Gold card about a year after I got my ICICI card. I used the Citibank Gold card for 2-3 purchases, paid all the dues in time, and Citibank increased my credit limit every time.

Encouraged by their response (I got very nice emails from their customer service) and actions (increase of credit limit), I started using the Citibank Gold card more frequently. In a few months’ time, I got a free-for-life Citibank Platinum card with all these attractive features and benefits. I even got an invitation to join a wine club though I’m more of a rum and whiskey guy. Don’t blame them though; getting your hands on such kind of consumer lifestyle/preferences data will be next to impossible in India.

I have now almost forgotten the ICICI Gold card; I use it very rarely these days. The credit limit given to me 3 years back still remains the same. And I have never received a single email or communication from ICICI. I also know that ICICI bank outsources its Customer Analytics to an Analytics Service Provider in Mumbai. So where is the up sell analytics? Doesn’t their data show the fact that I am “almost” leaving now? That again reveals the fact that customer spending information is not at all analyzed and they are not doing much about customer churn either.

On a different note, I am a post-paid mobile customer of India’s largest telecom service provider, Airtel. Every month, whenever my bill is generated I receive about 6 SMSs from Airtel within the next 5-6 days. The messages can be summarized as:

1. Your bill has been generated…
2. You can view your bill at your online account…
3. Your bill has been emailed to abc@gmail.com and the password to open it is… and if you haven’t received it… (this is actually 2 SMSs because of the length of the message)
4. Your bill amount is XXX…
5. Your bill amount is XXX and the last date of payment is…

For the last 3 years or so, I have been paying all my bills before the due date. Once I got so irritated that I emailed their customer service, and their reply? These are server generated messages and we can’t do anything about it. I got more irritated and asked my email to be forwarded to Airtel’s CRM, Business Intelligence, Analytics or whatever the team there like to call themselves!

I got just one SMS alert when my next month’s bill was generated. But it was back to square one from the 2nd month onwards.

My question is which “smart” manager came up with the idea that 6 SMSs should be sent to all their post-paid customers every month? Why doesn’t one SMS saying “Your bill amount of XXX has been generated and the due date is ABC” suffice? Has anyone at Airtel calculated the cost of sending 5-6 SMSs to all their post-paid customers, every month? Shouldn’t the last SMS be sent only to those customers who have a habit of making late payments? Do they send the second SMS to those customers who don’t have online accounts too? And why can’t these alerts be customized based on a customer’s usage and payment behavior?

I can give more examples of Indian companies in the retail, entertainment, and services sector that are not doing nothing or very little about all the customer data they have, inspite of mentioning or advertising that they use BI & Analytics. So how mature is the Business Intelligence, and CRM Analytics setup at Indian companies? And how skilled or knowledgeable are the senior people associated with it?

Dilbert on Data Mining

The other (lighter?) side of Data Mining :-)

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:-)

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.

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.

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.