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Mathematical models are developed in every area from atomic energy to bio technology. Models are generally designed to understand the real world phenomena mathematically/algorithmically. A model is a prototype of reality or an abstraction. Real world contains a lot of assumptions and axioms, despite this we can fit the real world process into a mathematical model.
Every process, business or company can be considered to contain a list of inputs, transformations / systems (working) and finally the output. Of course all of these operate in a set of external and internal conditions (called environment). This real world phenomena can possibly be represented in mathematical form in terms of flowchart & numbers.
Pictorial and mathematical representation will help in optimization and alignment to the business KPIs. Coming to KPIs, these are set of metrics which measure performance of the business. These are performance metrics that are tracked, measured & monitored on regular basis.
Mathematical models contains an additional loop (feedback loop) in normal flow chart, gets formed from output to system as a feedback of performance. This feedback contains information on what to improve and other intelligence that is fed back into the system. The feedback can be however not necessarily limited to effectiveness and efficiency of the system. First is the ability of the system to meet the desired output/ objectives. Second is the relationship between input and output, measure of how good is the relationship.
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Users must understand that all mathematical models will only help support a decision or help solve a problem. At the time of decision making a knowledge worker generally has one or more alternatives to solve the problem. A list of feasible solutions will need to be chalked out from all possible solutions. The best solution or optimum solution to be derived from the list of feasible solutions again based on criteria such as profitability, cost, response etc.
Companies are slowly adopting to the data mining as a concept, they are looking for quick and easy solution for their problems. On the other end there are whole host of companies which are offering software solution and tools for data mining.
There is a danger here, the easy use of GUI tools on large amount of data available is tempting and which makes users tempt to use black box methodologies available in the tools to solve their business problems. They mistakingly assume data mining is all about using a tool and running the data on the tool, this can be very hazardous and dangerous as actions are taken on business decisions.
Little knowledge is dangerous while applying powerful models.
Since the underlying logic used for model building in automated tools are unknown (their internal assumptions are unclear). Therefore it is very easy to do it in the wrong way assuming it is the right solution. Such mistakes are still made in companies.
People sometimes might argue that there is always a simpler solution to problems. I am the person who belives in Occam’s Razor, which means simplest explanation is always the best way. This means the interpretation of the model and the final solution should be as simple as possible. However, method of arriving at solution must be as detail as possible taking every factor, element into consideration. Analysts must not make the mistake of over simplyfying the method and make too many assumptions.
A lot of knowledge workers believe that predictive modeling process can be automated by using tools and statistical software. These software are certainly useful but cannot replace the intellectual of the analyst.
The processof building the model must be as detailed as possible, it has to tried & tested on various data and measured by various parameters. All possible factors must be considered while building the model.
Therefore Analyst must understand the underlying algorithm , process of arriving score, model design etc.
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Decision makers or knowledge workers are looking for information and knowledge at various kinds for making strategic or even mundane decisions. The process of extraction of information and knowledge from data is called data mining. This knowledge and information can be represented in various formats, this could be as simple as mean, median, mode, count or it could be in a graphical format such as histogram, line, pie chart, trend lines or moving averages. Advanced techniques like learning models, business optimizations are next level of knowledge requirment for the organization.
Even a using a tool as simple as a spreadsheet will be extremely helpful in providing a mental representation of business situation. Most commonly used statistical techniques can be implemented in a spreadsheet as simple as MS EXCEL.
There are some very important techniques to business intelligence analysis. Most importantly defining the objective and performance indicators, these are metrics that are used to estimate performance of an object (entity). The next is developing mathematical relationships between variables and metrics through finding patterns. The last is What-If analysis is by determining variations in the output metric by changing the input variables.
The advantage of using mathematical models is beyond increasing performance and ROI. It helps knowledge workers in deeper analysis of the business and underlying product/domain. This will increase awareness in the company, knowledge transfer within the company, and higher desire to learn better things. It encourages intellectual thinking within the company and promote people with good analytical skills who can offer great value to the company.\
There are many techniques like regression and classification, which are some of the popular mathematical models,however predictive analytics are not limited to these methods.
Regression: Linear Regression, kNN, CART, Neural Net
Classification: Logistic Regression, Bayesian Methods, Discriminant Analysis, Neural Net, kNN, CART.
There are some limitations and advantages of each of the methods. The right model and right mathematical technique to be choosen for each problem. The underlining business value that needs to be increased with each of the techniques.
Best techniques are formulates after and testing and evaluating each approach and measuring the impact of success or performance. All mathematical models use simple statistical techniques, however the value is in mapping the business problem into a mathematical problem, this requires some intellectual talent.
Models developed can be extremely useful in business critical process like sales, marketing and product.
More on such topics will be published on http://www.praveenkodur.com/blog/
Business Intelligence can be referred to a set of analysis methodologies & mathematical techniques that leverage the available data to derive knowledge / information that can be used to support/action business critical decisions. Call for a robust BI system comes from the need of making effective decisions, which has to be based on accurate information about business.
Decisions are taken by knowledge workers at all levels of the company. Most of the knowledge workers generally adopt decision making style based on experience, gut feel, instinctive & spontaneous techniques. Such methods soon become predictable & stagnant. In a long run it does not prove useful in rapidly changing economic, technology & business environment. In rapidly changing technological environment, process become increasingly complex,
To stay competitive in a dynamic business environment requires a decision making style which is supported by knowledge driven by information & data. Analytics can provide a strategic value in providing competitive advantage to the companies.
For example, Internet company observes a lot of customer attrition on their website. These users may switch to a competitive website. The company is looking to reduce this churn by offering a discount or a trial. To increase the effectiveness of campaign is to recognize those customers and estimate their probability to discontinue the service. From a given list of customers we can a implement a model to estimate the probabilities and arrive at a smaller subset of users to be targeted for the campaign. This smaller set of users will be the majority of the future discontinued users.
Such tools and methodologies support knowledge workers in making better decisions. Generally several options are available to users before making a decision. This actually helps them choose the best methodology.
Often there is a enormous amount of effort is required to extract knowledge from data. Data is present in most raw format almost incomprehensible. Data needs to be transferred to human readable format and then has to be processed (sometimes tortured) to extract information and then to knowledge to make it useful for knowledge workers.
More articles in this blog http://www.praveenkodur.com/blog will talk in detail about BI systems.
Extracting knowledge from data requires deployment of various systems, process and maintenance. Installing Transaction servers to maintaining data warehousing systems to creating decision support systems. The idea of a Decision support system is to promote a scientific and rational approach to management.
Business intelligence architecture must be designed to function efficiently with cost effective components. There are various components such as logging systems, data warehouse, BI methodologies.
Analytics plays a important role in customer relationship management. CRM is area which includes a whole host of activities which involves using the CRM software to campaign management tool to call tracking tool to database marketing tool etc. Each tool holds a customer information and in some way has a touch point with the customer. For building a good business it is important to deal with customer individually, rather than deal with competitor.
Focus for many companies such as Banks, Insurance companies, Telecommunication companies is to track customer end to end, from the time money spent on the customer acquisition to the time revenue is generated from the consumer for product/service until the customer attrites from the firm. Every activity in the chain must be closely observed to evaluate the true value of customer. Companies worldwide are creating process to deal with customers individually. This can help them devout more attention to customers who are more valuable to the business and let go customers not valuable to business.
Data Mining requires a lot of effort/techniques and focus to centre their business around the customer than a product. Companies have to constantly keep a watch on what their customers are doing, keep in mind their past actions, discover knowledge from their actions (gain knowledge) and finally use the knowledge cleverly to make decisions to make profit.
However data mining is not always beneficial for the user, consider the fact a model recommends a service to a user instead of the product A. however if the business makes more profit on product and very negligible amount on service. Model recommendations may not be implemented. However it helps in understanding such customers.
Good question to ask is how can a consumer company with large base of customers can individually deal with each consumer. This can be accomplished intelligently by deploying effective technology solutions which is customisable based on data mining models and techniques. Nowadays the customer is the data entry operator who enters data into the system at various points and these are captured by the system. Consider your bank, your touch points are ATM, Bank Branch, customer care who responds on phone, mail, hard mail & lastly your account /loan/credit card that you hold of the bank. Each transaction records your behaviour which can be an additional knowledge that bank is made aware of, this knowledge can be used by the bank to learn more about you and customise the next interaction or next touch point instance with you.
The transaction system records every instance of transaction of the user enabling the bank to analyse the nature of every transaction and update your profile with rich insights. The knowledge discovery doesnt end here. It needs support of data warehousing system along with extremely good data mining models to be able to take action / make decisions and deal with each customer. More on this will be written in more detail in coming articles.
The blog http://www.praveenkodur.com/blog/ will be updated with more such analytics, online marketing articles.
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