SPSS and Business Intelligence are two new words added to my MBA dictionary today. Words, being as deceptive as they are, usually hide a big picture behind them. For instance, 'An alley down the 4th street of Jaya Nagar, Bangalore' is a bigger word description than 'The Sahara'. But to understand the magnitude of the words, one has to see the big picture, philosophically and in this case of Jaya Nagar Alley vs Sahara, quite literally too!
So let me try to depict the big picture behind these two words. SPSS forms a subset of Business Intelligence Tools which in turn forms a subset of Data Mining Applications which in term forms a subset of Statistics Applications which is quite big enough a picture for us. So let us stop here and explore this picture in front of us. For starters, some Wiki definitions as follows:
Data Mining: Data mining, a branch of computer science and artificial intelligence, is the process of extracting patterns from data.
Business Intelligence: Business intelligence (BI) refers to computer-based techniques used in spotting, digging-out, and analyzing business data, such as sales revenue by products and/or departments, or by associated costs and incomes.
SPSS: A computer program for statistical analysis, original designed for Social Sciences.
An interesting example of an application of Data Mining that I spotted while mining some websites is this article, which tells about a unique application that detects spoilers in movie reviews and allows for readers to confidently read online reviews without the fear of general knowing who committed the murder. This application considers combinations of words as data and predicts spoilers using certain combinations of words and some linguistic logic. That is data well mined and well applied! Not in business strictly but this example may tell us that data is everywhere is gold is everywhere under it to be mined.
In Business Intelligence, data usually takes various forms for various purposes. In general, all these efforts are to answer one basic question "How well do you know your customer"? Experienced businessmen will admit that it took years to find answers to this question, and that too with a touch of uncertainty well masked. Such is the complexity of this single question. It does pose many branch questions which give clues to aid analysts to grope in the dark till they clutch the root.
SPSS is one intelligent pair of goggles used by the analyst to aid his search for truth/insight in the dark. As they are always, truth and insight only offer themselves to the deserving. So not all who wield the wand can do magic with SPSS or any other tool, because this tool is an intelligent servant who will do what is asked to be done and nothing more than that. Only the trained and experienced will know what to ask from this genie. There lies the route map to the root question mentioned above. As is said often, in business, one who has the right answers will always be a good employee and one who has the right questions will always be his master. SPSS can only be mastered with the understanding and the knack of finding the right question.
From the brief discussions we had in class today in the retail data, we ordered SPSS to show us the what, who and where of customer satisfaction in several lights. A sample below:
What: Customer dissatisfaction in service
Where: Store 2
Who: Female customers
Where exaclty: Clothing section in Store 2
'Why' is the ultimate question to be answered. After getting to know the above answers, the analysts throw several hypotheses to find the answer for the elusive 'Why'.
We also discussed about spurious associations in class today. Some people may remember Sania Mirza's skirts more vividly. What it emphasizes is that data, as obedient a server to a wise master, can also be as good a misleading conundrum to the hazy eyed questioner. Hence, it is always essential to check, re-check and cross-check to believe in a hypothesis.
Cross tabulation and frequencies, the specifics of today's discussions become still smaller in the larger picture as a subset of SPSS too. They are only descriptive methods, or in simple words, nothing but adjectives for data or a customizable display panel of data. What we did today was to learn to adjust our eyes to this display panel and ask what we want to see.
They will only direct us to hypothesis generation. Conclusive evidences on hypotheses can be arrived after some more playing with data. They are like night vision goggles. They are tools designed to show what can not be normally seen in the clutter. We have learned to adjust our eyes to these goggles today. For example, we were able to focus our sight to only to a negative customer contact in the clothes section of Store 2 today with these tools from a mountain of data. Who knows, there might be an irritating employee in that store section who is getting on the nerves of customers there. The answer is yet to be found out with conclusive evidence. Let us try to convict this black sheep conclusively by subjecting him to cross examination with other tools in the coming days.
Focus on the big picture!
- Gomathi Shankar K
So let me try to depict the big picture behind these two words. SPSS forms a subset of Business Intelligence Tools which in turn forms a subset of Data Mining Applications which in term forms a subset of Statistics Applications which is quite big enough a picture for us. So let us stop here and explore this picture in front of us. For starters, some Wiki definitions as follows:
Data Mining: Data mining, a branch of computer science and artificial intelligence, is the process of extracting patterns from data.
Business Intelligence: Business intelligence (BI) refers to computer-based techniques used in spotting, digging-out, and analyzing business data, such as sales revenue by products and/or departments, or by associated costs and incomes.
SPSS: A computer program for statistical analysis, original designed for Social Sciences.
An interesting example of an application of Data Mining that I spotted while mining some websites is this article, which tells about a unique application that detects spoilers in movie reviews and allows for readers to confidently read online reviews without the fear of general knowing who committed the murder. This application considers combinations of words as data and predicts spoilers using certain combinations of words and some linguistic logic. That is data well mined and well applied! Not in business strictly but this example may tell us that data is everywhere is gold is everywhere under it to be mined.
In Business Intelligence, data usually takes various forms for various purposes. In general, all these efforts are to answer one basic question "How well do you know your customer"? Experienced businessmen will admit that it took years to find answers to this question, and that too with a touch of uncertainty well masked. Such is the complexity of this single question. It does pose many branch questions which give clues to aid analysts to grope in the dark till they clutch the root.
 |
| Every row and column there can hide fascinating mysteries behind. Know which door to open. SPSS will open it for you. |
SPSS is one intelligent pair of goggles used by the analyst to aid his search for truth/insight in the dark. As they are always, truth and insight only offer themselves to the deserving. So not all who wield the wand can do magic with SPSS or any other tool, because this tool is an intelligent servant who will do what is asked to be done and nothing more than that. Only the trained and experienced will know what to ask from this genie. There lies the route map to the root question mentioned above. As is said often, in business, one who has the right answers will always be a good employee and one who has the right questions will always be his master. SPSS can only be mastered with the understanding and the knack of finding the right question.
From the brief discussions we had in class today in the retail data, we ordered SPSS to show us the what, who and where of customer satisfaction in several lights. A sample below:
What: Customer dissatisfaction in service
Where: Store 2
Who: Female customers
Where exaclty: Clothing section in Store 2
'Why' is the ultimate question to be answered. After getting to know the above answers, the analysts throw several hypotheses to find the answer for the elusive 'Why'.
We also discussed about spurious associations in class today. Some people may remember Sania Mirza's skirts more vividly. What it emphasizes is that data, as obedient a server to a wise master, can also be as good a misleading conundrum to the hazy eyed questioner. Hence, it is always essential to check, re-check and cross-check to believe in a hypothesis.
Cross tabulation and frequencies, the specifics of today's discussions become still smaller in the larger picture as a subset of SPSS too. They are only descriptive methods, or in simple words, nothing but adjectives for data or a customizable display panel of data. What we did today was to learn to adjust our eyes to this display panel and ask what we want to see.
They will only direct us to hypothesis generation. Conclusive evidences on hypotheses can be arrived after some more playing with data. They are like night vision goggles. They are tools designed to show what can not be normally seen in the clutter. We have learned to adjust our eyes to these goggles today. For example, we were able to focus our sight to only to a negative customer contact in the clothes section of Store 2 today with these tools from a mountain of data. Who knows, there might be an irritating employee in that store section who is getting on the nerves of customers there. The answer is yet to be found out with conclusive evidence. Let us try to convict this black sheep conclusively by subjecting him to cross examination with other tools in the coming days.
Focus on the big picture!
- Gomathi Shankar K
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