Data Mining and Business
Learn How you can "predict" the future with Data Mining!
What is Data Mining?
Extract from Wikepedia:
Data mining is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. It is an interdisciplinary subfield of computer science. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.
The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate.
The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps.
The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.
How businesses can benefit from Data mining?
Data Mining is a very powerful tool for businesses. Its accuracy in prediction of trends is 80%. Whether is it approprate in timing and business models utilised for business activities can be forumulated based on huge data. Data Mining will take into consideration the total data to analyse the situation before conclusion is drawn. Data Mining can go very deep -- e.g. zeroing into companies, sectors, industry, etc. It takes into consideration not only statistics (structured data) but also unstructured data such as trends, sentiments, market condition, political and etc.
Data Mining can be used in the following areas:
Planning to Expand Overseas
The country your business plans to expand into, is it receptive to your business? Is the industry you are in a sunset or sunrise industry in that country? Who are the major players in the country? How is the economy of the country? What industries it lacks? How is the political situation? And much more....
Appropriate of business models used
Can your current business model survived in current business conditions? Can you change your current business model to grab a bigger market share and maintain the lead? Don't forget Nokia and Motorola died because of their failure to change to suit the current trend. Data Mining is very good tool to identify changes required to remain relevant.
And much More!
Data Mining Examples
Everybody was forecasting that Clinton will become President. The Poll, newspapers, TVs and social media are all out to support Clinton. Yet, Trump came in as President to the shock of the liberals. Why?
Clinton used only one platform in addressing the people in all states. Whilst Trump used technology by tapping into the "Big Board" Data Mining (it gives real time informaiton about situation in the local areas - e.g. weather, news, etc). If you watch his speeches in the rallies in various states, you will note that he talked about different issues specifically to each state. That is he is addressing the main concerns of the voters.
My partner, Anoop, who is an expert in data mining, was working in UK when the British was talking about Britain exiting EU. From his data analysis, he knew that Briexit will go through.
He took actions to sell all his assets (UK stocks, properties, etc) and moved overseas to Singapore. The rest is history.
What will happen to Singapore the next 5 years?
Everybody knows and senses that Singapore economy will be bad for next few years. But how really bad will it be? There are too many bad indicators for Singapore.
These will / may happen to Singapore next 5 years:
- SGD will weaken against USD
- Interest rates will go up
- Foreign investments pull out from Singapore (already happening)
Many more SMEs will go belly up due to unable to make profits (already happening)
- More retrenchment and people out of jobs (already happening)
- Economy slow down or even goes into negative growth
- Neighboring countries overtake us in economic growth
- Singapore loses its importance as a deep sea port (thanks to China)
- Foreign Talents (FT) will move back to their own countries as they can't survive in Singapore due to bad economy and high cost of living.
- More crimes (already happening)
- Banks will have alot more bad debts (already started to happen)
- Singaporeans will kpkb (make noise) more against the government (already in social media)
- Those who are well to do will move their cash and assets overseas
Due to weak economy, properties price will plunge making many estate agents joining the rank of unemployed (alteady happening).
- Retailers will cry for help as less people has the money to shop. There will be many shops in large shopping malls vacant.
- Rental will drop drastically for both commercial and residential because of lack of tenants.
- Domino effect of bad debts will affect everybody
....And much more.
So if you run a business in Singapore, what can you do?
Do nothing, sit back and relax and believe wholesale that everything will soon pass. Like what PM Lee said, although Singapore economy is not gowing, it is still okay because the government has plans to move economy to other industries and find new ways to create jobs and prosperities; OR
Do something to protect your business from ending up as a casaulty.
If your business is MNC or have deep pocket, you can always move your operation to another country which is more conducive for business. For SMEs who do not have deep pocket, you have to sit down and think hard what to do.
For SMEs, the choices are limited. Click Here if you are interested to discuss about your concerns for your business future.
If your business is in these industries, you'd survive the coming bad times.
Disruptive technologies (DT)
DT such as Uber, Airbnb, Grab, etc are good example. Uber is the largest taxi operator and they don't even own any vehicle! If you can think of a DT business model and has the funds and technical expertise to develope it, then your chance of making it big and good is high. However, the road to success will be long and narrow and require much hard work.
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