Dos and Don’ts of Data Mining That Must Remember

Calling data the soul of every commercial activity is absolutely correct. As a thread control a puppet, the industrial structure is in the control of data. And the data is where the information lies. In addition to the government institutions, the corporate sector always stays in need of the data. Since all commercial entities are running in the marathon of taking lead, the accurate analysis of complex data helps them a lot. Customers’ behavior, their tastes, their investing patterns and many more vital facts can be extracted from the data. Let’s check below what wonder it can do.

Knowledge ascertaining-an ultimate goal of data mining:

Data is vast. And extracting useful information from it is no less than the churning of ocean. The answer of this complex question is hidden in data mining. What data mining companies actually do is discovering meaningful and useful information from the heap of the data. They deploy data mining techniques to extract, cleanse and then, classify diverse patterns. Otherwise, the information can be stayed rested in that. Thereby, the business predictions of a data analyst will not be able to parallel the accuracy.

Inputting data and preprocessing for data mining:

Sifting through data mining earns knowledge to the Companies dealing in data mining services. It clearly indicates mining of data plays dominant role in knowledge discovery from various databases. It’s sounds easy but actually it is not. This process begins with browsing and exploring files, flat files and relational data etc.. This is what the data miner needs to input data for further preprocessing it. The subsequent role is of data mining. It helps in decision-making. Obviously, this is done only after intense study of the analytical report.

Dos and Don’ts of Data Mining:

Dos:

1. Subtly define the goal: No serious decision is taken at random. It’s about considering serious questions regarding sales, purchases, customers and productivity for an entrepreneur. So, defining ‘what’ is not enough. ‘How’ and ‘when’ factors should also be added in defining the goals.
2. Suggest simple solutions: Any simple decision of business is easy to execute. And it discards the possibility of getting rejected. If an easy solution is executed, it will take less time to bag success.
3. Questing is must: Asking question opens the way to deep insight. Instead of adhering to an advanced and sophisticated tool of data mining, it is better to ask and understand.
4. Be ready to deal with complex data: The data miner has to trim the data by extracting it from various messy databases. This extraction ends up in multiple spreadsheets in diverse formats. So, be ready to extract, transform and load.
5. Be flexible in using more than one technique: Sticking to one data mining technique or algorithm can never fulfill every requirement for analysis. So, be flexible to switch from one technique to another.
6. Cross-examine with the original records: In order to get off the chances of errors, cross-checking the new databases with the originals statistics or records.
7. Be current on the latest technology of data mining: It will be an asset to current on the latest development of data mining world to add cutting edge to your knowledge.

Don’ts:

1. Don’t neglect the power of good data preparation: Preparing good data requires cleansing, transforming and aggregating model. If so is done systematic ally, an impactful database will give outstanding result.
2. Don’t allege algorithm or techniques: Playing the game of blames to data mining model is nothing but a sheer idea of refuging under wrong analysis. So, don’t rely on them and software for assumptions. Use your knowledge, algorithm and wisdom.
3. Don’t rely on default model accuracy metric: MSE (Mean Squared Error) and PCC (Percent Correct Classification) present default results after scanning errors and classifications respectively. These metrics are default that may interpret wrong results.
4. Don’t jumble wrong with correct data: Since big data has collection various patterns, so its recommended to use randomization tests to eliminate the chances of false patterns.  
5. Don’t play blind game of mining any data using data mining: Utilize your domain knowledge to cross-examine the variable used in data mining.
6. Don’t deny simple solution: A complex solution may reject your projection due to lack of understanding. So, give space to simpler and easy-to-understand solutions.
7. Don’t forget to maintain records of data: Documentation of all modeling steps and subsets of information should be recorded.