We like to learn new things. We look at the world with a certain perspective. Whatever we see we learn. The difference in this perspective is if you see from a hole in the wall or if you see view from that hole. The difference is in the range of vision. Let’s look at some perspectives on Analytics:
When we analyze data we have the same choice whether to look at small amount apertafarmacia24.com of data or to look at a large reference. This data is stored in a database. The collection of databases is also known as data warehouse. When a supermarket chain wants to analyze consumer data it can either analyze the data of one store or to analyze the entire data available for all the stores. The data of one store is the database. The combination of these databases in a central location is data warehousing. Data Warehousing is the maintenance of data at a central location in complete and organized form. DATA MINING So what does Data Mining do? Data mining gives us the ability to look at the big picture. It gives us the ability to examine the data warehouse. Data mining is analyzing the data from different perspectives and summarizing it into information. Data mining software gives us the ability to perform various statistical and analytical tools on this data. We can draw associations from this data. Data mining is the tool which essentially helps to find the needle in the hay stack. All analysis of course requires the right research objectives. It requires the description of the problem for which solution has to be found.
WHERE TO USE DATA MINING?
The answer to this question is – everywhere. Yes data mining if used correctly can help quite a bit in decision making. It can be used to analyze sales data, consumer data, advertisement data etc. It helps in 3 things 1. Checking accuracy – it checks the accuracy of a past decision. For example whether the advertising campaign is performing well. 2. Past Analysis – It helps to analyze if any changes need to be made in the current scenario on the basis of past experiments. For example, whether the current strategy will work or some modifications will benefit us more depending on a study of various strategies undergone and their results. 3. Future Predictions – it helps to forecast the trends based on the past performance. For example: will a promotional offer help in the sales of the product can be answered by studying the effect of promotions on sales.
TIME AND FEASIBILITY
Why do we require data mining? Since data is large and ever changing we need to be able to analyze our data on time otherwise by the time we take a decision the trend will change. So to study a large exhaustive data we need tools that are fast enough to give outputs that could help us in modifying our strategy at the onset of new trends. We need to be able to convert the staggering data into an actionable strategy which could lead to a better business model. Watch this space on FIIB’s MAC blog site for more on Data Mining…
The author of the blog is Anuradha Apan, Student, FIIB
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