What is Data Mining?
Data mining is looking at a lot of data and trying to get valuable information out of it.
Data mining deals with large data sets that would take too long to go through manually. So, data scientists create and use programs or software to look at these huge data sets and discover patterns in the data.
The core of data mining is turning data into information. It can be overwhelming to look at a lot of data and not see the value in it.
But what happens if you are a company and do not have so much information you need a program to determine the data you have?
When dealing with smaller sets of data, you don’t necessarily need to hire a team of data scientists. You need someone who understands data and can extract it enough to show you it visually.
HingePoint Data and Data Detective Work
Before you can mine your data for trends, you need to understand what data you have and if it is good or bad data. This is fundamental to using data to maximize your business.
We call this the Data Detective phase of our unique process. The goal is to get Hinge Point Data, which is data that if accessed and visualized the right way can revolutionize and transform your business.
A HingePoint is the point where something literally pivots. It’s the turning point where you are going to see a significant change take place.
We found this happens best when you have the right data to make the best decisions or keep employees and customers the best informed.
HingePoint executes this fundamental level of data detection with our data dictionary work. When we work with a customer, we go into the data and define every piece of data they have.
With our Data Dictionary we identify the following:
- Where is it?
- What database?
- What table?
- What system?
Ideally, we’d go into the actual database and look at the tables and categorize and classify each column and data set.
Once our customer understands what data they actually have, we can begin creating solutions that will mine for the data they want and need to maximize their business.