Every second of every day, billions of people interact, in some way, with thousands of global systems. Every single time you use your smartphone or tablet to check the news, make reservations, update your social media profiles, or check email, you are creating an interaction. Every purchase you make, or every time you stand at an intersection, or ride an elevator, systems are capturing these interactions. And, each one of these interactions is data. All these interactions are what constitute the huge mass of information known as big data.
Today, the business world realizes what a huge role big data plays in creating and maintaining successful companies. It goes without saying that data-driven companies perform better. It is no longer a difficult task to gather data, lots of it, but it is more important to have the ability to make sense of all that data, and to find what is relevant, and useable for your company. Thanks to big data, business intelligence, and data mining, companies no longer need to rely on trial and error, or learn from their mistakes in order to become successful and prosperous. Business intelligence gathers and stores information, and data mining makes sense of it. Companies get the knowledge they need to understand how they’re doing at any given moment, and where to go when moving ahead towards the future.
What’s the difference between business intelligence (BI) and data mining (DM)?
When trying to understand the relationship between these two aspects of business analysis, it is easier to consider them first in a basic manner. Think of BI as a machine whose job it is to gather data, store it, and then, to convert those masses of random data into useable information. Once the “machine” has churned through all the raw data and transformed it, this is where DM comes in. It takes the transformed data and discovers patterns. Then, those patterns are used by companies as knowledge that will help them make better strategic and operative decisions for the present, and future. Unfortunately, companies can’t use raw, or primary data to gain knowledge. This is not only because of the sheer amount of data, but also because raw data is in multiple formats like numbers, figures, or readings from gauges or other instruments. Another reason is that data is created in different ways. There is captured data, found through intentional research, or analysis. And, there is exhaust data, collected by machines such as cash registers, or smart phones. These machines collect data as a secondary function, as their main purpose is for something else.
What are the steps your company needs to take to mine data successfully?
So, how does it happen? How do you weed through all that data to make sense of it? You want to find those hidden patterns that will give you the knowledge and insight to help you make your business better. The purpose of the DM process at this point is to analyze data bases for patterns and trends hidden in those large data sets that BI has been gathering. Now it’s time to extract knowledge and transform it into understandable structures like graphs, charts, or tables. Something you can hold in your hand, study, and use as a decision making tool.
First of all, you must set a business objective, a goal to reach, a hypothesis, or a problem to solve, which you will achieve by using the data. For example, if your company is just starting out you may initially want to find out who your customers are, what are their likes and dislikes, etc. This way you can personalize your message and focus your marketing campaigns, so they are more cost efficient. You may be searching for new avenues of revenue, how to provide better customer services, how to gain a competitive edge over other businesses in the same industry, or possibly how to become more efficient in day-to-day operations. The list of goals or problems to solve is limitless, and personal.
The next phase of the DM process is choosing suitable techniques to search for those patterns in the data sets. Which technique you choose depends on the goal you have set in the first phase. Fortunately, there are tools available, like Python or R, to take care of the data analysis. But, it’s important to know which technique is right for your needs. Each of the following DM techniques employ their own unique algorithm to find certain patterns in the data sets. The techniques, or tasks are separated into two main categories: descriptive, which finds patterns that can be interpreted by humans, based on existing data; and, predictive, which discover values of attributes as they relate to other attributes, and make forecasts based on the data.
Techniques that are in the descriptive category are:
Clustering, which locates data that is grouped together based on logical relationships or consumer preferences
Associations, which identify common attributes and how things are associated with one another
Sequential patterns, which are used to mine data specific to the anticipation of behavior patterns and trends
In the predictive category is:
Classification, which locates data in predetermined groups
When the patterns have been discovered using the suitable extraction techniques, your data experts can look at the information and interpret it to obtain the knowledge to solve the problem, or reach the goal that you have set at the beginning of the DM process. At the end of the process, the data is ready to be presented as knowledge, in useful formats like graphs, charts, or tables.
How will mining data benefit your business?
When you’ve discovered those patterns that have been hiding in all your data, it’s exciting. But, it doesn’t mean anything if you don’t apply it to benefit your business. So, what do you do with all that new knowledge?
Refine, and boost your marketing strategy. With a better idea of who your customers are, when and where they are online, or what their buying habits are, you can focus your marketing more sharply.
Raise your customer loyalty statistics. Through better personalization, your customers see that you are thinking of them, and considering their needs. You are offering them what they want, and they’ll respond with more loyalty.
Gain higher profits when your marketing campaigns are sharper, and more focused, and customers more prevalent.
Lower operation costs, and run your business more efficiently. This can happen through error reduction, or understanding supply and demand more precisely.
Gain a competitive edge when your company increases customer loyalty, has better insight into future trends, and becomes more profitable.
Collecting information is a human characteristic that has been used for organization and progress since the beginning of time. It used to be a simple task, but as the internet grows exponentially, the increasing volume of information will be impossible to make sense of without the proper tools. By using business intelligence and data mining, your company can now harness the power of that data, turn it into knowledge, use it to make better business decisions, and reap the rewards.