Nowadays, the majority of the corporations possess the data they need for business intelligence. However how they use and analyze these data is what differs the winners from the losers, the successful ones from the failed businesses.
That is why the big companies and data intelligence specialists struggle to find unexpected patterns, trends, correlations, and clustering in the data. And this is where the core goals/objectives of data mining and business intelligence lie.
On this page:
- What is data mining?
- What are the two main objectives associated with data mining? Explained.
- An infographic in pdf for free download.
What is Data Mining?
The key basis of the business success is to constantly increase the awareness and knowledge of the decision makers.
How can the decision makers create the knowledge and information they need? Well, It comes from the data that has been collected, assessed and analyzed.
On the other hand, there is an enormous amount of data available. This data is of no use for the management decisions until it is turned into useful information.
So, what is data mining?
Furthermore, data mining tools are designed to allow you to predict future trends.
To look for patterns in large batches of data and to evaluate trends and probability of future events, the business intelligence experts use special software tools and sophisticated mathematical algorithms.
So as you see the definition, it is not hard to guess what are the two main objectives associated with data mining. Let’s see them in more details.
What are the two main objectives associated with data mining?
Uncovering trends and patterns
Uncovering trends and patterns is a great power for the businesses of all sectors and industries.
Companies which can successfully extract and uncover trends and patterns are able to know and understand better their customers. These companies are able to develop more effective marketing strategies, increase sales, decrease costs and etc.
That is why big organizations spent a significant amount of time and money implementing business intelligence and data mining tools in order to help identify trends and opportunities.
Moreover, the benefits are not only for the business organizations but they go further. Uncovering hidden patterns and trends bring a huge value for all aspects of life including policing, science, human rights, engineering, and more.
How can you use the trends and patterns discovered by data mining techniques?
Here are some most common examples and uses:
- In finance and banking, discovering hidden trends are used to create risk models for the different type of loans.
- In marketing area, spotting trends can help you for defining market segments through identifying the common characteristics of customers. This is a basis for developing smarter marketing campaigns and predicting customer loyalty. Also, you can predict which customers are likely to leave your company and turn to a competitor.
- Retail stores use the information for customer shopping habits to optimize the design of their stores in order to improve customer experience.
- Governing organizations use data mining to detect fraudulent transactions and which transactions are most probably to be fraudulent.
One of the best and most popular examples of data mining that uncovers patterns is the discovery that beer and diapers are often purchased together. The deeper market research efforts discovered that it is a common habit for Fathers to take some beer when they are going to buy diapers.
You can guess how valuable is this information for sales growth. The stores can use this information to place the beer and diapers in closeness.
The above is a great example of how your sales can growth by identifying valuable patterns in your customers (the Fathers) behaviors in the marketplace. Base on that pattern, you can take a valuable decision that will help you to make more money by placing the two goods together where they are actually more likely to be purchased.
So you see why uncovering insights, trends, and patterns are actually the two main objectives associated with data mining.
How can the hidden trends and patterns be uncovered?
With the help of:
- Automated prediction of trends and different behaviors. The process of finding predictive information in a huge database nowadays is a completely automated procedure. No more extensive hands-on analysis.
- Automated revelation of unknown patterns. Data mining software tools just dive in databases and identify hidden patterns.
You can detect connections and patterns with intuitive tools, analyze key data quality metrics and uncover important trends on the go, explore big data in no time on your mobile phone, iPad, iPhone, and etc.
And all of this just from one place. The automation and software save an enormous amount of time, energy, money and lead to successful data mining and business intelligent process.
In fact, without automation, many of data mining trends and patterns are not the results of intelligence at all, just guesswork.
The mission of every data analysis specialist is to achieve successfully the two main objectives associated with data mining i.e. to find hidden patterns and trends. This is a vital information of the hidden risks and untapped opportunities that organizations face.
Businesses desperately need the right information on who bought what, for how much, when, and etc in order to make the right managerial decisions.
Nowadays, there is too much data. Transforming that data into meaningful information is a key that opens the door of the success for organizations all over the world.
Today in our data-driven world, with the right data mining management, businesses are able to carry out deep analysis quickly on time. The result is different types of campaigns that bring the right message to the right people.
Silvia Vylcheva has more than 10 years of experience in the digital marketing world – which gave her a wide business acumen and the ability to identify and understand different customer needs.
Silvia has a passion and knowledge in different business and marketing areas such as inbound methodology, data intelligence, competition research and more.