Data mining is the element that bridges the gap between data analytics and businesses. It’s the reason why business owners and C-level executives understand the decisions they wind up with.
Encore better elaborated data mining through a visual representation. If data is the fuel that opens opportunities for growth, then data mining is the engine that converts the fuel to hum the insights [you gathered] to life.
In terms of business intelligence (BI), there can be both commercial and granular applications of data mining that promote different benefits to various facets of an organization.
However, with so many concepts surrounding BI, there appears to have some sort of overlap, making it hard to know which does what. We’re here to set the record straight and to shed light on what data mining contributes to the fulfillment of your business goals.
Data Mining vs. Business Intelligence
For starters, let’s clear the haze clouding the lines of data mining and business intelligence.
BI is a faculty of business encompassing extraction, standardization, filtration and utilization of data. On the other hand, data mining is the specific process of drawing out information from respective sources and transforming these raw data sets into useful information. The latter’s essence leans more on spotting patterns, trends, irregularities or correlations among databases.
Simply put, data mining is under the umbrella of BI. Together with its other related components (e.g. data analysis and data visualization), they make up an intensive system that helps key people in a company to make sound and smarter decisions.
What is data mining in business intelligence?
Data mining’s vital role in the whole BI process is to discover all the information businesses may need, depending on what their goal is. A business then proceeds with data analysis to determine the uncovered data’s relevance to the entity.
At a surface level, it [data mining] comes out simple, but there are more intricacies and technology hiding beneath this feat once started.
- In practice, data mining deals with relatively smaller data sets for a more concentrated insight on particular parts of the business — unlike BI which focuses on organizational-level acumen. The incorporation of the former in the latter is an effective initial step to understanding how the variables present in the business impact your overall performance.
- Before data is turned over for analysis, it is a must that it undergoes the preparation phase first. Data mining is a technique that is of great help in sifting and shaping information, especially because 80% of what you get from your sources is still unstructured. The data mining techniques have ways to transform files or documents to a more suitable data ready for analysis.
This step also involves removing or deleting data that is irrelevant, duplicate, incomplete, and corrupted which may cause errors, inaccurate forecasts, or unwanted outliers.
Understanding how data mining helps businesses
This is a case-to-case basis. Data mining has varying advantages for companies, depending on how they use it.
For example, now that businesses are shifting to a digital space or offering more digital products, there becomes a dramatic increase in your payment transactions and customer engagements online. As this momentum keeps up, the data you acquire piles up as well, and becomes more valuable since it contains your big-ticket info about your market. Through it, you can have a better grasp of your customers’ shopping and purchase behavior — resulting in new strategies, service lines, marketing approaches and many others.
The same application is imposed when BI is deployed in other areas of business, such as in finance and accounting and in operations.
A general overview of the data mining process
Your business objective is your driving force in implementing a BI initiative. And to get to your desired end point, you need to start by gathering of all necessary information and applying the data mining approach from the get-go. No matter what your goals are, how they change along the way or how you plan to work around it, this process for data mining remains pretty much ironclad.
- Having a business understanding
- Having a data understanding
- Doing data preparation
- Building a model
- Evaluating/Validating the results
- Implementation of the data model
Data mining techniques in BI
There are various sophisticated techniques in this area, with every approach distinctly highlighting an important aspect of data mining. Each method addresses issues with a different kind of insight.
Knowing the root problem you’re dealing with answers the question of which technique best yields the optimal results for your company. Let’s dip a toe into the different data mining techniques you can deploy.
- Association Rule Learning
- Regression Analysis
- Anomaly or Outlier Detection
- Data Warehousing
- Neural Networks
- Sequential Pattern Training
In our next installment, we will tackle more of the data mining process, techniques, tools and how you can efficiently implement business intelligence in your growing organization.
Ready to explore BI?
Data mining is just the initial leg of business intelligence. In the current business landscape, the software, tools and technology being used to conduct data mining are evolving in terms of capabilities while being more accessible, affordable and automated.
If you want a fresh perspective from your mountains of data, we can help you get on the right foot. With D&V Philippines, you can make your data work for your advantage through our tested business analysis and reporting solutions. We have in-house experts to guide you in this iterative process.
You can grab a copy of our guide The Rising Frontier: Harnessing The Power Of Business Analytics to learn more about the wonders of data analytics. You can also schedule a free consultation with us for a discussion about our services.