You’ve collected your data. But have you prepared, organized, and prioritized it? Now you have to go even further to turn this raw data into a source of strategic information.
This phase of data exploration is better known as data mining.
The difference between a rough diamond, fresh out of the ground, and the precious stone adorning the creation of a prestigious jeweler, is the ability of the diamond dealer to bring to life the different facets of the gem. This analogy describes precisely what data mining is all about. When the diamond cutter must demonstrate precision of gesture, rigor in his work and remarkable technicality, data mining is based on this same method of rigor.
The principle? To work on a raw material, most often made up of large volumes of data, in order to extract the most precious ore: knowledge.
Knowledge of a market, a customer, a sector of activity… Data mining is a demanding discipline that allows you to answer questions or problems of varying complexity with precision. Thanks to data mining, you will be able to identify emerging trends, to engage on the path of prediction to anticipate the expectations of a market or of different customer profiles.
The methodology behind data mining
Using statistical-based approaches and algorithms, data mining allows to detect anomalies, generate patterns and identify correlations in large datasets in order to make better decisions. To achieve this, however, you need to follow a specific methodology.
To begin, you will need to perform a fine-grained categorization of the available data. This first phase allows you to identify correlations. Within the framework of a data mining project, different analytical techniques can be used. In order to search for trends and thus ensure that you are always in step (and even ahead!) with a market, for example, you will use cluster analysis.
This statistical method consists of grouping the elements of a data portfolio according to their degree of similarity. The objective is to identify similar groups according to specific criteria. The larger the group, the stronger the trend!
On the other hand, data mining can be based on the anomaly detection method in order to identify unusual phenomena. This data mining technique reveals, for example, behaviors that do not conform to what other data show. This technique is very common in the banking sector, for example, in order to identify potential fraud.
What are the benefits of data mining?
By embarking on the challenging but promising path of data mining, you can streamline many internal practices and complete a wide range of projects. By leveraging data mining, you can:
- gain a better understanding of your customers’ expectations in order to define and adapt your business strategy,
- define models to anticipate changes in behavior or market trends,
- evaluate and consolidate your marketing strategies,
- anticipate risks related to an operation
- optimize your loyalty strategies and reduce churn,
- define an R&D roadmap in relation to customer or user segments…
Data mining opens the way to a wide range of optimization of your internal processes. It puts data at the center of all your projects, as a knowledge base that allows you to gain not only in relevance but also in accuracy!
To build a coherent data mining project, there is however one essential prerequisite. You must always be guided by a clearly identified business objective. Like the diamond cutter who starts to cut a precious stone, you must know on which facet to shine the light to bring out the knowledge!
Data mining: for which uses?
Data mining is likely to deliver its full potential in many sectors of activity and for a wide range of uses.
In medical and pharmaceutical research for instance, data mining is of considerable value because it allows to leverage large volumes of data. In marketing, it is also very valuable because it allows the positioning, the discourse, the packaging, and the distribution channels to better serve the development strategy of the company. In the retail sector (whether it is physical or ecommerce), data mining contributes to the knowledge of the customer in order to deliver exceptional customer experience. The industrial sector has also massively adopted data mining, both in the search for optimization of production flows and in the development of predictive maintenance.
As you can see, each industry and each business has its own way of exploring and exploiting the precious ore that is data. So what are you waiting for?
