Understanding data mining
As manager, director, or consultant, you want to get a clear understanding of what data mining is. Is it worth investing time and money in? What does it mean for your customers, processes, organization, and stakeholders? Data mining follows a completely different process from regular Business Analytics, and comes with its own set of questions:
- How do you make a business case for data mining?
- What’s the nature of the relationship between Big Data, artificial intelligence, and data mining?
- Who do you involve in the project and how do you get managerial support?
- What expertise and competencies do you need to successfully complete the project?
- Who are the actual users of data mining and how do you convince them?
If you have any of these questions , the data mining specialists of Passionned Group can help you obtain clear answers and solutions.
What is data mining? Data mining is a method of making complex connections and detecting patterns in (big) data that may not be obvious. After validating the results, you can place them in the data mining model. Whenever you mine new data that fits the pattern, you can instantly be alerted.
What does the data mining process look like?
Regular Business Analytics assumes that the user is asking a specific question. Data mining is a method of reversing this process: using the information to detect patterns. But here, too, expertise and knowledge about the contents of the information in question is crucial in achieving functional results. This is highlighted in CRISP-DM, the Cross-Industry Standard Process for Data Mining. This standard divides the complete process up into six steps:
- Company knowledge and understanding. This step is about building understanding of the data mining application so you can distinguish useful results from useless ones. The business consultant will play a meaningful role in this step.
- Data knowledge. Build an understanding of, and knowledge about, the data. This will allow you to determine the reliability and usefulness of the data.
- Data preparation. Here, you check the quality and completeness of the data. Where necessary, you can adjust or make improvements. This step usually falls to the data analyst.
- Modeling. This step is often seen as the “real” data mining, because this is where you study the information. The data scientist builds the model.
- Evaluation. Evaluate the results of the previous step, modeling. Confirm whether the information and results have given the desired answers, following the criteria from step 1. The business consultant, the data analyst, and the data scientist perform this evaluation together.
- Application and embedding. Now it’s time to apply the results of the data mining within the structure of the organization.
There are also simpler systems out there, but every system contains a step to prepare the information, a step involving the actual data mining, and a step to process and evaluate the results.
Predictive Analytics & Big Data
It’s no surprise that artificial intelligence shows its true value when combined with Big Data. You can now use large quantities of (unstructured) data to detect and visualize extremely complex relationships. These can be fit into descriptive, predictive, or prescriptive models. The model’s reliability is much greater when you use Big Data compared to using smaller quantities of data. With a much greater sample size, there are more opportunities to refine results and to test the sample quality. However, at the same time, this comes with its own challenges, as it can be harder to verify the quality of the data. In our Big Data & Predictive Analytics training course you can learn how to successfully implement a data mining project in your organization.
First analysis rarely yields good results
A different, equally important, aspect of data mining is the speed at which useful results are acquired. It’s rare to see great results right away after the first analysis, especially where large quantities of data are concerned. Usually, the first attempts just show that the filters aren’t tuned properly, or that you’re not looking for the correct parameters. You may also discover that your assumptions are incorrect. Ironically, these useless results are actually very useful: they are necessary in order to perform a correct data mining analysis, in the end.
Data mining in the public eye
Data mining has helped many organizations and companies to work smarter. ICS, International Card Services, may not be the most famous example, but their results speak for themselves. Every part of their process has been reinforced using data mining algorithms, from recruiting new customers to retaining existing ones, from acceptance to promoting the use of credit cards. The algorithms have increased the effectiveness of every step of the process. The amount of fraudulent transactions went down by 50% in just a year, and the use of credit cards increased by 20%. ICS weren’t the only ones to benefit from this, the customers did too.
5 advantages of data mining
✓ Customers satisfaction increases indirectly (customer-focused working).
✓ Possible increase in profitability (financial position).
✓ Simplification of processes (simplicity).
✓ Do more with fewer people (efficiency).
✓ Precision services and building new business models (differentiation).
Implementing data mining into your company
Do you want to start data mining in your organization? Freely contact the advisors and consultants of Passionned Group. With our experience and expertise we can help you achieve results.