More and more people are catching on to the idea that there is more to data mining than the “big data hype” would suggest. But there are still 5 very big, persistent lies (or myths, if you prefer) that plague conversations about data mining or predictive analytics.
If your organization can move past these lies you will be able to reap the benefits of data mining and predictive analytics as never before. Each lie holds you back from doing all you can, so it’s important to understand them and to debunk them, one by one.
1. All Companies Use Data the Same Way
A lot of people think “Amazon” or “Netflix” when they think about predictive analytics. It’s certainly true that predictive analytics can be used to offer individual recommendations to customers, and it is true that in certain industries that capability can increase sales.
But there are many other ways to apply data mining and predictive analytics. If you want to increase sales you can also use it to place products in an advantageous way. You can use it to adjust the wording or the timing of a sales letter to get a very profitable incremental improvement. If you want to decrease waste you can use it to decide exactly how many widgets to order. If you want to decrease fraud you can use its ability to target useful outliers to help you find the fraudulent and bring them to justice.
Every organization has different needs, different, pressing problems, and even different stores of data. All of that will help determine what the best and most profitable projects will be for your company.
2. Choosing the Right Software Package is the Most Important Step
Many organizations approach data mining backwards. They say, “Well, data mining is a big thing now. We should probably be doing some of that.” Then they send someone off to decide which software package to buy.
The result is an expensive purchase and an expensive implementation that goes absolutely nowhere.
Instead, the company should say something like this: “We really need to figure out why sales plummet in April and what we can do about it. Data analytics can help with that.”
The company can then turn to one of two resources to solve this business problem: any open-source data mining software, which is free, or Microsoft Excel, which companies already have. From there it’s a matter of preparing the data and setting up the project in a way that ensures that it is capable of actually asking the question.
3. You Need to Hire a Data Scientist
TMA has covered this misconception before. A data scientist is usually the last person you need.
Data scientists create algorithms. Many of those algorithms already exist inside of Excel or other programs. 90% of the time nobody needs a better algorithm. They need a better approach for using the algorithms they already have.
Does that mean there’s no value in developing more powerful algorithms? No, of course not. That’s like saying there’s n o value in creating a better truck. Most companies, however, don’t directly hire engineers to create better trucks. They buy the best trucks on the market, then look for qualified people to drive those trucks.
4. Data Mining is Only for Huge Organizations
Organizations of any shape and size can use data mining to some degree, depending on what data they are collecting and what they are trying to do.
Obviously size and budget do open up the number of tools you can buy. If you absolutely think it’s necessary to track the movement of shoppers around your store to solve your business problem then it helps to have the budget to buy the software and hardware that can do that. Often, however, shopping patterns can as easily be determined by digging into the sales records of any store.
Data mining is most often effective at creating incremental change. Small changes can often produce big results, and small changes can stack to create even bigger results. A 1% increase in the effectiveness of your sales letter campaign doesn’t sound like much until you think about just how many people you’re mailing, just how many new customers that 1% increase would represent and just how much more revenue will be generated. What would happen if you could increase that number by 1% again? Big things.
It’s not the size of the organization but the needs of the organization that matter.
5. You’ll need four more years of training to ever make this happen.
Training is absolutely key to learning how to use data mining correctly. However, it hardly requires anyone in your organization to go pursue a second 4 year degree. A few weeks of seminar training are often all that is necessary to fully realize the potential that data mining and predictive analytics hold for your organization.