The 7 Most Important Data Mining Techniques

The 7 Most Important Data Mining Techniques

Data mining is the process of looking at large banks of information to generate new information. Intuitively, you might think that data “mining” refers to the extraction of new data, but this isn’t the case; instead, data mining is about extrapolating patterns and new knowledge from the data you’ve already collected.

Relying on techniques and technologies from the intersection of database management, statistics, and machine learning, specialists in data mining have dedicated their careers to better understanding how to process and draw conclusions from vast amounts of information. But what are the techniques they use to make this happen?

Data Mining Techniques

Data mining is highly effective, so long as it draws upon one or more of these techniques:

1. Tracking patterns. One of the most basic techniques in data mining is learning to recognize patterns in your data sets. This is usually a recognition of some aberration in your data happening at regular intervals, or an ebb and flow of a certain variable over time. For example, you might see that your sales of a certain product seem to spike just before the holidays, or notice that warmer weather drives more people to your website.

2. Classification. Classification is a more complex data mining technique that forces you to collect various attributes together into discernable categories, which you can then use to draw further conclusions, or serve some function. For example, if you’re evaluating data on individual customers’ financial backgrounds and purchase histories, you might be able to classify them as “low,” “medium,” or “high” credit risks. You could then use these classifications to learn even more about those customers.

3. Association. Association is related to tracking patterns, but is more specific to dependently linked variables. In this case, you’ll look for specific events or attributes that are highly correlated with another event or attribute; for example, you might notice that when your customers buy a specific item, they also often buy a second, related item. This is usually what’s used to populate “people also bought” sections of online stores.

4. Outlier detection. In many cases, simply recognizing the overarching pattern can’t give you a clear understanding of your data set. You also need to be able to identify anomalies, or outliers in your data. For example, if your purchasers are almost exclusively male, but during one strange week in July, there’s a huge spike in female purchasers, you’ll want to investigate the spike and see what drove it, so you can either replicate it or better understand your audience in the process.

5. Clustering. Clustering is very similar to classification, but involves grouping chunks of data together based on their similarities. For example, you might choose to cluster different demographics of your audience into different packets based on how much disposable income they have, or how often they tend to shop at your store.

6. Regression. Regression, used primarily as a form of planning and modeling, is used to identify the likelihood of a certain variable, given the presence of other variables. For example, you could use it to project a certain price, based on other factors like availability, consumer demand, and competition. More specifically, regression’s main focus is to help you uncover the exact relationship between two (or more) variables in a given data set.

7. Prediction. Prediction is one of the most valuable data mining techniques, since it’s used to project the types of data you’ll see in the future. In many cases, just recognizing and understanding historical trends is enough to chart a somewhat accurate prediction of what will happen in the future. For example, you might review consumers’ credit histories and past purchases to predict whether they’ll be a credit risk in the future.

Data Mining Tools

So do you need the latest and greatest machine learning technology to be able to apply these techniques? Not necessarily. In fact, you can probably accomplish some cutting-edge data mining with  relatively modest database systems, and simple tools that almost any company will have. And if you don’t have the right tools for the job, you can always create your own.

However you approach it, data mining is the best collection of techniques you have for making the most out of the data you’ve already gathered. As long as you apply the correct logic, and ask the right questions, you can walk away with conclusions that have the potential to revolutionize your enterprise.

The 7 Most Important Data Mining Techniques

Your Data Is Sound, But How’s Your Dashboard? 5 Aspects to Consider

Your Data Is Sound, But How’s Your Dashboard? 5 Aspects to Consider

One of the biggest problems in data management and data science is being able to obtain “good” data. You need to gather sufficient data from a substantial array of subjects who fit your study’s requirements, and ensure the accuracy of the data… otherwise, any conclusions you draw could be biased or skewed.

But assume for a moment that your data is already solid. That’s no guarantee of success, unfortunately: It’s like having all the ingredients of a pizza in one place but lacking the ability to tie those ingredients together, and cook them appropriately.

Without the latter, you may not get the final product you seek. In addition to considering the quality of your data, consider the quality of your dashboard; it’s more important than you might assume.

Why Your Dashboard Matters

Here are some of the reasons your dashboard should matter as much as your data.

  • Access. First, you need to be able to call up as much of the data as possible. If your dashboard highlights a handful of key variables, but makes others harder to see or understand, it could lead you to false conclusions or undersell what you’ve been able to gather.
  • Manipulation. Your dashboard is also what empowers you to tweak different variables, generate comparative reports, play around with different timeframes and demographics, and ultimately give you the “full picture” of your subject matter.
  • Showcasing. Depending on your company and your position, you’ll probably need to make sure other people can see and understand the data before you can reap its true content. That’s where visualizations come into play. Your dashboard should make it easy for people to wrap their minds around your findings, regardless of whether they were involved in accumulating them.

Key Considerations

In the current data-driven marketplace,there are hundreds of unique dashboards  you can use to analyze and display information. How to choose which would be best for your needs?

  1. Ease of use. First, you should make sure your dashboard is easy to use, both for you and the others on your team. If it sucks up a few hours of study and playing around to learn the basic functions, it will probably include features you miss entirely. Beyond that, it may cost hours of company time to get new hires up to speed, and anyone outside your team who tries to use or view the platform could be baffled. Your dashboard should be more or less intuitive, if possible.
  2. Variable controls. You’ll also need a platform that has sufficient variable controls, which will allow you to create your own custom reports and change them dynamically as you spend more time on the platform. It should be relatively easy to account for new variables, reframe your data with new parameters, and dig deeper to unearth further insights. Cookie-cutter reports and controls aren’t likely to meet your needs in today’s business arena.
  3. Design aesthetics. Don’t discount the value of the aesthetics of your dashboard. Your data visuals should exist to tell a story about your data, both to people on your team and outside of it. If that story is hard to follow, or looks boring, your audience either won’t be able to draw accurate conclusions, or won’t be inspired to do so.
  4. Feature approachability. What good is a dashboard with a ton of features if you only need a few of them to obtain the results you need? You might be tempted to opt for a dashboard that offers lots of bells and whistles, but those perks won’t necessarily offer the best fit for your organization. Instead, find a platform with features that will contour to your needs, and are relatively easy to find and master.
  5. Access and share-ability. Finally, you need to make sure there won’t be any obstacles with regard to access or share-ability. Most organizations will want a dashboard with multiple “access” levels, including administrative and view-only accounts. You should also weigh how customized reports can be displayed, exported, and circulated to others. This is one of the most important functions of data gathering and distribution.

Your dashboard is more than just a user interface that allows you to get access to raw information. It’s a filter and a platform that can help you get the most out of your data.

Think carefully before you make the decision, and keep auditing that decision as you use the platform in your daily work, because something better may be on the way, or already available.

Your Data Is Sound, But How’s Your Dashboard? 5 Aspects to Consider

Will Workers in Obsolete Jobs Find Refuge in Data Analysis?

Will Workers in Obsolete Jobs Find Refuge in Data Analysis?

Like it or not, data-driven artificial intelligence algorithms and other high-tech robotic applications are coming to fill our jobs. An analysis by PwC estimated that up to 38 percent of current American jobs could be taken over by machines within the next 15 years.
Even white-collar jobs aren’t safe, since algorithms are capable of governing sophisticated tasks for machines in ways that previously were unthinkable, such as writing or distributing pharmaceuticals. The transition has given rise to fears.
On a small scale, individuals in potentially obsolete positions are worried they won’t be able to support their families. On a larger scale, some fear that full-scale job automation could lead to economic collapse.

Could there be a solution to these fears in the field of data analysis?

Why Data Analysis Is Safe

Number-crunching software has gotten pretty good at recognizing patterns: highlighting deviations from the norm and identifying trends. In fact, data-analysis algorithms may currently be able to outperform human data scientists.

So why would data analysis be a safe haven for human workers? Because the objective, numerical facet of data analysis represents only one piece of the process.

After the patterns have been identified, you have to figure out what they mean, and which actions to take based on those patterns in order to benefit the company. For the time being, this is too abstract and complex a task for algorithms, so it stays squarely in human minds.

In addition, people will have to be available to monitor the performance of advanced machines and algorithms, to recognize their inefficiencies and recommend changes to improve performance.

Widespread Applications

This migration could occur in many potential applications. In manufacturing and fabrication, for example, a specialist in plasma cutting could be replaced by a machine that can handle this process on its own.
However, the human worker could land a new position helping to design the algorithm responsible for the job, and monitoring its performance.
Similarly, in a more abstract example, a human journalist could monitor the performance of an algorithm designed to write like a human. He or she could run edits before an article is published, and monitor performance statistics to assess whether the algorithm needs adjustment before future writing assignments.

The Problems

The ready availability of data analysis seems like a safe haven, but a few potential problems arise in counting on it to prevent human jobs from becoming obsolete.

  • Pacing. Once technology gets to the point of being able to update itself, we may see advances in technology that outpace our expectations. At the current rate, in which jobs are replaced gradually and almost without notice, it’s no issue for skilled workers to forge new positions for themselves. But if a huge swath of jobs become obsolete simultaneously, this could be problematic.
  • Skill acquisition. Data analysis and algorithm assessment demand an entirely different skillset even from skilled workers. They might have to return to college, or invest in new training and development, which not everyone would be willing to do.
  • Job availability. Finally, a single machine or algorithm may only require one data analyst to monitor it, but it might replace multiple jobs. Only some jobs would be salvageable, while others truly would become obsolete.

It’s Evolution, Not Obsolescence

The bottom line is that it’s unlikely our jobs will suddenly vanish due to the onset of hyper-sophisticated machines and algorithms. In fact, the term “luddite,” which has long been used to describe someone who’s resistant to technological advancements, arose as a term to describe English textile workers who were afraid of their jobs disappearing during the Industrial Revolution .

Their fear was rooted in uncertainty, and wasn’t necessarily warranted. We’re facing a similar situation today.

It’s inevitable that robots will replace some of our jobs, but this will (mostly) improve our productivity and economy. Most jobs aren’t going to disappear; they’re going to evolve, and they’ll be available to anyone willing to evolve along with them.

Will Workers in Obsolete Jobs Find Refuge in Data Analysis?