Four key data mining techniques and how firms use them

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Until recently, the world of data mining was not particularly well known among business leaders. While data has been collected by firms for decades, it’s only been in recent years that the technological capacity for mass-scale data collection has emerged to a sufficient extent.

Times are changing, and businesses across sectors are now starting to reap the rewards of the data mining revolution. From healthcare providers who use data mining techniques to extract epidemiological insights to sales departments making sure that their product teams know what customers want, managing large amounts of data is becoming increasingly crucial. As this article will show, even governments are getting in on the act – suggesting that any business that wants to stay ahead of the curve needs to ensure their data mining game is up to scratch.

As data computers get faster and data mining gets more sophisticated, you might be considering a career switch into this growing field. And what better way to get started on that journey than to explore and learn about some of the most common (and the most effective) data mining techniques there are out there? This blog post will help you to do just that and also identify how they can be applied to your own career change plans.

Machine learning

 Anyone who has paid attention to new stories on data mining in recent years will no doubt have come across the concept of machine learning. Put simply, machine learning is a way for computers to take advantage of algorithms, which are essentially sets of rules that show computing technology how to acquire knowledge and information. This information is then used to achieve a specific end for the firm, such as parsing data in order for it to be analyzed by a human within the organization.

There are a range of options when it comes to setting up a machine learning system, and firms use different options in different ways. Supervised learning, for example, involves some level of human input: under this system, the algorithm provides the machine with the information it needs to put labels to data sources, and the machine then goes ahead and does it. This might be used in a context such as sales, where the data fields – like “location of customer” or “buying habit” – are already known. Sometimes, however, machines are able to even perform this classification process by itself – which is known as “unsupervised learning.”

It’s worth noting that machine learning is one of the most famous – or perhaps infamous – areas of artificial intelligence when it comes to replacing tasks carried out by humans. Machines are now able to conduct all sorts of data mining tasks that humans were once responsible for – and, crucially, they can often do it in a faster timeframe than a human.

This is partly a function of the fact that there is now a much larger amount of data out there: with people shopping online and using credit cards to pay for so much of what they buy compared to using cash, for example, companies have a wealth of information available to process to extract profitable insights. Therefore, it’s vital for companies to ensure that they are preserving this competitive advantage by utilizing all of this data.

Data graphics

 For many people, data is not something that gets presented graphically on a regular basis. While most people can recognize a bar chart when they see one, data tends to be associated in particular with spreadsheets or even other, more coded file types.

But the truth is that data is presented graphically in many different contexts. It could take the form of something simple, such as a pie chart on a slideshow, or it could be something much more complicated – such as an interactive heat map that shows the depth of a particular trend in a particular place or context. Overall, this discipline and practice of presenting data in a graphical format is generally known as “data visualization.”

Anyone who has ever worked in a data-heavy environment will know that it’s very common for data to be presented as part of various goals. Those who have to produce pitch decks are often competent in data visualization, which means that these techniques are popular in the corporate and professional services world. Data graphics can be used for new business pitches by firms trying to secure new client accounts. They’re also seen in the world of nonprofit organizations, where they are often used to demonstrate the impact of spent grants and donations for funders’ reports.

The range of data graphics options is huge. A network diagram, for example, draws lines between different points and explicates the links between each one. This can be used in many contexts but is often particularly useful in contexts of expansion. A firm that is looking to grow might feed its new business leads into a network diagram to see whether there are more links between them than appeared at first glance and to get a deeper understanding of how the different connections link.

It’s important to stress that data graphics can be produced in both a high-level style and also in a more amateur manner. For some people, using the inbuilt tools offered by something like Microsoft Excel is sufficient. But for many firms, more advanced data visualization is necessary. This is why it’s important for those who are looking to switch into this career to ensure that they invest properly in the training they need. In the UK, for example, Aston business analytics programmes focus on skills like pattern uncovering – and visualization is a key part of this.

 Finding anomalies

 Have you ever wondered how supermarkets and hypermarkets find out why some of their products are performing strongly and which ones don’t do so well? The straightforward answer, of course, is that in many cases, the trend speaks for itself for some obvious contextual reason. Sales of barbecues will rise in the summer, say, while hats and gloves are likely to fly off the shelves much more quickly in winter.

But what happens when something unusual happens? Often, firms use a data mining tool called an “outlier detector” to break the black box of explaining anomalies. Take the supermarket example again: if a store suddenly discovers that they are selling loaves of brown bread at a much higher rate than previously, there may not be an obvious immediate explanation. It might only be by applying an outlier detector system to loyalty card information that the retailer can discover that customers are also buying low-sugar goods across the board in higher numbers. From there, the retailer might use other data mining techniques to draw connections to social media, television and media trends – and use this to plan a new push towards healthier products, timed in the right way for maximum uptake.

Forecasting for the future

 The anomaly-finding example above suggests that businesses are often using data to plan and predict what courses of action might be best. But this is just the tip of the iceberg: there is a plethora of different prediction methods that fall under the data mining umbrella, and there’s one for every skillset and preference.

Take the example of forecast modeling. Under this system, a data controller can get through vast swathes of accumulated data from across time to reach specific conclusions that will benefit the business. A transport provider, for example, could apply forecast modeling to its ticket sale history to work out which days have the most travelers – and, therefore, which days are most likely to need the most trains, airplanes or buses. In this way, supply and demand can become much more attuned to each other and respond in a way that is positive for profits.

Another option is known as data clustering. This form of modeling allows for batches of data to be brought together in a way that highlights the overlapping pieces of information and allows for group insights to be taken. This is especially useful for clients who heavily rely on effective segmentation as a way of doing business. Advertising agencies, for example, often use cluster modeling as a way of predicting what’s likely to happen in terms of media consumption and usage. By clustering together pieces of data on the viewing habits of women aged between 45 and 54, for example, an advertising space seller can work out what advertisements might be most effective at what times of the day.

Forecasting is perhaps near-universal in terms of its utility. Even the government is using data forecasting. Everything from how much tax revenue is expected in a given fiscal year to enemy movements in an armed forces context can have data mining techniques applied to them in order to develop a greater understanding of what might come next. While the private sector often leads the way when it comes to using technology such as data mining and forecasting services, many governments are in fact trailblazers in this regard.

The Local Government Association in the UK, for example, found that some councils in Britain had developed their own data forecasting software to provide predictive analytics services. Hertfordshire County Council applied technology like motion sensors in the residences of those it was looking after under its social care obligations. This tracked everything from changes in temperature to frequency of movement and used this to forecast potential illnesses. For firms, and especially those in areas like healthcare, this means that the data-powered competition is coming not just from other companies but also from state actors – so it’s important to make sure your own data mining game is strong.

Limitations?

It’s worth pointing out at this juncture that there is of course no guaranteed way to forecast what will happen in the future, even with top data science tools at your fingertips. Anyone, or any business, which offers that is unlikely to be telling the truth, and even the most sophisticated data mining forecasting services can be (and have been proven) wrong.

But what data mining services are able to do is provide relatively high levels of accuracy and define what that level of relative accuracy looks like. In this sense, they are best understood as services that can help reduce and manage the risks posed by uncertainty. By having a developed understanding of what is likely to occur, how likely it is and what could occur instead, a business leader can make more effective decisions – while always having a plan B up their sleeve just in case.

In short, data mining is clearly a major part of the business and corporate landscapes these days. From techniques like the fascinating ability of unsupervised machines to visual practices like data graphic creation, data mining is happening at all levels of business and in most industries. For those who are debating whether or not to switch careers into this sector, it’s wise to get learning. The world of data mining is changing all the time, and keeping your knowledge levels up could well be what makes you stand out from others in the job market.

Data mining is a hugely interesting area to work in, and whether you’re planning to apply for jobs within a business or whether you’re looking to work for a client-focused data consulting firm and take on multiple clients, there are clearly plenty of specialization routes. Now all it will take will be a strong work ethic and some focus in order to get the qualifications you need to make your dream of a data mining career a reality.

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