What is data and big data mining? An easy guide

You have a lot of data, but how do you find the right data to make a business decision?

Data is powering almost all aspects of life, whether you're aware of its impact or not. Business decisions are based upon insights from information, whether automated or manually assessed. This information could be collected from customers, gleaned from machinery completing manufacturing processes, extracted from market information to decide the best route for production lines, supply chains and more.

Without data, businesses would not be as successful as competitive as they are today, services and products would not be adapted to address customer needs and firms would struggle to adapt to the changing market conditions.

But data isn't useful in its raw form. It needs to be analysed and key insights lifted out to provide value. Cloud computing has allowed data to be set free from the server. It allows huge swathes of data to be stored at scale, with real-time analysis possible at any time of the day. The key is assessing this data at lightning speed, and that's not possible using human processing power. There's just too much data to rifle through.

What is data mining?

Data mining works by examining data to discover patterns and anomalies within vast datasets. Mining data means you can autonomously predict what will happen in the future based upon the past and predict how your business will change, ensuring you're prepared for every eventuality.

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There are a vast range of methods to carry this out and an organisation overwhelmed with data can use data mining to grow the business, streamline costs, enhance relationships with customers and decrease risks.

Analytics giant SAS believes data mining is vital because it not only allows an organisation to discover the best data for whatever goals it is trying to achieve but it will also convert the most relevant data into meaningful information that has a heap more value.

Data mining allows businesses to sift through all the chaotic and repetitive noise in their data and understand what is relevant, then make good use of that information to assess likely outcomes. The process identifies patterns and insights that can't be found elsewhere, and by using automated processes to find the specific information, it not only speeds up the time it takes to find the data but also increases the reliability of the data.

Once the data is gathered, it can be analysed and modelled to convert it into actionable insights for the business to use.

Big Data mining

Big data mining is a form of analysis that involves taking vast quantities of data (big data) and turning that into meaningful information.

This approach is most commonly used as part of a business intelligence strategy that aims to create targetted insights for an organisation, including data about systems, processes, and anything else that involves consistent data collection over a prolonged period of time.

Big data, by its nature, usually takes far longer to collect, and is often stored in an unstructured format - so some structuring is required before it can be fully analysed.

Mining usually involves searching through a database, refining and then extracting that data to then be ordered into a meaningful structure, usually based on common features or types, using an algorithm.

As big data mining is essentially data mining on a much larger scale, it also needs far more computing power to do effectively. In some cases, only specialised equipment, such as research computers, are up to the task.

However, the core principles of data mining remain the same, regardless of the size of the data set.

Data mining techniques

Among the techniques, parameters and tasks in data mining are:

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  • Anomaly detection: unusual data records are identified that could be of interest if errors that need more study.
  • Dependency modelling: Looking for relationships between variables. For example, a supermarket will collect information about the purchasing habits of their customers. Using association rule learning, the supermarket can work out which products are bought together and use this for marketing.
  • Clustering: this searches for structures and groups in data that are similar, without using known data structures.
  • Classification: searching for patterns in new data using known structures. For example, when an email client classifies messages as spam or legitimate.
  • Regression: searching for functions that model data with the least amount of errors.
  • Summarisation: creating a compact dataset representation. This includes visualisation and report generation.
  • Prediction: Predictive analytics look for patterns in data that can be used to make reasoned forecasts about the future.
  • Association: a more straightforward approach to data mining, this technique allows for making simple correlations between two or more sets of data. For example matching people's buying habits, such as people who buy razors tend to buy shaving foam at the same time, which would allow for the creation of straightforward buying suggestions served to shoppers.
  • Decision trees: related to most of the above techniques, the decision tree model can be used as a means by which to select data for analysis or support the use of further data within a data mining structure. A decision tree essentially starts with a question that has two or more outcomes in turn connecting to other questions, eventually leading to an action, say send an alert or trigger an alarm if analysed data leads to particular answers. 

Advantages of data mining

There are a few ways in which organisations can benefit from data mining.

  • Predicting trends: finding predictive information in large datasets can be automated using data mining. Questions that used to require lots of analysis can now be answered more efficiently straight from the data.
  • Decision-making help: as organisations become more data-driven, decision making becomes more complex. By using data mining, organisations can objectively analyse the available data to make decisions.
  • Sales forecasting: businesses with repeat customers can keep track of the buying habits of these consumers by using data mining to foresee future purchase patterns so they can offer the best possible customer service. Data mining looks at when their customers have bought something and predicts when they will buy again.
  • Detecting faulty equipment: applying data mining techniques to manufacturing processes can help them detect faulty equipment quickly and come up with optimum control parameters. Data mining can be used to regulate these parameters to result in fewer errors during manufacturing and better-finished products.
  • Better customer loyalty: low prices and good customer service should ensure repeat custom. Businesses can decrease customer churn by using data mining, especially on social media data.
  • Discover fresh insights: data mining can help you discover patterns that reinforce your business practices and strategies, but it can also throw up unexpected information about your company, customers, and operations. This can lead to new tactics and approaches that can open up new revenue streams or find faults in your business that you would never have spotted or have thought to look for otherwise.

Disadvantages of data mining

As with anything in life, while there are many benefits associated with using data mining, there are also some few drawbacks too.

  • Privacy issues: Businesses collect information about their customers in many ways for understanding their purchasing behaviours trends, but such businesses aren't around forever, they could go bankrupt or be acquired by another company at any time, which would usually lead to the customers' personal information they own being sold to another or leaked.
  • Security issues: Security is a big concern for both businesses and their customers, especially due to the huge number of hacking cases where big data of customers have had their private information stolen. This is a possibility everyone needs to be aware of.
  • Misuse of information: Information collected through data mining for ethical reasons could be misused, such as being exploited by people or businesses to take benefits of vulnerable people or discriminate against a group of people.
  • Not always accurate: Information collected isn't always 100% accurate, and if used for decision-making, could cause serious consequences.
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