Data fuels almost everything around us and influences most aspects of our daily life, including significant business decisions.
These are often made based on insights from information, which can be either automated or manually assessed. This information is obtained through a number of ways, such as collected from customers or extracted from market information, and is then used to determine the best course for production lines, supply chains and more.
Many modern businesses would arguably be less successful or competitive if not for data, which contributes enormously to being able to adapt to the ever-changing market conditions or consumer needs.
Nevertheless, data isn’t much use in its original, raw state. In order to provide value, it requires analysis and being sifted for key insights. Thanks to cloud computing, large amounts of data can be liberated from the constraints of a limited-storage server and held at scale, with real-time analysis available 24/7. However, what is even more important is that these vast quantities of data need to be assessed at lightning speed in order to sift through the right information – a task that is not possible using human processing power.
What is data mining?
Data mining is defined by scrutinising large amounts of data in order to discover patterns and irregularities within the datasets. By mining data, you can create an independent forecast of the future of your business and predict scenarios of potential opportunities as well as challenges.
There are many different ways to mine and a data-swamped enterprise can use this opportunity to expand the business, streamline costs, mitigate risks, and strengthen relationships with clients
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
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.
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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:
- 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.
The future of data and data mining
With technology becoming more complex and the sheer volume of data being collected expected to explode in the coming years, data analytics will increasingly give businesses a competitive edge. With these tools becoming more accessible, too, competing organisations will be vying to build and deploy increasingly powerful services to give them more accurate insights.
Edge computing is one example of a technology that can instigate changes in data capture ad analysis, allowing businesses to process real-time data in the location it’s being gathered, rather than porting this to a data centre or the cloud. The launch of 5G, although still being deployed, should smoothen this process too with the the promise faster data transfer speeds. This should also benefit the Internet of Things (IoT) ecosystem.
Edge computing can also help with data mining when teams need to analyse small quantities of data in real-time, given that big data mining is, at present, limited to data centres and the cloud. Gartner research suggests 75% of enterprise-generated data will be created and processed outside of the traditional data centre by 2025, with the future of big data analytics lying firmly at the edge.
Machine learning equally promises to influence the future of data analytics, with more businesses deploying such applications with each passing year. This is because the technology is becoming more accessible with many tools just as easily available to small businesses as they are to data scientists. Some of the newest machine learning tools can provide businesses of all sizes with the capabilities to analyse complex datasets and derive useful insights, with the performance of these systems only set to improve.
In the age of rampant digital transformation, not only is data becoming more important, but so is the speed and accuracy of processing this data, and the quality of insights that organisations can derive.
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See the original article here: ITPro