Clarity Analytics specialists combine business acumen, existing data and statistical analysis to uncover unique insights into business processes.

Clarity has an un-rivalled team of data specialists who are highly experienced in helping customers extract greater value from their information assets.  We've had leading roles in a variety of projects across industries and technologies, including some of the largest and most successful information initiatives in the country.

Data Analytics and Data Mining

Data mining is referring to the process of extracting patterns from data. By extracting these patterns, data is transformed into information. The samples on which the data mining process is carried out have to be representative for the larger body. A certain pattern may be discovered in a sample, but it may be irrelevant for a larger body of data.

The increasing volume of data makes human approaches inefficient. Automatic data processing is obviously more efficient. Data mining can uncover hidden patterns in large bodies of data much more efficiently. By examining raw data, you can draw conclusions about the information contained in the body of data. Such conclusions can help with business decisions at all levels.

Data mining can sort through large data sets using software to identify patterns, while data analytics is focused on inference and drawing conclusions based on previous knowledge of the researchers. The distinction between data mining and data analytics refers to scope, purpose and focus of analysis.

Common Methods

A commonly used method in data mining is decision tree learning. Decision trees are predictive models where each branch is a classification question and the leaves are partitions of the dataset. Decision trees create a segmentation of the initial dataset. Marketing managers have been using segmentation of customers, products or sales regions. Segmentation can be used to predict important pieces of information. Due to the high level of automation and ease of translating decision tree models into SQL, this technology is easily integrated with existing IT processes.

Neural networks methods are used for data mining tasks, but require long training times. Neural networks have accurate predictive models. They are automated to the degree where users do not need to know much on how they work. The disadvantage is that they are very complex and sometimes difficult to understand even by experts. Much of the effort is spent today on increasing their clarity. They have a wide range of applications, and have been used in detecting fraudulent use of credit cards and credit risk prediction, or increasing the hit rate of targeted mailings.

Rule induction systems are perhaps the best of data mining techniques for identifying all possible predictive patterns in a database. While neural networks can say exactly what must be done, rule induction systems are like committees of trusted advisors that explain why something should be done.

Emerging Trends

Organisations should analyse the information they have collected to find out more about their customers or clients. Social networking analysis is becoming very popular. Social media can provide plenty of information about users, making it possible for advertisers to target users based on personal information listed on the profile pages. Agile methods allow for rapid development and testing of new developments. Social media introduction within an organisation can be better accomplished with agile methods and approach.

Keys to a successful project

In order to achieve maximum benefits from a data mining and data analytics project, communication is extremely important. It is vital that the consultants understand what the analysis is seeking to achieve (in business terms) and why. 

Careful negotiation on the budget, schedule for development, support and working with corporate infrastructure groups are important steps for the successful implementation of a project.  Consideration must also be given to how the analysis project will work with the technical data custodians such as the data warehouse teams or OLTP applications administrator to access raw data.  

However, all of this is wasted if the new information is not used!  Thought must then be given as to how analysed data is integrated back into a business process to ensure it is actionable.  Business data custodians are usually in the best able to advise the most relevant ways to achieve this.