Data Mining For Business Intelligence
Humans have been collecting data since the dawn of time. More and more has been collected over the thousands of years we have been on this planet. However, since the technology boom, this has increased exponentially.
Businesses are facing challenges to sieve through the useless data to discover patterns and understand the useful information.
Enter big data and data mining.
Big data is the computerised processing of large amounts of information; data (structured, unstructured and semi structured) that exceeds the capacity of conventional software to be captured, managed and processed within a reasonable time.
Big data tends to refer to the use of predictive analytics, user behaviour analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set.
Because there is such a large volume of data within these files, the right information must be obtained quickly.
Data sets are growing rapidly. In fact, the IDC predicts that by the year 2025, there will be 163 zettabytes of data. To put that into perspective, the current world’s largest SSD drive can hold up to 100 terabytes.
Taking the necessary data stored, then it is important to consider different techniques of data analysis, such as the association, clustering, text analytics or data mining.
Data mining is one of the most important, because it is the process of extracting data, analysing it from various perspectives to find patterns in the database. This information is then presented a useful way to the end user by data visualisation techniques.
There are two types of data mining:
- Descriptive: gives information about existing data;
- Predictive: makes forecasts based on the data
The data mining process is as follows:
Detecting anomalies – identifies unusual and uncommon data that are unexpected and do not follow the common pattern of other results. Any results that have found to be anomalies could be incorrect and will require investigating into.
Association rule learning – uses strict rules to identify relationships between the parameters used to obtain the data. Similar to machine learning, the machine uses algorithms to find the solutions, however, where it is differs is machine learning determines the algorithms itself and does not require the strict rules to be set.
Clustering – the machine groups together pieces of data that have similar properties to each other while leaving out data without those properties at the same time.
Classification – the system learns a function that finds data that hasn’t yet been defined and categorising it into a pre-defined class. The user will define a structure and the machine will categorise the data based on the rules of that defined structure.
Regression – analyses which function estimates the least incorrect results. It does this by understanding how the dependent variable reacts when independent variables are changed.
Summarisation – presents the data in a way that is more understandable to a user.
Data mining is now so important to businesses because it saves them a lot of time and money on researching new business opportunities and enable them to make more key strategic decisions.
So how does mining aid with Business Intelligence to provide insights?
Data Mining For Business Intelligence
Data mining and business intelligence are powerful tools to capture and use knowledge. Being able to use the information gathered is at least as important as gathering it. So, it is therefore important to have business intelligence (BI).
BI is the process of transforming data into useful data, and turning that useful data into business knowledge. Without BI, organisations will not be prepared in making strategic manoeuvres.
Business Intelligence combines data analysis applications, including ad hoc analysis and querying, enterprise reporting, online analytical processing (OLAP), mobile BI, real-time BI, operational BI, cloud and software as a service BI, open source BI, collaborative BI and location intelligence. BI technology also includes data visualisation, tools for building BI dashboards and KPIs.
The benefit of BI to businesses is that they are bale to gain competitive edges over their rivals and improves internal operations. Everything becomes more efficient and streamlined.
Other uses of BI include financial control, production planning, company profitability and many, many more.
It’s not just the business that benefits from BI and data mining; customers also see improvements in the relationship with the organisation. Mining identifies customer habits and patterns. The business is then in a far better position to recognise what customers are looking for, improving satisfaction and loyalty to the brand.
Data Mining And BI For Business Growth
Business intelligence acts as important voice in determining where a business should be going. The results obtained could indicate where things need improving internally in order for the business to scale quicker and optimise growth.
If there are any problems identified, the solutions are quicker to obtain and can be implemented quickly and efficiently.
It also helps with proposing to enter new markets based on evidence. The knowledge obtained from data mining and business intelligence can figure out where the company will succeed by predicting outcomes before even entering the market.
Data mining is used to generate business intelligence. It is an increasingly popular term representing the tools and systems that enable organisations and corporations to turn business knowledge into a profit.
Data mining and business intelligence have made it so much easier for businesses to access key information quickly and efficiently from data modelling. This enables them to make far better decisions. In addition, data mining technologies have bright future in business applications, making possible new opportunities by automated prediction of trends and behaviours in these businesses.
BI is no longer a futuristic idea or concept; it’s happening right now and will only improve as technology advances over the coming years.