Jet Global originally published this article. The whole article may be found here: What Role Does Data Mining Play in Business Intelligence?
Businesses in the current day are constantly on the lookout for a competitive advantage—something that will enable them to deliver goods or services at a lower cost, greater quality, and faster rate than their competitors. The first step is to determine the quality and quantity of data they can acquire.
From manufacturing to supply chain logistics to retail sales to customer experience to post-sale marketing and beyond, data is the key to making processes more efficient, production costs lower, profit margins larger, and marketing campaigns more effective.
However, data alone isn’t the answer—practically it’s meaningless without a way to engage with it and extract valuable information.
By combining online analytical processing (OLAP), location intelligence, enterprise reporting, and other features, business intelligence (BI) software can assist. Enterprise firms can utilise BI software to combine different data sources into a single unified source, compile and arrange data, and provide an interface for end users to extract reports and dashboards that can help them make better business choices.
So, how can a cutting-edge company match its plethora of data with the ability to successfully use it through BI software? Let’s start with a definition of data mining.
Toiling Away in the Data Mines
Data mining is the process of extracting data from various sources (such as retail point-of-sale software, logistics management tools, and IoT-enabled manufacturing machinery), analysing it, and summarising it with reports or dashboards that can assist businesses in gaining insight into their operations. If data is the fuel that drives optimization opportunities, data mining is the engine that transforms that raw fuel into forward momentum for your company.
Transforming your raw data into business insight via the process of data mining takes place over five steps:
Extract, Transform, and Load (ETL):
Data mining starts with extracting data from one or more sources (such as those mentioned above), translating it into a standardised format, and feeding it into a data warehouse.
Store and manage:
The data is then stored and managed in a multidimensional database system, such as OLAP or tabular cubes, by organisations.
Access:
Business analysts, IT specialists, or data scientists receive access to the data after it has been standardised, imported, and managed into the database to determine how it should be arranged.
Analysis:
Based on the end user’s inquiries, the application software analyses and sorts the data.
Present:
Data is evaluated and sorted before being given to the end user in an understandable manner, such as a report, chart, or graph.
While business intelligence (BI) is primarily concerned with monitoring datasets and tracking data against business objectives and key performance indicators (KPIs), data mining is concerned with analysing datasets in order to uncover emerging patterns and trends. Data mining accomplishes this by employing advanced approaches to data in order to assist companies in achieving a certain objective or purpose:
Classification:
Classification is used to gather data and metadata information, which is then used to help arrange data into different classes.
Clustering:
Clustering is a data mining approach for identifying data sets that are similar to one another. Clustering is a technique for grouping data and identifying differences and similarities.
Regression:
Regression is a useful tool for determining the relationship between two or more variables. Regression is a statistical technique for determining the impact of seemingly unrelated or independent factors on dependent variables.
Association Rules:
The association rules method aids in the discovery of connections between two or more things. The goal of association rules is to find hidden patterns in a data set.
Outer Detection:
Items in the collection that don’t match predicted patterns or behaviours are detected using outside detection. Intrusion detection and fraud detection are two common examples. Outlier Analysis or Outlier Mining are terms used to describe the process of detecting outliers.
Sequential Patterns:
Similar patterns or trends in data over a certain period, such as seasonality, can be identified using sequential patterns analysis.
Prediction:
Prediction analyses previous occurrences and forecasts future events using a combination of different data mining techniques (such as clustering, classification, trends, and so on).
While data mining can find hidden patterns in your data and reliably forecast the future based on past data, BI and analytics software is necessary to connect those predictions and patterns to business goals and KPIs.
Data Mining and Business Intelligence
On paper, data mining and business intelligence appear to be very distinct, but there is a lot of overlap in terms of output and how they might help your company succeed. When it comes to purifying, standardising, and exploiting company data, data mining is an essential component of business intelligence. It also improves your capacity to use that data to produce accurate and reliable forecasts, allowing you to operate at a higher level than merely relying on the past data you have and guessing at future events.
Businesses may utilise data mining to uncover the information they need, then apply business intelligence and analytics to figure out why it’s so critical. After you’ve decided to become more data-driven, the next step is to look into BI tools.
We’ve put up a full assessment of 7 of the top Dynamics BI and analytics systems to assist you choose the correct solution after spending the last 15 years working directly with Microsoft Dynamics customers. We aim to assist you in improving your visibility into trends and forecasting, so you can use that information to inform your business strategy. Start future-proofing your company right now.
