In the modern era, businesses are continually looking for a competitive advantage—something that will allow them to deliver goods or services at a lower cost, higher quality, and faster speed than their competitors. The path to doing so begins with the quality and volume of data they are able to collect.
Data drives everything in the business world, from manufacturing to supply chain logistics to retail sales to customer experience to post-sale marketing and beyond, data holds the secrets to making processes more efficient, production costs cheaper, profit margins higher and marketing campaigns more effective.
But data alone is not the answer—without a means to interact with the data and extract meaningful insight, it’s essentially useless.
Business intelligence (BI) software can help by combining online analytical processing (OLAP), location intelligence, enterprise reporting, and more. BI software offers enterprise businesses the opportunity to connect disparate data sources into one unified source, collate and structure the data, and offer an interface for end-users to extract reports and dashboards that can drive more informed business decisions.
So how does a leading-edge business find a way to marry their wealth of data with the opportunity to utilize it effectively via BI software? Let’s introduce the concept of data mining.
Toiling Away in the Data Mines
Essentially, data mining is the process of extracting data from different sources (such as retail point of sale software, logistics management tools, and IoT-equipped manufacturing machinery), analyzing it, and summarizing it with reports or dashboards that can help businesses gain insight into their operations. If data is the fuel driving opportunities for optimization, data mining is the engine—converting that raw fuel into forward motion for your business.
Transforming your raw data into business insight via the process of data mining takes place over five steps:
- Extract, Transform, and Load (ETL): The first stage in data mining involves extracting data from one or many sources (such as those referenced above), transforming it into a standardized format, and loading it into the data warehouse.
- Store and manage: Next, businesses store and manage the data in a multidimensional database system, such as OLAP or tabular cubes.
- Access: After the data has been standardized, loaded, and managed into the database, business analysts, IT professionals, or data scientists gain access to the data to determine how it should be organized.
- Analysis: Application software analyzes and sorts the data based on incoming queries from the end user.
- Present: After the data has been analyzed and sorted, it is presented to the end user in an understandable format, such as a report, chart or graph.
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While BI is primarily focused on monitoring datasets and tracking data against business goals and key performance indicators (KPIs), the practice of data mining is used to analyze datasets to detect emerging patterns and trends. Data mining does this by applying sophisticated techniques to data that help organizations achieve a specific purpose or goal:
Classification is used to retrieve information about data, and metadata and then that information is used to help sort data by different classes.
Clustering is a data mining analysis technique used to identify data sets that are like each other. Clustering helps to group data and recognize differences and similarities.
Regression is a powerful method for analyzing the relationship between variables. Regression is used to identify the impact of seemingly unrelated or independent variables on other dependent variables.
The association rules technique helps to find associations between two or more items. Association rules aim to discover hidden patterns in the data set.
Outer detection observes items in the dataset that don’t match expected patterns or behaviors. Common examples are intrusion detection and fraud detection. Outer detection is also sometimes referred to as Outlier Analysis or Outlier mining.
Sequential patterns analysis helps to identify similar patterns or trends in data over a specific period, such as seasonality.
Prediction relies on a combination of other data mining techniques (such as clustering, classification, trends, etc.) to analyze past events to forecast future events.
While data mining can apply sophisticated data models and algorithms to uncover hidden patterns in your data and accurately predict the future based on historical info, BI and analytics software is required to map those predictions and patterns against business goals and KPIs.
Data Mining and Business Intelligence
Data mining and BI may seem different on paper, but there’s a great deal of overlap in both the output and the way they can contribute to the success of your business. Data mining is an integral component of business intelligence when it comes to cleansing, standardizing, and utilizing business data. It also contributes to your abilty to use that data to make accurate and dependable predictions that can allow you to operate at a higher level than simply relying on the historical data that you have available to you, and guessing at future outcomes.
Businesses can use data mining to find the information they need and use business intelligence and analytics to determine why it is important. Once you’ve made the decision to become more data driven, the next forward is to evaluate BI software.
After spending the last 15 years working directly with Microsoft Dynamics customers, we have put together a detailed assessment of 7 of the top Dynamics BI and analytics platforms to help you choose the right solution. We want to help you optimize visibility into trends and predication—and use that insight to inform business strategy. Start future proofing your business today.