How Data Warehousing and Data Mining Work Together for Better BI
Data warehousing and data mining together serve as the backbone of business intelligence (BI). Data warehousing provides quality, governed data for the data mining process. This article explains the relationship between data warehousing and data mining and looks at several examples of how different industries are using data to gain competitive advantages.
What Is Data Warehousing?
Data warehousing is the process of securely storing data with the purpose of providing a central repository of information that can be explored and analyzed to produce business insights. A modern cloud data warehouse (CDW) is a “single source of truth” that stores quality data in a governed manner so business teams can analyze it to make better decisions.
The data in a CDW may originate from databases, applications, IoT devices, SaaS software, and other sources. Modern CDWs integrate with cloud-native tools that allow business teams to quickly extract insights from all their data sources, in any format.
What Is Data Mining?
Data mining is a part or subset of data analytics. It involves searching for and finding patterns, anomalies, associations, and correlations in very large data sets. The goal of data mining is to predict an outcome based on available data. Due to the amount of data inherent in data mining, machine learning is often used.
The process of data mining is typically made up of four steps:
1. Determine objectives
There is no end to the questions that a business might ask to become more competitive. But narrowing the focus to one particular question or set of questions at a time is crucial to the ability to act on the insights you generate. Precisely define the business problem, get clear on the context of the problem, and create specific objectives.
2. Gather and prepare data
The next step is to identify the data sets you need for your excavation and to prepare the data. Once the data has been cleaned and transformed, it’s ready to be used.
3. Investigate relationships and apply algorithms
At this stage in the process, things start to get interesting. Using SQL, you can identify patterns, anomalies, associations, and correlations that serve as insights into the problem you aim to solve. Depending on the data set, you may also apply machine learning algorithms.
4. Evaluate results
The final step is to evaluate and interpret the results of your investigation. At this point, meaningful insights should become clear. The questions you outlined in your objectives should be answered, and the insights you glean will inform your action plan.
How Data Warehousing and Data Mining Work Together
Data warehousing and data mining function together to support BI. Without a centralized repository, data mining wouldn’t be possible. The relationship between data warehousing and data mining could be compared to building construction, with the data warehouse being represented by a building supply store and data mining being represented by the construction process.
In order to construct the building in an efficient manner, the people building the house need a reliable location to source their supplies when they need them. Each day or week, the builders get what they need from the building supply store to complete their work.
Similarly, business teams who need to make data-driven decisions must have a centralized repository where they can reliably find quality, governed data for their work. Then, using data mining, they can generate the insights they need for strategic decision-making.
Examples of Data Warehousing and Mining in Action
Nearly every industry can benefit from data warehousing and data mining. Increasingly, successful organizations are using data to achieve competitive advantages. Let’s look at a few examples of data warehousing and data mining in action.
Retail and CPG: Market basket analysis
Retailers use internal data and third-party data to identify purchasing patterns and associations. With the insight they glean, they can display products together to increase transaction value and make product recommendations when a customer places a particular product in their cart.
Healthcare: Health forecasts
Data analytics is used in a multitude of ways in the healthcare industry. One example is the ability to make more-accurate diagnoses by aggregating a patient’s medical records, treatment patterns, and other information to identify the best possible treatment plan. Data mining can also help improve outcomes and reduce costs by identifying potential health risks and creating health forecasts for various population segments.
Financial services: Fraud detection
Financial services companies use data warehousing and data mining to better understand market risks, detect fraud, assist with regulatory compliance, optimize marketing messages, and more.
Media: Personalization and engagement
Many media companies use data analytics to better understand how content drives audience acquisition and engagement, increase personalization in advertising and content, and identify new revenue streams.
The Snowflake Data Cloud for Data Analytics
The Snowflake Data Cloud powers data-driven business decisions by providing quick and easy access to a single trusted source for all your data. It’s an ideal solution for data warehousing, and because it seamlessly integrates with all modern BI and machine learning tools, you can mine your data for insights efficiently.
Use all of your data
With Snowflake, you can make data-driven business decisions with quick and easy access to a single trusted source for all your data. Near-unlimited, low-cost cloud storage with 2–3x compression allows teams to efficiently make use of all relevant data. Additionally, Snowflake Data Marketplace and private data exchanges facilitate sharing data with customers and partners. Native support for geospatial data and analysis ensures efficiency.
Have confidence in security, governance, and privacy
Snowflake’s features keep your data secure, while providing governed access to authorized users. Certifications for SOC2 Type 2, ISO 27001, PCI DSS, HIPAA, FedRAMP, and more support compliance.
Enable fast and easy SQL analytics
Avoid bottlenecks or service interruptions with dedicated compute resources for every user and workload. Use Snowsight, the built-in visualization UI for Snowflake, or take advantage of optimized direct connectors for popular BI and analytics tools. The Snowflake Data Cloud is also fully ANSI SQL–compatible, with native support for disparate datasets.
Automate administration and maintenance
Snowflake automates data warehouse administration and maintenance, and it’s designed specifically for a seamless cross-cloud experience. Enable automatic query caching, planning, parsing, and optimization, automatic updates with no scheduled downtime, and cross-cloud data replication for seamless, global data access.
See Snowflake’s capabilities for yourself. To give it a test drive, sign up for a free trial.