Unit 8: Data Warehousing and Data-Driven Systems
In the last unit, you learned about data sharing, including the benefits and disadvantages. During your reading, you were given the knowledge to explain the continuous efforts by institutions and publishers to improve policies and guidelines for data sharing. Because of data sharing, researchers can investigate or reveal more information on an event through open access and data methods. Next, you will learn the final step in data management. This step covers the importance of data warehousing and data-driven systems. Business intelligence (BI) propels the evolution of data. Data warehouses exist to allow decision-makers the ability to understand and improve organizational performance. Data warehouses first entered the business arena in the 1980s. The purpose was to assist with data flow from an organization’s operational system into decision-support systems. Data-driven is a business term that describes using data to inform or improve processes, performance, revenue, and decision-making. Data-driven systems are specialized software used for acquiring, managing, and presenting information. Therefore, data-driven systems use information from data warehouses to support business decisions, actions, and policies. This unit will cover how data warehousing and data-driven systems can support organizations through real-time feeds and the ability to retrieve historical data. Also, the unit will review each element and how they assist in the overall scheme of a business.
Completing this unit should take you approximately 5 hours.
Upon successful completion of this unit, you will be able to:
- describe how data warehouses bring related information from disparate databases so that it can be analyzed;
- explain what data-driven systems can achieve in an organization;
- identify the most widely-used data warehousing architectures and schemas and how they are applied in practice; and
- use data warehouse and mining techniques to support decision-making based on business objectives.
8.1: Data-Driven Decisions
Currently, most organizations make data-driven decisions. Data-driven decisions examine an organization's problems by investigating the connection between possible problem variables. What does it mean for an organization to take a data-driven approach? This means that strategic and daily decisions are grounded on data analysis and interpretation. Therefore, organizations examine and organize data aimed at better serving customers and measuring key performance standards.
Watch this video and think back to the project lifecycle process. How well does the project lifecycle process address the criteria for data-driven decisions?
Read this article on being data-driven. Under the Project Lifecycle section, read the four sections: (1) Analyze and Plan, (2) Design and Develop, (3) Deploy and Implement and (4) Cross-Cutting: M&E.
8.1.1: KPIs and How They Relate to Business
Decision-makers use data to better understand customers and improve organizational performance. Overall organizational performance is measured using key performance indicators (KPIs) and certain data collected from internal and external sources. Next, you will learn more about KPIs and how organizations use data to measure performance.
KPIs move an organization forward toward an intended result or goal. KPIs are the strategic focus and process improvement created using the analytical decision-making approach. Therefore, a KPI is considered the measurable value used to compare productivity and business objectives.
Remember, KPIs are measurable values. Organizations use KPIs to gauge their success in reaching certain objectives. However, each department within an organization may use different KPIs to measure performance and outcomes. Thus, distinct KPI types measure success based on specific department goals and targets within an organization.Read this article and pay attention to how companies achieve high-quality performance for value creation. Focus on the selection of KPIs and the discussion. How can organizations improve sustainability in their strategies to improve certain KPIs?
8.2: Data Warehousing
Now, you understand KPIs and their association with business operations. Remember, business use of KPIs is vital for developing strategies and gauging performance, goals, and objectives. Next, we will discuss how to manage data used for KPIs and the purpose of data warehouses.
A data warehouse is a compilation of data from both internal systems and external sources. Data warehouses consist of historical data from both internal and external data sources. Data warehouses support decision-making through consolidation, analysis, and reporting of data results.
Think back to what you learned about key performance indicators (KPI) in Unit 8.1 and how businesses use them. A data warehouse is a system that processes and analyzes data used to measure organizational KPIs. Remember, data warehouses allow organizations to process data, support the analytical process, and drive decisions.
Data warehouses are also known as relational databases. This is a type of data storage that allows access to data that are related to each other. Therefore, organizations use data warehouses as the primary tool for analytical and business reporting. This means that data warehouses can consist of real-time and historical data from internal and external data sources to produce reports for data-driven decisions.8.2.1: Types of Data Warehousing
You learned that a data warehouse is a system used to gather internal and external data from a wide range of sources. Remember, data warehousing also supports data-driven decisions by serving as the data input to models in decision support systems. The actual analysis of the data will be through models created in a decision support system. However, these models would not be able to operate without the data stored in the data warehouse. There are three common data warehouse models in traditional architecture: virtual warehouse data, data marts, and enterprise data warehouses.
Virtual data warehouses (VDW) consist of separate databases. An analyst can query information from separate databases and store them together. As a result, analysts can access all data as if it is within a single database.
Organizations also might use a data mart. A data mart might be thought of as a smaller version of a data warehouse. A data mart is utilized to analyze specific business-line reporting. For example, there might be a data mart to support budgeting and financial planning. Think back to what you learned about KPIs and how businesses use them to measure goals and objectives. Organizational data is collected or combined from multiple source systems. The specific departments within the organization can use data to measure and report on KPIs.
Comparatively, enterprise data warehouses span across the entire organization. This data warehouse type requires data used in association with the entire organization. For example, a decision-maker can use enterprise data warehouses to uncover unknown dependencies between projects, study risks and opportunities, and provide strategic decisions on behalf of the entire organization.
One thing that both data marts and data warehouses have in common is that they are designed to be easily and quickly searched. Thus, instead of being structured as a formal, normalized relational database, a data warehouse might use a simple, non-normalized structure such as a star or hierarchy. The object is speed, not rigorous normalization.Read this lesson on data warehousing and data mining. Pay attention to the difference between data warehousing and data mining. Answer the practice question at the end of the lesson.
Watch this video on data warehousing architecture. Which schemas can be applied to one or more of the industry services listed in the previous article?
8.2.2: How Data Mining Works
Remember, you learned that virtual data warehouses are used to query information from different databases and used as one relational database. Data mart analyzes and reports on specific areas within the organization. Enterprise data warehouse analyzes and provides strategic decisions on behalf of the entire organization. This next section will discuss how data mining is used in organizations.
Data mining is the process of sorting, organizing, and analyzing enormous sets of data. Data mining is also known as knowledge discovery in the business world. Remember, data mining is not complete until analysts extract meaning or insight from the data.
Businesses use the data mining process to turn raw data into useful and beneficial information. The process includes data mining tools that predict consumer behaviors and future business trends. Data mining contributes to the overall ability to make proactive and knowledge-based decisions.Read this article and pay attention to the data mining techniques, classifier development, and evaluation criteria. Then take notes and understand the difference between supervised and unsupervised learning models. Finally, read the summary and discussion section of this article. What distinctions can be made about the three major purposes of problem-solving items using data-mining techniques?
There are different types of data warehouses, and each has a specific purpose within an organization. Remember, it is important to use the correct type of warehouse to support the "decision support" model being employed. Decision support techniques such as classification, prediction, time-series analysis, association, clustering, and so on will each have their own unique data needs. Correctly designing the data warehouse will ensure the best possible evidence to support strategic and daily decisions.
Managing data is an important function in the administrative process. Because organizations use data to guide decisions, decision-makers rely on you to produce a data management plan for sustainability, growth, and strategy. As you start to interact with decision-makers and the decision-support systems they use, you will also find that additional study of the models employed through a course on quantitative methods or decision-support technology will prove useful.
Study Guide: Unit 8
We recommend reviewing this Study Guide before taking the Unit 8 Assessment.
Unit 8 Assessment
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Take this assessment to see how well you understood this unit.
- This assessment does not count towards your grade. It is just for practice!
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