Data and Analytics

Business intelligence (BI) empowers your organization to analyze data and gain valuable insights to drive decision-making processes. Our skilled analysts and data scientists leverage an effective BI framework to uncover meaningful insights and utilize available data to provide answers.

For instance, when management seeks ways to improve website conversion rates, BI can identify potential causes, such as low engagement with website content. Using the BI system, analysts can validate if engagement truly impacts conversion and pinpoint the specific content responsible.

The tools and technologies that enable BI leverage data from various sources, including files, databases, data warehouses, and expansive data lakes. They execute queries, typically in SQL format, against the data and generate reports, dashboards, and visualizations. These resources facilitate the extraction of insights from the data, which are utilized by executives, mid-management, and employees in their day-to-day operations to make data-driven decisions.

A data warehouse (DWH) serves as a centralized repository for businesses to store and analyze vast amounts of data from diverse sources. It is a cornerstone of the business intelligence (BI) process, equipping organizations with the necessary tools to make informed decisions.

In essence, a DWH is a robust system for data management where organizations store both current and historical information spanning sales, marketing, finance, customer service, and more. It plays a pivotal role in facilitating BI processes by enabling organizations to generate queries and address their most critical analytical questions. Through this capability, companies can optimize performance and develop strategies based on accurate insights rather than relying solely on intuition.

To fully grasp the value of a data warehouse in a business environment, it is essential to differentiate it from a traditional database. While both are valuable for data storage and management, they serve distinct purposes. The following are key differences that shed light on the unique value a data warehouse brings:

1. Scope: A data warehouse accommodates massive data volumes from multiple sources, providing a comprehensive view of the organization's operations. In contrast, a database typically focuses on specific applications or operational functions.

2. Purpose: A data warehouse is designed for analytical purposes, enabling complex queries and data analysis to derive insights. On the other hand, a database is primarily focused on transactional operations, ensuring efficient data processing and retrieval.

3. Data Structure: Data warehouses employ optimized structures like star or snowflake schemas to facilitate efficient querying and analysis across multiple dimensions. Databases, on the other hand, typically utilize normalized schemas for transactional integrity.

The differences between databases and data warehouses are crucial in understanding their respective roles and value in a business environment. Here, we highlight the key disparities that set them apart.

Firstly, databases serve as repositories for recording data and transactions in a structured format, offering users the ability to access, manipulate, and retrieve information as needed. Their primary purpose is secure and organized data storage and retrieval. On the other hand, data warehouses store vast amounts of data from multiple sources, focusing on analytical purposes. They provide businesses with an environment to make queries and inform crucial strategies.

Secondly, processing methods distinguish the two systems. Databases employ OnLine Transactional Processing (OLTP) to handle simple transactions in real-time, such as inserts, replacements, and updates. Conversely, data warehouses utilize OnLine Analytical Processing (OLAP) to swiftly analyze extensive volumes of big data. OLTP focuses on immediate data processing, while OLAP enables data analysis at a significantly accelerated pace.

Lastly, databases typically cater to specific use cases, such as real-time data storage for each item sold on a website. They excel at processing numerous detailed queries rapidly. In contrast, data warehouses are "subject-oriented" and retrieve summarized data for complex queries, which are subsequently used for analysis and reporting purposes.

In the past, decision support applications were commonly used by organizations to drive data-driven decision-making. These applications directly queried and reported on data from transactional databases, without the presence of a data warehouse as an intermediary. This approach is similar to the current trend of storing vast amounts of unstructured data in data lakes and querying it directly.

There were 5 challenges encountered when relying solely on decision support applications without a data warehouse:

1. Unsuitable Data Form: Data often lacked the necessary structure and organization required for effective reporting and analysis.

2. Data Quality Issues: The data available for decision support purposes frequently suffered from quality issues, such as inaccuracies, duplication, or missing values.

3. Performance Strain: The processing demands of decision support activities placed a burden on transactional databases, resulting in reduced performance for operational systems.

4. Dispersed Data: Data was scattered across multiple systems, making it challenging to consolidate and derive meaningful insights.

5. Historical Information Gap: Transactional OLTP databases were not designed to retain historical information, depriving decision-makers of valuable long-term trends and patterns.

These challenges, along with others, prompted enterprises to adopt the data warehouse model. Even today, all five of these problems remain relevant in the context of data-driven decision-making. This raises the question: Can we forgo a data warehouse while still enabling efficient business intelligence (BI) and reporting?

While the concept of storing and querying data directly from data lakes has gained popularity, it is important to recognize that data lakes alone may not fully address the challenges outlined above. Data warehouses, with their optimized structures and data integration capabilities, offer several advantages:

1. Data Transformation: Data warehouses allow for data transformation and modeling to ensure that information is in a suitable form for reporting and analysis.

2. Data Quality Management: Robust data quality processes can be implemented within a data warehouse to address issues and improve the overall reliability of the data.

3. Performance Optimization: By separating analytical workloads from transactional systems, data warehouses minimize the impact on operational performance and provide faster query response times.

4. Centralized Data Repository: Data warehouses consolidate data from various sources, enabling a unified view and simplifying analysis.

5. Historical Data Retention: Data warehouses are designed to retain historical information, allowing organizations to uncover trends, patterns, and insights over time.

While data lakes have their advantages for storing and exploring unstructured data, incorporating a data warehouse into the BI architecture complements these capabilities and addresses the challenges that persist in the absence of a dedicated repository. By leveraging the strengths of both data lakes and data warehouses, organizations can achieve efficient BI and reporting, empowering decision-makers with accurate, timely, and comprehensive insights.

Business intelligence (BI) architecture encompasses various components and layers, each serving a specific purpose. Let's begin by analyzing the key elements that constitute a robust BI architecture framework.

1. Data Collection: The initial step involves gathering relevant data from diverse sources, both internal and external. These sources may include databases, ERP or CRM systems, flat files, APIs, and more. The data collection process aims to acquire comprehensive and accurate information for analysis.

2. Data Integration: Once the data is collected, it undergoes integration into a centralized system, often facilitated by Extract, Transform, Load (ETL) processes. During this stage, the collected data is cleansed, standardized, and prepared for analysis. Data integration ensures consistency and compatibility across different data sources.

3. Data Storage: This layer highlights the significance of a data warehouse (DWH). A data warehouse serves as a repository where structured data is stored, enabling efficient querying and analysis. It provides a consolidated and organized environment for data retrieval and reporting. The DWH ensures data availability and facilitates the generation of actionable insights.

4. Data Analysis: Once the information is processed, stored, and cleaned within the data warehouse, it is ready for analysis. Leveraging appropriate tools, the data is visualized and examined to identify patterns, trends, and correlations. Data analysis empowers organizations to derive insights that drive strategic decision-making.

5. Data Distribution: The insights gained from the analysis are disseminated in various formats to stakeholders within the organization. This can include online reporting, interactive dashboards, or embedding solutions. The data is presented through graphs, charts, and visual representations to facilitate easy comprehension and decision-making.

6. Actionable Insights and Decision-Making: The ultimate objective of the BI architecture is to extract actionable insights from the data and utilize them to make informed decisions. These insights, derived from the analysis and visualization of data, serve as a foundation for improving business processes, optimizing strategies, and driving company growth.

By establishing a comprehensive BI architecture framework encompassing these layers, organizations can effectively collect, integrate, store, analyze, distribute, and utilize data to gain a competitive advantage in the market. The data warehouse, as a central component, plays a pivotal role in enabling efficient data management and decision-making processes.

Having the right data in your data warehouse and the right business intelligence leveraging that data allows for many practices that can drive strategic decision-making. Options include, but are not limited to:

Data mining
Also known as knowledge discovery, data mining is a process used to extract usable data from a more extensive set of raw data. This process helps you discover trends, themes, or patterns in large amounts of big data.

Performance metrics
Metrics are used to measure the behavior, activities, and, yes, the performance of a business, its employees, or specific campaigns. While performance metrics are the result of analysis, those results can then be collected for further analysis. Performance metrics measure required data within a range, allowing a hypothesis to be formed, proven, or disproven according to previously determined business goals.

Querying
Within business intelligence and data warehouses, analysts and business teams query data to check its validity or accuracy. Successful BI helps businesses and organizations ask and answer questions of their data and have the right data in place to get reliable, quantitative information in those answers.

Statistical analysis
Data analysis has several components; statistical analysis is one of them. In the context of business intelligence and data warehousing, statistical analysis involves collecting and reviewing data samples. In statistics, a sample is a selection drawn from a total population of data. It is critical to have the data warehoused and connected to your BI processes for the analysis to be as accurate, thus leading to smart, strategic decisions.

Data visualization
Data visualization means taking data and representing it visually to improve understanding and better inform decisions. These can be charts, diagrams, data stories, and infographics to show answers to questions and provide data validation for decisions. Presenting data as a spreadsheet can be a cumbersome and dry experience, but visualizing data often helps bring information to life in a more compelling and effective way.

Data storytelling
Data storytelling is translating data analyses into layman's terms to influence or inform a strategic business decision. Having the right warehouse for your data and the most reliable business intelligence tools will make it easier to compile and the stories that much more pursuasive.

If you can afford to do it effectively, yes. While some organizations practice business intelligence without the use of a data warehouse, there are downfalls to that approach, usually due to time or budget. Namely, processing the needed data can put a strain on transactional databases, reduce performance, and increase load time. This slows the analysis-to-insight process. Also, by not combining your data sources, they prove less efficient and can lack accessible historical information. In other words, transactional databases cannot do the same job as a data warehouse. A strong relationship with your data is critical when it comes to making the right, timely decisions for your organization.

Using a robust data warehouse partnered with business intelligence best practices makes this possible. Learn more about how we work with our partners to provide data warehousing and BI solutions.