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Data Warehousing and Integrations: Modern Business Intelligence

Struggling with managing large data volumes from disparate sources? Read on to discover how modern data warehousing and integration techniques enhance business intelligence.

Key takeaways:

Data warehousing aggregates data from disparate sources into a central repository to support analysis and drive decision-making.

It provides many benefits, including eliminating silos, increasing data quality, speeding up analysis, and improving decision-making.

Adopting the right strategies can help you avoid common issues like data harmonization and scalability as data expands.

Businesses manage increasingly large volumes of data from various sources daily. Per IDC’s estimates, the annual data produced globally will reach 175 zettabytes by 2025. As more data becomes available, embracing modern solutions is crucial to stay competitive.

Data warehousing aids business intelligence by making data more usable. It provides a safe, structured way to store and centralize data, enhancing your ability to answer complex questions involving large data sets. Other benefits include better data quality, faster business insights, and more intelligent decision-making. You must address integration and scalability issues and other obstacles to unleash the full potential of data warehousing.

This article explores the crucial role of data warehousing in business intelligence and how integrating data sources for analytics provides an edge. You’ll also gain practical tips you can implement immediately to harness data effectively. Keep reading.

Fundamentals of data warehousing

Data warehousing aggregates data from disparate sources into a central repository (data warehouse) to support analysis and drive decision-making. Think of it as a process where digital trucks unload data into a large database, and then workers sort the data into rows of well-organized shelves for easy retrieval. This enables your organization to run powerful analytics on large volumes of historical data in a way impossible with standard databases.

A data warehouse architecture consists of three tiers with the following core components:

  • A data warehouse server (typically a columnar or relational database) that collates, cleanses, converts, and organizes data from disparate sources through Extract, Load, and Transform (ELT) or Extract, Transform, and Load (ETL) processes.
  • An online analytical processing (OLAP) engine that quickly runs complex queries. Depending on the existing database system, you can use one of three OLAP models: HOLAP, MOLAP, or ROLAP.
  • A business intelligence and reporting tool that allows you to present data visually and conduct ad-hoc analysis.

Data warehousing provides a more holistic view of your organization’s data than isolated source data. It’s also more sophisticated than a data lake, which centralizes data but in an unstructured manner, making it hard to retrieve the exact information you’re looking for. 

A data warehouse is a single source of truth for all data that’s easy to navigate and understand. Moreover, its non-volatile nature provides a reliable historical record since data can’t be erased or altered once entered.

The power of integrating data sources for analytics

When disconnected data sources exist in an organization, there’s a high chance of creating silos. For example, certain information may be accessible to some departments but unavailable to others, or some systems may lack the latest data. 

These disconnects complicate data management and analysis. Because each data store has a unique format and structure, it’s difficult to pull them together for a comprehensive view. Departments may end up with inaccurate, outdated, or conflicting data for their analyses and reports, invalidating or skewing their insights. Inconsistent data management practices and increased costs are likely to be a concern, too.

Data warehouse integration eliminates silos by connecting data sources into a single cohesive system for unified access. It standardizes data formats for compatibility and merges similar data points to remove redundancies. 

The integration acts as a cross-checker, ensuring data stored in separate locations match. This results in a centralized resource with easy access to information for analysis and business intelligence. It enhances data quality and simplifies data management, supporting more timely and accurate insights across the organization.

Leveraging data warehousing in business intelligence

As we’ve established, data warehouses allow more efficient and faster access to large data sets. They typically have the memory and computing resources needed to run complex queries, making it easy to derive valuable insights from data quickly. 

Consolidating data previously residing in different places allows you to quickly sort through multiple data sets to identify relationships and patterns that inform decision-making and help predict future trends. You quickly find the answers you need, when you need them, without the wait. You can also generate and distribute reports in minutes instead of hours or days.

Finding answers is made possible by the OLAP engines performing multidimensional analysis of large volumes of transactional and historical data at very high speeds. OLTP (online transactional processing) also plays a crucial role by allowing accurate, real-time execution of several database transactions by many people simultaneously over the internet.

Overcoming challenges in data warehousing and integration

While data warehousing in business intelligence brings many benefits, including better data quality, faster business insights, and smarter decision-making, it’s not without its fair share of obstacles. That’s why choosing an experienced software development partner to help with data warehousing is smart.

For example, integrating data from isolated repositories into a unified data warehouse can be time-consuming and complex. This is especially true if the data exists in different structures and formats. Implementing data integration maze tools to facilitate extraction, transformation, and loading (ETL) processes can help eliminate silos and overcome integration complexities. These tools ensure data from disparate sources is compatible and harmonized within the data warehouse.

As you start housing more data and needing a wide range of data and more advanced analytics, on-premise data warehouses may struggle to scale effectively, leading to delays in data processing, performance bottlenecks, and increased costs associated with hardware upgrades. These scalability issues can be solved by switching to cloud-based data warehousing solutions.

Another challenge you must overcome is poor data quality. Inconsistent, outdated, or inaccurate data undermine the integrity of analyses and decision-making processes. Establishing robust data governance practices is crucial to upholding the integrity and quality of data and maintaining stakeholders’ trust in the data warehouse. These include regular data cleansing, validation, and profiling. Mapping out source-to-target data transformation rules can also help enhance data accuracy and reliability.

The future of business intelligence with data warehousing

Integrating data warehousing systems into business intelligence strategies is a smart move. It helps you achieve a more holistic view of your organization’s data and eliminates silos, ensuring everyone is dealing with the same information. It also lets you leverage advanced analytics to glean critical insights and make informed decisions.

As more data becomes available globally, forward-thinking companies that embrace data warehousing for data-driven decision-making will have an edge. Therefore, you should position yourself early to avoid falling behind.

Let Susco help with your data warehousing needs. Start the conversation with a free consultation and move forward in control.

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