On their way to building a strong data culture, enterprise companies face a whole myriad of obstacles—from siloed data structures to the lack of accessibility and poor governance. Aiming to tackle them, organizations consider building an enterprise data warehouse (EDW), a large, centralized, and integrated vault that stores vast amounts of data from various sources and serves as a basis for data-driven decision-making and strategic insights.
Although for the past decade, the concept of an enterprise data warehouse has become common for today’s companies, only 26% of those investing in data warehouses report they are functioning effectively and fulfilling their role as a centralized hub for integrated data management and analysis.
How can leaders approach EDW development correctly and what aspects should they consider during the discovery phase and implementation? We provide a practitioner’s view further in the blog post.
- Approaching enterprise data management
- Understanding enterprise data warehouse
- How to build an efficient enterprise data warehouse
Approaching enterprise data management
As a rule, organizations start to think about building an EDW (enterprise data warehouse) when they find themselves in a challenging business scenario.
For example, for 10 years of a company’s presence in the market, the number of software systems they’ve been using has grown dramatically, creating vast amounts of diverse and distributed data. To maintain a competitive edge, the company has to be able to quickly make informed decisions. This requires centralized and managed access to the information stored within different departments and business units. In addition, the data quality should be enough to make decisions.
Another potential scenario is a company undergoing the mergers and acquisitions (M&A) process. Here data inconsistencies are a major roadblock to building stakeholder trust, evaluating the company’s achieving regulatory compliance, and assessing risks.
Poor data culture or its complete absence is the top reason why enterprise companies fail with advanced data analytics. No matter how mature their operations and business processes are, once they reach the tipping point, implementing data management will be the only way to move forward.
Let’s sum up. Approaching big data management and analytics is a native step in an enterprise business evolution. Sooner or later, to remain productive and efficient, businesses will need to take better (read: data-driven) decisions.
Tasks to solve with enterprise data warehouse
If we drill down to specific use cases and scenarios, we’ll observe a number of challenges and tasks businesses aim to solve with enterprise data management and EDWs in particular.
As you see, a large number of challenges that make part of typical business scenarios boil down to solving common tasks of enterprise data management. The reason for that is data inconsistencies that are often a consequence of a historic set of circumstances (company growth, changing strategic objectives, chaotic software development and implementation).
At Trinetix, we help enterprises transform their businesses from the inside, harnessing a strong data culture and keeping customers’ expectations first. Our experience shows that most often, on their way to building an enterprise data warehouse, organizations usually aim to solve the below tasks.
- Data silos and fragmentation. An average enterprise company uses a toolset of 187 applications. As a result, the data is scattered across different systems and departments, leading to data silos. Each department may have its own data storage and management systems, making it challenging to get a holistic view of the organization's data. An EDW can help break down these silos and integrate data from various sources into a centralized platform.
- Lack of data governance. The abundance of enterprise tools and systems also creates a lack of clear ownership and accountability for data, which leads to inadequate data management policies and processes, and inconsistent data definitions resulting in insufficient data quality controls. An EDW can improve data governance by providing a centralized platform for data storage, enabling consistent data practices, and facilitating better data management policies across the organization.
- Poor data quality. When each system within a company exists in isolation, it’s difficult to control the overall data quality. This means that while aggregating and consolidating the data, the organization risks getting data of poor quality—as a single system with low-quality data can spoil the overall picture. An EDW can incorporate data cleansing and data quality management processes, improving its overall accuracy and reliability.
- Lack of data transparency and accessibility. When companies approach building data views, they may find out that data in some systems is challenging to access, cannot be collected, or is missing at all. With an EDW, organizations can ensure that data is accessible, understandable, and traceable. This transparency fosters trust among stakeholders, enables compliance with data governance policies, and facilitates data-driven decision-making across the enterprise.
A Fortune 500 company got a 360-degree view of business development processes with a data management platform developed by Trinetix
Understanding enterprise data warehouse
Approaching enterprise data warehouse development, businesses shouldn’t think of it as a silver bullet. In fact, it’s just an asset to choose from many when solving data management tasks within your company.
Representing a repository that stores and integrates data from various sources, an enterprise data warehouse does not exist as a standalone structure. Let’s look at an example of modern data architecture to understand its role and dependencies with other components of the structure.
On the left, there are typical enterprise data sources: databases, a business software suite that encompasses systems like CRM, ERP, and HRM, and third-party applications. They produce raw structured data—the input.
On the right is the output that comes as a result of EDW implementation. It represents processed, integrated, and transformed data prepared for querying, reporting, and deriving insights through various analytical methods and BI tools.
In the middle, there are three key components that help enterprises efficiently turn raw data into valuable insights to be used for analytics:
- A data lake is a dedicated solution for collecting and storing large volumes of raw, unstructured, and structured data in its native format. It admits no data modeling or transformation.
- A data warehouse is a centralized repository that stores structured and integrated data. It is optimized for querying and analytical processing and allows users to retrieve and analyze data for business intelligence and decision-making purposes.
- A data mart is a smaller, department-specific subset of a data warehouse or data lake. It contains specific information tailored to a particular department or team's needs, making it easier for them to access and analyze relevant data for their specific requirements.
The process of data extraction, its transformation into a consistent and usable format, and load into a target data warehouse or data mart is originally called ETL. However, with time, the concept is often modified to ELT, where the sequence requires loading data first. The latter is considered more efficient for modern data warehousing as it takes advantage of the processing power of target systems.
Types of enterprise data warehouse
As the enterprise data warehouse concept continues to advance, organizations that approach its development may come across a few variations that allow for solving more complex data management tasks. Based on the specific needs, data requirements, and business objectives of an enterprise, modern data warehouses may have different architectures, features, and use cases.
Considering the vast variety of today’s enterprise data warehouse solutions, we decided to focus on the most modern and widely implemented ones.
Cloud data warehouse
An enterprise cloud data warehouse is a solution hosted and managed on cloud platforms like Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), or Snowflake.
Cloud data warehouses offer advantages like scalability, as they can easily scale up or down based on data volume and processing needs. They also provide flexibility and cost-effectiveness since organizations can pay for the resources they actually use. Leading cloud data warehouses offer robust security measures and seamless integration with other cloud services, making data warehousing more accessible and efficient.
Hosted and managed by a cloud service provider
Scalability, elasticity, and pay-as-you-go pricing; data is stored and processed in the cloud
Organizations with fluctuating data volumes, a need for rapid scaling, and operating in cloud-centric environments
A data lakehouse is a relatively new concept that combines elements of data lakes and data warehouses. It integrates data storage and data processing capabilities, providing a unified platform for storing both raw and processed data. It leverages open-source big data technologies like Apache Spark and Delta Lake to enable real-time data processing and analytics directly on the data lake. This enterprise data warehouse architecture simplifies data management and reduces data movement by processing and refining data within the same environment where it's stored.
The data lakehouse aims to provide both enterprise data warehouse benefits (structured data, fast querying) and the benefits of a data lake (flexibility, cost-effectiveness), offering organizations a powerful and efficient solution for data storage and analytics.
Integrates data lake and data warehouse capabilities into a unified platform
Supports both structured and unstructured data, enabling storage, processing, and analytics in the same system
Organizations dealing with diverse data sources, big data, and data requiring flexibility in storage and processing
Virtual data warehouse
A virtual data warehouse is an abstraction layer that sits on top of various data sources (e.g., databases, data lakes) and provides a unified, virtual view of the data. It allows users to query and analyze data from different sources as if they were part of a single data warehouse, without physically moving or replicating the data.
Virtual data warehouses simplify data integration, reduce data duplication, and improve data accessibility. They are especially useful when dealing with complex data landscapes where data resides in multiple systems.
Provides a logical abstraction layer over existing data sources without physically moving or replicating data
Enables data integration from multiple sources for analysis and reporting without the need for a centralized physical repository
Organizations with multiple data sources and agile data integration needs
Federated data warehouse
In a federated data warehouse architecture, data remains in separate data marts or warehouses, but they are connected through a middleware layer. This middleware enables data from different sources to be accessed and queried as a unified data source.
Federated data warehouses offer better data isolation, flexibility, and easier management of decentralized data. They are often implemented when organizations have data warehouses serving specific departments or business units, and the need arises to create a cohesive view of data across these individual warehouses.
Integrates data from multiple distributed sources in real-time or near-real-time without consolidating data physically
Offers a unified view of data without moving or copying data into a centralized repository
Organizations with distributed data environments and a need for real-time data access
Hybrid data warehouse
A hybrid data warehouse combines on-premises and cloud-based data storage in a single data warehousing solution. It allows organizations to leverage the benefits of both infrastructures, ensuring optimal performance, scalability, and data distribution.
Hybrid data warehouses are useful for organizations that have existing on-premises data warehouses but want to extend their capabilities with cloud-based storage and analytics. This approach provides greater flexibility and cost-effectiveness by allowing organizations to choose the best fit for different parts of their data warehousing infrastructure.
Combines on-premises and cloud-based data storage and processing to create a seamless integration between both environments
Offers scalability, cost optimization, data integration capabilities, and the ability to maintain data security across on-premises and cloud components
Organizations handling fluctuating workloads, complying with strict data regulations, supporting geographically distributed operations, and facilitating the integration of legacy systems
In addition to the types of enterprise data warehouses, organizations can use different data modeling techniques (schemas) to structure data for efficient storage, retrieval, and analysis. Most often, organizations turn to star- or snowflake-type schemas based on data complexity, query performance requirements, ease of data maintenance, and specific business needs.
However, before companies proceed to choose the types and techniques, there are a number of things to consider when building an efficient EDW.
How to build an efficient enterprise data warehouse
It’s estimated that today 82% of global companies prioritize data modernization. Along with that, only 20% of them manage to deliver improved business outcomes. Considering these statistics, we want to share the insights that help us at Trinetix deliver predicted business outcomes and exceed the expectations of our clients when building enterprise data warehouses.
Analyze current data and come up with objectives
Understanding the current state of enterprise data provides a crucial foundation for designing an effective EDW that addresses the organization's specific data challenges. It helps identify data gaps, redundancies, and opportunities for improvement.
This involves assessing the current organizational data landscape to gain a comprehensive understanding of the data maturity, culture, and measure key indicators.
- Data governance encompasses the policies, processes, and controls that ensure data is managed and used responsibly.
- Data transparency ensures that data is accessible and understandable to relevant stakeholders.
- Data quality involves determining data accuracy, completeness, and reliability.
- Data ownership identifies the individuals or departments responsible for data management and data oversight tasks.
- Data profiling involves examining the content and structure of data sources to uncover insights and patterns as well as identify data issues and inconsistencies.
Next, by engaging with stakeholders, including business users, analysts, and IT teams, the project team gathers essential information about the data needed for decision-making and strategic planning.
Proceed to master data management
Master data management (MDM) involves creating, managing, and maintaining a single, authoritative, and consistent version of master data elements, such as customers, products, vendors, and employees, across the organization. By establishing a robust MDM framework, the EDW ensures that essential data entities are accurate, reliable, and harmonized throughout various systems and applications.
Implementing MDM in conjunction with the EDW provides two major benefits:
- Solving data discrepancies and duplicates. Byeliminating data silos, and promoting a unified view of master data across the organization, companies can enhance data quality and integrity within the EDW, making it a reliable source for decision-making processes.
- Streamlining data governance. Establishing clear data ownership and data management duties within the organization allows companies to get better control over critical data assets. This ensures that data is managed responsibly and aligns with compliance and data security requirements.
How to build a robust data governance strategy?
Establish a data integration architecture
Building a data integration architecture is a blueprint that outlines how data from various sources will be collected, transformed, and loaded into the EDW. It encompasses the design of data pipelines, movement mechanisms, and transformation processes required to consolidate and harmonize data from disparate systems. In fact, it’s the basis for creating a unified and cohesive data environment and the next step in creating an efficient EDW.
A crucial component of establishing the data integration architecture is data cataloging. It involves creating an inventory of data assets, their characteristics, and relationships. This helps data integration teams identify relevant data for the EDW, and determine mappings, transformation rules, and loading strategies, ensuring accurate and efficient integration.
Set up integration and ETL processes
Once the data integration architecture is established, organizations can proceed to implement data integration and ETL processes based on the architecture defined.
- Data integration involves consolidating data from various heterogeneous sources, transforming it into a unified format that aligns with the EDW's data model, and loading it into the warehouse for analysis and reporting.
- During the ETL process, the data is extracted from source systems, cleansed and standardized, and loaded into the EDW.
Efficient data integration and ETL processes are essential for ensuring that the EDW contains accurate and up-to-date information.
Integrate data visualization tools
As the implementation of an EDW requires keeping all the parties involved in the process aligned and updated on the progress, data visualization plays a key role in its success.
Integrating data visualization tools allow organizations to transform complex data into customizable reports and real-time dashboards that promote operational efficiencyand facilitate effective communication of data findings to stakeholders. This enhances data understanding and drives strategic decision-making across the company.
Ramp up your business opportunities with our strategic approach to data management
Assess. Optimize. Repeat
The last step in developing an efficient enterprise data warehouse involves a cyclic process of assessing the performance and effectiveness of the EDW, identifying areas for optimization, implementing improvements—and then repeating the cycle to ensure the solution evolves and remains relevant over time.
- Assess. Evaluating the performance and usage of the EDW will allow the organization to understand if the data warehouse still aligns with the evolving business requirements. The assessment of dedicated performance metrics, data quality, and user feedback help the company understand any existing challenges or limitations.
- Optimize. Based on the assessment findings, data professionals can improve data integration processes, refine data models for better query performance, or enhance data visualization capabilities. Data quality and governance practices may be strengthened, and any bottlenecks in the system may be addressed.
- Repeat. Continuous monitoring and evaluation ensure that the data warehouse stays in sync with changing business needs, technological advancements, and emerging trends. The iterative nature of this stage allows the EDW to evolve and adapt to the organization's dynamic data requirements.
Building an enterprise data warehouse is impossible without harnessing a strong data culture within an organization. On their way to becoming data-driven, enterprises need a reliable and experienced transformation partner. At Trinetix, we provide tailored technology-agnostic data enablement and intelligence solutions for your business.
Starting with an in-depth analysis of the company data culture and expectations, we leverage the best practices of enterprise data management to provide business-specific recommendations, develop, and implement a practical future-forward solution.
If you feel like giving your business strategy a data-driven boost, let’s chat about building a modern enterprise data warehouse that keeps your challenges and opportunities first.