A data warehouse ecosystem is crucial for today’s data management so that businesses can gain the best benefit from their most strategic asset, data. It organizes business data so that key stakeholders have access to its advanced insights and use cases.
Companies need data to maximize their revenue, get an edge over their competitors, and decrease overhead costs. Data management through data warehouses is critical to optimize the insights and usefulness, not only for IT but executive leadership and other crucial departments as well.
A data warehouse is a data ecosystem that organizes information for reporting, analysis, and decision making. The data warehouse stores and makes available detailed and aggregated data from multiple sources in a single location to enable advanced analytics and support data-driven decisions. It can manage vast volumes of historical data to optimize data mining, analysis, artificial intelligence (AI), and machine learning (ML).
The concept of data warehouses is not new. Businesses have set up warehouse systems since the 1980s; however, the technology supporting them, their use cases, and the amount of data continues to evolve. This evolution allows more users and use cases to get more value from their data.
Many organizations choose to invest in a data warehouse because of the clear advantages and business insights it can provide:
Data warehouses can accomplish a wide variety of use cases across organizations, including:
When it comes to making a data warehouse that works for the needs of an organization, there are multiple design considerations to keep in mind.
There are three architectural approaches to housing a data warehouse that have evolved over the years:
On-Prem
Organizations can choose to buy a data warehouse license and deploy it in their on-premises infrastructure. It is a more expensive solution than the cloud, but government entities and organizations that need to comply with strict security regulations sometimes choose this solution. However, the cost is often prohibitive to all but the largest enterprises.
Appliance
A data warehouse appliance is a bundle of software and hardware (including operating system, storage, CPUs, and data warehouse software) that is pre-integrated so that the organization connects and starts using as-is. Like the software, appliances can be a more costly solution that makes it affordable to only larger organizations.
Cloud
A cloud data warehouse is built to run seamlessly in the cloud and is provided to organizations as a managed service. In the cloud model, the physical warehouse is hosted, maintained, and managed by the cloud provider so that the organization doesn’t have the upfront costs of hardware and doesn’t have to spend the time or money to maintain the solution over time. The initial cost to entry is often very low but does require maintaining an active subscription for the life of the platform.
Cloud data warehouses have grown in popularity over the past five to ten years as cloud services become a faster, easier, and more cost-effective way for businesses to scale. The cloud offers companies the ease, scalability, and management of an appliance without the extensive up-front hardware and licensing costs. Plus, the cloud allows organizations to reduce their on-premise data center footprint.
Cloud data warehouses allow most businesses to maintain work from anywhere. As more organizations embrace remote and hybrid work, the ability to perform analytics from home is critical. Cloud data warehouses enable IT, leadership, and key players to perform their work effectively and seamlessly anywhere.
Many businesses now use a combination of data lake and data warehouse. They both have a place in most companies, depending on the organization’s needs.
A data lake can be a more flexible structure for housing data than a data warehouse. It has a more fluid method for storing data, and the data is only structured as it moves to the application layer or it might be stored in a semi-structured format typically in JSON, AVRO or Parquet. .
Although data lakes are not as user friendly and take more time and effort to structure, they do have specific purposes and use cases for businesses:
With a data lake, the raw data comes in a variety of formats and structures and has less governance. It is ideal for situations like models for analysis and testing hypotheses around data and raw information. For data scientists who understand and use complex data, data lakes can be perfect for their needs.
A data lake is a centralized repository for data, but less effort is done to clean, standardize, or establish governance over it. This means that the information in a data lake is in a more raw (or at least medium rare) state which means it could be redundant and may lack the accuracy and standardization needed for consumption that requires more curated data.
However, a data warehouse is designed explicitly for data analytics and business-critical reporting, which involve reading a large amount of data to understand relationships and trends across the data. This requires the information to be highly curated and governed so that it serves as a definitive and auditable view of the truth.
A data lake is ideal for consumers who don’t have a clear objective for their data yet. It can allow them to hold their information in a single and safe location until they understand what they need it for and create a data warehouse to meet their needs. It also can be used as a sandbox to quickly onboard new data and investigate its value before going through a larger effort to govern and model it into the data warehouse.
When it comes to getting started and ensuring success, it is often best to start small. The concept of building an enterprise data warehouse can be daunting for those who are new to the process. So instead of addressing every data need and problem that may be facing the business, identifying key areas with the most impact to start with can help to get quick successes. Find a significant use case that fits within the long-term roadmap that may require the least complex effort.
Just like no two organizations have the same business needs, no two organizations will have the same journey implementing their data warehouses. However, there is some commonality in the steps and they often start with small steps and a focus on business benefits to ensure success and drive immediate value.
Phase 1: Establishing the Business Objectives
One of the biggest mistakes that organizations make is deciding to implement a data warehouse without defining the ways that the business will drive value from it. The “if you build it, they will come” approach simply doesn’t work, since a data warehouse built without a purpose almost never meets anyone’s needs.
It’s absolutely critical to keep the end in mind when you start out, and it is essential that business stakeholders, department leads, and data science and analyst users intended to be consumers of the information are engaged very early about their highest priority needs for data and the insights they hope to gain from it.
Most businesses will find that they already have a pent up demand for a data warehouse, but didn’t recognize it as being such: a growing backlog of requests to IT for data extracts or refreshed data sets, hundreds of similar or seemingly identical reports that may or may not even be used, departmental “islands” of data that are actually workarounds for not having a more centralized source of data, and repeated data cleanup “fire drills” when new projects startup or when there are concerns about the real source of “truth” or quality of data throughout the enterprise.
During this phase, organizations should inventory these needs and prioritize the ways in which data will enable improved business efficiency, increase customer impact, reduce institutional risk, and drive revenue opportunities. These should be specific objectives that are both strategic for long-term needs and tactical ones that address immediate pain points.
Phase 2: Taking Stock of Your Existing Environment
While focusing attention on the future, it is also important to get a good sense of what exists today to establish a baseline for the current data landscape. This is a combination of sources of data, flows between systems, technologies that are in use, processes, and policies that directly impact how data is captured, moved, and used.
This can be a daunting task, but knowing what you have and also recognizing what you don’t know about your data landscape can help to inform strategy, architecture, and platform needs for your data warehouse. Even just compiling a list of what you have today helps existing sources of data (what is persisted/generated internally and also received from external providers), where data is flowing today inside and outside the organization, the location, and purpose of the department or workgroup-focused point solutions, and listings of reports, datasets, and dashboards that exist today can be important in understanding your current state.
It should also be noted that understanding current processes and policies for data governance, security, privacy, and quality management should also be captured, since some of these may need to be revisited along the way.
Phase 3: Formulation and Platform Selection
After having a good understanding of the current landscape and outlining broad goals and objectives, organizations can then decide on their optimal deployment options and architectural design approach when structuring the data warehouse. They will select their data warehouse technology based on a number of factors, including sizing and scale, the volume of data flows, reporting and consumption needs, and privacy/security requirements.
There is no “one size fits all” solution, and many businesses find that the business needs change quickly as well, so it is important in this process to make decisions not just to solve today’s needs but also for what is known about future direction. The flexibility of the data warehouse platform and its ability to scale and grow to meet business needs is a primary factor in selecting the right solution since you may start small but will want to expand as needed.
Phase 4: Creating the Value Roadmap and Positioning Organizational Roles
Once clear goals and platform decisions are in place, this phase will establish the roadmap for implementation, usually in the form of quarterly goals for implementation. These can be a series of deployment phases, with the understanding that establishing the foundation may comprise much of the early work, with more expansion and delivery of tactical use cases accelerating over time.
When determining what goes in each phase, the business priorities should be a key driver in the decision process. However, each phase should consist of a combination of high-priority business needs that can drive immediate value balanced with ongoing infrastructure and platform enhancements to the data warehouse. This ensures that continuous value is delivered, but also that improvements to make the management and monitoring of the data warehouse are not forgotten along the way.
In parallel with this, it is also critical to put the right organizational roles in place, which includes aligning the implementation with business sponsors that already have use cases in mind where value can be clearly shown. There rarely is a single role that has ownership over data initiatives even when organizations have established a Chief Data Officer (CDO). Strategy, funding, and prioritization is often a joint effort of a broader Data Steering Committee with representation from business lines, IT, infrastructure, security, and audit. This level of partnership across a variety of roles and various skills is critical to ensure that a data warehouse is a success.
There are, however, some key roles that must be put in place to implement and maintain a data warehouse long-term.
Phase 5: Creating the Data Warehouse Architecture and Design
This phase includes establishing the overall architecture of the data warehouse, designing individual data flows in and out, and even profiling existing data sources to confirm assumptions about the existence and quality of data that will be needed.
Some of the analysis that goes into this includes:
Decisions about hosting of the data warehouse (Cloud, on-premise, hybrid, etc.) are made at this time as well, and how security policies, encryption approaches, data access provisioning, and compliance aspects will be addressed.
Detailed design will include planning out how data will flow in and out of the data warehouse, ultimately resulting in highly detailed data mapping and lineage details. The tools that will also be used for consuming the data to create reports, dashboards, analysis, and models are also planned for and their implementation and rollout approaches are outlined.
Phase 6: Implementing the Data Warehouse
This phase is where all of the planning and design come together–building integration/ETL processes to move data in and out of the data warehouse, configuring the security controls, building and validating reports and dashboards, and working with analysts and data scientists to ensure their data needs are met.
Quality and performance testing is also conducted during this phase, and often involves reconciling data back to originating sources. However, in many organizations, an agile approach to ongoing implementation efforts often help to continually focus implementation efforts on the highest priority needs, which allows teams to incorporate new business needs and also account for improvements/enhancements to existing capabilities.
Phase 7: Launch
Launching an organization’s data warehouse includes the rollout of the platform and data migration, but it is also when business users are introduced to the data warehouse and maybe the first time that business intelligence or other analysis tools are made available to end-users. Careful communication, training, and ongoing support are all needed to make sure that the adoption of the data warehouse is not stalled due to a lack of understanding or apprehension about the use of the tools.
Phase 8: Business As Usual Operations and Continuous Improvement
After the data warehouse has successfully launched, organizations need to put operational processes in place to continually support its users and create a mechanism for feedback and enhancement requests. Most data initiatives, including data warehouse implementations, don’t just “finish” after a first rollout, and organizations must establish mechanisms for ongoing expansion and improvement over time so that the data warehouse grows and adapts to business, market, and end-user needs.
Most importantly, stakeholders must be involved at every step, and successes need to be spotlighted. It is easy to begin to capture metrics that show the impact of the data warehouse, whether it is an increase in projects velocity because data is readily accessible, a decreased business user dependence on IT to make information available, elimination of data quality problems that may have plagued implementations in the past, or even true revenue and efficiency gains based on trusted information that is available whenever it is needed.
The concept of creating a data warehouse is intimidating for most organizations. However, creating a data warehouse is critical for the advanced analytics and insights that inform better decision-making. It is crucial that companies take concrete steps to create and maintain their data warehouses to get more use from their data.
Start the process by deciding on the most critical areas of your organization that would benefit from a data warehouse. The quick wins and early success will help fuel enthusiasm to expand your efforts. Our experts have the knowledge and experience to assist organizations of all types in creating data warehouses that meet their needs. If you are not sure where to begin or aren’t sure how to best optimize your data warehouse, Caserta can help.
Not so long ago, having a data warehouse was sufficient. It was like everyone knew that collecting data was important, but had no real idea how to leverage that data to boost business performance and market shares.
So why do organizations wait to master data? Often, it is easier to accrue technical debt than tackle this area, especially because it can feel like a daunting task and companies don’t know where to start.
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Keith Belanger, Sr. Data Warehouse Data Architect, Liberty Mutual Insurance
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