As mentioned in my post about Data Warehouses, a Data Warehouse is a centralized archive for storing and managing large amounts of data from various sources. Organizations design Data Warehouses to support the efficient querying and analysis of data, typically for business intelligence and decision-making purposes. The power of a Data Warehouse is that you can easily merge data from databases from different applications into management information based on one source of truth: the Data Warehouse.
There are several reasons why a data warehouse is essential:
- Data integration: A data warehouse allows you to integrate data from multiple sources, such as transactional databases, log files, and social media feeds, into a single, cohesive repository, making it easier to analyze and understand the data.
- Data quality: A data warehouse helps to ensure the quality and consistency of the data by applying standardization and cleansing rules to the incoming data, helping to reduce errors and inconsistencies that can affect the accuracy of your analysis.
- Data performance: you optimize a Data W for fast querying and analysis, which is essential when working with large amounts of data. It uses techniques such as indexing and materialized views to speed up queries and improve the performance of analytical queries.
- Data security: a data warehouse typically includes security and access controls to ensure that only authorized users can access the data, which is vital for protecting sensitive or confidential information.
- Data governance: a data warehouse helps to ensure that data is appropriately managed and governed, with clear policies and procedures in place for data quality, security, and access. Data governance helps to ensure the integrity and reliability of the data.
Building a Data Warehouse can be compared with building a home. Once you have created an expensive house, you want to live in it for the rest of your life. You don’t want your home to collapse because of a bad foundation, and if your house becomes too small, you want to expand it as cost-efficiently as possible. You want to avoid destroying your home and rebuilding it entirely: that would be a waste of time and money.
If you keep the previous paragraph in mind, you will conclude that it is critical to set up a stable Data Warehouse that is scalable in the future to secure its success. You must keep the “first time right” approach in mind, and careful planning is required to complete this task. You can best do this by setting up a dedicated Data Warehouse project.
How to start a Data Warehouse Project?
As mentioned before, Data Warehouses are increasingly crucial for companies in all industries, mainly because these companies want to become more data-driven. However, a lot goes wrong when building Data Warehouses, despite putting a lot of time and money into Data Warehouse projects. For example, about 80% of Data Warehouse projects fail to achieve their intended goals. If this is at large companies, it sometimes makes it into the public eye. In all other cases, the high failure rate of these projects never surfaces and is preferably kept quiet.
A successful Data Warehouse project begins with the realization that you need a solid and reliable Data Warehouse. Investing time and money in creating, organizing, and securing your data management comes next.
One of the first data storage concepts started in the 1980s and had its roots at IBM. Researchers Barry Devlin and Paul Murphy were responsible for developing the first business Data Warehouse. Their goal was to create an architectural model for data flow from operational systems to environmental decision support.
The concept of data storage evolved in the development of actual Data Warehouses during the 1990s and 2000s because of the immense cultural and technological changes that took place: computerization, globalization, and networking. Because of this, more data became available. More data sounds good, but only if this data is structured and organized. Corporations did not store data in a structured way yet, which meant that they had to cope with lousy system integration of data in which inconsistent and fragmented data was stored, making it impossible to generate helpful business information required for decision-making. To structure the data, corporations developed a solution to support them in consolidating the data they took from all databases. This structured data could help them in their decision-making: the Data Warehouse.
The benefits of a Data Warehouse are clear: increased efficiency and accountability, rapid delivery, improved forecasting, and the ability to better adapt and respond to changing markets. Yet it is challenging to demonstrate the Return On Investment (ROI) of a data warehouse. Big promises, goals, and investments do not yield precise short-term results. Six months to a year after inception, the project can still feel like a leap of faith without tangible results. Because of the uncertainty of Data Warehouse project requirements, organizations often design Data Warehouse projects to track the complexity and progress of the project itself closely. As long as progress is noticeable, the funding remains in place, and the prospect is that it will eventually work. Data Warehouse projects that look only at the process and fail to meet business objectives are set to fail.
Business Objectives as the starting point
Mapping out the project requirements for your Data Warehouse should start with staff who knows the business goals best, not those who know the databases inside out. By starting with a clear business goal, you can work in reverse to identify the specific reporting requirements needed to achieve it. After this, you can set up your Data Warehouse to facilitate these requirements. Then you build the Data Warehouse little by little with a structured approach. For example, you start by connecting key data sources for your financial information flow so that you can report on critical financial KPIs. Over time, you can scale up so that your Data Warehouse can include and connect Operations, HR, Sales, and more until you have added all the necessary applications and connected all stakeholders. Involving all stakeholders and introducing them to the functionalities and the benefits of your Data Warehouse is essential because you want business-wide commitment and support for your Data Warehouse, and all departments/stakeholders should benefit from the Data Warehouse.
Step 1: The Data Warehouse Blueprint
It is best to start by creating a blueprint that answers below questions:
- What applications is your organization using?
- What is the level of data detail?
- What are the critical KPIs for both now and in the future?
The answers to these questions allow you to build a Data Warehouse that can quickly address fundamental reporting needs. Still in addition to this, you will also have a Data Warehouse that can change your business. Next, you can create a simple Data Warehouse in just a few weeks. Then new data sources, offices, regions, and so on are connected, and additional you can roll out increasing complexity at a pace that suits your business.
Step 2 Involve (Key) Users in the project
Another vital element is to involve users in your Data Warehouse project. It would help if you convinced your (key) users of the power of a data-driven corporate culture, and they need to understand what goes along with it. You also have to fully convince the executives in your company that data moves the organization forward. When you set up your Data Warehouse, it is essential to get the entire company to make their contribution to it. At the same time, employees will be inspired and excited about their data-driven future.
Step 3 Select the right project partner
Often, organizations that are not “tech-savvy” run into problems. They know they need a Data Warehouse but have no idea where to start. The number of standard and custom data solutions available on the market is overwhelming, leading many organizations to hire experts. In doing so, you may get stuck with a specific expertise of that expert. These experts may have their personal preferences and then build your Data Warehouse based on that preference: open source, Microsoft, Oracle, or, for example, a best-of-breed solution. It is better to choose an expert who is flexible and does not have a strong preference.
It is also wise to work with a data specialist who understands your industry. Their prior knowledge will help the specialist build a Data Warehouse that can serve your critical KPIs so you can report on them quickly and reliably. Also, with a specialist who understands your industry, you can make adjustments faster. They can guide you in expanding and improving your Data Warehouse to suit your industry’s changes, opportunities, and requirements.
Step 4 Testing the Data Warehouse
If there was skepticism beforehand about the success of your Data Warehouse project, compiling the first reports is an exciting moment. If there are concerns about the correctness of the data in your Data Warehouse during these first reports, it can spell the end of the project. These incidents happen in many companies, and it should be no surprise if disagreements about corporate data existed even before the Data Warehouse project started. For example, headquarters might produce figures that employees in regional offices think are way off. Internal politics, lack of attention to detail, or the desire to remain strictly compliant can create data silos that keep companies from communicating openly and transparently about their data.
Step 5 Good quality of data
A successful Data Warehouse project requires a good quality of data. Setting up a structured data workflow and creating a system of checks and balances and approvals promote data accuracy. By building this in, those familiar with the data sets can approve them. It also makes it easier to detect inaccurate or altered data, helping to create a culture of accountability and responsibility, ensuring that the stored data is reliable.
Step 6 The importance of Data Warehouses
Data Warehouses are needed for companies to become data-driven, solid, reliable, and scalable. On this basis, accurate and fast KPI reports are possible. Your organization can meet all data management requirements, making a Data Warehouse increasingly an asset that your organization can no longer do without.
Final Thoughts
Centrally storing and managing all your data is a must-have, and a Data Warehouse is the best way to do this. In a Data Warehouse, you can store, integrate, clean, and analyze your data for reporting and decision-making purposes. It allows an organization to get a comprehensive view of its operations and performance and to make data-driven decisions based on trends and patterns identified in the data.
I strongly advise approaching a Data Warehouse as a project. The main reasons for this:
- Complexity: setting up a Data Warehouse is a complex task involving many aspects, such as data integration, data transformation, data modeling, and data security. It is essential to approach this process as a project to ensure that all of these aspects are properly planned and executed.
- Time and resources: Setting up a Data Warehouse requires significant time and resources, including hardware, software, and personnel. By approaching this process as a project, you can better allocate these resources and track progress toward your goals.
- Stakeholder involvement: A Data Warehouse project typically involves a wide range of stakeholders, including business analysts, data engineers, and IT staff. It is vital to approach this process as a project to ensure that all stakeholders are appropriately engaged and aligned toward the same goals.
- Change management: A Data Warehouse project often involves significant changes to an organization’s data management processes and infrastructure. It is important to approach this process as a project to ensure that you properly manage these changes and that there is clear communication and coordination among all involved parties.
- Risk management: Setting up a data warehouse involves a range of risks, including technical risks, data quality risks, and security risks. It is important to approach this process as a project to identify and mitigate these risks to ensure its success.
Don’t just “start on the fly,” and don’t take setting up a sustainable Data Warehouse too lightly. Take a “first time right” approach and make a Roadmap to build your Data Warehouse properly. When your organization has not been working with data in a dedicated way and does not have a Business Intelligence/Data department, make sure to acquire external help: don’t reinvent the wheel.
Feel free to contact me if you have questions or in case you have any additional advice/tips about this subject. If you want to keep me in the loop if I upload a new post, make sure to subscribe so you receive a notification by e-mail.
