Data warehouse implementation is the process of launching a data warehouse in your organisation to consolidate data from multiple sources.
Having a single repository of all your data allows you to get a top-down view of all your information and enables cross-channel reporting.
However, the process of implementing a data warehouse can seem difficult for agencies when they are first introduced to it.
Keep reading to learn how you can easily implement a data warehouse in your agency, what its benefits are, and what problems it can solve for marketing agencies.
What is a Data Warehouse?
A data warehouse combines data from several sources into a single, coherent repository. It serves as a single source of truth, allowing agencies to perform comprehensive data analysis and reporting. A data warehouse is also the foundation of a business intelligence system.
Data warehouses are designed to support decision-making by providing a consistent view of the organisation’s data.
They are typically populated with data from multiple sources, including transactional systems, operational databases, and external data sources. The data in a data warehouse is typically organised by subject area, such as customers, products, sales, etc.
What is Data Warehouse Implementation?
Data warehouse implementation is the process of designing, building, and launching a new data warehouse. This process generally includes four main phases:
- Planning and designing
- Component selection
- Testing and launching
Each of these phases has its own set of activities and deliverables that need to be completed before moving on to the next phase.
In this guide, we’ll take a closer look at each phase of data warehouse implementation. Most importantly, you’ll learn exactly what you need to do to ensure success while strictly following the best data warehouse implementation practices.
4 Steps to Successfully Implementing a Data Warehouse
Let’s delve into each step of implementing a data warehouse to see what exactly the process involves:
#1. Planning and Designing
The first step in any data warehouse implementation is planning and design. The phase of data warehouse planning is critical to the success of the project because it sets the foundation for everything that comes after.
During the planning and design phase, you’ll need to answer important questions such as:
- What is the purpose of the data warehouse?
- What data needs to be included?
- How will the data warehouse be structured?
- What tools and technologies will be used?
Answering these questions will help you create a detailed plan for the project that can be used to guide the rest of the implementation.
#2. Component Selection
Once the planning and design phase is complete, it’s time to start selecting the components of the data warehouse. This includes choosing the hardware, software, and other tools that will be used to build and operate the data warehouse.
Some of the most important decisions you’ll need to make during this phase include:
- Data sources: These are the systems where data is stored and accessed. Data warehouses typically incorporate data from multiple sources, including transactional databases, OLAP databases, and flat files.
- Metadata: In simple words, this is data about data. It includes information such as titles of tables and columns, data types, length of data, etc. It’s used to describe the structure of the data in a data warehouse and how it is related to other data in the warehouse. Metadata also gives users information on what’s included in a data warehouse.
- Data staging area: This is a temporary location where data from the various data sources is combined and transformed.
- ETL (Extract, Transform, and Load) tools: These tools are used for extracting data from source systems, transforming it into a format that can be loaded into the data warehouse, and then loading it into the data warehouse. This process happens in the staging area.
- Database: This is the central repository where data from the various data sources is combined and made available for reporting and analysis. Data warehouses are typically OLAP databases.
- Data marts: These are subsets of the data warehouse that are designed for specific use cases or groups of users. Data marts contain only the data that is relevant to those use cases, and they are typically much smaller in size than the entire data warehouse. Data marts can be built using ETL tools, or they can be created manually.
- Reporting and analysis tools: These are the tools that the agency would use to access and analyse data in the data warehouse. Reporting and analysis tools can include SQL query tools, OLAP tools, and data visualisation tools.
After the components have been selected, it’s time to start putting everything together. This is where the data warehouse will be built and configured according to the plans created in the earlier phases.
The implementation phase generally includes the following activities:
- Installing and configuring the data warehouse platform
- Creating the data storage environment
- Building the ETL process
- Configuring the reporting and analysis tools
#4. Testing and Launching
Once the data warehouse has been built and configured, it’s time to put it through its paces. This is important to ensure that everything is working as expected and that there are no major issues that need to be addressed before launch.
The testing phase generally includes the following activities:
- Determining data accuracy
- Measuring performance
- Conducting user acceptance testing
Once the data warehouse has been tested and validated, it’s time to launch it. This generally includes making the data warehouse available to employees and training them on how to use it.
There are a few key things to keep in mind as you work through each phase of data warehouse implementation. First, be sure to involve all stakeholders in the project from the beginning. This will help ensure that everyone is on the same page and that there are no surprises later on.
Second, make sure to document everything as you go. Documentation will come in handy if there are any issues that need to be debugged or if changes need to be made down the road. And finally, don’t forget to test, test, test!
Advantages of Implementing a Data Warehouse in Your Agency
There are many advantages to implementing a data warehouse in your agency. A data warehouse can give you the ability to:
- Combine data from multiple sources: Data warehouses allow you to consolidate all your data, making it easier to get a complete picture of your agency’s operations.
- Access data in real time: Data warehouses allow you to access data in real time, giving you the most up-to-date information possible.
- Generate reports and analytics: Data warehouses give you the ability to generate cross-channel reports and draw analytics that can help you improve your agency’s operations.
- Improve decision making: Data warehouses can help you make well-informed decisions by providing accurate and up-to-date information.
- Save time and money: Data warehouses can help you save time and money by reducing the need for manual data entry and data cleansing.
Data Warehouse Implementation Trends in 2022
The trends of data warehousing in 2022 are transforming the industry. Here are some of them you should be aware of:
#1. Cloud-Based Data Warehouses
Cloud-based data warehouses are becoming increasingly popular, as they offer a number of advantages over traditional on-premises data warehouses. Cloud-based data warehouses are more scalable, more flexible, and easier to manage.
As of 2022, over 60% of all corporate data is stored in the cloud. This is up from 30% in 2015.
#2. Real-Time Data Access
There is an increasing demand for real-time data access in data warehousing. Real-time data can help organisations make better decisions and respond more quickly to changes in the marketplace.
The global data warehousing market is projected to reach $51.18b by 2028.
#3. Self-Service Data
Having self-service data is another growing trend in data warehouse implementation. Data warehouses equip agencies with self-service data, which allows them to access and analyse data without having to rely on IT staff.
#4. Big Data
Big data is another trend that is impacting data warehouse implementation. Big data refers to data sets that are too large and complex to be processed by traditional database management systems.
As such, organisations are turning to custom data warehouse solutions to store and analyse big data.
Implementing a Data Warehouse to Solve Common Problems in Marketing Agencies
There’s no question that easy access to data is becoming increasingly important in the world of marketing.
Agencies are under constant pressure to deliver targeted, personalised campaigns and show ROI for their efforts. But in many cases, agencies are struggling to keep up with the sheer volume and complexity of data they need to deal with on a daily basis.
There are a number of common problems marketing agencies face that can be solved by implementing a data warehouse. Let’s see what they are and how a data warehouse can solve them:
#1. Inaccurate or Outdated Data
Data warehouses help solve the problem of working with inaccurate or outdated data by providing a single source of truth for an organisation.
The data warehouse is updated and cleaned on a regular basis with the latest data from operational databases. This ensures that the data in the warehouse is accurate and up to date.
#2. Siloed Data
One of the benefits of using a data warehouse is that it can help break down silos of information. When data is stored in separate databases, it can be difficult to get a complete picture of what is happening.
By consolidating data into a single database, analysts can more easily discover relationships and patterns to unlock new insights.
#3. Lack of Visibility Into the Business
Data warehouses help organisations get a 360-degree view of their business by providing a consolidated view of their data.
Data warehouses also provide a historical view of the data, which is important for understanding how a business has changed over time.
This allows for enhanced business intelligence and allows agencies to make data-driven decisions.
Implement a Data Warehouse With the Help of Acuto
Data warehouse implementation can be a complex and costly process, but it can also be extremely beneficial for organisations.
By following the latest trends in data warehouse implementation, organisations can ensure that their data warehouses are up-to-date and ready to meet the demands of the future.
Acuto can help you implement a custom data warehouse quickly and easily, and at a lower cost than pre-built data warehouse solutions. Contact us today to learn more about our services.
Need a Custom Data Warehousing Solution?
By now, you should be familiar with the entire process of data warehouse implementation. Let’s go over the main points that we covered:
- A data warehouse is a centralised repository of all your agency’s data.
- Data warehouse implementation is the process of designing, building, testing, and launching a data warehouse in your agency.
- Using a data warehouse in your agency helps you combine data from multiple sources, analyse data in real time, generate cross-channel reports, make informed decisions, and save time and money.
- By following the latest trends in data warehousing, you can ensure that you are ready to meet the demands of the future.
- Data warehouses are especially useful for marketing agencies because they clean inaccurate data, break down data silos, and help you get a 360-degree view of your business.