Data Warehouse 101 - A Complete Guide
Every business gathers data. But how do you organise and transform it into an understandable format? Explore this guide on data warehouses and gain the business insights you've always wanted.
In today's data-driven world, businesses require more than just collecting data—they need to harness it.
A data warehouse is essential for storing vast amounts of data and transforming it into actionable insights.
From integrating siloed data sources and ensuring consistency to offering real-time reports and historical context, a data warehouse is a game-changer.
But what does it take to set one up?
And how do you ensure it aligns with your business objectives?
In this blog, we'll dive deep into the essence of data warehousing, its undeniable benefits, and the leading platforms revolutionising how businesses use data.
What is a data warehouse?
A data warehouse is a big, digital storage space where a company can send all its data to be stored.
Once stored, data can be formatted into easy-to-understand visuals like graphs and charts.
All in the hope of gaining valuable business insights to improve business performance.
What challenges does a data warehouse solve for businesses?
1. Unintegrated Data Silos
One of the most common challenges businesses of all sizes face.
It's when a company has numerous software that all work independently and don't share performance data.
As a result, businesses can not get a complete view of their performance.
To achieve this, they would have to export all the data independently and combine it. A process that takes a long time and is highly susceptible to error.
How a data warehouse eliminates siloed data
A data warehouse integrates every source of data a business can capture. Whilst the platforms still don't function as one, the results and actions are stored in one place so users can get a holistic understanding of business operations.
2. Inconsistent Data
With every business department creating its reports to generate a combined management report, there is plenty of opportunity for incorrect data.
Every department might have different access to data and use varied formats and standards.
How a data warehouse enhances data quality and consistency
A data warehouse will become the sole platform for reporting and analytics, standardising and cleaning data from several sources to create the 'single source of truth' clients often request.
3. Lack of Real-Team Insights
The problem with static data is that the information is out-of-date the moment it is exported.
A problem when the key to a successful business is the ability to react to issues or, even better, predict challenges before they occur.
As a manager or an owner, your decisions are only as good as the information you have, and good information is up-to-date.
How a data warehouse provides real-time insights
Data warehouses often offer real-time or near-real-time data integration and analysis capabilities so that users can access live data snapshots, view real-time KPI reports and respond to emerging undesirable statistics.
4. Limited Historical Data
Historical data is essential for monitoring performance and spotting trends that only occur over long periods.
How a data warehouse provides historical data
Data warehouses are designed to store large amounts of historical data that can be used to identify trends, conduct time-series analysis and create accurate forecasts.
5. Difficulty in Generating Reports
Most businesses collect data. However, businesses' greatest challenge is formatting data to be easily understandable. Large data sheets are hard to understand, preventing management teams from gaining insight and spotting trends.
How a data warehouse creates efficient reporting and analysis
Data warehouse platforms either come with a reporting and analytics feature built-in or can integrate with one. Taking the mass of raw data and feeding it into visual formats such as charts and graphs.
Analytics dashboards can contain all the regularly used reports for rapid access with easy-to-use filtering and sorting capabilities.
How do you create and install a data warehouse?
Creating and installing a data warehouse is a complex project, so we'll skip the serious tech talk and describe what needs to be considered from an organisational point of view.
If you want a detailed plan, please contact us here.
- Define business objectives - consider why your business needs a data warehouse, how you intend to benefit from it, which business processes need refining, and, most importantly, identify the key performance indicators.
- Assess current IT systems - it's time to map out your processes, identify the data sources and evaluate your existing platforms' effectiveness using a defined scoring system.
Download a free software evaluation scoring spreadsheet.
- Gather organisational requirements - Consult with all your departments to understand their data needs so you can create a mock data dashboard by identifying the figures required to deliver the reports requested.
- Select your data warehouse technology - either search for solutions yourself or employ the services of a tech consultant to provide you with a shortlist. Create a scoring matrix to reveal the solution that best meets your business's objectives, budget, current IT solutions you intend to keep and technical requirements.
- Define data governance policies - you must understand the data governance policies and laws your business needs to comply with so the new data warehouse solution meets all the quality, security and privacy requirements.
- Implement data warehouse - now you've chosen your data warehouse platform, install the solution, connect it to all the identified data sources and load the initial datasets to validate quality.
- Launch reports dashboard - with historical data imported and live data streams set up, create the analytics dashboard containing all the reports identified during step 3.
- Training and support - regardless of the simplicity of the solution, you will need to provide training to the end-users in the form of documentation or ongoing support provided by the data warehouse platform, in-house IT support or IT consultant (if hired).
What job titles will you need to hire in-house to install and maintain a data warehouse and analytics environment?
Data Architect/IT Project Manager: Responsible for designing the data warehouse architecture, including the structure, schema, and all the technical specifications.
Database Administrator/Engineer: Manages the database environment, ensures optimal performance, handles backups & recovery, and ensures database security.
Data Analyst/Scientist: Works closely with business units to understand their data needs and performs data analysis to generate insights and reports.
Business Intelligence (BI) Analyst: Specialises in creating visualisations, dashboards, and reports to make data-driven decisions by analysing trends and patterns.
Technical Support Specialist: Provides ongoing technical support and troubleshoots any issues users might encounter with the data warehouse or analytics tools.
What are the best data warehouse systems?
The ten best data warehouse and analytics platforms
Our top pick - Zoho Analytics
Zoho Analytics allows you to import data from various sources, including databases, cloud storage, online applications, and other business software. Once the data is imported, it's stored in a centralised repository within Zoho Analytics, effectively acting as a data warehouse. It also offers features to clean, transform, and blend data from different sources, preparing it for analysis.
Real-Time Analytics and Dashboards: Zoho Analytics offers real-time analytics capabilities. It provides a range of visualisation options, including charts, pivot tables, and widgets to create comprehensive dashboards. These dashboards can be set up to refresh in real-time or at scheduled intervals, depending on the data source. If the data source supports it, Zoho Analytics can display real-time data on the dashboards.
Integration with Other Zoho Apps: If you use other Zoho applications, such as Zoho CRM or Zoho Finance, the integration is often seamless, enabling real-time analytics with less configuration.
Connectivity with External Databases: Zoho Analytics also provides connectivity to external databases, allowing you to connect and fetch real-time data from those databases directly.
APIs for Custom Integration: Zoho Analytics offers APIs allowing you to build custom integrations with other systems to bring real-time data.
Alerts and Notifications: You can set up threshold-based alerts and receive real-time notifications when key metrics cross predefined limits.
Tableau (from Salesforce)
Use: Visual analytics and business intelligence.
Features: Offers powerful data visualisation, dashboard creation, and supports real-time data analytics. Connects to various data sources and provides robust integration capabilities.
Microsoft Power BI
Use: Business analytics and data visualisation.
Features: Allows the creation of interactive reports and dashboards. It supports real-time data analytics and integrates well with various Microsoft products and other data sources.
Looker (from Google)
*Was called Data Studio
Use: Data exploration and business intelligence.
Features: Provides a platform for data exploration, visualisation, and sharing of real-time insights. Supports integration with various databases and big data platforms.
QlikView/Qlik Sense
Use: Business intelligence and visual analytics.
Features: Offers an associative model for data analytics, allowing for complex data discovery and preparation, interactive dashboards, and analytics applications.
Amazon QuickSight
Use: Business intelligence and visualisation.
Features: AWS's BI tool that allows for the creation of interactive dashboards. Supports ML insights and real-time analytics with SPICE (Super-fast, Parallel, In-memory Calculation Engine).
Domo
Use: Business intelligence and data visualisation.
Features: A fully mobile, cloud-based operating system that unifies every business component. Offers real-time data analytics and visualisation.
Sisense
Use: Business intelligence and data analytics.
Features: Offers a unified data and insights platform with options for embedded analytics. Supports integration with various data sources.
Snowflake
Use: Cloud-based data warehousing.
Features: Provides a scalable and high-performance data warehouse as a service. Integrates with various data integration and business intelligence tools.
TIBCO Spotfire
Use: Data analytics and business intelligence.
Features: Offers analytics software for visual data discovery, predictive analytics, and dashboard creation. Supports real-time data analytics.
In Conclusion
Every business collects data, whether intentional or not, and most even buy data for insight or marketing. Data warehouses are transforming the business landscape by seamlessly integrating various data streams, ensuring data consistency, providing real-time insights, and offering a historical perspective.
They mitigate challenges like data silos and inconsistent reporting, empowering businesses to make informed decisions.
Moreover, with platforms ranging from Zoho Analytics to Snowflake, data warehousing is no longer the preserve of large corporations but is accessible and scalable for businesses of all sizes.
A data warehouse is not just an IT solution but a strategic asset, bridging the gap between raw data and actionable business intelligence. For modern businesses aiming for growth and efficiency, investing in a data warehouse is one of the few business assets that can create organised information, save time and act as the catalyst for genuinely profit-boosting decisions.
