Do any of these scenarios sound familiar?
- The data I need to run my business lives in 3 different mission-critical applications, and combining that data to get the information I need is a monumental task.
- The reporting coming out of my ERP is just not enough.
- My current reporting is decent, but we have to run it after business hours or our systems crash.
If any of these ring true, it’s probably time to consider a data warehouse.
A quick definition: a data warehouse is a data management system that:
- gathers data from a variety of sources (order entry, CRM, ERP, and so on),
- combines it, and
- transforms it for reporting, querying, and analytics.
Common use cases for data warehousing
Use case: critical data spread across multiple systems
Picture this scenario: your sales team uses a CRM to track customer interactions and deals, sales orders and accounting transactions live in your ERP, and the manufacturing department relies on a custom solution built for its needs.
Now you need reporting that follows the entire lifecycle — from prospect to closed deal, through ordering and manufacturing, all the way to payment receipts.
A data warehouse gathers the information from all of these systems, then organizes and relates it in one place, opening the door to advanced reporting and analysis.
Use case: historical trending
Most systems used to run a business focus on current values, not historical ones. That makes it difficult to analyze trends over time or track changes to things like pricing or product naming.
A data warehouse can be configured to capture those changes, enabling trending and historical analysis.
Use case: business intelligence platforms
The rise of business intelligence tools like Power BI, Tableau, and Domo has made it possible to build BI dashboards and reports quickly. Out of the box, these tools can connect to a variety of data sources and generate reports across systems. However, tying that data together in real time within the BI tool itself can be challenging and slow. The ETL (Extract, Transform, and Load) process of a data warehouse does the heavy lifting of connecting the data, so your reporting platform can focus on displaying useful, fast-performing metrics with ease.
Use case: data analysis
From one-off query requests to projections — and even analysis of how accurate those projections turned out to be — a data warehouse lets you take deeper dives into operational data.
For example: a company runs a one-time project over a 3-month period. During that time, the project touches data in multiple systems across the organization. Since the project will rarely, if ever, happen again, there’s no point in building a dashboard or standing reports to track it. But there is real interest in querying the data to understand the project’s impact and results. Because that data is already tracked in the data warehouse, queries can be run directly against the warehouse — no exporting data from various systems and stitching it together with pivot tables.
Use case: performance concerns
Complex reports can take a long time to run — or worse, they can drag down the performance of the source system, slowing transaction times for everyone using it.
A data warehouse ingests the source data, transforms it, and loads it into services designed specifically for reporting. The result: faster report performance, with no negative impact on the people using your operational systems.
Who benefits from a data warehouse?
People at all levels of an organization can benefit from a data warehouse:
- Management: A well-executed BI dashboard driven by a data warehouse gives management teams both big-picture and detailed views of company operations. It also lets that information be “sliced and diced” by region, department, product, customer — or any other way that helps drive informed decisions.
- Data scientists: With all the data in one place, the data science team can focus their energy on analysis and modeling rather than gathering data from the various operational systems that store it. And because a data warehouse is purpose-built for reporting and analysis, it’s fine-tuned to deliver results faster than the individual systems can.
- Sales: Sales teams can pull data on their prospects’ or customers’ sales history, trends, closing rates, and more.
When is a data warehouse NOT needed?
A data warehouse won’t solve every business problem. Here are some scenarios where one isn’t needed — or should be put off until later:
- Overkill: A small business runs on a single application, and the reports it provides are more than adequate for running the business.
- Lack of access: The data in a critical system simply isn’t accessible. Some software systems don’t allow their data to be accessed, and in that case the data warehouse has nothing to ingest and transform.
- Bad data: If the data in your systems is unreliable or inconsistent, the effort required to clean it in a data warehouse can become an obstacle. It may be better to fix the bad-data problem first, then implement a data warehouse solution.
How do I turn the data into valuable information I can use to run my business?
It’s true: the warehouse stores data, not insights. You’ll need additional functionality to harvest valuable, actionable information from your data warehouse:
- A BI platform: Common business intelligence software — like Microsoft’s Power BI, Tableau, or Domo — can connect to a data warehouse and be used to design BI dashboards, advanced reports, and KPIs that surface the critical information hiding in your data.
- Data science tools: Data science teams employ a unique set of tools, including languages like Python and F#, that let them efficiently analyze warehouse data, build one-off queries, and develop models for projecting things such as market trends, inflation, and sales.
Why can’t I just use my existing systems for analysis?
Sometimes you can.
Most business systems include reporting modules that help paint a picture of what’s going on in an organization. The trouble is that these reports are often limited:
- They can only report on data found within that one system.
- They traditionally can’t report on data trends over time.
- They can sometimes drain resources from the mission-critical system you rely on to operate the business.
Where are data warehouses hosted?
A data warehouse can be hosted either on-prem (on your office network) or in the cloud. Many cloud providers offer powerful tools — like Azure Data Factory or AWS Data Pipeline — that can ingest and transform your data. Once transformed, the data can be stored in cloud storage services or loaded into on-prem solutions like SQL Server.
I think I need a data warehouse. How can I get started?
Want to talk with our team about your data warehousing needs? Let’s talk.
Here are some questions worth thinking about ahead of our conversation:
- What systems are currently in place in your organization?
- What KPIs or metrics are you interested in?
- Why are the reports from your current systems insufficient?
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