Managing Data: Is it time for a data warehouse or even a data lake? 

By: Tenny Chen

Does every business need to have a data warehouse? 

Not necessarily. Advanced data solutions may be overkill for small businesses thriving on analysis from raw output files like .csv or Excel, or getting sufficient insights from their ERP system reports. Basic data cleaning and consolidation tools, such as Power Query, often suffice in these cases. You can reference The Power of Query: Case Studies to learn more about this.

However, it might be time to reconsider your approach if you repeatedly perform the same data-cleaning steps. Warning signs that indicate a need for data reorganization include:

1. Queries taking longer to run as your business grows
2. Reporting is becoming an increasingly burdensome task
3. Constantly recreating identical data-cleaning processes

In these cases, reorganizing your data structure could significantly improve efficiency and reduce workload.

What exactly is a data warehouse?

The data management landscape is replete with seemingly analogous terms: databases, data warehouses, data lakes, and now, with the advent of Microsoft Fabric, we’re encountering the concept of “data lakehouses.” Let’s clarify these distinctions to navigate this complex terrain better.

At the core of every business operation lies a fundamental process: data capture. Each business transaction, whether it’s the purchase of raw materials, the hiring of a new employee, or the addition of a new account, generates a set of related records. These records are systematically organized into tables within databases. These databases typically operate behind the scenes, powering the various software applications your business relies on. At this stage, the data remains in its raw, unprocessed form, isolated within the backend of each specific software system.

Once your data is organized, you can derive valuable insights, such as identifying seasonal sales patterns through quarter-by-quarter aggregation. Sales trends can be crucial in determining how many temporary employees should be hired. However, it’s also essential to consider the number of potential new customers in the pipeline.

During this process, you must clean the tables in each database to be consistent across software for later comparisons. It would also be helpful if they were together in one place. This export, transform, and load (ETL) process and the cleaned tables form a data warehouse.

ß(traditional enterprise data warehousing architecture from Data Mozart)

Up to this point, we’ve been discussing data in tabular format, which is commonly referred to as “structured data.” But what if your data needs to be extended beyond tables? Consider scenarios where you want to store diverse content like your company’s logo history or customer reviews from social media platforms. These data can’t be easily put in a table and are called structured and unstructured data. When you start including the semi-structured and unstructured data in the data warehouse, you build a data lake.

ß (Data lake architecture from Databricks)

How can data warehouse help me in the financial area?

Whether you are a CFO, a controller, an analyst, or a bookkeeper, there had to have been a time when you needed to organize, consolidate, and analyze data. This process can quickly become a headache, especially when you are working with our old friend, Excel spreadsheet. As business grows, the time spent looking for a tab, a file, or a folder can pile up tremendously – that’s when you need a data warehouse to help.

So, what can a data warehouse do for you?

• Centralize scattered financial data into one place to make the data look nice and clean
•  Simplify (or I would even say “eliminate” because your scheduled queries are doing that for you) the data cleaning process and reduce risks and errors associated with it (so there is no chance of missing any zeros or multiplying with the wrong rate!).
• Improve data quality and accuracy by making sure the cleaning and validating steps are consistent (once it is set up, the code will run with the same logic each time)
• Reduce the effort of data maintenance (data transformation steps are repeatable)
• Offer robust reporting and analysis capabilities (Once configured, you can see customized KPIs and reports)
• Enhance data security with various authentication methods (restricted access to data and workflows)

If you are thinking one step forward — to use historical data to predict future trends, it is also easier to integrate artificial intelligence and machine learning with data warehouse.

There you have it! A data warehouse can make the whole consolidating and analyzing process much smoother. Based on your organization’s needs and data structure, you can choose the right architecture home for your data. If you need clarification or a second opinion, please contact our Technology Advisory team; we are happy to help!

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