Data warehouse for finance teams

Finance teams in small to medium businesses often juggle spreadsheets, local files and manual reports. That scatter of data can lead to:

• Version confusion when multiple copies float around

• Delays in analysis as teams hunt down the latest figures

• Increased risk of errors from manual consolidation

A centralised store solves these issues by giving your team a single source of truth.

Why desktop files cause pain

1. Inconsistent data

   • Team members may use different versions of a report

   • It becomes hard to know which number is correct

2. Inefficient workflows

   • Time wasted copying and merging files

   • Delays in month-end or budgeting cycles

3. Limited visibility

   • Management can’t get up to date insights on cash flow or revenue

   • Forecasts and scenario planning suffer

Types of data warehouse

Choosing the right model depends on your budget, skills and scale. Common options include:

• On premise

  Your own servers and software. High control but heavy upfront cost and maintenance.

• Cloud based

  Hosted by providers such as Google BigQuery, Amazon Redshift or Microsoft Azure Synapse. Scales on demand with minimal IT overhead.

• Open source

  Tools like Apache Hive or PostgreSQL can work as a warehouse, but require in-house expertise.

A brief history

1. 1980s

   Early data warehouses emerged to support decision support systems. Bill Inmon promoted an enterprise warehouse while Ralph Kimball advocated dimensional models.

2. 2000s

   Appliances and specialised hardware made warehouses faster but still expensive.

3. 2010s to now

   The rise of cloud based platforms shifted cost from capital expenditure to operational expenditure.

The rise of cloud based warehousing and data lakes

Cloud warehouses let you load, store and query large data sets without buying hardware. Over the same period, data lakes emerged as a place to land raw data (structured and unstructured) before moving cleansed subsets into a warehouse.

Feature	Cloud warehouse	Data lake
Data type	Structured tables	Raw files, logs, images
Compute	Scales per query	Separate compute engines or serverless
Governance	Mature security and access controls	Emerging tooling for data cataloguing
Typical use case	Financial reporting and budgeting	Ad hoc analytics and machine learning

A fit for purpose strategy

A practical warehouse strategy for finance teams should be:

• Functional

  Load your accounting, billing and payroll data in one place

• Scalable

  Grow as your transaction volume and user base expand

• Cost effective

  Pay only for storage and compute you use

• User friendly

  Enable self service reporting without IT bottlenecks

Why choose Pentify Insights

Pentify Insights is a boutique data and analytics business with a mission to enable your organisation to harness the full potential of your data Pentify Insights. We leverage over 25 years of combined experience to:

• Review your current data setup and spot the blockers

• Design a cloud based warehouse solution that meets finance needs today and tomorrow

• Optimise your existing technology stack for faster insights

• Support you through implementation and change management

If you’re ready to move beyond random desktop files and get your data working for you, we can help bring your warehouse to life.

Get in touch at Nick@pentifyinsights.com to discuss how a fit for purpose strategy can transform your finance reporting.