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We all want beautifully structured business intelligence reports from clear, organised and trusted data that allows us to garner real, strategic business insights: but it’s not easy.

Data from different data sources is rarely, if ever, structured in a way that it can be used directly without some form of prior manipulation. It often requires cleansing, normalising the data and removing duplicates as well as refining routines or logic to address anomalies, poor quality or missing data. Some forms of data, such as narratives and commentary may not even be suitable for reporting, but contain important insights for your organisation.

All of which presents a challenge when you consider building a data warehouse.

Does it have to be a trade-off between your business vision and the data challenge?

Anyone that’s ever tried to build a data warehouse will have encountered missing data in supposedly reliable source systems, content stored in the wrong locations, poorly structured data with missing relationships and incorrect values. It means that building a data warehouse often becomes a trade-off between your business vision and the challenges and limitations inherent within the multiple data sources that it’s drawn from.

The 7 proven steps to great data insight:

At illumo digital, we have designed and built solutions for clients across sectors with large and complex data volumes as you would imagine existing in finance, energy, healthcare and emergency services. We have developed a proven methodology for dealing with the challenges involved, which means we can deliver solutions that allow organisations to derive real business insights from the data they store across the business. Our process can be distilled into the following seven steps:

  1. Understand your reporting requirements: This important step is often one of the most challenging, especially when dealing with legacy systems that can lack documentation, or where documentation has not kept pace with changes to the system. This will help form a roadmap and target design to achieve your vision.
  2. Analyse the data available: This allows us to refine the appropriate data transformations then transpose the required data from the source data system into the new data warehouse.
  3. Revise the data warehouse design when necessary: Sometimes the source data will be overly difficult to transform and it may be cheaper and easier to modify the design to accommodate legacy data than to modify the legacy data source itself.
  4. Script the data transformation process: Typically, the output of the above transformation process will then be encoded into a scripted data transformation process that can be run on a regular basis – usually overnight, depending on needs – to populate the data warehouse quickly and easily.
  5. Test: We test the performance of the loading and updating processes and check the ability of the data model to deliver the information required by the business. This means our customers know they will receive a product that works as expected.
  6. Verify outputs: Checking the integrity of extracted and transformed data against the predefined business outputs is another key part of our quality assurance approach so that your data warehouse can be a trusted source.
  7. Integrate data visualisation tools: We can help organisations to maximise the value of their data by integrating data visualisation tools, such as SSRS, QlikView or Tableau. This gives you easy to consume business insight that helps you to make more informed decisions.

We’ve been building data warehouses and delivering actionable business insights for over 7 years. Organisations such as the RNLI rely on the business intelligence derived from a illumo digital built data warehouse to make important strategic decisions.

If you’d like to find out what we could do for you, click the contact button below and we’d be delighted to arrange a no-nonsense assessment of whether data warehousing is right for your organisation and, if so, how we can support you on your data insight journey.

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