Today, almost every organization (small or large) holds a repository of integrated data entities from multiple business domains.

And guess what? Data is something that grows over time and can become challenging to manage and extract meaningful insights from.

Having said that, not always does an organization require a Data Warehouse (DWH) to handle its data for analysis. In some cases, it may be enough to have a BI Application extract data directly from the data sources of your operational systems.

A DWH is typically required when there is a large volume of data at hand and complex integration & manipulation logic is required to reach a data-model that is analysis-ready.

So, when and why does Nogamy recommend building a DWH as part of your Data Analytics array?

  • Clear business terminology – A data model that is built into a DWH allows for a clear definition of measurements and KPIs that depict the “organizational truth”. This truth is important for creating a unified terminology across the business stakeholders and for reducing the probability of contradictory \ erroneous measurement reporting.
  • Objective reporting – The fact that the DWH crosses business domains by nature and is not biased by any specific unit in the organization, creates an objective data infrastructure and definitions for measuring performance .
  • Ease of analysis – Information in a DWH in organized in a way which makes analysis and query simple so that the users of dashboard \ reports \ queries can enjoy a quick and easy framework for analysis.
  • Support Agile work – A DWH can be built in a phased manner but without having to do re-work when introducing a new data domain each time and\or when data volumes grow.
  • Better performance – a DWH data model is built in a way as to support the need-for-speed when querying large amounts of data. Most of the heavy lifting of processing is done at the backend and thus does not impact business users querying the data.
  • Utilizing AI tools – A physical data model that exists in a DWH supports the use of advanced statistical analysis tools (Machine learning, Deep learning).
  • Reduce dependency on BI Application vendors – A DWH is a proprietary asset that you create for your organization. A rubust part of the relevant business logic of your organization will reside there. The BI App. Is then used only as a visualization layer for analysis and reporting. This is opposed to putting all that business logic inside the BI application when you don’t have a DWH.

 

Replacing your BI Application is a lot easier if you have a DWH in place!
Building a DWH is a major process in an organization which requires readiness from aspects of both technology and business. Building a DWH the right way is not an easy task. It must be built to maximize the business benefits you will receive from the data in your organization to a new level.

We at Nogamy have built dozens of DWHs for a wide range of industry sectors. We will be happy to serve you as well.

Contact us for further details.

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Data Warehouse - why and for who ?

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