An Enterprise Data Model (EDM) enables your organization to build a holistic and platform independent reporting ecosystem.
Reading this article will give you an overview on why organizations often struggle with data silos and how an Enterprise Data Model can contribute to fulfill both Enterprise Business Intelligence and Self-Service Business Intelligence requirements.
But why should you avoid data silos and how can an Enterprise Data Model contribute?
Data silos are collections of data and information that are often isolated and only accessible by individuals or certain departments of an organization which use specific technologies or tools. Sooner or later this leads to several challenges that various organizations will face. These challenges – in terms of provisioning data in a secure, timely and well governed manner - occur especially for fast growing companies that at some point are not able to provide trusted data for all departments and employees. In order to combine and report on those silos individual departments often invest in own integration or reporting solutions, which can create an additional silo on top of the existing ones.
There are several reasons why data silos occur, to name three overarching topics:
Data silos can arise when departments are not collaborating and hence not communicating with each other in a proper way.
Departments within an organization can use various technologies and tools which are not compatible with each other.
Widespread ‘silo thinking’ in the minds of employees and absence of an organizational data- and knowledge-sharing culture.
Based on the above, data silos generate an incomplete view of the organizations business. If each department has its own data silo, it can get very challenging to retrieve trusted and well governed data - especially in case of missing or insufficient communication within the organization.
Duplicate information between departments leads to an inefficient use of IT resources and thus also to avoidable costs.
Storing same information in different departments can also provide inconsistent data, e.g. when an information is updated in system A while the same information in system B is not considered in the update. In case of new data source or a change in the source system, all data silos have to be adjusted accordingly, which creates additional efforts.
To avoid data silos and the associated drawbacks, it is crucial to have a central hub for organizational data such as an Enterprise Data Model, which will be introduced in the following section.
An Enterprise Data Model (EDM) can help with breaking up the before mentioned data silos by combining security aspects, audited calculations and trusted data sources which can be used be a variety of analytics- and visualization-tools. This helps organizations to build a well governed holistic and platform independent reporting ecosystem.
Having an Enterprise Data Model can break up data silos within the organization. In a real-world scenario a single data source is used multiple times by various users and departments to fulfill their reporting needs – sometimes even with different visualization tools.
In many cases this also leads to duplicate data transformations within the data pipeline and the addition of key figures or metrics on top of the data source. This common scenario – which often occurs in practice - has several downsides. To name three:
An Enterprise Data Model – along with organizational governance - can help to solve those challenges and provide a holistic Single Source of Truth for corporate data sources. An EDM can help to establish data standards within the organization and enables the coexistence of both Enterprise Business Intelligence (BI) and Self-Service Business Intelligence (SSBI). Users can stick to curated data sources with verified transformations, both standardized and trusted metrics and are still free to build additional transformations or metrics to fulfill their individual reporting needs.
In summary, an Enterprise Data Model can come with a bunch of advantages, to name a few:
In the next chapter of this article we will have a focus on Enterprise Business Intelligence with Microsoft Power BI, where we will outline various use cases for Enterprise Reporting via the XMLA endpoint.
How can the before mentioned advantages of an Enterprise Data Model look like when it comes to practical usage and use cases within an organization? Below we outlined practical examples from the report consumers view.
Gartner’s’ Magic Quadrant names Microsoft as a leader when it comes to Analytics and Business Intelligence Platforms. In an article by my colleague, we pointed out why. With the ability to connect to a Power BI Premium Dataset via the XMLA endpoint the platform offers huge opportunities for Enterprise Reporting and the provisioning of an Enterprise Data Model.
The Power BI Dataset – which represents the EDM in this example – can contain data of various source systems of different departments within the organization. The queries and transformations are verified, it contains validated and standardized metrics and on top of this access management is handled.
From the pure reporting perspective, this Power BI Dataset can now be used in many scenarios, as a central data source for …
These are just a few possible scenarios to benefit from an Enterprise Data Model. The individual improvement opportunities can be identified when taking a deep look at the organizations use cases, needs and existing data and reporting landscape. As a conclusion this also leads to the point that the organizational reporting landscape can be harmonized by having a Single Source of Truth – which also provides the same view on the data across tools without any inconsistencies.
Microsoft is continuously progressing and providing new features to its Modern Enterprise BI platform. We stay excited to follow the new features and how they can provide value for the data solutions of our customers. To learn more about the new Enterprise BI features, Microsoft presented on the Ignite, you should have a look on this article.
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