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How Sval successfully implemented their application retirement strategy

Bilde av Anja Loug Helland
by Anja Loug Helland

26. Aug 2022, 3 minutes reading time

How Sval successfully implemented their application retirement strategy

How Sval successfully implemented their application retirement strategy

As organizations grow either through M&A or organically, business applications may become redundant and eventually retired. This leads to a growing risk involving legacy infrastructure-, system-, data- and competency-requirements. This blog post highlights key takeaways from a Gartner Paper addressing data retention and application retirement, and provides a suggested solution illustrated through a customer example where Sval successfully retired 3 legacy financial systems. 

In June 2022, Gartner Group published ‘How to Address Data Retention and Application Retirement’ (ID G00765896) written by Michael Hoeck and Stefan Van Der Zijden. This paper explains the considerations and evaluations I&O leaders should take into account when establishing a strategy for application retirement.

Key challenges with legacy applications
The Gartner paper highlights the following key challenges associated with legacy applications:
  • Financial risks, such as application vendor support and maintenance
  • Operational risks, such as capacity and knowledge to use the application
  • Business risks, such as lack of access to data
  • Security risks, such as applications running on unsupported databases and/or OSs

 To mitigate risk, Gartner suggests using a specific solution to save data to a modern database and develop applicable data access procedures to access data.
By using efficient tools together with a clear application retirement strategy your organization will reduce risk and save costs on licenses, storage, and infrastructure. Elements of the strategy will include a clear definition of what type of data to retain, what type of data to migrate, and eventually, what will be accessible or archived.

 Customer story
"In the initial project, we were able to retire 3 legacy financial systems by using Xpert BI to do a ‘carve-out’ of the data needed" 

Sval Energy is a Norwegian energy company that has grown substantially through several acquisitions over the last years. This has left the organization with a range of different financial legacy systems both on premises and in the cloud (SaaS). With limited resources and a clear growth agenda, the IT manager at the time, Ånen Brimsøe, saw the need for a strategy and tools to remove technical debt, improve and standardize ease-of-access to data, and at the same time ensure regulatory compliance on the financial data.

The chosen solution and method involved the following steps:
  • Identify business data requirements and compliance data requirements
  • Using automation to copy the data into a modern standardized database (Raw data)
  • Create a simplified data model with a user interface in Excel (data model)
  • Test data extracts and reports
  • Terminate license(s) for the application
The technical components are Microsoft SQL Server for data storage, Xpert BI for automating, standardizing and documenting data extractions and simple data modelling, and Excel as user interface for accessing data sets and data models. The solution is also cloud-ready and can easily be merged to Microsoft Azure.

“In the initial project last year, we were able to retire 3 legacy financial systems by using Xpert BI to do a ‘carve-out’ of the data we needed. In an acquisition process earlier this year we used Xpert BI for both data retention from SAP and Contiki, and data migration from Contiki to HouseOfControl. We also have a new acquisition this fall and will use the same standardized approach.” Ånen Brimsøe, (Delivery Lead, Sval Energy)
Anja Loug Helland

Anja Loug Helland

Anja is Head of Advisory and co-founder of BI Builders and has worked with data warehousing and data platforms for over a decade. Having experience from a range of different clients, Anja is an expert in understanding our customers' needs and map that to data and data models to get business value.

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