{"id":4907,"date":"2016-06-28T10:47:06","date_gmt":"2016-06-28T09:47:06","guid":{"rendered":"http:\/\/www.etlsolutions.com\/?p=4907"},"modified":"2018-06-14T15:50:27","modified_gmt":"2018-06-14T14:50:27","slug":"how-to-ensure-data-quality-after-a-migration","status":"publish","type":"post","link":"https:\/\/etlsolutions.com\/how-to-ensure-data-quality-after-a-migration\/","title":{"rendered":"How to ensure data quality after a migration"},"content":{"rendered":"

By Emmanuel Udeh, Subsurface Data Analyst at Shell.<\/a><\/h3>\n

Quality information and data are critical for effective performance of any business. It is essential that the strategic and operational decisions which ensure schedules and budgets are met are underpinned by quality business intelligence.<\/p>\n

An operator\u2019s ability to leverage Information and data as key business assets is therefore an important success factor in developing, executing and managing projects and facilities. This mitigates risk, ensures safety, and maintains compliance in a lower for longer regime.<\/p>\n

Information technology, together with data management, plays a critical role in supporting an organization\u2019s systems and tools. This helps personnel to access data from a multitude of sources. They can then effectively integrate information to drive decision making and collaborate with partners, both externally and internally.<\/p>\n

The data migration challenge<\/h4>\n

A 2011 study by Geosoft Inc<\/a> showed that system, database and service maintenance costs were the most important concern of Information Management (IM) and Information Technology (IT) departments when thinking about maintaining any data management solution or program.<\/p>\n

IM\/IT departments are constantly challenged with providing access to the quality information and data required to help energy businesses deliver value to their shareholders. There is an ever-increasing volume of data, both unstructured and structured, to be managed. It is very important that all companies update their systems, applications and platforms regularly, in order to give personnel access to the most up-to-date and best quality data they need for decision making.<\/p>\n

Data migration is the transfer of data from an old system to a new system. This may be due to several factors such as technological advancements or a change in business processes. In order to ensure data quality post migration, with the minimal operational interruption, a clear strategy for migration of system software and data needs to be clearly outlined and followed.<\/p>\n

Data migration strategies<\/h4>\n

While the general perception towards change management activities can make data migration seem problematic and difficult to apply, no one actually doubts in its necessity. Generally, there are two principal migration strategies that businesses planning a data migration can consider: either a big bang migration or a trickle migration. Big bang migrations involve completing the entire migration in a small, defined processing window. This approach can seem attractive, in that it completes the migration in the shortest possible time, but it carries several risks. These include significant downtime of core systems and erosion of data quality due to increased pressure on migration teams to deliver on schedule. Trickle migrations take an incremental approach to migrating data. Rather than aiming to complete the whole event in a short time frame, a trickle migration involves running the old and new systems in parallel and migrating data in phases. This method inherently provides minimal downtime to any core systems and applications constantly required by the business.<\/p>\n

To choose the most suitable methodology, the IM\/IT team needs to answer key questions, particularly how to limit the business impact of the data migration. The team generally makes their decision after considering factors such as:<\/p>\n