Why data transformations fail and what you should do about it

By Credera

Many businesses today consider data as a major asset and look to utilise it to generate further value for their organisation. In many cases, however, the setup of a data practice or initiation of a data transformation programme ends up being both inefficient and costly. As a result, we find ourselves asking – what is impacting these programmes and are these challenges avoidable?

Credera has shared some of the most common issues that they have come across within organisations and these include:

  • Lack of a clear vision and strategy for data within the organisation. Without understanding how the data will be used by the business (generally via business use cases) and implemented by technology, transformation programmes can become substantially more complicated and expensive to run.
  • Clear ownership and accountability is another factor that affects the success of any transformation programme. This generally stems from poorly defined operating models and employees not having a complete understanding of their roles and responsibilities.
  • Failure to adapt to new technology paradigms, where poor utilisation of tooling can lead to inconsistent metadata capture and storage leading, reducing the value of the data being analysed as a result. The Cloud is also a disruptive driver of new data architectures, with new approaches (such as data mesh) implemented in leading companies. There is a risk of being left behind with traditional architectures that are bloated and static.
  • Inadequate understanding and implementation of data management – for example, data quality, metadata, master & reference data, and governance. An organisation with an inconsistent approach to data quality can end up making incorrect decisions and spend large amounts of money undoing them.
  • Failure to manage the change in data culture can leave employees unsure about the data transformation that has occurred and unwilling to follow new policies, procedures, or standards. This is one of the more intangible issues but it is an important one to give thought to because there is little point in developing a new platform or data solution if the people you are building it for are not going to use it.

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