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Achieving a successful data transformation


Key steps to focus on in your data transformation
Key steps to focus on in your data transformation

Key steps to focus on in your data transformation:









1. Establish a Decision-Making Framework:

The first step in driving an effective data transformation is establishing a decision-making framework. This involves creating a single view of data, which means moving away from traditional data processing methods. Instead of dealing with multiple raw data sources and databases, the goal is to integrate them into one data model. This requires identifying a strategic information management platform that can support this integration. This is a crucial step as it helps in gathering streamlined insights to inform business solutions.


2. Data Integration:

The process of bringing together multiple data sources and databases and rationalising reporting solutions can be quite challenging. However, it is a necessary step that requires significant time and effort. By standardising and automating data loading processes, you can create an efficient and secure storage system for data. This step not only improves data quality but also provides multiple options for presenting the data, making it more accessible for business teams.


3. Ensure Robust Data Analytics Capabilities:

To further streamline operations and ensure strong data analytics, it is essential to consider cloud analytics capabilities. Replacing legacy reporting solutions with interactive data visualisation software, such as Power BI, can significantly enhance your data analysis process. This software allows for automated data refreshes, enabling near-real-time data analysis. Moreover, it also offers monitoring and alerting capabilities, turning data sources into coherent, relevant, and actionable insights.


4. Support Effective Adoption of Tech:

Introducing new tools and technologies is only half the battle. It is equally important to train your team to use these tools effectively. This includes training sessions for super-users, focusing on enabling sustained delivery and management of data analytics needs. This ensures that the team can effectively adopt and use the new technologies, leading to a more successful data transformation.

Conclusion

In conclusion, driving an effective data transformation involves several key steps. From establishing a decision-making framework to ensuring robust data analytics capabilities, each step plays a crucial role in the process. Supporting the effective adoption of tech is also vital to ensure the successful implementation of the transformation. With these steps, businesses can leverage data to its fullest potential, leading to more informed decision-making and improved business solutions.


Looking for expertise in data transformation at the core of programmes and projects? Look no further! Richard Keenlyside is an expert in this field, driving numerous FTSE 250 transformation programmes.

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