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Data Culture

Data culture helps organisations optimise their data value chain

In today’s data-driven world, organisations need to treat data as a strategic asset

In our current digital landscape, data has become a vital strategic asset for any organisation in its journey to success. These organisations understand that a successful business maintains a strong data culture which grows, mines, uses and closely manages this asset. In 2023 and beyond, any business that does not appreciate this is missing a golden opportunity to leapfrog its competition. It also runs the risk of stagnating while more forward-looking competitors begin to place greater emphasis on the importance of managing data across the data value chain.

Data integrates into every dimension of a business’s operations and employees, which is fundamental to corporate thinking and objectives. A strong data culture democratises data access, calibrates quality decision-making faster, breeds collaboration, and fosters data literacy while maintaining the right balance of control – fostered through robust data governance.

Development of a strong data culture

The challenging task of creating a compelling data culture to manage data as a strategic asset requires a deliberate approach that considers all stakeholders, driven by a deep understanding of each stakeholder’s needs and touch points in the data value chain.

Understanding that data culture – which includes how data is captured, seen, used and managed – underpins the efficiency of an organisation’s data value chain. How should a business implement a data culture to optimise its data value chain?

Of course, sponsorship by C-suite executives and support from senior leadership is crucial to developing a strong data culture. A top-down approach designed by and for the business users and enabled through data teams and IT is obviously critical. A committee of business and IT professionals responsible for continuously driving the data culture within the organisation should champion the initiative.

Assessing Data Maturity – Data Culture

The first step to creating a pervasive data culture is to assess a business’s current data maturity level. This requires organisations to map their data inventory and understand their current state accurately. This, in turn, means describing the current architecture and tools, mapping data inputs, outputs, interfaces and flows and the current level of data and user skills within the organisation and identifying the strengths and weaknesses of their current state.

Determining the “To-Be” state

The next step is determining the “To-Be” state – the business objectives and how they see their data architecture supporting this in the ideal world. Amongst some of the many questions to be asked are: what data is required, where does it reside, what integrations are required, how will the data be used, will it be commercialised or for internal use only, will there be machine learning and AI applications, what skills are required to support the data architecture, what level of upskilling needs to be done with the user community to allow them to use the data effectively.

Conducting a Data Gap Analysis in a data culture

Whilst the temptation is to look to technology at this early stage, businesses must first conduct a gap analysis between their as-is state and their to-be scenario before considering technology recommendations to close that gap. The gap analysis, in effect, sets out the requirements for implementing the data culture-defining, amongst other things, the desired technology features, data governance procedures, and organisational data literacy initiatives.

Once the business has defined its data requirements, it has a roadmap for its data journey. This will allow them to break down each gap into manageable tasks encompassing technology selection and implementation, data project priorities, and business and technical upskilling.

Ultimately, implementing a comprehensive and pervasive data culture within organisations, along with data professionals such as Insight Consulting, is essential for optimising the data value chain. This approach allows businesses to stay ahead of their competition, mitigate risk and drive the bottom line through enhanced and accurate decision-making as well as operational efficiency for long-term business growth.

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