• • • DATA CENTRE MANAGEMENT • • •
complex process. There are a myriad of tools and solutions available, but different data sources, different data structures make this a complex process. Failure to understand the implications of different data constraints – such as inconsistent data – can, again, derail the process and undermine data confidence.
Context After the collection and combination stages of the data lifecycle, the context stage is fundamental for business growth and to make effective change happen. Data may have intrinsic value, but its only true value to the business is the information it provides. Therefore, contextualisation is crucial in order to create this information and deliver actionable insights, in turn, enabling intelligent decision-making. Without an effective data model, there can’t be a
clear vision of how to add that context, whether it is a business or operational context. The ability to present that data as information to the right people and deliver real insights from the data will not succeed. This can be particularly difficult for small- medium businesses (SMBs), because this is an analytical process that requires specific skills –
changes may occur, or need to take place, to make the cycle, and end-results, more effective. For example, if the business requires more data
to understand how a particular operation is achieved, changes need to be made in the ‘data collection’ stage. It is important to remain agile and flexible throughout the process, learning from business findings in each stage, and identifying the business areas that need improvement. This is a continually evolving cycle, and businesses need to repeat and change where necessary.
skills that may be lacking in-house. Working with an independent data expert can help businesses to understand their data, and by applying algorithms derived from Machine Learning and Artificial Intelligence to produce insights, organisations can derive value from the data more quickly and benefit from the insights produced.
Change The most critical aspect of the data lifecycle (collect, combine, context, change) is to remember that it is a ‘cycle,’ and not a finite process. While businesses undertake each of these stages,
Conclusion Data is the essential ingredient in the digital transformation journey, and in order to be successful, it is crucial that businesses have an appropriate strategy in place to get their data right. By going through the data lifecycle, making
changes where necessary, and leveraging insights from new analytics, businesses can become data- driven, making better informed decisions, which in turn, will act as a catalyst to accelerate the digital transformation journey.
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