Latest Advisory

Hopes and Dreams for When Data Mesh Meets Reality

The notion of data mesh is highly appealing, as business stakeholders desire ready access to data for analytics. In their eyes, they own the data and should have easy access in a manner that avoids the delays of a centralised data management approach. However, many organisations are buying into a simplistic and unworkable definition of data mesh, often treating the concept as a technology solution, rather than a governance philosophy. IBRS discusses the role of data mesh in organisations and how data analytics teams must function.

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Creating Effective Digital/Physical Workplaces

The evolving workplace has been highly influenced by an organisation’s adaptability to trends, productivity and (more recently) wellness. With technology now more instrumental in shaping the physical office environment, how can leadership teams ensure that the digital workplace promotes more effective collaboration, improved wellbeing, and increased productivity?

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The Collaboration Paradox: Fragmented and Siloed Knowledge

Organisations that employ digital collaboration tools in an uncontrolled manner find that the very tools intended to streamline communications throughout the organisation result in the opposite: increasingly siloed departmental group thinking and, worse, silos of information hidden from the organisation at large. How can enterprises avoid the tsunami of fragmented knowledge?

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Six Killers of Customer Segmentation & Personalisation

Cross-functional tasks among various departments provide better collaboration, especially when customer personalisation and customisation are employed. How should enterprises deal with the challenges that prevent them from analysing their customer behaviour, attributes, and trends?

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Generative AI for Enterprise Use – An Overview of the State-of-the-Art

With more vendors such as Microsoft, Amazon Web Services (AWS), Google, and SalesForce embedding AI technology into their products for enterprise use, organisations will soon have more tools with AI capabilities. However, more solutions do not always mean producing meaningful outcomes, since most of these products will still fall short of expectations due to the current limitations of language models, and some types of work that still cannot be easily replicated by AI.

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