In the fast-paced realm of data analytics, crafting an effective data strategy and promoting data literacy are crucial for organisations aiming to leverage data to drive business success. Drawing upon recent Data Leaders Who’s Who articles, we delve into the insightful responses of three esteemed professionals in the field: Kathryn Gulifa, Pieter Vorster, and Elisa Koch who shed light on the key elements that set apart a good data strategy and discuss success in raising data literacy.
Read MoreWhat is a data strategy?
Data strategy is a series of steps, a long-term plan to enable business strategy by managing and utilising an enterprise’s information.
Like any other transformation program, it involves people, process and technology to carve a pragmatic, realistic roadmap and to realise outcomes /business benefits.
Read MoreEffective data management and data governance is critical to the success of any artificial intelligence (AI) journey. We know that poor data quality and insufficient data governance can lead to flawed or biased AI models, resulting in incorrect or unreliable results. On the other hand, well-managed data and strong data governance can enable the development of accurate and reliable AI models that can drive business value and improve decision-making. Fundamentally data management and data governance can make or break an AI journey.
Read MoreDespite advancements in AI technologies, trust has not gotten any closer between business and data science teams. AI governance might be an answer. AI governance seems to be “born” out of data governance. Data governance aims at appropriate information consumption through various processes and frameworks. Arguably, AI governance shares similar overarching objectives — but given its technical complexities, it needs a dedicated focus. They are related but different.
Read MoreAgainst this backdrop, leaders who want to transform culture must introspect if they’re really willing to do what it takes. Sustaining behaviourally-designed operating rhythms is a tremendous commitment, often requiring structural change and management overhead to generate accountability through both ‘means’ and ‘ends’.
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