The customer journey is a dynamic, multi-faceted experience encompassing every touchpoint a customer has with your brand, from initial awareness to post-purchase interactions. Traditionally, businesses viewed these touchpoints as isolated events. However, the reality is far more intricate. Customers seamlessly navigate between online and offline channels, influenced by a multitude of factors like advertising, social media, and word-of-mouth recommendations. They weave a complex web of interactions, making it crucial to understand the complete picture.

So how can organisations grasp the full spectrum of the various offline and online journeys a customer undertakes before proceeding with or abandoning a purchase?

Data quality is more than just a technical concern; it’s about ensuring that the information we rely on is trustworthy and actionable. Whether it's setting long-term goals, streamlining operations, or complying with regulations, data quality forms the bedrock of successful business outcomes. In this blog we will explore the importance of assessing data quality, examining its key dimensions and methodologies for evaluation. Later using the assessment framework as the foundation, we will provide you with a step-by-step guide to building a data quality strategy.

In an era defined by the deluge of data, the significance of data science pervades every aspect of contemporary life, offering a pathway to uncovering hidden patterns, predicting future trends, and fostering meaningful advancements across industries. In this blog, we'll delve into the key features of and methodologies within data science, its importance in today's world, and actionable advice on how to initiate your organisation’s journey into the realm of data science. 

Power BI reports rely heavily on the quality and efficiency of the data they connect with. Yet, there are instances where the required data is not readily available or does not align with report requirements. In such cases, developers resort to manual adjustments, risking inefficiency and unsustainability in the long run. Power BI developers need to dedicate significant efforts to optimising this transformation process to achieve the best possible report performance. To truly leverage its capabilities, it is imperative to optimise the way you use your data.