Your Data. Our Expertise.
Your Data. Our Expertise.
Your Data. Our Expertise.
The ability to deliver on promises is what sets successful companies apart. Metrics like DIFOT (Delivery In Full, On Time) and SIFOT (Shipped In Full, On Time) aren’t just numbers—they’re indicators of your commitment to your customers. These key supply chain metrics will transform your customer experience, reduce stockouts, and streamline your operations.
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In today’s digital business world, data accuracy and usage can be the game-changers that propel your business forward. But what if there are hidden gaps in your data practices that are holding you back? We’ve developed the Great Lakes Analytics Data Insight Assessment to help you uncover those gaps and make sure your data is working as hard, if not harder, than you.
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While corporate strategy resets happen more frequently than we might like to admit, and each time they do, it necessitates a comprehensive overhaul of all data assets and processes to align with the new strategic direction. This involves reclassifying historical data, redefining business segments, recalculating financial metrics, and much more. The process of refreshing the strategy is both time-consuming and costly, often overlooked because the primary focus is on satisfying investor expectations rather than considering the company’s data infrastructure.
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A critical aspect of harnessing the power of data lies in how it is presented. Data visualizations play a crucial role in conveying complex information succinctly and effectively. Good visualizations can transform raw data into actionable insights, making it easier for decision-makers to grasp essential trends and patterns. Let’s delve deeper into the top factors that contribute to creating impactful and meaningful data visualizations.
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Digital transformation in the field of data analytics is not just a technological change but a fundamental shift in how organizations operate, make decisions, and compete in the market. For a successful transformation, especially in data analytics, gaining buy-in from executives is crucial.
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A data lakehouse provides a comprehensive solution for managing and analyzing diverse data types. While it requires significant investment in setup and maintenance, its unified storage, streamlined data processing, and cost-effective scalability make it a compelling option for data-driven businesses. For companies deciding between a data lakehouse or traditional solutions, consider the current and future data needs, infrastructure capabilities, and how seamlessly they can integrate existing systems. By choosing the right approach, businesses can unlock the full potential of their data assets.
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This comprehensive guide will explore the concept of data lakes, delineate their pros and cons, and provide insights into their optimal use, especially in the context of data science and analytics. Additionally, we will address the critical aspect of educating leadership about data lakes, including a detailed template for discussions with upper management.
Read moreIn this week’s “Featured Engineering”, we explore the intricacies of setting up sound ERP systems, focusing on the necessity of aligning business processes and the role of a data advocate in steering clear of decisions made purely on a financial basis
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n the rapidly evolving field of data science, there’s a common misconception that being a “full stack” data professional—proficient in every coding language and tool—is the pinnacle of achievement. This perspective, however, overlooks the true essence of data science.
Read moreThe reliance on data for decision-making, strategic planning, and operational improvements cannot be overstated. However, this reliance brings with it a significant amount of pressure on the teams responsible for managing and executing these data projects.
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