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The Reality of Data: Why Business Context Matters to Drive Business Value

May 25 @ 5:00 pm - 7:30 pm MDT

A clear understanding the business processes and context is critical to interpreting data to drive better business decisions.

Organizations collect so much data. From airlines to grocery stores, weather stations to social media, data is being captured all around us. In the world of business all kinds of details are being recorded to make decisions: improving efficiency, saving money, increasing customer loyalty, and so on.

With this exponential growth in the data space, more organizations are embarking on projects to make data more available and accessible, and these projects will involve data activities such as; architecting, modelling, engineering, and report building, to name a few. Whether a project involves a few or all these layers, the final product is still for the business to reap the benefits of the data and effect positive change.

There’s a secret to data hiding in plain view – the data is not perfect. Measurements can be inaccurate, data for continuous events are only sampled on specific intervals, and perhaps not every attribute is collected to describe the event (Has anyone recorded your mood when making a purchase?). As such we need to consider the context around when the data was collected – who was involved, what were they doing, what was the intended outcome, and what was it that we were interested in capturing. In short, what was the business process?

Business processes are a key part in data understanding and are not exclusive to business-facing tasks. Any activity that uses data can benefit from the knowledge of these processes so join us to find out why they are important each step of the way in delivering successful data projects.


Patrick Marchand is a Principal Consultant with Improving and an experienced IT professional in Advanced Analytics, Data Warehouses and Business Intelligence having analyzed, architected, developed, and implemented solutions that cover data and dimensional modeling, ETL, cube analysis, and reporting. He has worked on and delivered numerous BI and data warehouse solutions based on Kimball design principles. His expertise lies in business and data analysis, data architecture, dimensional modeling, ETL development, performance tuning, and mentoring.

Patrick’s ability to gather requirements and grasp the overarching business process and objectives behind the needs of the business – coupled with his expertise database processing, data management best practices and advanced analytics – are at the core of his delivery of successful projects. Patrick’s solutions are architected and implemented with the foresight to achieve sustainability and scalability without sacrificing performance.


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