Category

Data

The Challenge of Data Accessibility

By | Data

Today’s IT data investment dollars are often funneled into the creation of new advanced analytics capabilities: big data integration, machine learning, and data science solutions. In this blog, Stephen Galloway explains why these initiatives will fail unless the challenge of data accessibility is addressed alongside these complementary initiatives.

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Hacking Data to Serve a Community

By | Community, Culture, Data, Volunteering

Last fall, St. Aloysius, an organization and school that provides psychiatric services to Cincinnati youth, faced a big problem; the “no-show” rate for their programs was almost as high as 30%. In this blog, Lauren McDonald explains how a group of SEI-Cincinnati consultants and the dedicated staff members of St. Aloysius partnered together to solve this problem during a “data-hack-a-thon.”

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Approaching Machine Learning

By | Data, Strategy, Technical

From predictively queuing a show for a video subscriber to watch next to mapping the building numbers for every street in France, Machine Learning offers a viable solution. But where do you start? In this blog, Sara Showalter explains why framing the problem and selecting the right team are critical to success.

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Does your data strategy deliver?

By | Data, Strategy

With marketplaces rapidly evolving, the data required to support decision making processes are changing at an equally breakneck pace. Is your technology delivery pipeline equipped to support the data demands of your business stakeholders? In this blog, Stephen Galloway explores how to overcome the disconnect between an organization’s data strategy and the tactical delivery capabilities that support it.

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Data Discovery: Putting your data under the lights

By | Data, Technical

In a previous blog, Storytelling with Data, my colleague offered some great tips on keeping a presentation focused on the data. Unfortunately, the data doesn’t always seem to tell a coherent story. Maybe the principal developer is gone, the documentation is stale, or perhaps the subject matter experts each have their own swirling interpretations of the content. What now? Analytical opportunity is knocking and it’s time for data discovery! There are challenges with any data discovery initiative, however. To help you overcome these challenges and improve your analytical skills, I offer several tips to help you through the data discovery process.

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Analytics for the Masses

By | Business Intelligence, Collaboration, Data, Demand Management

My colleagues and I were recently presented with an interesting challenge while building out a business intelligence solution for a mid-size client. The client wanted to archive, for potential future use, a large amount of data over and above the current reporting requirements.  Unfortunately, the client’s proprietary database system was primarily designed for data analysis, not storage, and could not be leveraged as a solution.  To identify a solution, our collaboration focused on evaluating options made possible through significant changes in the data analytics field.  As we discussed the new technologies and methodologies, I found myself drawing parallels to how Apple Macintosh, in 1984, brought computing power from the mainframe to the masses. 

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