International Institute for Analytics

Today’s Data Governance best practices are built on the lessons of decades of missteps and frustration. The new approach focuses on building lightweight user-centric approaches, enabled with robust and flexible tools. And these lessons and practices are directly transferred to BI Governance, where the explosion of users leveraging a vast array of self-service tools demands the right governance approach. In this webinar we will cover: - The key principles for driving effective BI Governance - How to leverage effective BI Governance to engages users - A framework for BI Governance connecting enterprise strategy to critical daily action

Inquiry:

We’re looking for guidance about how we can create a set of standards and guidelines for use of systems and tools. Our data scientists and analysts are drifting away from expectations of how aspects of the environment should be used.

Inquiry:

With everyone working remote these days, do you have any suggestions for ensuring new employees feel connected to the team? Also, how can we keep our employees engaged in general?

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We have a large Agile team working on multiple projects with shared resources. We run one sprint for all of them, with one scrum master. It’s not working well. Is there a way to make it work?

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We are organized in a modified hub-and-spoke way with enterprise data scientists in the hub reporting to the hub, and analysts in the business reporting to the business. In the hub we find it difficult to manage the cross-functional, enterprise-scale projects and the more ad hoc needs of senior leadership.

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We’re developing a data science job matrix that should clearly spell out the career paths within our new data hub. What worked and didn’t work for others?

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We want our supply chain team to expand their efforts beyond demand forecasting. Where are the low-hanging fruits where a centralized advanced analytics team could provide value? Alternatively, is demand forecasting actually a good use of our resources?

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What are some best practices for writing third-party vendor contracts to ensure that we receive the best possible transactional data? One challenge is that we don’t pay for the data we’re receiving.

As companies seek to use advanced analytics to drive improved decision-making, knowing how and when to embed those analytics in business processes, in order to eliminate human variance and interference, becomes a critical skill. Embedded analytics raises a host of questions–how do you know your embedded analytics will make consistently superior decisions when compared to your company’s best decision-makers? And how do you address the naturally-arising concerns decision-makers have about being “replaced” by algorithms? Join Marc as he lays out the issues walks through the process and describes a framework for navigating this difficult transition.

Inquiry:

As a global manufacturing company with factories and distribution centers in many countries we’d like to do more with supply chain analytics. How do we get started?