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It is often difficult to know whether your data engineering team is as productive and competent as it should be. When a data engineering team can't get ahead of its backlog and business users complain incessantly, it's tempting to question the team's effectiveness and capabilities. But if you don't have deep knowledge of data engineering practices, you won't know whether a two-week turnaround on a data request is good or bad. 

This webinar of seasoned data architects and data engineers will discuss the telltale signs that your data engineering team is working productively or not. They will also examine how to rehabilitate an unproductive team and how to set KPIs to measure their output in an objective way. If you've ever questioned the approach or capabilities of your data engineering team, you won't want to miss this session! 

You Will Learn:

-Symptoms of an ineffective data engineering team 

-What questions to ask your data engineering team 

-KPIs for measuring data engineering output 

-When it makes sense to hire more data engineers and not 

-Data engineering best practices your team should follow

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There are data quality issues where the cause and remedy are obvious. For instance, users are fat-fingering data entry tasks so you need to implement a drop-down selection list. Or ETL loads are misfiring so you need to industrialize ETL monitoring. Or key metrics yield conflicting results so you need a standardized definition and calculation.

Then, there are other data quality issues that are just.... nasty. The cause is not obvious - it might be a combination of outdated systems, poor internal processes, or synchronization issues with external providers. It might take an army of people to track down root causes and build new processes and validations to improve data quality and consistency.

In this webinar, our consultants will explore how to deal with the nastiest, gnarliest, and most pernicious data quality issues. And we'll tackle your use cases and do some real-time problem-solving! So bring your worst data nightmares and let's have some fun!

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AI in all its forms promises to transform virtually every business process.  Yet for every example of success there are multiple cases of failure.   As a data community we recognize the importance of serving high quality and well-structured data to these transformations. However, as business process and analytics become increasingly mingled in the AI journey, this is not enough. Our models need to become “digital twins” of our organization.

This webinar will provide the case for building a digital twin, and a step-by-step approach to implement then at the center of your transformation.

You Will Learn:

● The definition of a digital twin

● How digital twins fit into the AI journey

● Techniques for building and modeling a digital twin using data and process

● Digital twin tools

● Digital twin use cases

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Too many companies struggle to ensure adequate Power BI performance and optimize usage. If you're in this camp, tune in to this webinar to hear our consultants describe how to make effective use of Power BI, improve performance, and avoid costly upgrades.

Based on real client experiences, our consultants will present a methodology for analyzing the root causes of Power BI performance issues and steps you can take to optimize Power BI resources and licensing.

This Webinar Will Cover:

● Workspace design and management

● DAX and SQL techniques

● Power BI applications

● Semantic models 

● Change management  

● System resource utilization 

● Power BI service vs desktop

● Self-service practices

● Golden datasets 

● Aggregate tables 

● And more...

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Many business and data executives salivate with the idea of monetizing data. Most see the volumes of transactional data as an underutilized and expensive corporate asset that could make millions of dollars or more a year, easing corporate budgets and strengthening bottom lines.

Although a few companies make tens or hundreds of millions of dollars selling corporate data sets, most do not. The journey to monetizing data is not a quick or easy one. There are many challenges and pitfalls along the way that executives need to address before they have a realistic chance of profiting from this traditional back-office asset.

In this webinar, Wayne Eckerson and Michael Hejtmanek explore the top pitfalls organizations face when attempting to monetize data, drawing on practical experience and real-world examples.

You will learn how to overcome these 7 pitfalls:

1. Assuming your data is valuable — without proof

2. Thinking your data is ‘ready’ when it’s not

3. Underestimating internal resistance

4. Overlooking legal, privacy or compliance risks until it’s too late

5. Building the wrong product — one that’s interesting, but not useful

6. Failing to treat data like a real product

7. Treating monetization as a one-off project, not an ongoing process