Data visualization is the cornerstone of building data analytics capabilities for faster, more accurate decision-making and enhanced forecasting. If you are primarily a dashboard consumer rather than creator and frequent data analyst, this data visualization primer is for you.
What makes your data valuable? What are the obstacles preventing rapid time-to-value? How do you truly achieve rapid value from Business Intelligence (BI) and Analytics? If you have found yourself asking these questions about your organization’s data, you are not alone.
Tableau Desktop 2020.1 was recently released, and it may be the greatest release since Hyper. This one is packed full of features both practical and salesy that viz developers have been clamoring for.
Onebridge congratulates Alteryx for being named a Challenger in Gartner’s 2019 Magic Quadrant for Data Science and Machine Learning Platforms. This is the sixth time in a row that Alteryx has been recognized in this Magic Quadrant.
Technology continues to advance at a record pace – autonomous cars, a 5G Super Bowl Broadcast, cognitive services available in all major cloud providers for anyone to use, the list goes on. So why in the year 2020 do we still see such a gap in how we collect and manage polling data?
Having company data but no way to dissect and understand it is just words and numbers jumbled on a screen or piece of paper. Well-planned dashboards are created in an effort to quickly and effectively understand data. The key phrase here is well-planned.
By now we've all heard that data scientist is the "sexiest job in America." I should know... meaning sometimes I read the New York Times.
Recently, current and prospective clients have asked me for ideas about improving extract performance in Tableau Server. A couple of the first options to consider are hiding unused columns and using an incremental load instead of a full refresh. But here is the most important thing you can ever do to improve Tableau extract performance.
With data analytics playing a larger role in business performance each year, the ability to visualize results has become a useful skill for anyone looking to share the results of their work or anticipate future outcomes.
Finance teams are often surprised to learn how much time they can save thanks to data analytics solutions. Their teams are freed up to do the work they are really good at and passionate about, rather than staying buried under manual reporting responsibilities or solving problems caused by bad data.
Whether it’s siloed information lacking in insightful connection points, massive quantities of data too daunting to process, or negative past experiences, key decision makers are making moves based on gut instinct rather than facts.
Digital transformation is at the core of keeping a business competitive. Relying on the old formula of people, processes and technology alone doesn’t work. Organizations must now embrace the fourth pillar of digital transformation—data.
We’ve covered six ways to create effective dashboards, but it’s also important to discuss common mistakes. Why? To help you prevent having to fix issues later. Even knowing best practices you could still miss simple mistakes that should be avoided for your dashboards to be effective.
With data analytics playing a larger role in business performance each year, the ability to visualize results has become a useful skill for anyone looking to share the results of their work or anticipate future outcomes.Knowing which pitfalls and mistakes to avoid can make your visual reporting more impactful and helpful for your audience.
Around the Onebridge office, we have a lot of love for tools like Power BI because they allow businesses to use their data as an asset, and that’s what we’re all about. Stick around for a moment and let us give you a short introduction into the power of this tool.
It’s important to understand the various types of data analytics so you can identify where you are on your journey to data literacy and analytics empowerment.
There are so many amazing stories about data making life better, we don't want you to miss out. Check out our most recent favorites.
Power BI can not only import data from a variety of sources--it can even help with cleaning and formatting the data it pulls. But how easy is it to do this?
Every business should consider these options when building a mature data strategy. We’ve compiled the most important info about all three for your consideration.
Have you run across data-related terms that weren’t clearly defined? Do you want to understand more about the key components to building a data strategy? In this post, we’re focusing on Master Data Management (MDM).