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.
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.
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.
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.
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.
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.
The agile approach to software development focuses on delivering incremental value to customers in quick iterative product creation and enhancement. Agile methods push developers to gather feedback constantly so that they maximize the benefit of their efforts and minimize waste – rework and failed adoption by users.
Consulting firms charge $250 per hour to tell you what you already know, stating what you should do, and then they hope that because they confirmed your suspicions of replacing the 40-year-old-COBOL system that you’ll buy the execution as well.
Knowing how to put the SLA, KPI, CSF puzzle together helps IT gain credibility and promote value in partnerships between IT and the business we serve.
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.
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).
Learn how to use Microsoft Power BI's variety of data visualization options to share powerful data insights with your team.
You’ve got your sources loaded and have started building visuals and exploring your data in Microsoft Power BI. Now it’s time to take your data analysis to the next level and start looking at your data with fresh perspectives.