This site uses different types of cookies, including analytics and functional cookies (its own and from other sites). To change your cookie settings or find out more, click here. If you continue browsing our website, you accept these cookies.
Upgrading Server to version 22.1? We’ve enabled the faster AMP engine by default. Read about how these system settings changes might affect you.
We’re aware of an intermittent issue with our My Alteryx login and are actively working to have the issue corrected. If you run into an error when logging in, please try clearing all cookies or accessing the community on a different browser. Thank you for your patience!
Ready for some good news, bad news? Good news — analytic investment is on the rise and Forester is seeing a year over year increase. Bad news - business decision makers are NOT satisfied with the current analytics process. 81% of business decision makers are dissatisfied in the speed in which analytics are delivered today, and worse yet 69% are dissatisfied with the quality of work.1
Most organizations approach analytics in a daisy chain or hub and spoke model where tickets are submitted to IT in order to extract a needed dataset, that dataset then gets passed on to data specialist or data scientist who build sophisticated analytic models, which finally get passed on to the line-of-business analysts, who hopes that the dataset is right, to create reports and share insights.
81% of business decision makers are dissatisfied with the speed in which analytics are delivered today
69% of business decision makers are dissatisfied with the quality of work
The problem with this approach is the simple fact that is it is not solving the key problem with analytics in organizations today, which contrary to popular belief isn’t a lack of the right skills; it’s the inability to deliver the insights that decision makers need in the time they need them.
Traditional analytic tools and approaches are failing organizations everywhere as they reinforce analytic process silos. Traditional tools were built to address specific portions of the analytics process: data extraction, cleansing, blending; model creation; insight sharing and dashboards. In addition, these tools have been designed for specific players in the analytics process: IT, data scientist, and line-of-business analysts. And though some of these tools are trying to expand their traditional capabilities to bleed into other areas, none of them can fully address all key components in the analytic process. In addition, a lot of the traditional tools can’t enable all players within the processes equally — sophistication of analysis comes at the cost of ease of use or vice versa.
Analysts need better tools and processes that offer the right balance between ease-of-use and complexity of analysis, and enable friction-free analytics all while respecting the data governance requirements of IT. Yet, very few organizations have been able to break through and scale their analytics processes in such a way that it empowers analysts of all skill levels the ability to complete the full spectrum of analytics tasks. Those that have effectively broken down the data silos of traditional analytics processes have done so because they have embraced self-service data analytics.
“Analyst and leaders at world-class enterprises such as Hyatt, JPMorgan Chase and Southwest are embracing the self-service data analytics shiftand in the process are setting themselves apart.”
Analyst and leaders at world-class enterprises such as Hyatt, JPMorgan Chase and Southwest are embracing the self-service data analytics shift, and in the process are setting themselves apart from their peers and delivering the analytic insights that their business decision makers need and in the time they need them.
Learn more about how about what self-service data analytics is and is not; and how Hyatt, JPMorgan Chase and Southwest have embraced self-service data analytics to improve processes, eliminate repetitive tasks, build better relationships with IT, and deliver deeper insights faster in this white paper, Achieving Self-Service for Enterprise Data Analysts.