Use Case
Allow data science teams and business users to focus on uncovering insights from data that then get turned into actionable insights
Free up IT and data science teams from long backlogs of analytics and data cleansing tasks
Let workers focus on high-value projects and advanced analytics, instead of spending time on mundane tasks
Reduce mundane, repetitive tasks and automatically connect data sources
Eliminate mistakes by automating repetitive, manual tasks
Because of the pace at which data is growing, companies can no longer rely on last-generation tools like spreadsheets and pivot tables for insight. Those tools can provide the means to study a snapshot of historical data, but aren’t powerful enough to be used to gain insights into emerging trends or make predictions. Even at their most robust, the analytics in such tools are labor-intensive and by the time business leaders have derived insight from them, many opportunities have already passed.
When automated, analytics has the potential to give decision makers context around data and transform data into useful information. Automation eliminates the repetitive, manual labor of turning data into insights. Companies that automate analytics can scale out their data operations toward digital transformation.
Automating calculations is just one of the many tasks that can be accomplished with analytics. Workflows can chain analytics together to complete complex tasks in a series, which can then be made repeatable. For example, the month-end close in accounting consists of similar steps, calculations, and report formats. Automated analytics would be ideal for removing the labor component involved with month-end close.
Users can work with automated analytics tools at a high level, without having to understand the math, statistics, and algebra taking place below the surface. The tools empower knowledge workers, with expertise in business problems, to leverage analytics without needing expertise in data science.
With Alteryx, you can:
