When researching content for our latest blog we came across a post on Reddit in the r/dataanalytics group where a user asked "What are your biggest Pain Points as a Data Analyst?"
To get the conversation started he shared the following:
It's fun to come across these community threads and to empathize with our fellow data professionals. A few themes that surfaced in the ensuing discussion thread were data access, data source complexity, changing business requirements, and urgency of delivery.
We've written in past blogs that data source complexity is the enemy of great decision making and this post in reddit confirms data source complexity is an issue.
In a recent project with one customer, their biggest challenge was reconciling sales forecast data with inventory planning or optimization. The inventory data was sitting in cryptic ERP tables and the forecast data was in their CRM system. The objectives of the analytic exercise was to ensure they could either meet sales and demand forecasts or to ensure any products with slower moving inventory could be surfaced in reports so that the business leaders could make real-time decisions on promotions to move the product (inventory) more quickly.
Historically, this customer leaned on I.T. teams to source, clean, merge, and transform the data into a usable format to create the reports. Luckily with eyko, this team of analysts in the finance and sales teams were able to do this on their own without leaning heavily on I.T.
To learn more, here is a good white paper on Data Democratization and how tools like eyko help organizations beat the data source and data pipeline tool complexity.