More than 60% of the collected data is not used for analytics. This is due to the excessive load on the IT department to handle all data requests while troubleshooting and providing maintenance support.
Self-serving analytics can solve the problem and help employees make the most of data by running analytics at each vertical and department in the enterprise. The self-serving framework is a part of the big data implementation project.
Convert the business needs into use cases to define the analytical framework in the enterprise. It helps create a proper data flow for uninterrupted data analytics and insights.
The big data architecture should align with the business needs and long-term goals. It should be flexible, scalable, and secure.
Which existing applications are important for the business? How do the applications use the insights derived from the big data model? Integrate the systems to streamline the workflow.
Get rid of poor quality and duplicate data by establishing data governance regulations. Derive better and more accurate insights.
It’s time to turn the design into code and build the big data pipeline in the enterprise (either on-premises or cloud servers).
Finally, train and empower employees to use data analytics and data visualization tools to derive insights without relying on the IT department.
They have the experience and agility to understand what’s possible and deliver to our expectations.
