Every organization wants to harness the power of big data, yet most businesses find it unworkable and inefficient due to the costs of developing and maintaining internal systems and solutions. Even though the data insights that analysis could lead to could be the difference between success and failure, many firms find it difficult to justify the costs of in-house development and huge data analysis teams.
Analytics-as-a-service can help with that. Companies that can't afford to staff an internal data team can obtain robust data analysis capabilities by using analytics-as-a-service. They don't need expensive server space or data professionals because they can use cloud-based BI solutions to analyze massive data and generate insights.
With user-friendly analytics tools, it’s much easier for the average person to analyze data. Even those with little to no technical experience can, with a little coaching, learn how to run reports, build dashboards, and connect to data sources. This way, everyone in an organization can use data to make timely and accurate business decisions.
With these powerful tools, businesses can also perform more complex analytics, using things like pre-built machine learning applications and artificial intelligence to drive deeper insight from their data. If you do have data science experts in-house, these platforms often offer full-code solutions in common scripting languages like Python, allowing them to build their own data-science models within their BI tool.