Why an integrated analytics platform is the right choice
Companies realize that in order to grow, connect products and services, or protect their business, they need to become data-driven. In selecting the tools to realize these goals, organizations effectively have two choices: a self-selected combination of analytics tools and applications or a unified platform that handles all. In this blog we will discuss the challenges of the former choice that will provide justification for the latter.
Let’s take a step back and ask: what do organizations need in terms of analytics to realize their data-driven goals? What is needed to combat customer churn, provide a predictive maintenance service or identify fraud as it happens? One thing is clear: it is not one single analytical capability. Implementing innovative and differentiating business use cases is not simply selecting the perfect data warehouse solution and calling it good. Today’s solutions require more than a better individually functional tool. Going from data to insight to action demands acomplete range of capabilities that spans the data life cyclefrom the edge to AI.
The lifecycle starts when data is collected or ingested from any source. With the advent of 5G, this includes ever more data that’s streamed and generated in real-time. The data needs to be enriched before it can be analyzed and reported on in traditional data warehousing solutions and operational dashboards. Yet evermore, organizations are squeezing insight from their data through data science and machine learning. Based on volumes of historical data, these models allow prediction of the future or identification of the extraordinary. Brought to production at scale, machine-learned insight helps mature analytics from the mundane descriptive and diagnostic to the differentiating predictive and prescriptive. The data lifecycle as a whole is not linear as this paragraph describes. Rather, it is a fluent to-and-fro between the different stages. Ah, and incidentally, it needs to happen with consistent security and governance in order to meet the needs of both internal as well as external (regulatory) compliance.
Analytics for the data lifecycle implemented by combining individual analytics systems and applications may well provide the ‘best’ capabilities for collecting, enriching, reporting or predicting.
