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 a complete range of capabilities that spans the data life cycle from the edge to AI.
Analytics for the data lifecycle
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. Yet what they offer in narrow functionality, they lack in flexibility at the macro level, each effectively representing its own silo. Although each may secure and govern data and analytics, they do so for their own realm only and using their own framework. Each has its own choice of infrastructures to deploy to and occasionally uses proprietary technology and storage to deliver the best possible performance. The responsibility to ensure consistent data security and governance is delegated to the customer, requiring them to invest in resources and skills to bring it to a good end. The complexity leaves significant room for error and risk. The success of the project implemented or even the organization as a whole is directly connected to, locked onto, the proprietary vendors of technology and providers of infrastructure. Achieving or maintaining compliance to ever-increasing data privacy regulation becomes a project akin to the act where they juggle running chainsaws in the circus: exciting to watch but terrifying to do yourself and disastrous if it goes wrong.
The better choice: enterprise data cloud
The alternative, an integrated analytics platform, has a clear opportunity to do away with most of the operational overheads by providing consistent security and governance across the whole platform. Gone is the effort to synchronize between different silos and compliance becomes a near by-product of using the platform. The platform should provide the range of analytics across the data lifecycle that is on par or better than individual siloed solutions so that it meets the needs of the end-users. Crucially, a solid base in open source will ensure not only fast innovation but also independence from proprietary development. Your success is no longer dependent on your vendor. Finally, this independence should also extend to the infrastructure upon which the platform is deployed to offer complete choice and flexibility.
Together, these four capabilities (analytics for the data lifecycle, deployed to any cloud and data center, with consistent security and governance, in a platform that’s based on open source and open frameworks) are known as an enterprise data cloud. With it, organizations can unlock the power of their data to serve customers better, operate with greater efficiency, and strengthen security to protect their business.
To find out what to look for when evaluating an integrated analytics platform, join our webinar: Considerations for Selecting an Integrated Analytics Platform.
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