Analytics Fail
Other / March, 02 2021

Why do so many analytics projects fail to implement?

For a few years now, organizations have relied on big data and analytics applications, and now a change is taking place. Companies are beginning to move from “experimental analytics” to “industrialized analytics” as they become more aware of the kinds of business results they can achieve.


But it is not the kind of massive change we might expect. Companies are struggling to translate technical project results into viable solutions that deliver measurable business results. In this way, many analytical applications are not successfully translated from the laboratory to practice.

Why does this happen?

Often, companies use analytics tools without establishing results or use cases. Instead, many projects carried out by internal teams can experiment to see which solutions are feasible. Without a basis in actual business results or viable uses, many projects (which seem to go on and on) yield disappointing results in the end. Without demonstrable evidence, funding and excitement wane, and those projects are shelved, along with the other failed experiments of the past.

Even when the discovery phase of an analytics project is successful, and you already have sponsors, a lot still needs to happen. Releases must often be made based on data, infrastructure, and environment to ensure that the first is available in real or near real-time. Once these models are in place, there is a constant need to improve and maintain them.

Integration is another big task. It is necessary to integrate the analytics apps to the existing ones related to the business and the processes, such as your CRM or MRP solutions or Salesforce. Integration is the critical step that turns analytics into real business benefits.

The field of analysis continues to evolve and mature. More options are available to help businesses. End-to-end analytics platforms combine scalable infrastructure and pre-built templates and configurations.
The services needed to identify good use cases, find the right data sources, ask the right questions, and integrate insights are included.

That not only reduces the relationship between time and value but also reduces risk. And because platform solutions are built on scalable cloud infrastructure, analytical workloads always operate on “right-sized” resources that can be added or subtracted as needed.


All companies can benefit from analytics. And when you consider what a platform-based approach can offer versus a do-it-yourself approach, it’s easier to see that your company can finally get its ideas out of the lab and into action.

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